From 59d41406c65edd206c9547d7492b893688790986 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 31 Jan 2024 15:12:33 -0500
Subject: [PATCH 01/41] updated train.py to reflect changes in cli.py train &
 trainlaunch

---
 deepliif/{ => scripts}/train.py | 102 +++++++----
 train.py                        | 291 ++++++++++++++++++++++++++++----
 2 files changed, 328 insertions(+), 65 deletions(-)
 rename deepliif/{ => scripts}/train.py (78%)
 mode change 100755 => 100644

diff --git a/deepliif/train.py b/deepliif/scripts/train.py
old mode 100755
new mode 100644
similarity index 78%
rename from deepliif/train.py
rename to deepliif/scripts/train.py
index 39f38be..34eb5cd
--- a/deepliif/train.py
+++ b/deepliif/scripts/train.py
@@ -1,4 +1,5 @@
 """
+This script is used by cli.py trainlaunch command for DDP.
 Keep this train.py up-to-date with train() in cli.py!
 They are EXACTLY THE SAME.
 """
@@ -14,17 +15,13 @@
 import numpy as np
 from PIL import Image
 
-from deepliif.data import create_dataset, AlignedDataset, transform
-from deepliif.models import inference, postprocess, compute_overlap, init_nets, DeepLIIFModel
-from deepliif.util import allowed_file, Visualizer
+from deepliif.data import create_dataset
+from deepliif.models import create_model
+from deepliif.util import Visualizer
+from deepliif.options import Options, print_options
 
 import torch.distributed as dist
-import os
-import torch
-
-import numpy as np
-import random
-import torch
+    
 
 def set_seed(seed=0,rank=None):
     """
@@ -66,8 +63,9 @@ def set_seed(seed=0,rank=None):
               help='name of the experiment. It decides where to store samples and models')
 @click.option('--gpu-ids', type=int, multiple=True, help='gpu-ids 0 gpu-ids 1 or gpu-ids -1 for CPU')
 @click.option('--checkpoints-dir', default='./checkpoints', help='models are saved here')
-@click.option('--targets-no', default=5, help='number of targets')
+@click.option('--modalities-no', default=4, type=int, help='number of targets')
 # model parameters
+@click.option('--model', default='DeepLIIF', help='name of model class')
 @click.option('--input-nc', default=3, help='# of input image channels: 3 for RGB and 1 for grayscale')
 @click.option('--output-nc', default=3, help='# of output image channels: 3 for RGB and 1 for grayscale')
 @click.option('--ngf', default=64, help='# of gen filters in the last conv layer')
@@ -83,7 +81,6 @@ def set_seed(seed=0,rank=None):
 @click.option('--init-type', default='normal',
               help='network initialization [normal | xavier | kaiming | orthogonal]')
 @click.option('--init-gain', default=0.02, help='scaling factor for normal, xavier and orthogonal.')
-@click.option('--padding-type', default='reflect', help='network padding type.')
 @click.option('--no-dropout', is_flag=True, help='no dropout for the generator')
 # dataset parameters
 @click.option('--direction', default='AtoB', help='AtoB or BtoA')
@@ -126,6 +123,7 @@ def set_seed(seed=0,rank=None):
               help='learning rate policy. [linear | step | plateau | cosine]')
 @click.option('--lr-decay-iters', type=int, default=50,
               help='multiply by a gamma every lr_decay_iters iterations')
+@click.option('--seed', type=int, default=None, help='basic seed to be used for deterministic training, default to None (non-deterministic)')
 # visdom and HTML visualization parameters
 @click.option('--display-freq', default=400, help='frequency of showing training results on screen')
 @click.option('--display-ncols', default=4,
@@ -146,15 +144,30 @@ def set_seed(seed=0,rank=None):
 @click.option('--save-by-iter', is_flag=True, help='whether saves model by iteration')
 @click.option('--remote', type=bool, default=False, help='whether isolate visdom checkpoints or not; if False, you can run a separate visdom server anywhere that consumes the checkpoints')
 @click.option('--remote-transfer-cmd', type=str, default=None, help='module and function to be used to transfer remote files to target storage location, for example mymodule.myfunction')
+@click.option('--dataset-mode', type=str, default='aligned',
+              help='chooses how datasets are loaded. [unaligned | aligned | single | colorization]')
+@click.option('--padding', type=str, default='zero',
+              help='chooses the type of padding used by resnet generator. [reflect | zero]')
+# DeepLIIFExt params
+@click.option('--seg-gen', type=bool, default=True, help='True (Translation and Segmentation), False (Only Translation).')
+@click.option('--net-ds', type=str, default='n_layers',
+              help='specify discriminator architecture for segmentation task [basic | n_layers | pixel]. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator')
+@click.option('--net-gs', type=str, default='unet_512',
+              help='specify generator architecture for segmentation task [resnet_9blocks | resnet_6blocks | unet_512 | unet_256 | unet_128]')
+@click.option('--gan-mode', type=str, default='vanilla',
+              help='the type of GAN objective for translation task. [vanilla| lsgan | wgangp]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.')
+@click.option('--gan-mode-s', type=str, default='lsgan',
+              help='the type of GAN objective for segmentation task. [vanilla| lsgan | wgangp]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.')
+# DDP related arguments
 @click.option('--local-rank', type=int, default=None, help='placeholder argument for torchrun, no need for manual setup')
-@click.option('--seed', type=int, default=None, help='basic seed to be used for deterministic training, default to None (non-deterministic)')
-def train(dataroot, name, gpu_ids, checkpoints_dir, targets_no, input_nc, output_nc, ngf, ndf, net_d, net_g,
-          n_layers_d, norm, init_type, init_gain, padding_type, no_dropout, direction, serial_batches, num_threads,
+def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, ndf, net_d, net_g,
+          n_layers_d, norm, init_type, init_gain, no_dropout, direction, serial_batches, num_threads,
           batch_size, load_size, crop_size, max_dataset_size, preprocess, no_flip, display_winsize, epoch, load_iter,
           verbose, lambda_l1, is_train, display_freq, display_ncols, display_id, display_server, display_env,
           display_port, update_html_freq, print_freq, no_html, save_latest_freq, save_epoch_freq, save_by_iter,
           continue_train, epoch_count, phase, lr_policy, n_epochs, n_epochs_decay, beta1, lr, lr_decay_iters,
-          remote, local_rank, remote_transfer_cmd, seed):
+          remote, remote_transfer_cmd, seed, dataset_mode, padding, model, 
+          modalities_no, seg_gen, net_ds, net_gs, gan_mode, gan_mode_s, local_rank):
     """General-purpose training script for multi-task image-to-image translation.
 
     This script works for various models (with option '--model': e.g., DeepLIIF) and
@@ -166,19 +179,36 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, targets_no, input_nc, output
     plot, and save models.The script supports continue/resume training.
     Use '--continue_train' to resume your previous training.
     """
+    assert model in ['DeepLIIF','DeepLIIFExt','SDG'], f'model class {model} is not implemented'
+    if model == 'DeepLIIF':
+        seg_no = 1
+    elif model == 'DeepLIIFExt':
+        if seg_gen:
+            seg_no = modalities_no
+        else:
+            seg_no = 0
+    else: # SDG
+        seg_no = 0
+        seg_gen = False
+    
+    d_params = locals()
+
+    if gpu_ids and gpu_ids[0] == -1:
+        gpu_ids = []
+        
     local_rank = os.getenv('LOCAL_RANK') # DDP single node training triggered by torchrun has LOCAL_RANK
     rank = os.getenv('RANK') # if using DDP with multiple nodes, please provide global rank in env var RANK
 
     if len(gpu_ids) > 0:
         if local_rank is not None:
             local_rank = int(local_rank)
-            torch.cuda.set_device(gpu_ids[local_rank])
-            gpu_ids=[gpu_ids[local_rank]]
+            torch.cuda.set_device(local_rank)#gpu_ids[local_rank])
+            gpu_ids = [local_rank]
         else:
             torch.cuda.set_device(gpu_ids[0])
 
     if local_rank is not None: # LOCAL_RANK will be assigned a rank number if torchrun ddp is used
-        dist.init_process_group(backend='nccl')
+        dist.init_process_group(backend="nccl", rank=int(os.environ['RANK']), world_size=int(os.environ['WORLD_SIZE']))
         print('local rank:',local_rank)
         flag_deterministic = set_seed(seed,local_rank)
     elif rank is not None:
@@ -187,28 +217,42 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, targets_no, input_nc, output
         flag_deterministic = set_seed(seed)
 
     if flag_deterministic:
-        padding_type = 'zero'
+        d_params['padding'] = 'zero'
         print('padding type is forced to zero padding, because neither refection pad2d or replication pad2d has a deterministic implementation')
 
+    # infer number of input images
+    dir_data_train = dataroot + '/train'
+    fns = os.listdir(dir_data_train)
+    fns = [x for x in fns if x.endswith('.png')]
+    img = Image.open(f"{dir_data_train}/{fns[0]}")
+    
+    num_img = img.size[0] / img.size[1]
+    assert int(num_img) == num_img, f'img size {img.size[0]} / {img.size[1]} = {num_img} is not an integer'
+    num_img = int(num_img)
+    
+    input_no = num_img - modalities_no - seg_no
+    assert input_no > 0, f'inferred number of input images is {input_no}; should be greater than 0'
+    d_params['input_no'] = input_no
+    d_params['scale_size'] = img.size[1]
+    d_params['gpu_ids'] = gpu_ids
+
     # create a dataset given dataset_mode and other options
-    dataset = AlignedDataset(dataroot, load_size, crop_size, input_nc, output_nc, direction, targets_no, preprocess,
-                             no_flip, phase, max_dataset_size)
+    # dataset = AlignedDataset(opt)
 
-    dataset = create_dataset(dataset, batch_size, serial_batches, num_threads, max_dataset_size, gpu_ids)
+    opt = Options(d_params=d_params)
+    print_options(opt, save=True)
+    
+    dataset = create_dataset(opt)
     # get the number of images in the dataset.
     click.echo('The number of training images = %d' % len(dataset))
 
     # create a model given model and other options
-    model = DeepLIIFModel(gpu_ids, is_train, checkpoints_dir, name, preprocess, targets_no, input_nc, output_nc, ngf,
-                          net_g, norm, no_dropout, init_type, init_gain, padding_type, ndf, net_d, n_layers_d, lr, beta1, lambda_l1,
-                          lr_policy, remote_transfer_cmd)
+    model = create_model(opt)
     # regular setup: load and print networks; create schedulers
-    model.setup(lr_policy, epoch_count, n_epochs, n_epochs_decay, lr_decay_iters, continue_train, load_iter, epoch,
-                verbose)
+    model.setup(opt)
 
     # create a visualizer that display/save images and plots
-    visualizer = Visualizer(display_id, is_train, no_html, display_winsize, name, display_port, display_ncols,
-                            display_server, display_env, checkpoints_dir, remote, remote_transfer_cmd)
+    visualizer = Visualizer(opt)
     # the total number of training iterations
     total_iters = 0
 
diff --git a/train.py b/train.py
index 205b6ac..d326a08 100644
--- a/train.py
+++ b/train.py
@@ -12,18 +12,23 @@
 from deepliif.options.train_options import TrainOptions
 from deepliif.data import create_dataset
 from deepliif.models import create_model
+from deepliif.options import read_model_params, Options, print_options
 from deepliif.util.visualizer import Visualizer
+from PIL import Image
 import os
 
 import numpy as np
 import random
 import torch
 
+import click
 
 def set_seed(seed=0,rank=None):
     """
     seed: basic seed
     rank: rank of the current process, using which to mutate basic seed to have a unique seed per process
+    
+    output: a boolean flag indicating whether deterministic training is enabled (True) or not (False)
     """
     os.environ['DEEPLIIF_SEED'] = str(seed)
 
@@ -43,61 +48,275 @@ def set_seed(seed=0,rank=None):
         torch.backends.cudnn.deterministic = True
         torch.use_deterministic_algorithms(True)
         print(f'deterministic training, seed set to {seed_final}')
+        return True
     else:
         print(f'not using deterministic training')
+        return False
 
 
-if __name__ == '__main__':
+@click.command()
+@click.option('--dataroot', required=True, type=str,
+              help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
+@click.option('--name', default='experiment_name',
+              help='name of the experiment. It decides where to store samples and models')
+@click.option('--gpu_ids', type=int, multiple=True, help='gpu-ids 0 gpu-ids 1 or gpu-ids -1 for CPU')
+@click.option('--checkpoints_dir', default='./checkpoints', help='models are saved here')
+@click.option('--modalities_no', default=4, type=int, help='number of targets')
+# model parameters
+@click.option('--model', default='DeepLIIF', help='name of model class')
+@click.option('--input_nc', default=3, help='# of input image channels: 3 for RGB and 1 for grayscale')
+@click.option('--output_nc', default=3, help='# of output image channels: 3 for RGB and 1 for grayscale')
+@click.option('--ngf', default=64, help='# of gen filters in the last conv layer')
+@click.option('--ndf', default=64, help='# of discrim filters in the first conv layer')
+@click.option('--net_d', default='n_layers',
+              help='specify discriminator architecture [basic | n_layers | pixel]. The basic model is a 70x70 '
+                   'PatchGAN. n_layers allows you to specify the layers in the discriminator')
+@click.option('--net_g', default='resnet_9blocks',
+              help='specify generator architecture [resnet_9blocks | resnet_6blocks | unet_512 | unet_256 | unet_128]')
+@click.option('--n_layers_d', default=4, help='only used if netD==n_layers')
+@click.option('--norm', default='batch',
+              help='instance normalization or batch normalization [instance | batch | none]')
+@click.option('--init_type', default='normal',
+              help='network initialization [normal | xavier | kaiming | orthogonal]')
+@click.option('--init_gain', default=0.02, help='scaling factor for normal, xavier and orthogonal.')
+@click.option('--no_dropout', is_flag=True, help='no dropout for the generator')
+# dataset parameters
+@click.option('--direction', default='AtoB', help='AtoB or BtoA')
+@click.option('--serial_batches', is_flag=True,
+              help='if true, takes images in order to make batches, otherwise takes them randomly')
+@click.option('--num_threads', default=4, help='# threads for loading data')
+@click.option('--batch_size', default=1, help='input batch size')
+@click.option('--load_size', default=512, help='scale images to this size')
+@click.option('--crop_size', default=512, help='then crop to this size')
+@click.option('--max_dataset_size', type=int,
+              help='Maximum number of samples allowed per dataset. If the dataset directory contains more than '
+                   'max_dataset_size, only a subset is loaded.')
+@click.option('--preprocess', type=str,
+              help='scaling and cropping of images at load time [resize_and_crop | crop | scale_width | '
+                   'scale_width_and_crop | none]')
+@click.option('--no_flip', is_flag=True,
+              help='if specified, do not flip the images for data augmentation')
+@click.option('--display_winsize', default=512, help='display window size for both visdom and HTML')
+# additional parameters
+@click.option('--epoch', default='latest',
+              help='which epoch to load? set to latest to use latest cached model')
+@click.option('--load_iter', default=0,
+              help='which iteration to load? if load_iter > 0, the code will load models by iter_[load_iter]; '
+                   'otherwise, the code will load models by [epoch]')
+@click.option('--verbose', is_flag=True, help='if specified, print more debugging information')
+@click.option('--lambda_L1', default=100.0, help='weight for L1 loss')
+@click.option('--is_train', is_flag=True, default=True)
+@click.option('--continue_train', is_flag=True, help='continue training: load the latest model')
+@click.option('--epoch_count', type=int, default=0,
+              help='the starting  epoch count, we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>')
+@click.option('--phase', default='train', help='train, val, test, etc')
+# training parameters
+@click.option('--n_epochs', type=int, default=100,
+              help='number of epochs with the initial learning rate')
+@click.option('--n_epochs_decay', type=int, default=100,
+              help='number of epochs to linearly decay learning rate to zero')
+@click.option('--beta1', default=0.5, help='momentum term of adam')
+@click.option('--lr', default=0.0002, help='initial learning rate for adam')
+@click.option('--lr_policy', default='linear',
+              help='learning rate policy. [linear | step | plateau | cosine]')
+@click.option('--lr_decay_iters', type=int, default=50,
+              help='multiply by a gamma every lr_decay_iters iterations')
+@click.option('--seed', type=int, default=None, help='basic seed to be used for deterministic training, default to None (non-deterministic)')
+# visdom and HTML visualization parameters
+@click.option('--display_freq', default=400, help='frequency of showing training results on screen')
+@click.option('--display_ncols', default=4,
+              help='if positive, display all images in a single visdom web panel with certain number of images per row.')
+@click.option('--display_id', default=1, help='window id of the web display')
+@click.option('--display_server', default="http://localhost", help='visdom server of the web display')
+@click.option('--display_env', default='main',
+              help='visdom display environment name (default is "main")')
+@click.option('--display_port', default=8097, help='visdom port of the web display')
+@click.option('--update_html_freq', default=1000, help='frequency of saving training results to html')
+@click.option('--print_freq', default=100, help='frequency of showing training results on console')
+@click.option('--no_html', is_flag=True,
+              help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/')
+# network saving and loading parameters
+@click.option('--save_latest_freq', default=500, help='frequency of saving the latest results')
+@click.option('--save_epoch_freq', default=100,
+              help='frequency of saving checkpoints at the end of epochs')
+@click.option('--save_by_iter', is_flag=True, help='whether saves model by iteration')
+@click.option('--remote', type=bool, default=False, help='whether isolate visdom checkpoints or not; if False, you can run a separate visdom server anywhere that consumes the checkpoints')
+@click.option('--remote_transfer_cmd', type=str, default=None, help='module and function to be used to transfer remote files to target storage location, for example mymodule.myfunction')
+@click.option('--dataset_mode', type=str, default='aligned',
+              help='chooses how datasets are loaded. [unaligned | aligned | single | colorization]')
+@click.option('--padding', type=str, default='zero',
+              help='chooses the type of padding used by resnet generator. [reflect | zero]')
+# DeepLIIFExt params
+@click.option('--seg_gen', type=bool, default=True, help='True (Translation and Segmentation), False (Only Translation).')
+@click.option('--net_ds', type=str, default='n_layers',
+              help='specify discriminator architecture for segmentation task [basic | n_layers | pixel]. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator')
+@click.option('--net_gs', type=str, default='unet_512',
+              help='specify generator architecture for segmentation task [resnet_9blocks | resnet_6blocks | unet_512 | unet_256 | unet_128]')
+@click.option('--gan_mode', type=str, default='vanilla',
+              help='the type of GAN objective for translation task. [vanilla| lsgan | wgangp]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.')
+@click.option('--gan_mode_s', type=str, default='lsgan',
+              help='the type of GAN objective for segmentation task. [vanilla| lsgan | wgangp]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.')
+# DDP related arguments
+@click.option('--local-rank', type=int, default=None, help='placeholder argument for torchrun, no need for manual setup')
+def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, ndf, net_d, net_g,
+          n_layers_d, norm, init_type, init_gain, no_dropout, direction, serial_batches, num_threads,
+          batch_size, load_size, crop_size, max_dataset_size, preprocess, no_flip, display_winsize, epoch, load_iter,
+          verbose, lambda_l1, is_train, display_freq, display_ncols, display_id, display_server, display_env,
+          display_port, update_html_freq, print_freq, no_html, save_latest_freq, save_epoch_freq, save_by_iter,
+          continue_train, epoch_count, phase, lr_policy, n_epochs, n_epochs_decay, beta1, lr, lr_decay_iters,
+          remote, remote_transfer_cmd, seed, dataset_mode, padding, model, 
+          modalities_no, seg_gen, net_ds, net_gs, gan_mode, gan_mode_s, local_rank):
+    """General-purpose training script for multi-task image-to-image translation.
+
+    This script works for various models (with option '--model': e.g., DeepLIIF) and
+    different datasets (with option '--dataset_mode': e.g., aligned, unaligned, single, colorization).
+    You need to specify the dataset ('--dataroot'), experiment name ('--name'), and model ('--model').
+
+    It first creates model, dataset, and visualizer given the option.
+    It then does standard network training. During the training, it also visualize/save the images, print/save the loss
+    plot, and save models.The script supports continue/resume training.
+    Use '--continue_train' to resume your previous training.
+    """
+    assert model in ['DeepLIIF','DeepLIIFExt','SDG'], f'model class {model} is not implemented'
+    if model == 'DeepLIIF':
+        seg_no = 1
+    elif model == 'DeepLIIFExt':
+        if seg_gen:
+            seg_no = modalities_no
+        else:
+            seg_no = 0
+    else: # SDG
+        seg_no = 0
+        seg_gen = False
+    
+    d_params = locals()
+
+    if gpu_ids and gpu_ids[0] == -1:
+        gpu_ids = []
+        
+    local_rank = os.getenv('LOCAL_RANK') # DDP single node training triggered by torchrun has LOCAL_RANK
+    rank = os.getenv('RANK') # if using DDP with multiple nodes, please provide global rank in env var RANK
+
+    if len(gpu_ids) > 0:
+        if local_rank is not None:
+            local_rank = int(local_rank)
+            torch.cuda.set_device(local_rank)#gpu_ids[local_rank])
+            gpu_ids = [local_rank]
+        else:
+            torch.cuda.set_device(gpu_ids[0])
+
+    if local_rank is not None: # LOCAL_RANK will be assigned a rank number if torchrun ddp is used
+        dist.init_process_group(backend="nccl", rank=int(os.environ['RANK']), world_size=int(os.environ['WORLD_SIZE']))
+        print('local rank:',local_rank)
+        flag_deterministic = set_seed(seed,local_rank)
+    elif rank is not None:
+        flag_deterministic = set_seed(seed, rank)
+    else:
+        flag_deterministic = set_seed(seed)
+
+    if flag_deterministic:
+        d_params['padding'] = 'zero'
+        print('padding type is forced to zero padding, because neither refection pad2d or replication pad2d has a deterministic implementation')
 
-    opt = TrainOptions().parse()   # get training options
-    set_seed(opt.seed)
-    dataset = create_dataset(opt)  # create a dataset given opt.dataset_mode and other options
-    dataset_size = len(dataset)    # get the number of images in the dataset.
-    print('The number of training images = %d' % dataset_size)
-
-    model = create_model(opt)      # create a model given opt.model and other options
-    model.setup(opt)               # regular setup: load and print networks; create schedulers
-    visualizer = Visualizer(opt)   # create a visualizer that display/save images and plots
-    total_iters = 0                # the total number of training iterations
-
-    for epoch in range(opt.epoch_count, opt.n_epochs + opt.n_epochs_decay + 1):    # outer loop for different epochs; we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>
-        epoch_start_time = time.time()  # timer for entire epoch
-        iter_data_time = time.time()    # timer for data loading per iteration
-        epoch_iter = 0                  # the number of training iterations in current epoch, reset to 0 every epoch
-        visualizer.reset()              # reset the visualizer: make sure it saves the results to HTML at least once every epoch
-
-        for i, data in enumerate(dataset):  # inner loop within one epoch
-            iter_start_time = time.time()  # timer for computation per iteration
-            if total_iters % opt.print_freq == 0:
+    # infer number of input images
+    dir_data_train = dataroot + '/train'
+    fns = os.listdir(dir_data_train)
+    fns = [x for x in fns if x.endswith('.png')]
+    img = Image.open(f"{dir_data_train}/{fns[0]}")
+    
+    num_img = img.size[0] / img.size[1]
+    assert int(num_img) == num_img, f'img size {img.size[0]} / {img.size[1]} = {num_img} is not an integer'
+    num_img = int(num_img)
+    
+    input_no = num_img - modalities_no - seg_no
+    assert input_no > 0, f'inferred number of input images is {input_no}; should be greater than 0'
+    d_params['input_no'] = input_no
+    d_params['scale_size'] = img.size[1]
+    d_params['gpu_ids'] = gpu_ids
+
+    # create a dataset given dataset_mode and other options
+    # dataset = AlignedDataset(opt)
+
+    opt = Options(d_params=d_params)
+    print_options(opt, save=True)
+    
+    dataset = create_dataset(opt)
+    # get the number of images in the dataset.
+    click.echo('The number of training images = %d' % len(dataset))
+
+    # create a model given model and other options
+    model = create_model(opt)
+    # regular setup: load and print networks; create schedulers
+    model.setup(opt)
+
+    # create a visualizer that display/save images and plots
+    visualizer = Visualizer(opt)
+    # the total number of training iterations
+    total_iters = 0
+
+    # outer loop for different epochs; we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>
+    for epoch in range(epoch_count, n_epochs + n_epochs_decay + 1):
+        # timer for entire epoch
+        epoch_start_time = time.time()
+        # timer for data loading per iteration
+        iter_data_time = time.time()
+        # the number of training iterations in current epoch, reset to 0 every epoch
+        epoch_iter = 0
+        # reset the visualizer: make sure it saves the results to HTML at least once every epoch
+        visualizer.reset()
+
+        # https://pytorch.org/docs/stable/data.html#torch.utils.data.distributed.DistributedSampler
+        if local_rank is not None or os.getenv('RANK') is not None: # if DDP is used, either on one node or multi nodes
+            if not serial_batches: # if we want randome order in mini batches
+                dataset.sampler.set_epoch(epoch)
+
+        # inner loop within one epoch
+        for i, data in enumerate(dataset):
+            # timer for computation per iteration
+            iter_start_time = time.time()
+            if total_iters % print_freq == 0:
                 t_data = iter_start_time - iter_data_time
 
-            total_iters += opt.batch_size
-            epoch_iter += opt.batch_size
-            model.set_input(data)         # unpack data from dataset and apply preprocessing
-            model.optimize_parameters()   # calculate loss functions, get gradients, update network weights
+            total_iters += batch_size
+            epoch_iter += batch_size
+            # unpack data from dataset and apply preprocessing
+            model.set_input(data)
+            # calculate loss functions, get gradients, update network weights
+            model.optimize_parameters()
 
-            if total_iters % opt.display_freq == 0:   # display images on visdom and save images to a HTML file
-                save_result = total_iters % opt.update_html_freq == 0
+            # display images on visdom and save images to a HTML file
+            if total_iters % display_freq == 0:
+                save_result = total_iters % update_html_freq == 0
                 model.compute_visuals()
                 visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
 
-            if total_iters % opt.print_freq == 0:    # print training losses and save logging information to the disk
+            # print training losses and save logging information to the disk
+            if total_iters % print_freq == 0:
                 losses = model.get_current_losses()
-                t_comp = (time.time() - iter_start_time) / opt.batch_size
+                t_comp = (time.time() - iter_start_time) / batch_size
                 visualizer.print_current_losses(epoch, epoch_iter, losses, t_comp, t_data)
-                if opt.display_id > 0:
-                    visualizer.plot_current_losses(epoch, float(epoch_iter) / dataset_size, losses)
+                if display_id > 0:
+                    visualizer.plot_current_losses(epoch, float(epoch_iter) / len(dataset), losses)
 
-            if total_iters % opt.save_latest_freq == 0:   # cache our latest model every <save_latest_freq> iterations
+            # cache our latest model every <save_latest_freq> iterations
+            if total_iters % save_latest_freq == 0:
                 print('saving the latest model (epoch %d, total_iters %d)' % (epoch, total_iters))
-                save_suffix = 'iter_%d' % total_iters if opt.save_by_iter else 'latest'
+                save_suffix = 'iter_%d' % total_iters if save_by_iter else 'latest'
                 model.save_networks(save_suffix)
 
             iter_data_time = time.time()
-        if epoch % opt.save_epoch_freq == 0:              # cache our model every <save_epoch_freq> epochs
+
+        # cache our model every <save_epoch_freq> epochs
+        if epoch % save_epoch_freq == 0:
             print('saving the model at the end of epoch %d, iters %d' % (epoch, total_iters))
             model.save_networks('latest')
             model.save_networks(epoch)
 
-        print('End of epoch %d / %d \t Time Taken: %d sec' % (epoch, opt.n_epochs + opt.n_epochs_decay, time.time() - epoch_start_time))
-        model.update_learning_rate()                     # update learning rates at the end of every epoch.
+        print('End of epoch %d / %d \t Time Taken: %d sec' % (
+            epoch, n_epochs + n_epochs_decay, time.time() - epoch_start_time))
+        # update learning rates at the end of every epoch.
+        model.update_learning_rate()
+
+
+if __name__ == '__main__':
+    train()

From 2f808f7b3a0a7260d5037be70ef8db92083d4305 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 31 Jan 2024 15:13:03 -0500
Subject: [PATCH 02/41] updated trainlaunch for single node multi gpu

---
 cli.py | 57 ++++++++++++++++++++++++++++++++++++++++++++-------------
 1 file changed, 44 insertions(+), 13 deletions(-)

diff --git a/cli.py b/cli.py
index 40e1204..604838c 100644
--- a/cli.py
+++ b/cli.py
@@ -134,6 +134,7 @@ def cli():
               help='learning rate policy. [linear | step | plateau | cosine]')
 @click.option('--lr-decay-iters', type=int, default=50,
               help='multiply by a gamma every lr_decay_iters iterations')
+@click.option('--seed', type=int, default=None, help='basic seed to be used for deterministic training, default to None (non-deterministic)')
 # visdom and HTML visualization parameters
 @click.option('--display-freq', default=400, help='frequency of showing training results on screen')
 @click.option('--display-ncols', default=4,
@@ -158,8 +159,6 @@ def cli():
               help='chooses how datasets are loaded. [unaligned | aligned | single | colorization]')
 @click.option('--padding', type=str, default='zero',
               help='chooses the type of padding used by resnet generator. [reflect | zero]')
-@click.option('--local-rank', type=int, default=None, help='placeholder argument for torchrun, no need for manual setup')
-@click.option('--seed', type=int, default=None, help='basic seed to be used for deterministic training, default to None (non-deterministic)')
 # DeepLIIFExt params
 @click.option('--seg-gen', type=bool, default=True, help='True (Translation and Segmentation), False (Only Translation).')
 @click.option('--net-ds', type=str, default='n_layers',
@@ -170,14 +169,16 @@ def cli():
               help='the type of GAN objective for translation task. [vanilla| lsgan | wgangp]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.')
 @click.option('--gan-mode-s', type=str, default='lsgan',
               help='the type of GAN objective for segmentation task. [vanilla| lsgan | wgangp]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.')
+# DDP related arguments
+@click.option('--local-rank', type=int, default=None, help='placeholder argument for torchrun, no need for manual setup')
 def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, ndf, net_d, net_g,
           n_layers_d, norm, init_type, init_gain, no_dropout, direction, serial_batches, num_threads,
           batch_size, load_size, crop_size, max_dataset_size, preprocess, no_flip, display_winsize, epoch, load_iter,
           verbose, lambda_l1, is_train, display_freq, display_ncols, display_id, display_server, display_env,
           display_port, update_html_freq, print_freq, no_html, save_latest_freq, save_epoch_freq, save_by_iter,
           continue_train, epoch_count, phase, lr_policy, n_epochs, n_epochs_decay, beta1, lr, lr_decay_iters,
-          remote, local_rank, remote_transfer_cmd, seed, dataset_mode, padding, model, 
-          modalities_no, seg_gen, net_ds, net_gs, gan_mode, gan_mode_s):
+          remote, remote_transfer_cmd, seed, dataset_mode, padding, model, 
+          modalities_no, seg_gen, net_ds, net_gs, gan_mode, gan_mode_s, local_rank):
     """General-purpose training script for multi-task image-to-image translation.
 
     This script works for various models (with option '--model': e.g., DeepLIIF) and
@@ -213,12 +214,12 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
         if local_rank is not None:
             local_rank = int(local_rank)
             torch.cuda.set_device(gpu_ids[local_rank])
-            gpu_ids=[gpu_ids[local_rank]]
+            gpu_ids=[local_rank]
         else:
             torch.cuda.set_device(gpu_ids[0])
 
     if local_rank is not None: # LOCAL_RANK will be assigned a rank number if torchrun ddp is used
-        dist.init_process_group(backend='nccl')
+        dist.init_process_group(backend="nccl", rank=int(os.environ['RANK']), world_size=int(os.environ['WORLD_SIZE']))
         print('local rank:',local_rank)
         flag_deterministic = set_seed(seed,local_rank)
     elif rank is not None:
@@ -244,6 +245,7 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
     assert input_no > 0, f'inferred number of input images is {input_no}; should be greater than 0'
     d_params['input_no'] = input_no
     d_params['scale_size'] = img.size[1]
+    d_params['gpu_ids'] = gpu_ids
 
     # create a dataset given dataset_mode and other options
     # dataset = AlignedDataset(opt)
@@ -336,8 +338,9 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
               help='name of the experiment. It decides where to store samples and models')
 @click.option('--gpu-ids', type=int, multiple=True, help='gpu-ids 0 gpu-ids 1 or gpu-ids -1 for CPU')
 @click.option('--checkpoints-dir', default='./checkpoints', help='models are saved here')
-@click.option('--targets-no', default=5, help='number of targets')
+@click.option('--modalities-no', default=4, type=int, help='number of targets')
 # model parameters
+@click.option('--model', default='DeepLIIF', help='name of model class')
 @click.option('--input-nc', default=3, help='# of input image channels: 3 for RGB and 1 for grayscale')
 @click.option('--output-nc', default=3, help='# of output image channels: 3 for RGB and 1 for grayscale')
 @click.option('--ngf', default=64, help='# of gen filters in the last conv layer')
@@ -353,7 +356,6 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
 @click.option('--init-type', default='normal',
               help='network initialization [normal | xavier | kaiming | orthogonal]')
 @click.option('--init-gain', default=0.02, help='scaling factor for normal, xavier and orthogonal.')
-@click.option('--padding-type', default='reflect', help='network padding type.')
 @click.option('--no-dropout', is_flag=True, help='no dropout for the generator')
 # dataset parameters
 @click.option('--direction', default='AtoB', help='AtoB or BtoA')
@@ -396,6 +398,7 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
               help='learning rate policy. [linear | step | plateau | cosine]')
 @click.option('--lr-decay-iters', type=int, default=50,
               help='multiply by a gamma every lr_decay_iters iterations')
+@click.option('--seed', type=int, default=None, help='basic seed to be used for deterministic training, default to None (non-deterministic)')
 # visdom and HTML visualization parameters
 @click.option('--display-freq', default=400, help='frequency of showing training results on screen')
 @click.option('--display-ncols', default=4,
@@ -416,8 +419,22 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
 @click.option('--save-by-iter', is_flag=True, help='whether saves model by iteration')
 @click.option('--remote', type=bool, default=False, help='whether isolate visdom checkpoints or not; if False, you can run a separate visdom server anywhere that consumes the checkpoints')
 @click.option('--remote-transfer-cmd', type=str, default=None, help='module and function to be used to transfer remote files to target storage location, for example mymodule.myfunction')
+@click.option('--dataset-mode', type=str, default='aligned',
+              help='chooses how datasets are loaded. [unaligned | aligned | single | colorization]')
+@click.option('--padding', type=str, default='zero',
+              help='chooses the type of padding used by resnet generator. [reflect | zero]')
+# DeepLIIFExt params
+@click.option('--seg-gen', type=bool, default=True, help='True (Translation and Segmentation), False (Only Translation).')
+@click.option('--net-ds', type=str, default='n_layers',
+              help='specify discriminator architecture for segmentation task [basic | n_layers | pixel]. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator')
+@click.option('--net-gs', type=str, default='unet_512',
+              help='specify generator architecture for segmentation task [resnet_9blocks | resnet_6blocks | unet_512 | unet_256 | unet_128]')
+@click.option('--gan-mode', type=str, default='vanilla',
+              help='the type of GAN objective for translation task. [vanilla| lsgan | wgangp]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.')
+@click.option('--gan-mode-s', type=str, default='lsgan',
+              help='the type of GAN objective for segmentation task. [vanilla| lsgan | wgangp]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.')
+# DDP related arguments
 @click.option('--local-rank', type=int, default=None, help='placeholder argument for torchrun, no need for manual setup')
-@click.option('--seed', type=int, default=None, help='basic seed to be used for deterministic training, default to None (non-deterministic)')
 @click.option('--use-torchrun', type=str, default=None, help='provide torchrun options, all in one string, for example "-t3 --log_dir ~/log/ --nproc_per_node 1"; if your pytorch version is older than 1.10, torch.distributed.launch will be called instead of torchrun')
 def trainlaunch(**kwargs):
     """
@@ -448,6 +465,7 @@ def trainlaunch(**kwargs):
             elif args[i-1] not in l_arg_skip and arg not in l_arg_skip:
                 # if the previous element is not an option name to skip AND if the current element is not an option to remove
                 args_final.append(arg)
+    
 
     ## add quotes back to the input arg that had quotes, e.g., experiment name
     args_final = [f'"{arg}"' if ' ' in arg else arg for arg in args_final]
@@ -457,16 +475,29 @@ def trainlaunch(**kwargs):
 
     #### locate train.py
     import deepliif
-    path_train_py = deepliif.__path__[0]+'/train.py'
+    path_train_py = deepliif.__path__[0]+'/scripts/train.py'
+    
+    #### find out GPUs to use
+    gpu_ids = [args_final[i+1] for i,v in enumerate(args_final) if v=='--gpu-ids']
+    if len(gpu_ids) > 0 and gpu_ids[0] == -1:
+        gpu_ids = []
+    
+    if len(gpu_ids) > 0:
+        opt_env = f"CUDA_VISIBLE_DEVICES=\"{','.join(gpu_ids)}\""
+    else:
+        opt_env = ''
 
     #### execute train.py
     if kwargs['use_torchrun']:
         if version.parse(torch.__version__) >= version.parse('1.10.0'):
-            subprocess.run(f'torchrun {kwargs["use_torchrun"]} {path_train_py} {options}',shell=True)
+            cmd = f'{opt_env} torchrun {kwargs["use_torchrun"]} {path_train_py} {options}'
         else:
-            subprocess.run(f'python -m torch.distributed.launch {kwargs["use_torchrun"]} {path_train_py} {options}',shell=True)
+            cmd = f'{opt_env} python -m torch.distributed.launch {kwargs["use_torchrun"]} {path_train_py} {options}'
     else:
-        subprocess.run(f'python {path_train_py} {options}',shell=True)
+        cmd = f'{opt_env} python {path_train_py} {options}'
+    
+    print('Executing command:',cmd)
+    subprocess.run(cmd,shell=True)
 
 
 

From 04db7ce991ea90e9368c2fdaf8c91720a616d348 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Tue, 27 Feb 2024 14:50:14 +0000
Subject: [PATCH 03/41] added auto-determination of tile size for data
 augementation

---
 Image_Processing/Augmentation.py               | 18 ++++++++++--------
 .../Image_Processing_Helper_Functions.py       | 17 +++++++++++++----
 2 files changed, 23 insertions(+), 12 deletions(-)

diff --git a/Image_Processing/Augmentation.py b/Image_Processing/Augmentation.py
index b215eb0..a3fa06a 100644
--- a/Image_Processing/Augmentation.py
+++ b/Image_Processing/Augmentation.py
@@ -6,12 +6,13 @@
 
 
 class Augmentation:
-    def __init__(self, images):
+    def __init__(self, images, tile_size=512):
         self.images = images
         self.shape = self.images[list(self.images.keys())[0]].shape
         self.rotation_angle = np.random.choice([0, 90, 180, 270], 1)[0]
         # self.zoom_value = random.randint(0, 5)
         self.alpha_affine = 0.1
+        self.tile_size = tile_size
 
     def pipeline(self):
         """
@@ -30,12 +31,13 @@ def zoom(self):
         :return:
         """
         new_size = random.randint(int(self.shape[0] * 0.75), self.shape[0])
+        assert self.shape[1] - new_size >= 0, f'self.shape[1] - new_size ({self.shape[1]} - {new_size})should not be negative'
         start_point = (random.randint(0, self.shape[0] - new_size), random.randint(0, self.shape[1] - new_size))
         for key in self.images.keys():
             try:
-                self.images[key] = cv2.resize(self.images[key][start_point[0]: start_point[0] + new_size, start_point[1]: start_point[1] + new_size], (512, 512))
-            except:
-                print(key + ' not available')
+                self.images[key] = cv2.resize(self.images[key][start_point[0]: start_point[0] + new_size, start_point[1]: start_point[1] + new_size], (self.tile_size, self.tile_size))
+            except Exception as e:
+                print(e)
 
     def rotate(self):
         """
@@ -47,8 +49,8 @@ def rotate(self):
         for key in self.images.keys():
             try:
                 self.images[key] = ndimage.rotate(self.images[key], self.rotation_angle, reshape=False)
-            except:
-                print(key + ' not available')
+            except Exception as e:
+                print(e)
 
     def elastic_transform(self, random_state=None):
         """
@@ -78,5 +80,5 @@ def elastic_transform(self, random_state=None):
         for key in self.images.keys():
             try:
                 self.images[key] = cv2.warpAffine(self.images[key], M, shape_size[::-1], borderMode=cv2.BORDER_REFLECT_101)
-            except:
-                print(key + ' not available')
\ No newline at end of file
+            except Exception as e:
+                print(e)
diff --git a/Image_Processing/Image_Processing_Helper_Functions.py b/Image_Processing/Image_Processing_Helper_Functions.py
index 8f2e6b2..b71e552 100644
--- a/Image_Processing/Image_Processing_Helper_Functions.py
+++ b/Image_Processing/Image_Processing_Helper_Functions.py
@@ -87,7 +87,7 @@ def create_training_testing_dataset_from_given_directory(input_dir, output_dir,
             cv2.imwrite(os.path.join(all_dirs[i], filename), all_images[filename])
 
 
-def augment_set(input_dir, output_dir, aug_no=9, modality_types=['hematoxylin', 'CD3', 'PanCK'], tile_size=512):
+def augment_set(input_dir, output_dir, aug_no=9, modality_types=['hematoxylin', 'CD3', 'PanCK']):
     """
     This function augments a co-aligned dataset.
 
@@ -105,20 +105,29 @@ def augment_set(input_dir, output_dir, aug_no=9, modality_types=['hematoxylin',
     """
     if not os.path.exists(output_dir):
         os.makedirs(output_dir)
-    images = os.listdir(input_dir)
-    for img in images:
+    images_original = os.listdir(input_dir)
+    print(f'{len(images_original)} images found')
+    
+    count = 0
+    for i,img in enumerate(images_original):
         augmented = 0
         while augmented < aug_no:
             images = {}
             image = cv2.imread(os.path.join(input_dir, img))
+            if i == 0:
+                tile_size = image.shape[0]
+            assert image.shape[1] >= len(modality_types) * tile_size, f'image width ({image.shape[1]}) is not enough for {len(modality_types)} modalities with tile size {tile_size}'
             for i in range(0, len(modality_types)):
                 images[modality_types[i]] = image[:, i * tile_size: (i + 1) * tile_size]
             new_images = images.copy()
-            aug = Augmentation(new_images)
+            aug = Augmentation(new_images, tile_size)
             aug.pipeline()
             cv2.imwrite(os.path.join(output_dir, img.replace('.png', '_' + str(augmented) + '.png')),
                         np.concatenate(list(new_images.values()), 1))
             augmented += 1
+        count += 1
+        if count % 10 == 0 or count == len(images_original):
+             print(f'Done {count}/{len(images_original)}')
 
 
 def augment_created_dataset(input_dir, output_dir, aug_no=9, modality_types=['hematoxylin', 'CD3', 'PanCK'], tile_size=512):

From d0033f4268c2129c45dd7357441568998b7e19de Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Tue, 27 Feb 2024 14:55:38 +0000
Subject: [PATCH 04/41] allowed change of subfolder & batchsize when loading
 data (the former for val during training and the latter for batchsize change
 during val/test)

---
 deepliif/data/__init__.py        | 14 +++++++-------
 deepliif/data/aligned_dataset.py |  5 ++---
 2 files changed, 9 insertions(+), 10 deletions(-)

diff --git a/deepliif/data/__init__.py b/deepliif/data/__init__.py
index 6a4e6c4..2966985 100644
--- a/deepliif/data/__init__.py
+++ b/deepliif/data/__init__.py
@@ -55,28 +55,28 @@ def get_option_setter(dataset_name):
     return dataset_class.modify_commandline_options
 
 
-def create_dataset(opt):
+def create_dataset(opt, phase=None, batch_size=None):
     """Create a dataset given the option.
 
     This function wraps the class CustomDatasetDataLoader.
         This is the main interface between this package and 'train.py'/'test.py'
     """
-    return CustomDatasetDataLoader(opt)
+    return CustomDatasetDataLoader(opt, phase=phase if phase else opt.phase, batch_size=batch_size if batch_size else opt.batch_size)
 
 
 class CustomDatasetDataLoader(object):
     """Wrapper class of Dataset class that performs multi-threaded data loading"""
 
-    def __init__(self, opt):
+    def __init__(self, opt, phase=None, batch_size=None):
         """Initialize this class
 
         Step 1: create a dataset instance given the name [dataset_mode]
         Step 2: create a multi-threaded data loader.
         """
-        self.batch_size = opt.batch_size
+        self.batch_size = batch_size if batch_size else opt.batch_size
         self.max_dataset_size = opt.max_dataset_size
         dataset_class = find_dataset_using_name(opt.dataset_mode)
-        self.dataset = dataset_class(opt)
+        self.dataset = dataset_class(opt, phase=phase if phase else opt.phase)
         print("dataset [%s] was created" % type(self.dataset).__name__)
 
         sampler = None
@@ -95,7 +95,7 @@ def seed_worker(worker_id):
             self.dataloader = torch.utils.data.DataLoader(
                 self.dataset,
                 sampler=sampler,
-                batch_size=opt.batch_size,
+                batch_size=batch_size,
                 shuffle=not opt.serial_batches if sampler is None else False,
                 num_workers=int(opt.num_threads)
             )
@@ -106,7 +106,7 @@ def seed_worker(worker_id):
             self.dataloader = torch.utils.data.DataLoader(
                 self.dataset,
                 sampler=sampler,
-                batch_size=opt.batch_size,
+                batch_size=batch_size,
                 shuffle=not opt.serial_batches if sampler is None else False,
                 num_workers=int(opt.num_threads),
                 worker_init_fn=seed_worker,
diff --git a/deepliif/data/aligned_dataset.py b/deepliif/data/aligned_dataset.py
index aa5928b..b1e7501 100644
--- a/deepliif/data/aligned_dataset.py
+++ b/deepliif/data/aligned_dataset.py
@@ -11,7 +11,7 @@ class AlignedDataset(BaseDataset):
     During test time, you need to prepare a directory '/path/to/data/test'.
     """
 
-    def __init__(self, opt):
+    def __init__(self, opt, phase='train'):
         """Initialize this dataset class.
 
         Parameters:
@@ -19,7 +19,7 @@ def __init__(self, opt):
         """
         BaseDataset.__init__(self, opt.dataroot)
         self.preprocess = opt.preprocess
-        self.dir_AB = os.path.join(opt.dataroot, opt.phase)  # get the image directory
+        self.dir_AB = os.path.join(opt.dataroot, phase)  # get the image directory
         self.AB_paths = sorted(make_dataset(self.dir_AB, opt.max_dataset_size))  # get image paths
         assert(opt.load_size >= opt.crop_size)   # crop_size should be smaller than the size of loaded image
         self.input_nc = opt.output_nc if opt.direction == 'BtoA' else opt.input_nc
@@ -95,7 +95,6 @@ def __getitem__(self, index):
                 A = AB.crop((w2 * i, 0, w2 * (i+1), h))
                 A = A_transform(A)
                 A_Array.append(A)
-            
             for i in range(self.input_no, self.input_no + self.modalities_no + 1):
                 B = AB.crop((w2 * i, 0, w2 * (i + 1), h))
                 B = B_transform(B)

From 4aa5194b7462acd8153b079d138832f9202263b1 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Tue, 27 Feb 2024 14:57:25 +0000
Subject: [PATCH 05/41] updated segmentation metrics calculation for
 deepliifext

---
 DeepLIIF_Statistics/ComputeStatistics.py    | 16 ++++++++++------
 DeepLIIF_Statistics/Segmentation_Metrics.py | 10 +++++++---
 2 files changed, 17 insertions(+), 9 deletions(-)

diff --git a/DeepLIIF_Statistics/ComputeStatistics.py b/DeepLIIF_Statistics/ComputeStatistics.py
index 804945e..7b35192 100644
--- a/DeepLIIF_Statistics/ComputeStatistics.py
+++ b/DeepLIIF_Statistics/ComputeStatistics.py
@@ -21,7 +21,7 @@
 parser.add_argument('--gt_path', type=str, required=True)
 parser.add_argument('--model_path', type=str, required=True)
 parser.add_argument('--output_path', type=str, required=True)
-parser.add_argument('--model_name', type=str, required=False, default='DeepLIIF')
+parser.add_argument('--model_name', type=str, required=False, default='')
 parser.add_argument('--mode', type=str, default='Segmentation',
                     help='Mode of the statistics computation including Segmentation, ImageSynthesis, All')
 parser.add_argument('--raw_segmentation', action='store_true')
@@ -30,7 +30,8 @@
                     help='Batch size to use')
 parser.add_argument('--num_workers', type=int, default=8,
                     help='Number of processes to use for data loading')
-parser.add_argument('--image_types', type=str, default='Hema,DAPI,Lap2,Marker')
+parser.add_argument('--image_types', type=str, default='Hema,DAPI,Lap2,Marker', help='These are non-seg modalities to be evaluated.')
+parser.add_argument('--seg_type', type=str, default='Seg', help='This is the seg modality to be evaluated.')
 
 
 class Statistics:
@@ -45,6 +46,7 @@ def __init__(self, args):
         self.num_workers = args.num_workers
         self.device = args.device
         self.image_types = args.image_types.replace(' ', '').split(',')
+        self.seg_type = args.seg_type
 
         # Image Similarity Metrics
         self.inception_avg = collections.defaultdict(float)
@@ -184,8 +186,9 @@ def compute_segmentation_metrics(self):
         thresh = 100
         boundary_thresh = 100
         noise_size = 50
-        print(thresh, noise_size)
-        self.segmentation_info, self.segmentation_metrics = compute_segmentation_metrics(self.gt_path, self.model_path, self.model_name, image_size=512, thresh=thresh, boundary_thresh=boundary_thresh, small_object_size=noise_size, raw_segmentation=self.raw_segmentation)
+        
+        self.segmentation_info, self.segmentation_metrics = compute_segmentation_metrics(self.gt_path, self.model_path, self.model_name, image_size=512, thresh=thresh, boundary_thresh=boundary_thresh, small_object_size=noise_size, raw_segmentation=self.raw_segmentation, suffix_seg=self.seg_type)
+        assert len(self.segmentation_info) > 0, 'No segmentation results returned; one typical cause is a wrong --seg-type and/or --image-types that cannot be found in any image filename'
         self.write_list_to_csv(self.segmentation_info, self.segmentation_info[0].keys(),
                                filename='segmentation_info_' + self.mode + '_' + self.model_name + '_' + str(thresh) + '_' + str(noise_size) + '.csv')
         for key in self.segmentation_metrics:
@@ -202,14 +205,14 @@ def compute_statistics(self):
         self.create_all_info()
 
     def write_dict_to_csv(self, info_dict, csv_columns, filename='info.csv'):
-        print('Writing in csv')
+        print('Writing in csv',os.path.join(self.output_path, filename))
         info_csv = open(os.path.join(self.output_path, filename), 'w')
         writer = csv.DictWriter(info_csv, fieldnames=csv_columns)
         writer.writeheader()
         writer.writerow(info_dict)
 
     def write_list_to_csv(self, info_dict, csv_columns, filename='info.csv'):
-        print('Writing in csv')
+        print('Writing in csv',os.path.join(self.output_path, filename))
         info_csv = open(os.path.join(self.output_path, filename), 'w')
         writer = csv.DictWriter(info_csv, fieldnames=csv_columns)
         writer.writeheader()
@@ -228,3 +231,4 @@ def write_list_to_csv(self, info_dict, csv_columns, filename='info.csv'):
         stat.compute_segmentation_metrics()
     elif stat.mode == 'ImageSynthesis':
         stat.compute_image_similarity_metrics()
+    stat.create_all_info()
diff --git a/DeepLIIF_Statistics/Segmentation_Metrics.py b/DeepLIIF_Statistics/Segmentation_Metrics.py
index 65a62f2..4e2c50a 100644
--- a/DeepLIIF_Statistics/Segmentation_Metrics.py
+++ b/DeepLIIF_Statistics/Segmentation_Metrics.py
@@ -102,12 +102,16 @@ def compute_aji(gt_image, mask_image):
     return aji
 
 
-def compute_segmentation_metrics(gt_dir, model_dir, model_name, image_size=512, thresh=100, boundary_thresh=100, small_object_size=20, raw_segmentation=True):
+def compute_segmentation_metrics(gt_dir, model_dir, model_name, image_size=512, thresh=100, boundary_thresh=100, small_object_size=20, raw_segmentation=True, suffix_seg=None):
     info_dict = []
     metrics = collections.defaultdict(float)
     images = os.listdir(model_dir)
+
     counter = 0
-    postfix = '_Seg.png' if raw_segmentation else '_SegRefined.png'
+    if suffix_seg is not None:
+        postfix = f'_{suffix_seg}.png'
+    else:
+        postfix = '_Seg.png' if raw_segmentation else '_SegRefined.png'
     for mask_name in images:
         if postfix in mask_name:
             counter += 1
@@ -123,7 +127,7 @@ def compute_segmentation_metrics(gt_dir, model_dir, model_name, image_size=512,
             positive_mask[positive_mask > 0] = 1
             negative_mask[negative_mask > 0] = 1
 
-            gt_img = cv2.cvtColor(cv2.imread(os.path.join(gt_dir, mask_name.replace(postfix, '.png'))), cv2.COLOR_BGR2RGB)
+            gt_img = cv2.cvtColor(cv2.imread(os.path.join(gt_dir, mask_name)), cv2.COLOR_BGR2RGB)
             gt_img = cv2.resize(gt_img, (image_size, image_size))
 
             positive_gt = gt_img[:, :, 0]

From bcd4d926328fa608b78de90374e5988c79a6aa69 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Tue, 5 Mar 2024 16:01:04 +0000
Subject: [PATCH 06/41] added resizeconv, currently inferred from model name /
 dir

---
 deepliif/models/SDG_model.py | 14 ++++++++------
 deepliif/models/networks.py  | 24 ++++++++++++++++++------
 2 files changed, 26 insertions(+), 12 deletions(-)

diff --git a/deepliif/models/SDG_model.py b/deepliif/models/SDG_model.py
index ed0896d..5798eed 100644
--- a/deepliif/models/SDG_model.py
+++ b/deepliif/models/SDG_model.py
@@ -15,6 +15,8 @@ def __init__(self, opt):
         BaseModel.__init__(self, opt)
 
         self.mod_gen_no = self.opt.modalities_no
+        opt.resize_conv = 'resizeconv' in opt.checkpoints_dir or 'resizeconv' in opt.name
+            
 
         # weights of the modalities in generating segmentation mask
         self.seg_weights = [0, 0, 0]
@@ -55,7 +57,7 @@ def __init__(self, opt):
         self.netG = [None for _ in range(self.mod_gen_no)]
         for i in range(self.mod_gen_no):
             self.netG[i] = networks.define_G(self.opt.input_nc * self.opt.input_no, self.opt.output_nc, self.opt.ngf, self.opt.net_g, self.opt.norm,
-                                             not self.opt.no_dropout, self.opt.init_type, self.opt.init_gain, self.opt.gpu_ids, self.opt.padding)
+                                             not self.opt.no_dropout, self.opt.init_type, self.opt.init_gain, self.opt.gpu_ids, self.opt.padding, resize_conv=opt.resize_conv)
             print('***************************************')
             print(self.opt.input_nc, self.opt.output_nc, self.opt.ngf, self.opt.net_g, self.opt.norm,
                                              not self.opt.no_dropout, self.opt.init_type, self.opt.init_gain, self.opt.gpu_ids, self.opt.padding)
@@ -159,15 +161,15 @@ def backward_G(self):
         for i in range(self.mod_gen_no):
             self.loss_G_L1.append(self.criterionSmoothL1(self.fake_B[i], self.real_B[i]) * self.opt.lambda_L1)
 
-        #self.loss_G_VGG = []
-        #for i in range(self.mod_gen_no):
-        #    self.loss_G_VGG.append(self.criterionVGG(self.fake_B[i], self.real_B[i]) * self.opt.lambda_feat)
+        self.loss_G_VGG = []
+        for i in range(self.mod_gen_no):
+            self.loss_G_VGG.append(self.criterionVGG(self.fake_B[i], self.real_B[i]) * self.opt.lambda_feat)
 
         # self.loss_G = (self.loss_G_GAN[0] + self.loss_G_L1[0]) * self.loss_G_weights[0]
         self.loss_G = torch.tensor(0., device=self.device)
         for i in range(0, self.mod_gen_no):
-            self.loss_G += (self.loss_G_GAN[i] + self.loss_G_L1[i]) * self.loss_G_weights[i]
-        #    self.loss_G += (self.loss_G_GAN[i] + self.loss_G_L1[i] + self.loss_G_VGG[i]) * self.loss_G_weights[i]
+            #self.loss_G += (self.loss_G_GAN[i] + self.loss_G_L1[i]) * self.loss_G_weights[i]
+            self.loss_G += (self.loss_G_GAN[i] + self.loss_G_L1[i] + self.loss_G_VGG[i]) * self.loss_G_weights[i]
         self.loss_G.backward()
 
     def optimize_parameters(self):
diff --git a/deepliif/models/networks.py b/deepliif/models/networks.py
index ea99f6a..989639f 100644
--- a/deepliif/models/networks.py
+++ b/deepliif/models/networks.py
@@ -125,7 +125,7 @@ def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]):
     return net
 
 
-def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[], padding_type='reflect'):
+def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[], padding_type='reflect', resize_conv=False):
     """Create a generator
 
     Parameters:
@@ -156,7 +156,7 @@ def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, in
     norm_layer = get_norm_layer(norm_type=norm)
 
     if netG == 'resnet_9blocks':
-        net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9, padding_type=padding_type)
+        net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9, padding_type=padding_type, resize_conv=resize_conv)
     elif netG == 'resnet_6blocks':
         net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6, padding_type=padding_type)
     elif netG == 'unet_128':
@@ -332,9 +332,12 @@ class ResnetGenerator(nn.Module):
     """Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations.
 
     We adapt Torch code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style)
+    
+    Resize-conv: optional replacement of ConvTranspose2d
+    https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/190#issuecomment-358546675
     """
 
-    def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='zero'):
+    def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='zero', resize_conv=False):
         """Construct a Resnet-based generator
 
         Parameters:
@@ -346,6 +349,7 @@ def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_d
             n_blocks (int)      -- the number of ResNet blocks
             padding_type (str)  -- the name of padding layer in conv layers: reflect | replicate | zero
         """
+        print('resize_conv',resize_conv)
         assert(n_blocks >= 0)
         super(ResnetGenerator, self).__init__()
         if type(norm_layer) == functools.partial:
@@ -378,11 +382,19 @@ def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_d
 
         for i in range(n_downsampling):  # add upsampling layers
             mult = 2 ** (n_downsampling - i)
-            model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),
+            if resize_conv:
+                upsample_layer = [#nn.Upsample(scale_factor = 2, mode='bilinear',align_corners=True),
+                                  nn.Upsample(scale_factor = 2, mode='nearest'),
+                                  nn.ReflectionPad2d(1),
+                                  nn.Conv2d(ngf * mult, int(ngf * mult / 2),
+                                  kernel_size=3, stride=1, padding=0)]
+            else:
+                upsample_layer = [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),
                                          kernel_size=3, stride=2,
                                          padding=1, output_padding=1,
-                                         bias=use_bias),
-                      norm_layer(int(ngf * mult / 2)),
+                                         bias=use_bias)]
+            model += upsample_layer
+            model += [norm_layer(int(ngf * mult / 2)),
                       nn.ReLU(True)]
 
         if padding_type == 'reflect':

From c03245b94dd030d7ef8a56af365630f2cefd9b9c Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Thu, 7 Mar 2024 20:56:10 +0000
Subject: [PATCH 07/41] fixed case when SDG has only 1 input modality

---
 deepliif/models/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/deepliif/models/__init__.py b/deepliif/models/__init__.py
index 6c51259..61845d6 100644
--- a/deepliif/models/__init__.py
+++ b/deepliif/models/__init__.py
@@ -231,7 +231,7 @@ def run_dask(img, model_path, eager_mode=False, opt=None):
     model_dir = os.getenv('DEEPLIIF_MODEL_DIR', model_path)
     nets = init_nets(model_dir, eager_mode, opt)
     
-    if opt.input_no > 1:
+    if opt.input_no > 1 or opt.model == 'SDG':
         l_ts = [transform(img_i.resize((opt.scale_size,opt.scale_size))) for img_i in img]
         ts = torch.cat(l_ts, dim=1)
     else:

From a497cf46883b46511c34c05af231c176a822287a Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Thu, 7 Mar 2024 20:59:54 +0000
Subject: [PATCH 08/41] added an experimental submodule to facilitate model
 evaluation and metric calculation

---
 deepliif/stat/ComputeStatistics.py           | 268 ++++++++
 deepliif/stat/Create_Loss_Diagram.py         |  81 +++
 deepliif/stat/HelperFunctions.py             | 667 +++++++++++++++++++
 deepliif/stat/PostProcessSegmentationMask.py | 171 +++++
 deepliif/stat/Segmentation_Metrics.py        | 237 +++++++
 deepliif/stat/__init__.py                    | 323 +++++++++
 deepliif/stat/fid.py                         | 334 ++++++++++
 deepliif/stat/fid_official_tf.py             | 370 ++++++++++
 deepliif/stat/inception_score.py             | 100 +++
 deepliif/stat/swd.py                         | 158 +++++
 10 files changed, 2709 insertions(+)
 create mode 100644 deepliif/stat/ComputeStatistics.py
 create mode 100644 deepliif/stat/Create_Loss_Diagram.py
 create mode 100644 deepliif/stat/HelperFunctions.py
 create mode 100644 deepliif/stat/PostProcessSegmentationMask.py
 create mode 100644 deepliif/stat/Segmentation_Metrics.py
 create mode 100644 deepliif/stat/__init__.py
 create mode 100644 deepliif/stat/fid.py
 create mode 100644 deepliif/stat/fid_official_tf.py
 create mode 100644 deepliif/stat/inception_score.py
 create mode 100644 deepliif/stat/swd.py

diff --git a/deepliif/stat/ComputeStatistics.py b/deepliif/stat/ComputeStatistics.py
new file mode 100644
index 0000000..ca471fb
--- /dev/null
+++ b/deepliif/stat/ComputeStatistics.py
@@ -0,0 +1,268 @@
+import os
+import cv2
+import numpy as np
+import csv
+from numba import cuda
+import time
+
+from .Segmentation_Metrics import compute_segmentation_metrics
+#from fid_official_tf import calculate_fid_given_paths
+from .fid import calculate_fid_given_paths
+from .inception_score import calculate_inception_score
+
+from skimage.metrics import structural_similarity as ssim
+from skimage.metrics import peak_signal_noise_ratio as psnr
+from skimage.metrics import mean_squared_error
+from skimage import img_as_float, io, measure
+from skimage.color import rgb2gray
+import collections
+from .swd import compute_swd
+
+
+"""
+params:
+  gt_path
+  model_path
+  output_path
+  model_name
+  mode: Mode of the statistics computation including Segmentation, ImageSynthesis, All
+  raw_segmentation
+  device: Device to use. Like cuda, cuda:0 or cpu
+  batch_size: Batch size to use
+  num_workers: Number of processes to use for data loading
+  image_types: These are non-seg modalities to be evaluated.
+  seg_type: This is the seg modality to be evaluated.
+"""
+
+class Statistics:
+    def __init__(self, gt_path, model_path, output_path, model_name='', mode='Segmentation',
+                 raw_segmentation=False, device='cuda', batch_size=50, num_workers=8, 
+                 image_types='Hema,DAPI,Lap2,Marker', seg_type='Seg'):
+        self.gt_path = gt_path
+        self.model_path = model_path
+        self.output_path = output_path
+        self.model_name = model_name
+        self.mode = mode
+        self.raw_segmentation = raw_segmentation
+        self.batch_size = batch_size
+        self.num_workers = num_workers
+        self.device = device
+        self.image_types = image_types.replace(' ', '').split(',')
+        self.seg_type = seg_type
+
+        # Image Similarity Metrics
+        self.inception_avg = collections.defaultdict(float)
+        self.inception_std = collections.defaultdict(float)
+
+        self.mse_avg = collections.defaultdict(float)
+        self.mse_std = collections.defaultdict(float)
+
+        self.ssim_avg = collections.defaultdict(float)
+        self.ssim_std = collections.defaultdict(float)
+
+        self.psnr_avg = collections.defaultdict(float)
+        self.psnr_std = collections.defaultdict(float)
+        
+        self.fid_value = collections.defaultdict(float)
+        self.swd_value = collections.defaultdict(float)
+
+        self.all_info = {}
+        self.all_info['Model'] = self.model_name
+
+        # Segmentation Metrics
+        self.segmentation_metrics = collections.defaultdict(float)
+        self.segmentation_info = None
+
+        if not os.path.exists(self.output_path):
+            os.makedirs(self.output_path)
+
+    def compute_mse_ssim_scores(self):
+        for img_type in self.image_types:
+            images = os.listdir(self.model_path)
+            mse_arr = []
+            ssim_arr = []
+            # mse_info = []
+            for img_name in images:
+                if img_type in img_name:
+                    orig_img = img_as_float(rgb2gray(io.imread(os.path.join(self.gt_path, img_name))))
+                    mask_img = img_as_float(rgb2gray(io.imread(os.path.join(self.model_path, img_name))))
+
+                    mse_mask = mean_squared_error(orig_img, mask_img)
+                    ssim_mask = ssim(orig_img, mask_img, multichannel=True, gaussian_weights=True, sigma=1.5, use_sample_covariance=False, data_range=1)
+
+                    mse_arr.append(mse_mask)
+                    ssim_arr.append(ssim_mask)
+                    
+                    # mse_info.append({'image_name': img_name, 'image_type':img_type, 'mse': mse_mask, 'ssim': ssim_mask})
+            # self.write_list_to_csv(mse_info, mse_info[0].keys(),
+            #                        filename='inference_info_' + img_type + '_' + self.model_name + '.csv')
+            self.mse_avg[img_type], self.mse_std[img_type] = np.mean(mse_arr), np.std(mse_arr)
+            self.ssim_avg[img_type], self.ssim_std[img_type] = np.mean(ssim_arr), np.std(ssim_arr)
+        self.all_info['mse_avg'] = self.mse_avg
+        self.all_info['mse_std'] = self.mse_std
+        self.all_info['ssim_avg'] = self.ssim_avg
+        self.all_info['ssim_std'] = self.ssim_std
+    
+    def compute_psnr_scores(self):
+        """
+        Peak signal-to-noise ratio
+        """
+        for img_type in self.image_types:
+            images = os.listdir(self.model_path)
+            mse_arr = []
+            score_arr = []
+            # mse_info = []
+            for img_name in images:
+                if img_type in img_name:
+                    orig_img = img_as_float(rgb2gray(io.imread(os.path.join(self.gt_path, img_name))))
+                    mask_img = img_as_float(rgb2gray(io.imread(os.path.join(self.model_path, img_name))))
+                    #print(orig_img)
+
+                    score_arr.append(psnr(orig_img, mask_img, data_range=1))
+            self.psnr_avg[img_type], self.psnr_std[img_type] = np.mean(score_arr), np.std(score_arr)
+        self.all_info['psnr_avg'] = self.psnr_avg
+        self.all_info['psnr_std'] = self.psnr_std
+
+    def compute_inception_score(self):
+        for img_type in self.image_types:
+            images = os.listdir(self.model_path)
+            real_images_array = []
+            for img in images:
+                if img_type in img:
+                    image = cv2.imread(os.path.join(self.model_path, img))
+                    image = cv2.resize(image, (299, 299))
+                    real_images_array.append(image)
+            real_images_array = np.array(real_images_array)
+            self.inception_avg[img_type], self.inception_std[img_type] = calculate_inception_score(real_images_array)
+
+    def compute_fid_score(self):
+        for img_type in self.image_types:
+            os.environ['CUDA_VISIBLE_DEVICES'] = self.gpu
+            self.fid_value[img_type] = calculate_fid_given_paths([self.gt_path, self.model_path], None, low_profile=False)
+            print("FID: ", self.fid_value[img_type])
+            # self.fid_value[img_type] = calculate_fid_given_paths(paths=[self.gt_path, self.model_path], batch_size=self.batch_size, dims=self.fid_dims, num_workers=self.num_workers, mod_type='_' + img_type)
+
+            device = cuda.get_current_device()
+            device.reset()
+
+    def compute_swd(self):
+        for img_type in self.image_types:
+            orig_images = []
+            mask_images = []
+            images = os.listdir(self.model_path)
+            for img_name in images:
+                if img_type in img_name:
+                    orig_img = cv2.cvtColor(cv2.imread(os.path.join(self.gt_path, img_name)), cv2.COLOR_BGR2RGB)
+                    mask_img = cv2.cvtColor(cv2.imread(os.path.join(self.model_path, img_name)), cv2.COLOR_BGR2RGB)
+                    orig_images.append(orig_img)
+                    mask_images.append(mask_img)
+
+            self.swd_value[img_type] = compute_swd(np.array(orig_images), np.array(mask_images), self.device)
+
+    def compute_image_similarity_metrics(self):
+        self.compute_mse_ssim_scores()
+        print('SSIM Computed')
+        self.compute_inception_score()
+        print('inception Computed')
+        self.compute_fid_score()
+        print('fid Computed')
+        self.compute_swd()
+        print('swd Computed')
+
+        for key in self.mse_avg:
+            self.all_info[key + '_' + 'MSE_avg'] = self.mse_avg[key]
+            self.all_info[key + '_' + 'MSE_std'] = self.mse_std[key]
+            self.all_info[key + '_' + 'ssim_avg'] = self.ssim_avg[key]
+            self.all_info[key + '_' + 'ssim_std'] = self.ssim_std[key]
+            self.all_info[key + '_' + 'inception_avg'] = self.inception_avg[key]
+            self.all_info[key + '_' + 'inception_std'] = self.inception_std[key]
+            self.all_info[key + '_' + 'fid_value'] = self.fid_value[key]
+            self.all_info[key + '_' + 'swd_value'] = self.swd_value[key]
+
+    def compute_IHC_scoring(self):
+        images = os.listdir(self.gt_path)
+        IHC_info = []
+        for img in images:
+            gt_image = cv2.cvtColor(cv2.imread(os.path.join(self.gt_path, img)), cv2.COLOR_BGR2RGB)
+            if 'DeepLIIF' in self.model_name:
+                mask_image = cv2.cvtColor(cv2.imread(os.path.join(self.model_path, img.replace('_Seg', '_Seg_Refined'))), cv2.COLOR_BGR2RGB)
+            else:
+                mask_image = cv2.cvtColor(cv2.imread(os.path.join(self.model_path, img)), cv2.COLOR_BGR2RGB)
+            gt_image[gt_image < 10] = 0
+            label_image_red_gt = measure.label(gt_image[:, :, 0], background=0)
+            label_image_blue_gt = measure.label(gt_image[:, :, 2], background=0)
+            number_of_positive_cells_gt = (len(np.unique(label_image_red_gt)) - 1)
+            number_of_negative_cells_gt = (len(np.unique(label_image_blue_gt)) - 1)
+            number_of_all_cells_gt = number_of_positive_cells_gt + number_of_negative_cells_gt
+            gt_IHC_score = number_of_positive_cells_gt / number_of_all_cells_gt if number_of_all_cells_gt > 0 else 0
+
+            mask_image[mask_image < 10] = 0
+            label_image_red_mask = measure.label(mask_image[:, :, 0], background=0)
+            label_image_blue_mask = measure.label(mask_image[:, :, 2], background=0)
+            number_of_positive_cells_mask = (len(np.unique(label_image_red_mask)) - 1)
+            number_of_negative_cells_mask = (len(np.unique(label_image_blue_mask)) - 1)
+            number_of_all_cells_mask = number_of_positive_cells_mask + number_of_negative_cells_mask
+            mask_IHC_score = number_of_positive_cells_mask / number_of_all_cells_mask if number_of_all_cells_mask > 0 else 0
+            diff = abs(gt_IHC_score * 100 - mask_IHC_score * 100)
+            IHC_info.append({'Model': self.model_name, 'Sample': img, 'Diff_IHC_Score': diff})
+        self.write_list_to_csv(IHC_info, IHC_info[0].keys(),
+                               filename='IHC_Scoring_info_' + self.mode + '_' + self.model_name + '.csv')
+
+    def compute_segmentation_metrics(self):
+        # max_dice = [0, 0, 0]
+        # max_AJI = [0, 0, 0]
+        # for thresh in range(60, 150, 10):
+        #     for noise_size in range(10, 80, 20):
+        thresh = 100
+        boundary_thresh = 100
+        noise_size = 50
+        
+        self.segmentation_info, self.segmentation_metrics = compute_segmentation_metrics(self.gt_path, self.model_path, self.model_name, image_size=512, thresh=thresh, boundary_thresh=boundary_thresh, small_object_size=noise_size, raw_segmentation=self.raw_segmentation, suffix_seg=self.seg_type)
+        assert len(self.segmentation_info) > 0, 'No segmentation results returned; one typical cause is a wrong --seg-type and/or --image-types that cannot be found in any image filename'
+        self.write_list_to_csv(self.segmentation_info, self.segmentation_info[0].keys(),
+                               filename='segmentation_info_' + self.mode + '_' + self.model_name + '_' + str(thresh) + '_' + str(noise_size) + '.csv')
+        for key in self.segmentation_metrics:
+            self.all_info[key] = self.segmentation_metrics[key]
+            print(key, self.all_info[key])
+        print('-------------------------------------------------------')
+
+    def create_all_info(self):
+        self.write_dict_to_csv(self.all_info, list(self.all_info.keys()), filename='metrics_' + self.mode + '_' + self.model_name + '.csv')
+
+    def compute_statistics(self):
+        self.compute_image_similarity_metrics()
+        self.compute_segmentation_metrics()
+        self.create_all_info()
+
+    def write_dict_to_csv(self, info_dict, csv_columns, filename='info.csv'):
+        print('Writing in csv',os.path.join(self.output_path, filename))
+        info_csv = open(os.path.join(self.output_path, filename), 'w')
+        writer = csv.DictWriter(info_csv, fieldnames=csv_columns)
+        writer.writeheader()
+        writer.writerow(info_dict)
+
+    def write_list_to_csv(self, info_dict, csv_columns, filename='info.csv'):
+        print('Writing in csv',os.path.join(self.output_path, filename))
+        info_csv = open(os.path.join(self.output_path, filename), 'w')
+        writer = csv.DictWriter(info_csv, fieldnames=csv_columns)
+        writer.writeheader()
+        for data in info_dict:
+            writer.writerow(data)
+    
+    def run(self,write_to_csv=False):
+        if self.mode == 'All':
+            self.compute_statistics()
+            self.compute_IHC_scoring()
+        elif self.mode == 'Segmentation':
+            self.compute_segmentation_metrics()
+        elif self.mode == 'ImageSynthesis':
+            self.compute_image_similarity_metrics()
+        elif self.mode == 'SSIM':
+            self.compute_mse_ssim_scores()
+        elif self.mode == 'Upscaling':
+            self.compute_mse_ssim_scores()
+            self.compute_psnr_scores()
+        if write_to_csv:
+            self.create_all_info()
+        return self.all_info
+    
diff --git a/deepliif/stat/Create_Loss_Diagram.py b/deepliif/stat/Create_Loss_Diagram.py
new file mode 100644
index 0000000..586489e
--- /dev/null
+++ b/deepliif/stat/Create_Loss_Diagram.py
@@ -0,0 +1,81 @@
+import collections
+import numpy as np
+import matplotlib.pyplot as plt
+
+def isfloat(num):
+    try:
+        float(num)
+        return True
+    except ValueError:
+        return False
+
+
+def read_losses(file_name):
+    losses = {}
+    with open(file_name) as f:
+        lines = f.readlines()
+    for line in lines:
+        if line.startswith('(epoch'):
+            line = line.replace(',', '').replace('(', '').replace(')', '').replace(':', '').strip()
+            values = line.split(' ')
+            current_losses = {}
+            epoch_number = -1
+            for i in range(len(values)):
+                if values[i] == 'epoch':
+                    epoch_number = int(values[i + 1])
+                else:
+                    if not isfloat(values[i]) and values[i] != 'time' and values[i] != 'iters' and values[i] != 'data':
+                        print(values[i], values[i + 1])
+                        current_losses[values[i]] = float(values[i + 1])
+
+            losses[epoch_number] = current_losses
+    loss_values = collections.defaultdict(list)
+    for key in losses:
+        loss_values['epoch'].append(key)
+        for k in losses[key].keys():
+            loss_values[k].append(losses[key][k])
+
+    return loss_values
+
+
+def create_loss_diagram(file_name):
+    loss_values = read_losses(file_name)
+    x = loss_values['epoch']
+    plt.figure(figsize=(12, 4))
+    for i in range(1, 6):
+        plt.plot(x, loss_values['G_GAN_' + str(i)], label='G_GAN_' + str(i))
+    # plt.legend()
+    # plt.show()
+
+    # plt.figure()
+    for i in range(1, 6):
+        plt.plot(x, loss_values['G_L1_' + str(i)], label='G_L1_' + str(i))
+    # plt.legend()
+    # plt.show()
+
+    # plt.figure()
+    for i in range(1, 6):
+        plt.plot(x, loss_values['D_real_' + str(i)], label='D_real_' + str(i))
+    # plt.legend()
+    # plt.show()
+
+    # plt.figure()
+    for i in range(1, 6):
+        plt.plot(x, loss_values['D_fake_' + str(i)], label='D_fake_' + str(i))
+    # plt.legend()
+    # plt.gca().legend(loc='center left', bbox_to_anchor=(1, 0.5), ncol=5)
+    plt.legend(ncol=5)
+    plt.show()
+    # plt.scatter(x, y)
+    # plt.plot(x, y)
+    # plt.title("Connected Scatterplot points with line")
+    # plt.xlabel("epoch")
+    # plt.ylabel("G_GAN_1")
+    # plt.show()
+    # figure.tight_layout()
+    # print(loss_values)
+
+
+# create_loss_diagram('D://DeepLIIF//checkpoints//DeepLIIF_Empty_500_Model//loss_log.txt')
+# create_loss_diagram('C://Users//localadmin//Desktop//loss_log_SpectralNorm_SYN.txt')
+create_loss_diagram('C://Users//localadmin//Desktop//loss_log_DeepLIIF.txt')
\ No newline at end of file
diff --git a/deepliif/stat/HelperFunctions.py b/deepliif/stat/HelperFunctions.py
new file mode 100644
index 0000000..11ed519
--- /dev/null
+++ b/deepliif/stat/HelperFunctions.py
@@ -0,0 +1,667 @@
+import csv
+import json
+import math
+import os
+import h5py
+import numpy as np
+# import staintools
+from matplotlib.colors import LinearSegmentedColormap
+from numba import jit
+from scipy import ndimage
+import cv2
+import random
+from skimage.feature import peak_local_max
+import skimage.segmentation
+from skimage.morphology import watershed as ws, remove_small_objects
+import matplotlib.pyplot as plt
+
+def remove_small_objects_from_image(red_channel, min_size=100):
+    red_channel_copy = red_channel.copy()
+    red_channel_copy[red_channel > 0] = 1
+    red_channel_copy = red_channel_copy.astype(np.bool)
+    removed_red_channel = remove_small_objects(red_channel_copy, min_size=min_size).astype(np.uint8)
+    red_channel[removed_red_channel == 0] = 0
+    return red_channel
+
+
+def read_BC_detection_mask(img_name, data_type='test'):
+    img_type = img_name.split('.')[-1]
+    base_dir = '/home/parmida/Downloads/BCData'
+    annotations_dir = os.path.join(base_dir, 'annotations')
+    images_dir = os.path.join(base_dir, 'images')
+    negative_dir = os.path.join(annotations_dir, data_type, 'negative')
+    positive_dir = os.path.join(annotations_dir, data_type, 'positive')
+    images_dir = os.path.join(images_dir, data_type)
+    print(os.path.join(negative_dir, img_name.replace('.png', '.h5')))
+    gt_file_negative = h5py.File(os.path.join(negative_dir, img_name.replace('.' + img_type, '.h5')))
+    coordinates_negative = np.asarray(gt_file_negative['coordinates'])
+    gt_file_positive = h5py.File(os.path.join(positive_dir, img_name.replace('.' + img_type, '.h5')))
+    coordinates_positive = np.asarray(gt_file_positive['coordinates'])
+
+    positive_mask = np.zeros((640, 640), dtype=np.uint8)
+    negative_mask = np.zeros((640, 640), dtype=np.uint8)
+    for coord in coordinates_positive:
+        positive_mask[coord[1], coord[0]] = 255
+
+    for coord in coordinates_negative:
+        negative_mask[coord[1], coord[0]] = 255
+
+    return positive_mask, negative_mask
+
+def read_BC_detection_point(img_name, data_type='test'):
+    img_type = img_name.split('.')[-1]
+    base_dir = '/home/parmida/Downloads/BCData'
+    annotations_dir = os.path.join(base_dir, 'annotations')
+    images_dir = os.path.join(base_dir, 'images')
+    negative_dir = os.path.join(annotations_dir, data_type, 'negative')
+    positive_dir = os.path.join(annotations_dir, data_type, 'positive')
+    images_dir = os.path.join(images_dir, data_type)
+    print(os.path.join(negative_dir, img_name.replace('.png', '.h5')))
+    gt_file_negative = h5py.File(os.path.join(negative_dir, img_name.replace('.' + img_type, '.h5')))
+    coordinates_negative = np.asarray(gt_file_negative['coordinates'])
+    gt_file_positive = h5py.File(os.path.join(positive_dir, img_name.replace('.' + img_type, '.h5')))
+    coordinates_positive = np.asarray(gt_file_positive['coordinates'])
+
+    return coordinates_positive, coordinates_negative
+
+
+
+def compute_TP_FP_of_each_class(image, marked_class):
+    labeled, nr_objects = ndimage.label(image > 0)
+    TP = 0
+    FP = 0
+    for c in range(1, nr_objects):
+        component = np.zeros_like(image)
+        component[labeled == c] = image[labeled == c]
+        component = cv2.morphologyEx(component, cv2.MORPH_DILATE, kernel=np.ones((5, 5)), iterations=1)
+        TP, FP = compute_component_TP_FP(component, marked_class, TP, FP)
+    return TP, FP
+
+
+@jit(nopython=True)
+def compute_component_TP_FP(component, marked_class, TP, FP):
+    indices = np.nonzero(component)
+    cell_flag = False
+    for i in range(len(indices[0])):
+        if marked_class[indices[0][i], indices[1][i]] > 0:
+                TP += 1
+                cell_flag = True
+    if not cell_flag:
+        FP += 1
+    return TP, FP
+
+
+def compute_precision_recall_f1(TP, FP, FN):
+    precision = TP / (TP + FP) if (TP + FP) > 0 else 1
+    recall = TP / (TP + FN) if (TP + FN) > 0 else 1
+    F1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0
+    return precision, recall, F1
+
+
+def mark_Shiraz_image_with_markers(image, immunopositive, immunonegative, immunoTIL):
+    marked_image = image.copy()
+    positive = cv2.morphologyEx(immunopositive, cv2.MORPH_DILATE, kernel=np.ones((5,5)))
+    negative = cv2.morphologyEx(immunonegative, cv2.MORPH_DILATE, kernel=np.ones((5,5)))
+    TIL = cv2.morphologyEx(immunoTIL, cv2.MORPH_DILATE, kernel=np.ones((5,5)))
+    marked_image[positive > 0] = (0,0,255)
+    marked_image[negative > 0] = (255,0,0)
+    marked_image[TIL > 0] = (0,255,0)
+    return marked_image
+
+def read_NuClick_mask(img_name, dir_type='Train'):
+    # image_dir = '/home/parmida/Pathology/IHC_Nuclick/images/Train'
+    mask_dir = '/home/parmida/Pathology/IHC_Nuclick/masks/' + dir_type
+    mask = np.load(os.path.join(mask_dir, img_name.replace('.png', '.npy')))
+    labeled_mask = np.zeros((mask.shape[0], mask.shape[1], 3), dtype=np.uint8)
+    final_mask = np.zeros((mask.shape[0], mask.shape[1], 3), dtype=np.uint8)
+    labels_no = np.max(mask) + 1
+    color_dict = {}
+    color_dict[0] = (0, 0, 0)
+    for i in range(1, labels_no):
+        color_dict[i] = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
+    for i in range(mask.shape[0]):
+        for j in range(mask.shape[1]):
+            labeled_mask[i, j] = color_dict[mask[i, j]]
+            final_mask[i, j] = (0, 0, 0)
+    boundaries = cv2.Canny(labeled_mask, 100, 200)
+    # boundaries = cv2.dilate(boundaries, kernel=np.ones((3, 3), np.uint8))
+    labeled_mask_bw = cv2.cvtColor(labeled_mask, cv2.COLOR_RGB2GRAY)
+    final_mask[labeled_mask_bw > 0] = (0, 0, 255)
+    # final_mask[labeled_mask_bw == 0] = (0, 0, 255)
+    # final_mask[boundaries > 0] = (255, 255, 255)
+
+    contours, hierarchy = cv2.findContours(boundaries,
+                                           cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
+    cv2.drawContours(final_mask, contours, -1, (255, 255, 255), 2)
+    # cv2.imshow('labeled_mask', labeled_mask)
+    # cv2.imshow('boundaries', boundaries)
+    # cv2.imshow('final', final_mask)
+    # cv2.waitKey(0)
+    boundaries[boundaries > 0] = 255
+    return final_mask
+
+
+def get_detection_points(seg_img):
+    seg_img = cv2.resize(seg_img, (640, 640))
+    det_img = np.zeros_like(seg_img)
+    thresh = 50
+    det_img[np.logical_and(seg_img[:, :, 2] > thresh, seg_img[:, :, 2] > seg_img[:, :, 0] + 50)] = (0, 0, 255)
+    det_img[np.logical_and(seg_img[:, :, 0] > thresh, seg_img[:, :, 0] >= seg_img[:, :, 2])] = (255, 0, 0)
+    det_img[seg_img[:, :, 1] > thresh] = 0
+    det_img[:, :, 0] = remove_small_objects_from_image(det_img[:, :, 0], 80)
+    det_img[:, :, 2] = remove_small_objects_from_image(det_img[:, :, 2], 80)
+    det_img[:, :, 0] = ndimage.binary_fill_holes(det_img[:, :, 0]).astype(np.uint8) * 255
+    det_img[:, :, 2] = ndimage.binary_fill_holes(det_img[:, :, 2]).astype(np.uint8) * 255
+    # det_img[:, :, 0] = cv2.morphologyEx(det_img[:, :, 0], cv2.MORPH_ERODE, kernel=np.ones((3, 3)), iterations=2)
+    # det_img[:, :, 2] = cv2.morphologyEx(det_img[:, :, 2], cv2.MORPH_ERODE, kernel=np.ones((3, 3)), iterations=2)
+    # cv2.imshow('det_img', det_img)
+    det_img = np.squeeze(det_img).astype(np.uint8)
+    cells = watershed(det_img)
+    final_cells = []
+    positive_points = []
+    negative_points = []
+    seen = np.zeros((seg_img.shape[0], seg_img.shape[1]), dtype=np.uint8)
+    for i in range(len(cells)):
+        p1 = cells[i]
+        x1, y1, c1 = int(p1[1]), int(p1[0]), int(p1[2])
+        flag = False
+        seen[x1][y1] = 1
+        for j in range(len(cells)):
+            p2 = cells[j]
+            x2, y2, c2 = int(p2[1]), int(p2[0]), int(p2[2])
+            if seen[x2][y2] == 0:
+                if abs(x1 - x2) < 20 and abs(y1 - y2) < 20:
+                    flag = True
+                    # new_cell = int((x1 + x2) / 2), int((y1 + y2) / 2), int((c1 + c2)/2)
+                    # final_cells.append(new_cell)
+        if not flag:
+            final_cells.append(p1)
+            if c1 == 2:
+                positive_points.append((x1, y1))
+            elif c1 == 0:
+                negative_points.append((x1, y1))
+    return final_cells, positive_points, negative_points
+
+def detect_circles(component, output):
+    gray_blurred = cv2.blur(component, (3, 3))
+
+    # Apply Hough transform on the blurred image.
+    detected_circles = cv2.HoughCircles(gray_blurred,
+                                        cv2.HOUGH_GRADIENT, 1, 20, param1=100,
+                                        param2=20, minRadius=1, maxRadius=40)
+    # circles = cv2.HoughCircles(component, cv2.HOUGH_GRADIENT, 1, 10)
+    # ensure at least some circles were found
+    if detected_circles is not None:
+
+        # Convert the circle parameters a, b and r to integers.
+        detected_circles = np.uint16(np.around(detected_circles))
+
+        for pt in detected_circles[0, :]:
+            a, b, r = pt[0], pt[1], pt[2]
+
+            # Draw the circumference of the circle.
+            cv2.circle(output, (a, b), r, (0, 255, 0), 2)
+
+            # Draw a small circle (of radius 1) to show the center.
+            # cv2.circle(output, (a, b), 1, (0, 0, 255), 3)
+            # cv2.imshow("Detected Circle", output)
+            # cv2.waitKey(0)
+            # cv2.imshow('component', component)
+
+def watershed(pred):
+    cells=[]
+    for ch in range(3):
+        gray=pred[:,:,ch]
+        D = ndimage.distance_transform_edt(gray)
+        localMax = peak_local_max(D, indices=False, min_distance=10,exclude_border=False,labels=gray)
+        markers = ndimage.label(localMax, structure=np.ones((3, 3)))[0]
+        labels = ws(-D, markers, mask=gray)
+        for label in np.unique(labels):
+            if label == 0:
+                continue
+            mask = np.zeros(gray.shape, dtype="uint8")
+            mask[labels == label] = 255
+            cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[-2]
+            c = max(cnts, key=cv2.contourArea)
+            ((x, y), _) = cv2.minEnclosingCircle(c)
+            cells.append([x,y,ch])
+    return np.array(cells)
+
+def read_PathoNet_data(img_addr):
+    print(img_addr)
+    points = np.loadtxt(img_addr.replace('.jpg', '_points.txt'))
+    image = cv2.imread(img_addr)
+    # positive_mask = np.zeros((640, 640), dtype=np.uint8)
+    # negative_mask = np.zeros((640, 640), dtype=np.uint8)
+    positive_points = []
+    negative_points = []
+    for p in points:
+        if int(p[2]) == 1:
+            image[int(p[1]), int(p[0])] = (255, 0, 255)
+            negative_points.append((int(p[1]), int(p[0])))
+        else:
+            image[int(p[1]), int(p[0])] = (0, 255, 255)
+            positive_points.append((int(p[1]), int(p[0])))
+    # cv2.imshow('image', image)
+    # cv2.waitKey(0)
+    return positive_points, negative_points
+
+
+def crop_modalities(input_dir, img_name, img_types, location, size, output_dir):
+    for img_type in img_types:
+        image = cv2.imread(os.path.join(input_dir, img_name + img_type + '.png'))
+        crop = image[location[0]:location[0] + size[0], location[1]: location[1] + size[1]]
+        cv2.imwrite(os.path.join(output_dir, 'MYC_' + img_type + '.png'), crop)
+
+
+def read_mask_rcnn_segmentation_masks(input_dir, image_size):
+    images = os.listdir(input_dir)
+    masks = {}
+    for img in images:
+        if '.png' in img and len(img.split('_')) > 5:
+            print(img)
+            splitted = img.split('_')
+            image_name = ''
+            for i in range(0, len(splitted) - 3):
+                image_name += splitted[i] + '_'
+            image_name += splitted[-3]
+            cell_type = 'positive' if splitted[-2] == 1 else 'negative'
+            image = cv2.imread(os.path.join(input_dir, img))
+            image = cv2.resize(image, (image_size, image_size))
+            image_bw = np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)
+            image_bw[image[:,:,0] > 250] = 1
+            image_bw[image[:,:,1] > 250] = 1
+            image_bw[image[:,:,2] > 250] = 1
+            if image_name not in masks.keys():
+                masks[image_name] = {'positive': np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8), 'negative': np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8), 'binary': np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)}
+            masks[image_name][cell_type][image_bw > 0] = 1
+            masks[image_name]['binary'][image_bw > 0] = 1
+    return masks
+
+
+def read_mask_rcnn_detection_masks(input_dir, image_size):
+    images = os.listdir(input_dir)
+    masks = {}
+    for img in images:
+        if '_' in img and '.png' in img:
+            splitted = img.split('_')
+            image_name = ''
+            for i in range(0, len(splitted) - 3):
+                image_name += splitted[i] + '_'
+            image_name += splitted[-3]
+            cell_type = 'positive' if splitted[-2] == '1' else 'negative'
+            image = cv2.imread(os.path.join(input_dir, img))
+            image = cv2.resize(image, (image_size, image_size))
+            image_bw = np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)
+            image_bw[image[:,:,0] > 250] = 1
+            image_bw[image[:,:,1] > 250] = 1
+            image_bw[image[:,:,2] > 250] = 1
+            points = np.nonzero(image_bw)
+            x = points[0]
+            y = points[1]
+            bounding_box = [np.min(x), np.min(y), np.max(x), np.max(y)]
+            center = (int((bounding_box[0] + bounding_box[2]) / 2), int((bounding_box[1] + bounding_box[3]) / 2))
+            if image_name not in masks.keys():
+                masks[image_name] = {'positive': [], 'negative': [], 'binary': []}
+            masks[image_name][cell_type].append(center)
+            masks[image_name]['binary'].append(center)
+    return masks
+
+
+def read_unetplusplus_unet(input_npy, npy_path):
+    # results = np.load(input_dir)
+    imgs = np.load(input_npy)
+    with open(npy_path + 'dict_name.txt', 'r') as f:
+        names = json.load(f)
+    res_path = os.path.dirname(input_npy)
+    for i in range(imgs.shape[0]):
+        img = imgs[i]
+        print(img.shape)
+        plt.imsave(os.path.join(res_path, names[str(i)] + '.png'), (img * 255).astype(np.uint8))
+
+
+def read_Unet_plusplus_detection_masks(input_dir, image_size):
+    images = os.listdir(input_dir)
+    masks = {}
+    for img in images:
+        if '.png' in img:
+            image = cv2.imread(os.path.join(input_dir, img))
+            image = cv2.resize(image, (image_size, image_size))
+            # cv2.imshow('image1', image)
+            img = img.replace('.png', '')
+            new_image = np.zeros_like(image)
+            new_image[image[:,:,0] > 150] = (255, 0, 0)
+            new_image[image[:,:,2] > 150] = (0, 0, 255)
+            # cv2.imshow('image2', new_image)
+            det_img = np.squeeze(new_image).astype(np.uint8)
+            cells = watershed(det_img)
+            final_cells = []
+            positive_points = []
+            negative_points = []
+            seen = np.zeros((new_image.shape[0], new_image.shape[1]), dtype=np.uint8)
+            for i in range(len(cells)):
+                p1 = cells[i]
+                x1, y1, c1 = int(p1[1]), int(p1[0]), int(p1[2])
+                flag = False
+                seen[x1][y1] = 1
+                for j in range(len(cells)):
+                    p2 = cells[j]
+                    x2, y2, c2 = int(p2[1]), int(p2[0]), int(p2[2])
+                    if seen[x2][y2] == 0:
+                        if abs(x1 - x2) < 20 and abs(y1 - y2) < 20:
+                            flag = True
+                            # new_cell = int((x1 + x2) / 2), int((y1 + y2) / 2), int((c1 + c2)/2)
+                            # final_cells.append(new_cell)
+                if not flag:
+                    final_cells.append(p1)
+                    if c1 == 2:
+                        positive_points.append((x1, y1))
+                    elif c1 == 0:
+                        negative_points.append((x1, y1))
+
+            # for p in positive_points:
+            #     new_image[p[0]-5:p[0]+5, p[1]-5:p[1]+5] = (255,0,255)
+            # for p in negative_points:
+            #     new_image[p[0]-5:p[0]+5, p[1]-5:p[1]+5] = (0,255,255)
+
+            # cv2.imshow('image3', new_image)
+            # cv2.waitKey(0)
+            masks[img] = {'positive': [], 'negative': []}
+            masks[img]['positive'] = positive_points
+            masks[img]['negative'] = negative_points
+            # masks[img]['binary'].append(center)
+    return masks
+
+
+def read_Unet_plusplus_segmentation_masks(input_dir, image_size):
+    images = os.listdir(input_dir)
+    masks = {}
+    for img in images:
+        if '.png' in img:
+            image = cv2.imread(os.path.join(input_dir, img))
+            image = cv2.resize(image, (image_size, image_size))
+            img = img.replace('.png', '')
+            new_image = np.zeros_like(image)
+            new_image[image[:,:,0] > 0] = (255, 0, 0)
+            new_image[image[:,:,2] > 0] = (0, 0, 255)
+            masks[img] = {'positive': np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8), 'negative': np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8), 'binary': np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)}
+            masks[img]['positive'][image[:,:,0] > 0] = 1
+            masks[img]['negative'][image[:,:,2] > 0] = 1
+            masks[img]['binary'][image[:,:,0] > 0] = 1
+            masks[img]['binary'][image[:,:,2] > 0] = 1
+    return masks
+
+def read_DeepLIIF_segmentation_masks(input_dir, image_size, thresh=100):
+    images = os.listdir(input_dir)
+    masks = {}
+    for img in images:
+        if '_fake_B_5.png' in img:
+            image = cv2.imread(os.path.join(input_dir, img))
+            image = cv2.resize(image, (image_size, image_size))
+            img = img.replace('_fake_B_5.png', '')
+            new_image = np.zeros_like(image)
+            new_image[np.logical_and(image[:,:,0] > thresh, image[:,:,0] > image[:,:,2])] = (255, 0, 0)
+            new_image[np.logical_and(image[:,:,2] > thresh, image[:,:,2] >= image[:,:,0])] = (0, 0, 255)
+            # new_image[image[:,:,1] > thresh] = 0
+            # new_image[image[:,:,0] > 100] = (255, 0, 0)
+            # new_image[image[:,:,2] > 100] = (0, 0, 255)
+            # cv2.imshow('image', image)
+            # cv2.imshow('new_image', new_image)
+            # cv2.waitKey(0)
+
+            positive_mask = new_image[:, :, 0]
+            negative_mask = new_image[:, :, 2]
+            positive_mask = ndimage.binary_fill_holes(positive_mask, structure=np.ones((5, 5))).astype(np.uint8)
+            negative_mask = ndimage.binary_fill_holes(negative_mask, structure=np.ones((5, 5))).astype(np.uint8)
+            positive_mask = remove_small_objects_from_image(positive_mask, 50)
+            negative_mask = remove_small_objects_from_image(negative_mask, 50)
+            positive_mask[negative_mask > 0] = 0
+            masks[img] = {'positive': np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8), 'negative': np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8), 'binary': np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)}
+            # masks[img]['positive'][image[:,:,0] > thresh] = 1
+            # masks[img]['negative'][image[:,:,2] > thresh] = 1
+            # masks[img]['binary'][image[:,:,0] > thresh] = 1
+            # masks[img]['binary'][image[:,:,2] > thresh] = 1
+            masks[img]['positive'][positive_mask > 0] = 1
+            masks[img]['negative'][negative_mask > 0] = 1
+            masks[img]['binary'][positive_mask > 0] = 1
+            masks[img]['binary'][negative_mask > 0] = 1
+    return masks
+
+
+def read_ki67_detection_points(img):
+    input_dir = '/media/parmida/Work/DetectionDataset/test_Ki67'
+    image_size = 512
+    image = cv2.imread(os.path.join(input_dir, img))
+    image = image[:,5*512:]
+    image = cv2.resize(image, (image_size, image_size))
+    cv2.imshow('mask', image)
+    # cv2.waitKey(0)
+    positive_image = np.zeros((image.shape[0], image.shape[1]))
+    positive_image[image[:, :, 2] > 0] = 1
+    positive_mask = get_centers_of_objects(positive_image)
+
+    negative_image = np.zeros((image.shape[0], image.shape[1]))
+    negative_image[image[:, :, 0] > 0] = 1
+    negative_mask = get_centers_of_objects(negative_image)
+
+    return positive_mask, negative_mask
+
+
+def get_centers_of_objects(image):
+    mask = np.zeros((image.shape[0], image.shape[1]))
+    labeled, nr_objects = ndimage.label(image > 0)
+    for c in range(1, nr_objects):
+        component = np.zeros_like(image)
+        component[labeled == c] = image[labeled == c]
+
+        points = np.nonzero(component)
+        x = points[0]
+        y = points[1]
+        bounding_box = [np.min(x), np.min(y), np.max(x), np.max(y)]
+        center = (int((bounding_box[0] + bounding_box[2]) / 2), int((bounding_box[1] + bounding_box[3]) / 2))
+        mask[center[0], center[1]] = 255
+    return mask
+
+
+def read_Unet_plusplus_boundary_mask_image(img_addr, image_size):
+    print(img_addr)
+    image = cv2.cvtColor(cv2.imread(img_addr), cv2.COLOR_BGR2RGB)
+    image = cv2.resize(image, (image_size, image_size))
+    new_image = np.zeros_like(image)
+    res_image = np.zeros_like(image)
+    new_image[image[:,:,0] > 100] = (255, 0, 0)
+    new_image[image[:,:,2] > 100] = (0, 0, 255)
+    positive_mask = new_image[:,:,0]
+    negative_mask = new_image[:,:,2]
+    contours, hierarchy = cv2.findContours(positive_mask,
+                                           cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
+    cv2.drawContours(res_image, contours, -1, (255, 0, 0), 2)
+    contours, hierarchy = cv2.findContours(negative_mask,
+                                           cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
+    cv2.drawContours(res_image, contours, -1, (0, 0, 255), 2)
+    cv2.imwrite(img_addr.replace('.png', '_Seg.png'), cv2.cvtColor(new_image, cv2.COLOR_BGR2RGB))
+    return res_image, new_image
+
+def read_DeepLIIF_boundary_mask_image(img_addr, image_size, thresh=120):
+    image = cv2.cvtColor(cv2.imread(img_addr), cv2.COLOR_BGR2RGB)
+    # cv2.imshow('image', image)
+    image = cv2.resize(image, (image_size, image_size))
+    new_image = np.zeros_like(image)
+
+    new_image[np.logical_and(image[:, :, 0] > thresh, image[:, :, 0] > image[:, :, 2])] = (255, 0, 0)
+    new_image[np.logical_and(image[:, :, 2] > thresh, image[:, :, 2] >= image[:, :, 0])] = (0, 0, 255)
+
+    new_image[image[:,:,1] > 80] = 0
+    # new_image[image[:,:,0] > 100] = (255, 0, 0)
+    # new_image[image[:,:,2] > 100] = (0, 0, 255)
+    # cv2.imshow('image', image)
+    # cv2.imshow('new_image', new_image)
+    # cv2.waitKey(0)
+
+    positive_mask = new_image[:, :, 0]
+    negative_mask = new_image[:, :, 2]
+    positive_mask = ndimage.binary_fill_holes(positive_mask, structure=np.ones((5, 5))).astype(np.uint8)
+    negative_mask = ndimage.binary_fill_holes(negative_mask, structure=np.ones((5, 5))).astype(np.uint8)
+    positive_mask = remove_small_objects_from_image(positive_mask, 50)
+    negative_mask = remove_small_objects_from_image(negative_mask, 50)
+    # positive_mask[negative_mask > 0] = 0
+    negative_mask[positive_mask > 0] = 0
+
+    # new_image[np.logical_and(image[:,:,0] > thresh, image[:,:,0] > image[:,:,2])] = (255, 0, 0)
+    # new_image[np.logical_and(image[:,:,2] > thresh, image[:,:,2] >= image[:,:,0])] = (0, 0, 255)
+    # new_image[image[:,:,1] > thresh] = 0
+    #
+    res_image = np.zeros_like(image)
+    # positive_mask = new_image[:,:,0]
+    # negative_mask = new_image[:,:,2]
+    # # positive_mask = cv2.morphologyEx(positive_mask, cv2.MORPH_DILATE, kernel=np.ones((3,3)))
+    # # negative_mask = cv2.morphologyEx(negative_mask, cv2.MORPH_DILATE, kernel=np.ones((3,3)))
+    # positive_mask = ndimage.binary_fill_holes(positive_mask, structure=np.ones((5,5))).astype(np.uint8)
+    # negative_mask = ndimage.binary_fill_holes(negative_mask, structure=np.ones((5,5))).astype(np.uint8)
+    # positive_mask = remove_small_objects_from_image(positive_mask, 50)
+    # negative_mask = remove_small_objects_from_image(negative_mask, 50)
+    # # positive_mask[negative_mask > 0] = 0
+    # negative_mask[positive_mask > 0] = 0
+    # negative_mask = mask.copy()
+    new_image = np.zeros_like(image)
+    new_image[positive_mask > 0] = (255,0,0)
+    new_image[negative_mask > 0] = (0,0,255)
+    # cv2.imshow('positive_mask', positive_mask*255)
+    # cv2.imshow('negative_mask', negative_mask*255)
+    # cv2.waitKey(0)
+
+    contours, hierarchy = cv2.findContours(positive_mask,
+                                           cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
+    cv2.drawContours(res_image, contours, -1, (255, 0, 0), 2)
+    contours, hierarchy = cv2.findContours(negative_mask,
+                                           cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
+    cv2.drawContours(res_image, contours, -1, (0, 0, 255), 2)
+    return res_image, new_image
+
+
+
+def create_log_area_mask_cell_type(predicted_mask, gt_mask, index=0, colormap='bwr'):
+    smooth = 0.0001
+    predicted = predicted_mask[:,:,index]
+    gt = gt_mask[:,:,index]
+    # gt[gt_mask[:,:,1] > 0]=0
+    final_mask = np.zeros((predicted.shape[0], predicted.shape[1]))
+
+    labeled, nr_objects = ndimage.label(predicted > 0)
+    labeled_gt, nr_objects_gt = ndimage.label(gt > 0)
+    for c in range(1, nr_objects):
+        component = np.zeros_like(predicted)
+        component[labeled == c] = predicted[labeled == c]
+        nonzeros = np.nonzero(component)
+        component_gt_size = 0
+        # cv2.imshow('component', component)
+        for i in range(len(nonzeros[0])):
+            if gt[nonzeros[0][i], nonzeros[1][i]] > 0 and labeled_gt[nonzeros[0][i], nonzeros[1][i]] > 0:
+                label = labeled_gt[nonzeros[0][i], nonzeros[1][i]]
+                component_gt = np.zeros_like(gt)
+                component_gt[labeled_gt == label] = gt[labeled_gt == label]
+                nonzeros_gt = np.nonzero(component_gt)
+                component_gt_size = len(nonzeros_gt[0])
+                # print(component_gt_size)
+                break
+        # if component_gt_size > 0:
+        component_size = len(nonzeros[0])
+        # print('component_gt_size:', component_gt_size)
+        # print('component_size:', component_size)
+        if component_gt_size == 0:
+            value = 5
+            # print('yes')
+        else:
+            value = np.log2((component_size+smooth)/(component_gt_size+smooth))
+            value = min(value, 2) if value >= 0 else max(value, -2)
+        # print((len(nonzeros[0])+smooth)/(component_gt_size+smooth))
+        # if value < 0:
+        #     print(value)
+        #     print(min(value, 2) if value >= 0 else max(value, -2))
+
+        final_mask[component > 0] = value
+        # cv2.waitKey(0)
+        # final_mask[predicted == 0] = 0
+    # color_range = np.max(final_mask)
+    image_log = np.zeros_like(gt_mask)
+    for i in range(0, final_mask.shape[0]):
+        for j in range(0, final_mask.shape[1]):
+            value = final_mask[i][j]
+            if value == 5:
+                # print('yes!!!!!!!')
+                image_log[i, j] = (255, 255, 0)
+            elif -0.5 <= value <= 0.5:
+                image_log[i, j] = (255, 0, 0) if colormap == 'positive' else (0, 0, 255)
+            elif value > 0.5:
+                image_log[i, j] = (int(127.5/value), 0, 0) if colormap == 'positive' else (0, 0, int(127.5/value))
+            elif value < -0.5:
+                image_log[i, j] = (255, 255 - int(127.5/abs(value)), 255 - int(127.5/abs(value))) if colormap == 'positive' else (255 - int(127.5/abs(value)), 255 - int(127.5/abs(value)), 255)
+                # print((255, 255 - int(127.5/abs(value)), 255 - int(127.5/abs(value))))
+    # plt.tight_layout()
+    # plt.savefig('temp.png', bbox_inches='tight', pad_inches=0)
+    #
+    # image_log = plt.imread('temp.png')
+    # image_log = cv2.resize(image_log, (512, 512))
+    # image_log[predicted_mask[:, :, index] == 0] = 0
+    return image_log
+
+
+def create_log_area_mask(predicted_mask, gt_mask):
+    log_positive = create_log_area_mask_cell_type(predicted_mask, gt_mask, index=0, colormap='positive')
+    log_negative = create_log_area_mask_cell_type(predicted_mask, gt_mask, index=2, colormap='negative')
+    log_positive = (log_positive[:,:,:3] * 255).astype(np.uint8)
+    log_negative = (log_negative[:,:,:3] * 255).astype(np.uint8)
+    log_image = np.zeros_like(predicted_mask)
+    log_image[predicted_mask[:,:,0] > 0] = log_positive[predicted_mask[:,:,0] > 0]
+    log_image[predicted_mask[:,:,2] > 0] = log_negative[predicted_mask[:,:,2] > 0]
+    return log_image
+
+
+
+def create_color_map_image(colormap):
+    image = np.zeros((400, 100))
+    for i in range(image.shape[1]):
+        for j in range(image.shape[0]):
+            image[j, i] = (j - 200) / 100
+    colormap_image = np.zeros((400, 100, 3), dtype=np.uint8)
+    for i in range(0, image.shape[0]):
+        for j in range(0, image.shape[1]):
+            value = image[i][j]
+            if -0.5 <= value <= 0.5:
+                colormap_image[i, j] = (255, 0, 0) if colormap == 'positive' else (0, 0, 255)
+            elif value > 0.5:
+                colormap_image[i, j] = (int(127.5/value), 0, 0) if colormap == 'positive' else (0, 0, int(127.5/value))
+            elif value < -0.5:
+                colormap_image[i, j] = (255, 255 - int(127.5/abs(value)), 255 - int(127.5/abs(value))) if colormap == 'positive' else (255 - int(127.5/abs(value)), 255 - int(127.5/abs(value)), 255)
+    colormap_image = cv2.rotate(colormap_image, cv2.ROTATE_90_COUNTERCLOCKWISE)
+    return colormap_image
+
+
+def read_image_write_crop_parts(input_dir, img_name, location, crop_size, output_dir):
+    types = ['', '_DAPI', '_DAPILap2', '_Hema', '_Ki67', '_Seg_Aligned_Bound']
+    for image_type in types:
+        image = cv2.imread(os.path.join(input_dir, img_name + image_type + '.png'))
+        crop = image[location[0]: location[0] + crop_size[0], location[1]: location[1] + crop_size[1]]
+        cv2.imwrite(os.path.join(output_dir, img_name + image_type + '.png'), crop)
+
+
+def overlay_ki67_on_DAPI(input_DAPI, input_ki67):
+    # overlaid_image = np.zeros((input_DAPI.shape[0], input_DAPI.shape[1], 3), dtype=np.uint8)
+    # overlaid_image[:,:,2] = input_ki67
+    # overlaid_image[:,:,0] = input_DAPI
+    # overlaid_image[:,:,1] = np.floor((input_DAPI + input_ki67) / 2)
+    # overlaid_image[:,:,1] = input_DAPI
+
+    # overlaid_image = cv2.addWeighted(input_DAPI, 0.6, input_Lap2, 0.4, 1)
+    # overlaid_image = cv2.addWeighted(input_DAPI, 0.9, input_ki67, 0.1, 1)
+    overlaid_image = input_DAPI.copy()
+    overlaid_image[input_ki67[:,:,2] >= 30] = input_ki67[input_ki67[:,:,2] >= 30]
+    # overlaid_image[input_ki67[:,:,2] < 30] = input_ki67[input_ki67[:,:,2] < 30] * 0.2 + overlaid_image[input_ki67[:,:,2]] * 0.8
+    return overlaid_image
+
+
+def count_cell_number(image, channel=1, thresh=0):
+    mask = image[:, :, channel]
+    labeled, nr_objects = ndimage.label(mask > thresh)
+    return nr_objects
+
diff --git a/deepliif/stat/PostProcessSegmentationMask.py b/deepliif/stat/PostProcessSegmentationMask.py
new file mode 100644
index 0000000..07e1681
--- /dev/null
+++ b/deepliif/stat/PostProcessSegmentationMask.py
@@ -0,0 +1,171 @@
+import os.path
+import cv2
+import numpy as np
+from scipy import ndimage
+from numba import jit
+from skimage import measure, feature
+
+
+def get_average_cell_size(image):
+    label_image = measure.label(image, background=0)
+    labels = np.unique(label_image)
+    average_cell_size = 0
+    for _label in range(1, len(labels)):
+        indices = np.where(label_image == _label)
+        pixel_count = np.count_nonzero(image[indices])
+        average_cell_size += pixel_count
+    average_cell_size /= len(labels)
+    return average_cell_size
+
+
+@jit(nopython=True)
+def get_average_cell_size_gpu(label_image, image_size, labels_no):
+    average_cell_size = 0
+    for _label in labels_no:
+        if _label == 0:
+            continue
+        pixel_count = 0
+        for index_x in range(image_size[0]):
+            for index_y in range(image_size[1]):
+                if label_image[index_x, index_y] == _label:
+                    pixel_count += 1
+        average_cell_size += pixel_count
+    average_cell_size /= len(labels_no)
+    return average_cell_size
+
+
+@jit(nopython=True)
+def compute_cell_mapping(new_mapping, image_size, small_object_size=20):
+    marked = [[False for _ in range(image_size[1])] for _ in range(image_size[0])]
+    for i in range(image_size[0]):
+        for j in range(image_size[1]):
+            if marked[i][j] is False and (new_mapping[i, j, 0] > 0 or new_mapping[i, j, 2] > 0):
+                cluster_red_no, cluster_blue_no = 0, 0
+                pixels = [(i, j)]
+                cluster = [(i, j)]
+                marked[i][j] = True
+                while len(pixels) > 0:
+                    pixel = pixels.pop()
+                    if new_mapping[pixel[0], pixel[1], 0] > 0:
+                        cluster_red_no += 1
+                    if new_mapping[pixel[0], pixel[1], 2] > 0:
+                        cluster_blue_no += 1
+                    for neigh_i in range(-1, 2):
+                        for neigh_j in range(-1, 2):
+                            neigh_pixel = (pixel[0] + neigh_i, pixel[1] + neigh_j)
+                            if 0 <= neigh_pixel[0] < image_size[0] and 0 <= neigh_pixel[1] < image_size[1] and marked[neigh_pixel[0]][neigh_pixel[1]] is False and (new_mapping[neigh_pixel[0], neigh_pixel[1], 0] > 0 or new_mapping[neigh_pixel[0], neigh_pixel[1], 2] > 0):
+                                cluster.append(neigh_pixel)
+                                pixels.append(neigh_pixel)
+                                marked[neigh_pixel[0]][neigh_pixel[1]] = True
+                cluster_value = None
+                if cluster_red_no < cluster_blue_no:
+                    cluster_value = (0, 0, 255)
+                else:
+                    cluster_value = (255, 0, 0)
+                if len(cluster) < small_object_size:
+                    cluster_value = (0, 0, 0)
+                if cluster_value is not None:
+                    for node in cluster:
+                        new_mapping[node[0], node[1]] = cluster_value
+    return new_mapping
+
+
+@jit(nopython=True)
+def remove_noises(channel, image_size, small_object_size=20):
+    marked = [[False for _ in range(image_size[1])] for _ in range(image_size[0])]
+    for i in range(image_size[0]):
+        for j in range(image_size[1]):
+            if marked[i][j] is False and channel[i, j] > 0:
+                pixels = [(i, j)]
+                cluster = [(i, j)]
+                marked[i][j] = True
+                while len(pixels) > 0:
+                    pixel = pixels.pop()
+                    for neigh_i in range(-1, 2):
+                        for neigh_j in range(-1, 2):
+                            neigh_pixel = (pixel[0] + neigh_i, pixel[1] + neigh_j)
+                            if 0 <= neigh_pixel[0] < image_size[0] and 0 <= neigh_pixel[1] < image_size[1] and marked[neigh_pixel[0]][neigh_pixel[1]] is False and channel[neigh_pixel[0], neigh_pixel[1]] > 0:
+                                cluster.append(neigh_pixel)
+                                pixels.append(neigh_pixel)
+                                marked[neigh_pixel[0]][neigh_pixel[1]] = True
+
+                cluster_value = None
+                if len(cluster) < small_object_size:
+                    cluster_value = 0
+                if cluster_value is not None:
+                    for node in cluster:
+                        channel[node[0], node[1]] = cluster_value
+    return channel
+
+
+def remove_noises_fill_empty_holes(label_img, size=200):
+    inverse_img = 255 - label_img
+    inverse_img_removed = remove_noises(inverse_img, inverse_img.shape, small_object_size=size)
+    label_img[inverse_img_removed == 0] = 255
+    return label_img
+
+
+def positive_negative_masks(mask, thresh=100, boundary_thresh=100, noise_objects_size=50):
+    positive_mask = np.zeros((mask.shape[0], mask.shape[1]), dtype=np.uint8)
+    negative_mask = np.zeros((mask.shape[0], mask.shape[1]), dtype=np.uint8)
+
+    red = mask[:, :, 0]
+    blue = mask[:, :, 2]
+    boundary = mask[:, :, 1]
+
+    # Filtering boundary pixels
+    boundary[boundary < boundary_thresh] = 0
+
+    positive_mask[red > thresh] = 255
+    positive_mask[boundary > 0] = 0
+    positive_mask[blue > red] = 0
+
+    negative_mask[blue > thresh] = 255
+    negative_mask[boundary > 0] = 0
+    negative_mask[red >= blue] = 0
+
+    cell_mapping = np.zeros_like(mask)
+    cell_mapping[:, :, 0] = positive_mask
+    cell_mapping[:, :, 2] = negative_mask
+
+    compute_cell_mapping(cell_mapping, mask.shape, small_object_size=noise_objects_size)
+    cell_mapping[cell_mapping > 0] = 255
+
+    positive_mask = cell_mapping[:, :, 0]
+    negative_mask = cell_mapping[:, :, 2]
+
+    # return remove_noises_fill_empty_holes(positive_mask, noise_objects_size), remove_noises_fill_empty_holes(negative_mask, noise_objects_size)
+    return positive_mask, negative_mask
+
+
+def create_final_segmentation_mask_with_boundaries(positive_mask, negative_mask):
+    refined_mask = np.zeros((positive_mask.shape[0], positive_mask.shape[1], 3), dtype=np.uint8)
+
+    refined_mask[positive_mask > 0] = (255, 0, 0)
+    refined_mask[negative_mask > 0] = (0, 0, 255)
+
+    edges = feature.canny(refined_mask[:,:,0], sigma=3).astype(np.uint8)
+    contours, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
+    cv2.drawContours(refined_mask, contours, -1, (0, 255, 0), 2)
+
+    edges = feature.canny(refined_mask[:,:,2], sigma=3).astype(np.uint8)
+    contours, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
+    cv2.drawContours(refined_mask, contours, -1, (0, 255, 0), 2)
+
+    return refined_mask
+
+
+def count_number_of_cells(input_dir):
+    images = os.listdir(input_dir)
+    total_red = 0
+    total_blue = 0
+    for img in images:
+        image = cv2.cvtColor(cv2.imread(os.path.join(input_dir, img)), cv2.COLOR_BGR2RGB)
+        image = image[:,5*512:]
+        red = image[:,:,0]
+        blue = image[:,:,2]
+        labeled_red, nr_objects_red = ndimage.label(red > 0)
+        labeled_blue, nr_objects_blue = ndimage.label(blue > 0)
+        total_red += nr_objects_red
+        total_blue += nr_objects_blue
+    return total_red, total_blue
diff --git a/deepliif/stat/Segmentation_Metrics.py b/deepliif/stat/Segmentation_Metrics.py
new file mode 100644
index 0000000..afb2aac
--- /dev/null
+++ b/deepliif/stat/Segmentation_Metrics.py
@@ -0,0 +1,237 @@
+import collections
+
+import numpy as np
+import cv2
+import os
+from numba import jit
+from skimage import measure
+import time
+from .PostProcessSegmentationMask import positive_negative_masks
+
+
+@jit(nopython=True)
+def compute_metrics_gpu(mask_img, gt_img, image_size):
+    TP, FP, FN, TN = 0, 0, 0, 0
+    for i in range(image_size[0]):
+        for j in range(image_size[1]):
+            if mask_img[i, j] > 0 and gt_img[i, j] > 0:
+                TP += 1
+            elif mask_img[i, j] > 0 and gt_img[i, j] == 0:
+                FP += 1
+            elif mask_img[i, j] == 0 and gt_img[i, j] > 0:
+                FN += 1
+            elif mask_img[i, j] == 0 and gt_img[i, j] == 0:
+                TN += 1
+
+    smooth = 0.0001
+    if TP == 0:
+        if np.count_nonzero(gt_img) > 0 or FP > 0:
+            IOU, precision, recall, Dice, f1, pixAcc = 0, 0, 0, 0, 0, 0
+        else:
+            IOU, precision, recall, Dice, f1, pixAcc = 1, 1, 1, 1, 1, 1
+    else:
+        IOU = (TP) / (TP + FP + FN)
+        precision = (TP) / (TP + FP)
+        recall = (TP) / (TP + FN)
+        f1 = 2 * precision * recall / (precision + recall)
+        Dice = (2 * TP) / (2 * TP + FP + FN)
+        pixAcc = (TP + TN) / (TP + TN + FP + FN)
+    return IOU, precision, recall, f1, Dice, pixAcc
+
+
+def compute_metrics(mask_img, gt_img):
+    smooth = 0.0001
+    intesection_TP = np.logical_and(gt_img, mask_img)
+    intesection_FN = np.logical_and(gt_img, 1 - mask_img)
+    intesection_FP = np.logical_and(1 - gt_img, mask_img)
+    intesection_TN = np.logical_and(1 - gt_img, 1 - mask_img)
+    union = np.logical_or(gt_img, mask_img)
+
+    iou_score = (np.sum(intesection_TP) + smooth) / (np.sum(union) + smooth)
+    precision_score = (np.sum(intesection_TP) + smooth) / (np.sum(intesection_TP) + np.sum(intesection_FP) + smooth)
+    recall_score = (np.sum(intesection_TP) + smooth) / (np.sum(intesection_TP) + np.sum(intesection_FN) + smooth)
+    f1_score = 2 * (precision_score * recall_score) / (precision_score + recall_score)
+    dice_score = (2 * np.sum(intesection_TP) + smooth) / (2 * np.sum(intesection_TP) + np.sum(intesection_FN) + np.sum(intesection_FP) + smooth)
+    pix_acc_score = (np.sum(intesection_TP) + np.sum(intesection_TN) + smooth) / (np.sum(intesection_TP) + np.sum(intesection_TN) + np.sum(intesection_FN) + np.sum(intesection_FP) + smooth)
+    return iou_score, precision_score, recall_score, f1_score, dice_score, pix_acc_score
+
+
+def compute_jaccard_index(set_1, set_2):
+    n = len(set_1.intersection(set_2))
+    return n / float(len(set_1) + len(set_2) - n)
+
+
+def compute_aji(gt_image, mask_image):
+    label_image_gt = measure.label(gt_image, background=0)
+    label_image_mask = measure.label(mask_image, background=0)
+    gt_labels = np.unique(label_image_gt)
+    mask_labels = np.unique(label_image_mask)
+    mask_components = []
+    mask_marked = []
+    for mask_label in mask_labels:
+        if mask_label == 0:
+            continue
+        comp = np.zeros((gt_image.shape[0], gt_image.shape[1]), dtype=np.uint8)
+        comp[label_image_mask == mask_label] = 1
+        mask_components.append(comp)
+        mask_marked.append(False)
+
+    total_intersection = 0
+    total_union = 0
+    total_U = 0
+    for gt_label in gt_labels:
+        if gt_label == 0:
+            continue
+        comp = np.zeros((gt_image.shape[0], gt_image.shape[1]), dtype=np.uint8)
+        comp[label_image_gt == gt_label] = 1
+        intersection = [0, 0, 0]    # index, intersection, union
+        for i in range(len(mask_components)):
+            if not mask_marked[i]:
+                comp_intersection = np.sum(np.logical_and(comp, mask_components[i]))
+                if comp_intersection > intersection[1]:
+                    union = np.sum(np.logical_or(comp, mask_components[i]))
+                    intersection = [i, comp_intersection, union]
+        if intersection[1] > 0:
+            mask_marked[intersection[0]] = True
+            total_intersection += intersection[1]
+            total_union += intersection[2]
+    for i in range(len(mask_marked)):
+        if not mask_marked[i]:
+            total_U += np.sum(mask_components[i])
+    aji = total_intersection / (total_union + total_U) if (total_union + total_U) > 0 else 0
+    return aji
+
+
+def compute_segmentation_metrics(gt_dir, model_dir, model_name, image_size=512, thresh=100, boundary_thresh=100, small_object_size=20, raw_segmentation=True, suffix_seg=None):
+    info_dict = []
+    metrics = collections.defaultdict(float)
+    images = os.listdir(model_dir)
+
+    counter = 0
+    if suffix_seg is not None:
+        postfix = f'_{suffix_seg}.png'
+    else:
+        postfix = '_Seg.png' if raw_segmentation else '_SegRefined.png'
+    for mask_name in images:
+        if postfix in mask_name:
+            counter += 1
+
+            mask_image = cv2.cvtColor(cv2.imread(os.path.join(model_dir, mask_name)), cv2.COLOR_BGR2RGB)
+            mask_image = cv2.resize(mask_image, (image_size, image_size))
+            if not raw_segmentation:
+                positive_mask = mask_image[:, :, 0]
+                negative_mask = mask_image[:, :, 2]
+            else:
+                positive_mask, negative_mask = positive_negative_masks(mask_image, thresh, boundary_thresh, small_object_size)
+
+            positive_mask[positive_mask > 0] = 1
+            negative_mask[negative_mask > 0] = 1
+
+            gt_img = cv2.cvtColor(cv2.imread(os.path.join(gt_dir, mask_name)), cv2.COLOR_BGR2RGB)
+            gt_img = cv2.resize(gt_img, (image_size, image_size))
+
+            positive_gt = gt_img[:, :, 0]
+            negative_gt = gt_img[:, :, 2]
+
+            positive_gt[positive_gt > 0] = 1
+            negative_gt[negative_gt > 0] = 1
+
+            # AJI_positive = compute_aji(positive_gt, positive_mask)
+            # start = time.time()
+            IOU_positive, precision_positive, recall_positive, f1_positive, Dice_positive, pixAcc_positive = compute_metrics_gpu(positive_mask, positive_gt, gt_img.shape)
+            # end = time.time()
+            # print(end - start)
+            # print('GPU: ', IOU_positive, precision_positive, recall_positive, f1_positive, Dice_positive, pixAcc_positive)
+            # start = time.time()
+            # IOU_positive, precision_positive, recall_positive, f1_positive, Dice_positive, pixAcc_positive = compute_metrics(positive_mask, positive_gt)
+            # end = time.time()
+            # print(end - start)
+            # print('CPU: ', end - start, IOU_positive, precision_positive, recall_positive, f1_positive, Dice_positive, pixAcc_positive)
+
+            # AJI_negative = compute_aji(negative_gt, negative_mask)
+            # start = time.time()
+            IOU_negative, precision_negative, recall_negative, f1_negative, Dice_negative, pixAcc_negative = compute_metrics_gpu(negative_mask, negative_gt, gt_img.shape)
+            # end = time.time()
+            # print('GPU: ', IOU_negative, precision_negative, recall_negative, f1_negative, Dice_negative, pixAcc_negative)
+            # start = time.time()
+            # IOU_negative, precision_negative, recall_negative, f1_negative, Dice_negative, pixAcc_negative = compute_metrics(negative_mask, negative_gt)
+            # end = time.time()
+            # print('CPU: ', end - start, IOU_negative, precision_negative, recall_negative, f1_negative, Dice_negative, pixAcc_negative)
+
+            info_dict.append({'Model': model_name,
+                              'image_name': mask_name,
+                              'cell_type': 'Positive',
+                              'precision': precision_positive * 100,
+                              'recall': recall_positive * 100,
+                              'f1': f1_positive * 100,
+                              'Dice': Dice_positive * 100,
+                              'IOU': IOU_positive * 100,
+                              'PixAcc': pixAcc_positive * 100
+                              # 'AJI': AJI_positive * 100
+                              })
+
+            info_dict.append({'Model': model_name,
+                              'image_name': mask_name,
+                              'cell_type': 'Negative',
+                              'precision': precision_negative * 100,
+                              'recall': recall_negative * 100,
+                              'f1': f1_negative * 100,
+                              'Dice': Dice_negative * 100,
+                              'IOU': IOU_negative * 100,
+                              'PixAcc': pixAcc_negative * 100
+                              # 'AJI': AJI_negative * 100
+                              })
+
+            precision = (precision_positive * 100 + precision_negative * 100) / 2
+            recall = (recall_positive * 100 + recall_negative * 100) / 2
+            f1 = (f1_positive * 100 + f1_negative * 100) / 2
+            Dice = (Dice_positive * 100 + Dice_negative * 100) / 2
+            IOU = (IOU_positive * 100 + IOU_negative * 100) / 2
+            pixAcc = (pixAcc_positive * 100 + pixAcc_negative * 100) / 2
+            # AJI = (AJI_positive * 100 + AJI_negative * 100) / 2
+
+            info_dict.append({'Model': model_name,
+                              'image_name': mask_name,
+                              'cell_type': 'Mean',
+                              'precision': precision,
+                              'recall': recall,
+                              'f1': f1,
+                              'Dice': Dice,
+                              'IOU': IOU,
+                              'PixAcc': pixAcc,
+                              # 'AJI': AJI
+                              })
+
+            metrics['precision'] += precision
+            metrics['precision_positive'] += precision_positive
+            metrics['precision_negative'] += precision_negative
+
+            metrics['recall'] += recall
+            metrics['recall_positive'] += recall_positive
+            metrics['recall_negative'] += recall_negative
+
+            metrics['f1'] += f1
+            metrics['f1_positive'] += f1_positive
+            metrics['f1_negative'] += f1_negative
+
+            metrics['Dice'] += Dice
+            metrics['Dice_positive'] += Dice_positive
+            metrics['Dice_negative'] += Dice_negative
+
+            metrics['IOU'] += IOU
+            metrics['IOU_positive'] += IOU_positive
+            metrics['IOU_negative'] += IOU_negative
+
+            metrics['PixAcc'] += pixAcc
+            metrics['PixAcc_positive'] += pixAcc_positive
+            metrics['PixAcc_negative'] += pixAcc_negative
+
+            # metrics['AJI'] += AJI
+            # metrics['AJI_positive'] += AJI_positive
+            # metrics['AJI_negative'] += AJI_negative
+
+    for key in metrics:
+        metrics[key] /= counter
+
+    return info_dict, metrics
+
diff --git a/deepliif/stat/__init__.py b/deepliif/stat/__init__.py
new file mode 100644
index 0000000..7daac75
--- /dev/null
+++ b/deepliif/stat/__init__.py
@@ -0,0 +1,323 @@
+
+
+import os
+import subprocess
+import shutil
+import time
+from .ComputeStatistics import Statistics
+
+from ..options.test_options import TestOptions
+from ..options import read_model_params, Options, print_options
+from ..data import create_dataset
+from ..models import create_model, init_nets, infer_modalities, infer_results_for_wsi
+from ..util.visualizer import save_images
+from ..util import html, allowed_file
+import torch
+import click
+
+from PIL import Image
+import json
+
+def ensure_exists(d):
+    if not os.path.exists(d):
+        os.makedirs(d)
+
+
+def format_file_structure(output_dir,source_folder='images',target_folder={'gt':'_real_B', 'pred':'_fake_B', 'input':'_real_A'}):
+    """
+    target_folder: keys are new folder names, values are unique token with which to filter the needed images for this folder
+        1) if segmentation metrics are needed and no seg images are generated from the main model, folder name "input" can be
+        specified when you want to run segmentation (using the seg model) on the original input as well
+        2) if you want to also run metrics on the original input (e.g., SSIM/PSNR for upscaling task), then you should also specify
+        "input""
+        otherwise only "gt" and "pred" are used
+    """
+    dir_source = os.path.join(output_dir,source_folder)
+    
+    for folder_name, unique_token in target_folder.items():
+        # create gt_dir and pred_dir
+        dir_folder = os.path.join(output_dir, folder_name)
+        if os.path.exists(dir_folder) and os.path.isdir(dir_folder):
+            shutil.rmtree(dir_folder)
+        os.makedirs(dir_folder)
+
+        # separate the images
+        subprocess.run(f"cp {dir_source}/*{unique_token}* {dir_folder}", shell=True, check=True)
+        
+        # rename files
+        fns = os.listdir(dir_folder)
+        for fn in fns:
+            os.rename(f'{dir_folder}/{fn}',f"{dir_folder}/{fn.replace(f'{unique_token}','_')}")
+
+
+def generate_predictions(dataroot, results_dir, checkpoints_dir, name='', num_test=10000,
+                         phase='val', gpu_ids=(-1,), batch_size=None, epoch='latest'):
+    """
+    a function version of cli.py test / test.py, to be used with evaluate()
+    params:
+      dataroot: reads images from here; expected to have a subfolder
+      results_dir: saves results here.
+      checkpoints_dir: load models from here.
+      name: name of the experiment, used as a subfolder under results_dir
+      num_test: only run test for num_test images
+      phase: this effectively refers to the subfolder name from where to load the images
+      gpu_ids: gpu-ids 0 gpu-ids 1 or gpu-ids -1 for CPU
+      batch_size: input batch size
+    """
+    # retrieve options used in training setting, similar to cli.py test
+    model_dir = os.path.join(checkpoints_dir, name)
+    opt = Options(path_file=os.path.join(model_dir,'train_opt.txt'), mode='test')
+    
+    if gpu_ids and gpu_ids[0] == -1:
+        gpu_ids = []
+    
+    # overwrite/supply unseen options using the values from the options provided in the command
+    setattr(opt,'checkpoints_dir',checkpoints_dir)
+    setattr(opt,'dataroot',dataroot)
+    setattr(opt,'name',name)
+    setattr(opt,'results_dir',results_dir)
+    setattr(opt,'num_test',num_test)
+    setattr(opt,'phase',phase)
+    setattr(opt,'gpu_ids',gpu_ids)
+    setattr(opt,'batch_size',batch_size)
+    setattr(opt,'epoch',str(epoch))
+        
+    if not hasattr(opt,'seg_gen'): # old settings for DeepLIIF models
+        opt.seg_gen = True
+    
+    # hard-code some parameters for test.py
+    opt.aspect_ratio = 1.0 # from previous default setting
+    opt.display_winsize = 512 # from previous default setting
+    opt.use_dp = True # whether to initialize model in DataParallel setting (all models to one gpu, then pytorch controls the usage of specified set of GPUs for inference)
+    opt.num_threads = 0   # test code only supports num_threads = 1
+    opt.batch_size = 1    # test code only supports batch_size = 1
+    opt.serial_batches = True  # disable data shuffling; comment this line if results on randomly chosen images are needed.
+    opt.no_flip = True    # no flip; comment this line if results on flipped images are needed.
+    opt.display_id = -1   # no visdom display; the test code saves the results to a HTML file.
+    print_options(opt)
+    
+    batch_size = batch_size if batch_size else opt.batch_size
+    dataset = create_dataset(opt, phase=phase, batch_size=batch_size)  # create a dataset given opt.dataset_mode and other options
+    model = create_model(opt)      # create a model given opt.model and other options
+    model.setup(opt)               # regular setup: load and print networks; create schedulers
+    torch.backends.cudnn.benchmark = False
+    # create a website
+    web_dir = os.path.join(opt.results_dir, opt.name, '{}_{}'.format(opt.phase, opt.epoch))  # define the website directory
+    if opt.load_iter > 0:  # load_iter is 0 by default
+        web_dir = '{:s}_iter{:d}'.format(web_dir, opt.load_iter)
+    print('creating web directory', web_dir)
+    webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.epoch))
+    # test with eval mode. This only affects layers like batchnorm and dropout.
+    # For [pix2pix]: we use batchnorm and dropout in the original pix2pix. You can experiment it with and without eval() mode.
+    # For [CycleGAN]: It should not affect CycleGAN as CycleGAN uses instancenorm without dropout.
+    model.eval()
+    # if opt.eval:
+    #     model.eval()
+
+    _start_time = time.time()
+
+    for i, data in enumerate(dataset):
+        if i >= opt.num_test:  # only apply our model to opt.num_test images.
+            break
+        
+        model.set_input(data)  # unpack data from data loader
+        model.test()           # run inference
+        visuals = model.get_current_visuals()  # get image results
+        img_path = model.get_image_paths()     # get image paths
+        if i % 5 == 0:  # save images to an HTML file
+            print('processing (%04d)-th image... %s (batch size %s)' % (i*batch_size, img_path[-1], batch_size))
+        save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize)
+
+    t_sec = round(time.time() - _start_time)
+    (t_min, t_sec) = divmod(t_sec, 60)
+    (t_hour, t_min) = divmod(t_min, 60)
+    print('Time passed: {}hour:{}min:{}sec'.format(t_hour, t_min, t_sec))
+    webpage.save()  # save the HTML
+
+
+def generate_predictions_inference(input_dir, output_dir, tile_size=None, model_dir='./model-server/DeepLIIF_Latest_Model/', 
+                                   gpu_ids=(-1,), region_size=20000, eager_mode=True, color_dapi=False, color_marker=False,
+                                   save_type_pattern='Seg'):
+    output_dir = output_dir or input_dir
+    ensure_exists(output_dir)
+
+    image_files = sorted([fn for fn in os.listdir(input_dir) if allowed_file(fn)])
+    print(len(image_files),'images found')
+    files = os.listdir(model_dir)
+    assert 'train_opt.txt' in files, f'file train_opt.txt is missing from model directory {model_dir}'
+    opt = Options(path_file=os.path.join(model_dir,'train_opt.txt'), mode='test')
+    opt.use_dp = False
+    
+    number_of_gpus_all = torch.cuda.device_count()
+    if number_of_gpus_all < len(gpu_ids) and -1 not in gpu_ids:
+        number_of_gpus = 0
+        gpu_ids = [-1]
+        print(f'Specified to use GPU {opt.gpu_ids} for inference, but there are only {number_of_gpus_all} GPU devices. Switched to CPU inference.')
+
+    if len(gpu_ids) > 0 and gpu_ids[0] == -1:
+        gpu_ids = []
+    elif len(gpu_ids) == 0:
+        gpu_ids = list(range(number_of_gpus_all))
+    
+    opt.gpu_ids = gpu_ids # overwrite gpu_ids; for test command, default gpu_ids at first is [] which will be translated to a list of all gpus
+    
+    # fix opt from old settings
+    if not hasattr(opt,'modalities_no') and hasattr(opt,'targets_no'):
+        opt.modalities_no = opt.targets_no - 1
+        del opt.targets_no
+    print_options(opt)
+
+    count = 0
+    with click.progressbar(
+            image_files,
+            label=f'Processing {len(image_files)} images',
+            item_show_func=lambda fn: fn
+    ) as bar:
+        for filename in bar:
+            if '.svs' in filename:
+                start_time = time.time()
+                infer_results_for_wsi(input_dir, filename, output_dir, model_dir, tile_size, region_size)
+                print(time.time() - start_time)
+            else:
+                img = Image.open(os.path.join(input_dir, filename)).convert('RGB')
+                images, scoring = infer_modalities(img, tile_size, model_dir, eager_mode, color_dapi, color_marker, opt)
+
+                for name, i in images.items():
+                    if save_type_pattern and save_type_pattern in name:
+                        i.save(os.path.join(
+                            output_dir,
+                            filename.replace('.' + filename.split('.')[-1], f'_{name}.png')
+                        ))
+
+                with open(os.path.join(
+                        output_dir,
+                        filename.replace('.' + filename.split('.')[-1], f'.json')
+                ), 'w') as f:
+                    json.dump(scoring, f, indent=2)
+                
+            count += 1
+            if count % 100 == 0 or count == len(image_files):
+                print(f'Done {count}/{len(image_files)}')
+    
+
+def evaluate(model_dir, input_dir, output_dir, seg_model_dir=None, phase='val', subfolder=None, image_types='B_1,B_2,B_3,B_4', seg_type='B_5', 
+         seg_gen=False, epoch='latest',mode='Segmentation', seg_on_input=False, include_input_metrics=False, batch_size=None, gpu_ids=(-1,),overwrite=False, verbose=False):
+    """
+    params:
+      model_dir: directory of the trained model
+      input_dir: input directory of the whole dataset, which should contain multiple subfolders like train and test
+      output_dir: path to the trained model
+      seg_model_dir: only used when seg_gen=False; this is the model to generate segmentation mask for both gt and pred images
+      phase: this effectively is the subfolder name under model_dir in which is the input data
+      subfolder: this effectively is the subfolder name under output_dir in which is the folder for predictions "./images" locates
+      image_types: unique marker in filename for each modality; in model class DeepLIIFExt and SDG, it could be a string like B_1,B_2,B_3
+      seg_type: unique marker in filename for segmentation, if exists; in model class DeepLIIFExt and SDG, it could be a string like B_4
+      seg_gen: True (Translation and Segmentation), False (Only Translation).
+      epoch: the name of epoch to load, default to "latest"
+      mode: Mode of the statistics computation including Segmentation, ImageSynthesis, All, SSIM, Upscaling
+      seg_on_input: a flag to indicate whether to run the seg model on the input image; this applies only to the situation where 
+            seg_gen=False, mode includes segmentation metrics, and seg_model_dir is provided; if True, 2 sets of segmentation metrics will
+            be returned (seg on fake vs seg on real, seg on input vs seg on real)
+      batch_size: batch size for inference; if not set, default is opt.batch_size
+      overwrite: overwrite results; otherwise, if output_dir already exists, prediction generation will be skipped
+      verbose: print more info if True
+    """
+    if seg_gen == False and mode in ['Segmentation','All']:
+        if not seg_model_dir:
+            mode = 'SSIM'
+            print('seg_gen is False, cannot run segmentation metrics; mode is changed to SSIM')
+    
+    d_res = {'elapsed_time':[]}
+    if verbose:
+        params = locals()
+        for k,v in params:
+            print(k,v)
+    
+    if not subfolder:
+        subfolder = f'{phase}_{epoch}'
+    
+    print('Generating predictions...')
+    time_s = time.time()
+    if os.path.exists(os.path.join(output_dir, subfolder)):
+        print(f'Folder {os.path.join(output_dir, subfolder)} already exists')
+        print(f'overwrite: {overwrite}')
+        if not overwrite:
+            print('Skip prediction generation')
+        else:
+            print('Deleting folder and regenerating predictions...')
+            shutil.rmtree(output_dir)
+            generate_predictions(checkpoints_dir=model_dir, name='.', dataroot=input_dir,
+                         results_dir=output_dir, gpu_ids=gpu_ids, phase=phase, 
+                         batch_size=batch_size, epoch=epoch)
+    else:
+        generate_predictions(checkpoints_dir=model_dir, name='.', dataroot=input_dir,
+                         results_dir=output_dir, gpu_ids=gpu_ids, phase=phase,
+                         batch_size=batch_size, epoch=epoch)
+    d_res['elapsed_time'].append(time.time() - time_s)
+    
+    if seg_gen == False and seg_model_dir:
+        print('Generating segmentation mask...')
+        time_s = time.time()
+        seg_output_dir = os.path.join(output_dir, subfolder, 'seg')
+        if os.path.exists(seg_output_dir):
+            print(f'Folder {os.path.join(output_dir, subfolder, "seg")} already exists')
+            print(f'overwrite: {overwrite}')
+            if not overwrite:
+                print('Skip segmentation mask generation')
+            else:
+                print('Deleting folder and regenerating segmentation mask...')
+                shutil.rmtree(os.path.join(output_dir, subfolder, 'seg'))
+                generate_predictions_inference(input_dir=os.path.join(output_dir,subfolder,'images'),
+                                               output_dir=seg_output_dir, tile_size=512, model_dir=seg_model_dir,
+                                               gpu_ids=gpu_ids, eager_mode=True)
+        else:
+            generate_predictions_inference(input_dir=os.path.join(output_dir,subfolder,'images'),
+                                               output_dir=seg_output_dir, tile_size=512, model_dir=seg_model_dir,
+                                               gpu_ids=gpu_ids, eager_mode=True)
+        d_res['elapsed_time'].append(time.time() - time_s)
+
+    print('Preparing folder structure and formating filenames...')
+    time_s = time.time()
+    format_file_structure(os.path.join(output_dir,subfolder)) # the folder name val_latest
+    d_res['elapsed_time'].append(time.time() - time_s)
+
+    if seg_gen == False and seg_model_dir:
+        print('Preparing folder structure and formating filenames...')
+        time_s = time.time()
+        format_file_structure(os.path.join(output_dir,subfolder),source_folder='seg',target_folder={'gt_seg':'_real_B_1_SegRefined','pred_seg':'_fake_B_1_SegRefined', 'input_seg':'_real_A_SegRefined'}) # the folder name val_latest
+        d_res['elapsed_time'].append(time.time() - time_s)
+
+    print('Starting ComputeStatistics.py...')
+
+    time_s = time.time()
+    if seg_gen == False and seg_model_dir and mode in ['Segmentation', 'All']:
+        gt_dir = os.path.join(output_dir, subfolder, 'gt_seg')
+        pred_dir = os.path.join(output_dir, subfolder, 'pred_seg')
+        stats = Statistics(gt_path=gt_dir, model_path=pred_dir, output_path=output_dir,
+                           image_types=image_types, seg_type=seg_type, mode='Segmentation')
+        d_stat = stats.run()
+
+        if mode == 'All':
+            gt_dir = os.path.join(output_dir, subfolder, 'gt')
+            pred_dir = os.path.join(output_dir, subfolder, 'pred')
+            stats = Statistics(gt_path=gt_dir, model_path=pred_dir, output_path=output_dir,
+                               image_types=image_types, seg_type=seg_type, mode='ImageSynthesis')
+            d_stat_imagesynthesis = stats.run()
+            d_stat = {**d_stat, **d_stat_imagesynthesis}
+    else:
+        gt_dir = os.path.join(output_dir, subfolder, 'gt')
+        pred_dir = os.path.join(output_dir, subfolder, 'pred')
+        stats = Statistics(gt_path=gt_dir, model_path=pred_dir, output_path=output_dir,
+                           image_types=image_types, seg_type=seg_type, mode=mode)
+        d_stat = stats.run()
+
+        if include_input_metrics:
+            input_dir = os.path.join(output_dir, subfolder, 'input')
+            stats = Statistics(gt_path=gt_dir, model_path=input_dir, output_path=output_dir,
+                           image_types=image_types, seg_type=seg_type, mode=mode)
+    d_res['elapsed_time'].append(time.time() - time_s)
+
+
+    return {**d_stat, **d_res}
+
+
diff --git a/deepliif/stat/fid.py b/deepliif/stat/fid.py
new file mode 100644
index 0000000..d57a50a
--- /dev/null
+++ b/deepliif/stat/fid.py
@@ -0,0 +1,334 @@
+#!/usr/bin/env python3
+''' Calculates the Frechet Inception Distance (FID) to evalulate GANs.
+
+The FID metric calculates the distance between two distributions of images.
+Typically, we have summary statistics (mean & covariance matrix) of one
+of these distributions, while the 2nd distribution is given by a GAN.
+
+When run as a stand-alone program, it compares the distribution of
+images that are stored as PNG/JPEG at a specified location with a
+distribution given by summary statistics (in pickle format).
+
+The FID is calculated by assuming that X_1 and X_2 are the activations of
+the pool_3 layer of the inception net for generated samples and real world
+samples respectivly.
+
+See --help to see further details.
+'''
+
+from __future__ import absolute_import, division, print_function
+import numpy as np
+import os
+import gzip, pickle
+import tensorflow as tf
+from imageio import imread
+from scipy import linalg
+import pathlib
+import urllib
+import warnings
+
+class InvalidFIDException(Exception):
+    pass
+
+
+def create_inception_graph(pth):
+    """Creates a graph from saved GraphDef file."""
+    # Creates graph from saved graph_def.pb.
+    with tf.io.gfile.GFile( pth, 'rb') as f:
+        graph_def = tf.compat.v1.GraphDef()
+        graph_def.ParseFromString( f.read())
+        _ = tf.import_graph_def( graph_def, name='FID_Inception_Net')
+#-------------------------------------------------------------------------------
+
+
+# code for handling inception net derived from
+#   https://github.com/openai/improved-gan/blob/master/inception_score/model.py
+def _get_inception_layer(sess):
+    """Prepares inception net for batched usage and returns pool_3 layer. """
+    layername = 'FID_Inception_Net/pool_3:0'
+    pool3 = sess.graph.get_tensor_by_name(layername)
+    ops = pool3.graph.get_operations()
+    for op_idx, op in enumerate(ops):
+        for o in op.outputs:
+            shape = o.get_shape()
+            if shape._dims is not None:
+              #shape = [s.value for s in shape] TF 1.x
+              shape = [s for s in shape] #TF 2.x
+              new_shape = []
+              for j, s in enumerate(shape):
+                if s == 1 and j == 0:
+                  new_shape.append(None)
+                else:
+                  new_shape.append(s)
+              o.__dict__['_shape_val'] = tf.TensorShape(new_shape)
+    return pool3
+#-------------------------------------------------------------------------------
+
+
+def get_activations(images, sess, batch_size=50, verbose=False):
+    """Calculates the activations of the pool_3 layer for all images.
+
+    Params:
+    -- images      : Numpy array of dimension (n_images, hi, wi, 3). The values
+                     must lie between 0 and 256.
+    -- sess        : current session
+    -- batch_size  : the images numpy array is split into batches with batch size
+                     batch_size. A reasonable batch size depends on the disposable hardware.
+    -- verbose    : If set to True and parameter out_step is given, the number of calculated
+                     batches is reported.
+    Returns:
+    -- A numpy array of dimension (num images, 2048) that contains the
+       activations of the given tensor when feeding inception with the query tensor.
+    """
+    inception_layer = _get_inception_layer(sess)
+    n_images = images.shape[0]
+    if batch_size > n_images:
+        print("warning: batch size is bigger than the data size. setting batch size to data size")
+        batch_size = n_images
+    n_batches = n_images//batch_size # drops the last batch if < batch_size
+    pred_arr = np.empty((n_batches * batch_size,2048))
+    for i in range(n_batches):
+        if verbose:
+            print("\rPropagating batch %d/%d" % (i+1, n_batches), end="", flush=True)
+        start = i*batch_size
+        
+        if start+batch_size < n_images:
+            end = start+batch_size
+        else:
+            end = n_images
+        
+        batch = images[start:end]
+        pred = sess.run(inception_layer, {'FID_Inception_Net/ExpandDims:0': batch})
+        pred_arr[start:end] = pred.reshape(batch.shape[0],-1)
+    if verbose:
+        print(" done")
+    return pred_arr
+#-------------------------------------------------------------------------------
+
+
+def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
+    """Numpy implementation of the Frechet Distance.
+    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
+    and X_2 ~ N(mu_2, C_2) is
+            d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
+            
+    Stable version by Dougal J. Sutherland.
+
+    Params:
+    -- mu1 : Numpy array containing the activations of the pool_3 layer of the
+             inception net ( like returned by the function 'get_predictions')
+             for generated samples.
+    -- mu2   : The sample mean over activations of the pool_3 layer, precalcualted
+               on an representive data set.
+    -- sigma1: The covariance matrix over activations of the pool_3 layer for
+               generated samples.
+    -- sigma2: The covariance matrix over activations of the pool_3 layer,
+               precalcualted on an representive data set.
+
+    Returns:
+    --   : The Frechet Distance.
+    """
+
+    mu1 = np.atleast_1d(mu1)
+    mu2 = np.atleast_1d(mu2)
+
+    sigma1 = np.atleast_2d(sigma1)
+    sigma2 = np.atleast_2d(sigma2)
+
+    assert mu1.shape == mu2.shape, "Training and test mean vectors have different lengths"
+    assert sigma1.shape == sigma2.shape, "Training and test covariances have different dimensions"
+
+    diff = mu1 - mu2
+
+    # product might be almost singular
+    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
+    if not np.isfinite(covmean).all():
+        msg = "fid calculation produces singular product; adding %s to diagonal of cov estimates" % eps
+        warnings.warn(msg)
+        offset = np.eye(sigma1.shape[0]) * eps
+        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
+
+    # numerical error might give slight imaginary component
+    if np.iscomplexobj(covmean):
+        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
+            m = np.max(np.abs(covmean.imag))
+            raise ValueError("Imaginary component {}".format(m))
+        covmean = covmean.real
+
+    tr_covmean = np.trace(covmean)
+
+    return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean
+#-------------------------------------------------------------------------------
+
+
+def calculate_activation_statistics(images, sess, batch_size=50, verbose=False):
+    """Calculation of the statistics used by the FID.
+    Params:
+    -- images      : Numpy array of dimension (n_images, hi, wi, 3). The values
+                     must lie between 0 and 255.
+    -- sess        : current session
+    -- batch_size  : the images numpy array is split into batches with batch size
+                     batch_size. A reasonable batch size depends on the available hardware.
+    -- verbose     : If set to True and parameter out_step is given, the number of calculated
+                     batches is reported.
+    Returns:
+    -- mu    : The mean over samples of the activations of the pool_3 layer of
+               the incption model.
+    -- sigma : The covariance matrix of the activations of the pool_3 layer of
+               the incption model.
+    """
+    act = get_activations(images, sess, batch_size, verbose)
+    mu = np.mean(act, axis=0)
+    sigma = np.cov(act, rowvar=False)
+    return mu, sigma
+    
+
+#------------------
+# The following methods are implemented to obtain a batched version of the activations.
+# This has the advantage to reduce memory requirements, at the cost of slightly reduced efficiency.
+# - Pyrestone
+#------------------
+
+
+def load_image_batch(files):
+    """Convenience method for batch-loading images
+    Params:
+    -- files    : list of paths to image files. Images need to have same dimensions for all files.
+    Returns:
+    -- A numpy array of dimensions (num_images,hi, wi, 3) representing the image pixel values.
+    """
+    return np.array([imread(str(fn)).astype(np.float32) for fn in files])
+
+def get_activations_from_files(files, sess, batch_size=50, verbose=False):
+    """Calculates the activations of the pool_3 layer for all images.
+
+    Params:
+    -- files      : list of paths to image files. Images need to have same dimensions for all files.
+    -- sess        : current session
+    -- batch_size  : the images numpy array is split into batches with batch size
+                     batch_size. A reasonable batch size depends on the disposable hardware.
+    -- verbose    : If set to True and parameter out_step is given, the number of calculated
+                     batches is reported.
+    Returns:
+    -- A numpy array of dimension (num images, 2048) that contains the
+       activations of the given tensor when feeding inception with the query tensor.
+    """
+    inception_layer = _get_inception_layer(sess)
+    n_imgs = len(files)
+    if batch_size > n_imgs:
+        print("warning: batch size is bigger than the data size. setting batch size to data size")
+        batch_size = n_imgs
+    n_batches = n_imgs//batch_size + 1
+    pred_arr = np.empty((n_imgs,2048))
+    for i in range(n_batches):
+        if verbose:
+            print("\rPropagating batch %d/%d" % (i+1, n_batches), end="", flush=True)
+        start = i*batch_size
+        if start+batch_size < n_imgs:
+            end = start+batch_size
+        else:
+            end = n_imgs
+        
+        batch = load_image_batch(files[start:end])
+        pred = sess.run(inception_layer, {'FID_Inception_Net/ExpandDims:0': batch})
+        pred_arr[start:end] = pred.reshape(batch_size,-1)
+        del batch #clean up memory
+    if verbose:
+        print(" done")
+    return pred_arr
+    
+def calculate_activation_statistics_from_files(files, sess, batch_size=50, verbose=False):
+    """Calculation of the statistics used by the FID.
+    Params:
+    -- files      : list of paths to image files. Images need to have same dimensions for all files.
+    -- sess        : current session
+    -- batch_size  : the images numpy array is split into batches with batch size
+                     batch_size. A reasonable batch size depends on the available hardware.
+    -- verbose     : If set to True and parameter out_step is given, the number of calculated
+                     batches is reported.
+    Returns:
+    -- mu    : The mean over samples of the activations of the pool_3 layer of
+               the incption model.
+    -- sigma : The covariance matrix of the activations of the pool_3 layer of
+               the incption model.
+    """
+    act = get_activations_from_files(files, sess, batch_size, verbose)
+    mu = np.mean(act, axis=0)
+    sigma = np.cov(act, rowvar=False)
+    return mu, sigma
+    
+#-------------------------------------------------------------------------------
+
+
+#-------------------------------------------------------------------------------
+# The following functions aren't needed for calculating the FID
+# they're just here to make this module work as a stand-alone script
+# for calculating FID scores
+#-------------------------------------------------------------------------------
+def check_or_download_inception(inception_path):
+    ''' Checks if the path to the inception file is valid, or downloads
+        the file if it is not present. '''
+    INCEPTION_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
+    if inception_path is None:
+        inception_path = '/tmp'
+    inception_path = pathlib.Path(inception_path)
+    model_file = inception_path / 'classify_image_graph_def.pb'
+    if not model_file.exists():
+        print("Downloading Inception model")
+        from urllib import request
+        import tarfile
+        fn, _ = request.urlretrieve(INCEPTION_URL)
+        with tarfile.open(fn, mode='r') as f:
+            f.extract('classify_image_graph_def.pb', str(model_file.parent))
+    return str(model_file)
+
+
+def _handle_path(path, sess, low_profile=False):
+    if path.endswith('.npz'):
+        f = np.load(path)
+        m, s = f['mu'][:], f['sigma'][:]
+        f.close()
+    else:
+        path = pathlib.Path(path)
+        files = list(path.glob('*.jpg')) + list(path.glob('*.png'))
+        if low_profile:
+            m, s = calculate_activation_statistics_from_files(files, sess)
+        else:
+            x = np.array([imread(str(fn)).astype(np.float32) for fn in files])
+            m, s = calculate_activation_statistics(x, sess)
+            del x #clean up memory
+    return m, s
+
+
+def calculate_fid_given_paths(paths, inception_path, low_profile=False):
+    ''' Calculates the FID of two paths. '''
+    inception_path = check_or_download_inception(inception_path)
+
+    for p in paths:
+        if not os.path.exists(p):
+            raise RuntimeError("Invalid path: %s" % p)
+
+    create_inception_graph(str(inception_path))
+    with tf.Session() as sess:
+        sess.run(tf.global_variables_initializer())
+        m1, s1 = _handle_path(paths[0], sess, low_profile=low_profile)
+        m2, s2 = _handle_path(paths[1], sess, low_profile=low_profile)
+        fid_value = calculate_frechet_distance(m1, s1, m2, s2)
+        return fid_value
+
+
+if __name__ == "__main__":
+    from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
+    parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
+    parser.add_argument("path", type=str, nargs=2,
+        help='Path to the generated images or to .npz statistic files')
+    parser.add_argument("-i", "--inception", type=str, default=None,
+        help='Path to Inception model (will be downloaded if not provided)')
+    parser.add_argument("--gpu", default="", type=str,
+        help='GPU to use (leave blank for CPU only)')
+    parser.add_argument("--lowprofile", action="store_true",
+        help='Keep only one batch of images in memory at a time. This reduces memory footprint, but may decrease speed slightly.')
+    args = parser.parse_args()
+    os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
+    fid_value = calculate_fid_given_paths(args.path, args.inception, low_profile=args.lowprofile)
+    print("FID: ", fid_value)
diff --git a/deepliif/stat/fid_official_tf.py b/deepliif/stat/fid_official_tf.py
new file mode 100644
index 0000000..ab67ce5
--- /dev/null
+++ b/deepliif/stat/fid_official_tf.py
@@ -0,0 +1,370 @@
+"""
+@Brief:
+    Tensorflow implementation of FID score, should be the same as the official one
+    modified from official inception score implementation
+    [bioinf-jku/TTUR](https://github.com/bioinf-jku/TTUR)
+@Author: lzhbrian (https://lzhbrian.me)
+@Date: 2019.4.7
+@Usage:
+    # CMD
+        # from 2 precalculated stats
+        python fid_official_tf.py res/stats_tf/fid_stats_imagenet_valid.npz res/stats_tf/fid_stats_imagenet_train.npz --gpu 0
+
+        # from 1 precalculated stats, 1 image foldername/
+        python fid_official_tf.py res/stats_tf/fid_stats_imagenet_valid.npz /path/to/image/foldername/ --gpu 0
+
+        # from 2 image foldername/
+        python fid_official_tf.py /path/to/image/foldername1/ /path/to/image/foldername2/ --gpu 0
+
+    # used in code
+        ```
+        import tensorflow as tf
+
+        # load from precalculated
+        f = np.load('res/stats_tf/fid_stats_imagenet_train.npz')
+        mu1, sigma1 = f['mu'][:], f['sigma'][:]
+        f.close()
+
+        # calc from image ndarray
+        # images should be Numpy array of dimension (N, H, W, C). images should be in 0~255
+        with tf.Session() as sess:
+            sess.run(tf.global_variables_initializer())
+            mu2, sigma2 = fid_official_tf.calculate_activation_statistics(images, sess, batch_size=100)
+        fid_score = calculate_frechet_distance(mu1, sigma1, mu2, sigma2)
+        ```
+
+@Note:
+    Need to first download stats_tf of datasets in stats_tf/, see README.md
+
+    also, the same as inception_score_official_tf.py, the inception model used
+    contains resize and normalization layers
+    so the input of our images should be 0~255, and arbitrary HxW size
+
+    For calculating mu and sigma for foldername/, see precalc_stats_official_tf.py
+"""
+
+import numpy as np
+import os
+import tensorflow as tf
+import scipy.misc
+from scipy.misc import imread
+from scipy import linalg
+import pathlib
+import urllib
+import warnings
+from tqdm import tqdm
+
+
+cur_dirname = os.path.dirname(os.path.abspath(__file__))
+
+MODEL_DIR = '%s/res/' % cur_dirname
+
+class InvalidFIDException(Exception):
+    pass
+
+
+def create_inception_graph(pth):
+    """Creates a graph from saved GraphDef file."""
+    # Creates graph from saved graph_def.pb.
+    with tf.gfile.FastGFile(pth, 'rb') as f:
+        graph_def = tf.GraphDef()
+        graph_def.ParseFromString(f.read())
+        _ = tf.import_graph_def(graph_def, name='FID_Inception_Net')
+
+
+# -------------------------------------------------------------------------------
+
+
+# code for handling inception net derived from
+#   https://github.com/openai/improved-gan/blob/master/inception_score/model.py
+def _get_inception_layer(sess):
+    """Prepares inception net for batched usage and returns pool_3 layer. """
+    layername = 'FID_Inception_Net/pool_3:0'
+    pool3 = sess.graph.get_tensor_by_name(layername)
+    ops = pool3.graph.get_operations()
+    for op_idx, op in enumerate(ops):
+        for o in op.outputs:
+            shape = o.get_shape()
+            if shape._dims != []:
+                shape = [s.value for s in shape]
+                new_shape = []
+                for j, s in enumerate(shape):
+                    if s == 1 and j == 0:
+                        new_shape.append(None)
+                    else:
+                        new_shape.append(s)
+                o.__dict__['_shape_val'] = tf.TensorShape(new_shape)
+    return pool3
+
+
+# -------------------------------------------------------------------------------
+
+
+def get_activations(images, sess, batch_size=50, verbose=False):
+    """Calculates the activations of the pool_3 layer for all images.
+    Params:
+    -- images      : Numpy array of dimension (n_images, hi, wi, 3). The values
+                     must lie between 0 and 256.
+    -- sess        : current session
+    -- batch_size  : the images numpy array is split into batches with batch size
+                     batch_size. A reasonable batch size depends on the disposable hardware.
+    -- verbose    : If set to True and parameter out_step is given, the number of calculated
+                     batches is reported.
+    Returns:
+    -- A numpy array of dimension (num images, 2048) that contains the
+       activations of the given tensor when feeding inception with the query tensor.
+    """
+    inception_layer = _get_inception_layer(sess)
+    d0 = images.shape[0]
+    if batch_size > d0:
+        print("warning: batch size is bigger than the data size. setting batch size to data size")
+        batch_size = d0
+    n_batches = d0 // batch_size
+    n_used_imgs = n_batches * batch_size
+    pred_arr = np.empty((n_used_imgs, 2048))
+    for i in tqdm(range(n_batches)):
+        if verbose:
+            print("\rPropagating batch %d/%d" % (i + 1, n_batches))
+        start = i * batch_size
+        end = start + batch_size
+        batch = images[start:end]
+        pred = sess.run(inception_layer, {'FID_Inception_Net/ExpandDims:0': batch})
+        pred_arr[start:end] = pred.reshape(batch_size, -1)
+    if verbose:
+        print(" done")
+    return pred_arr
+
+
+# -------------------------------------------------------------------------------
+
+
+def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
+    """Numpy implementation of the Frechet Distance.
+    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
+    and X_2 ~ N(mu_2, C_2) is
+            d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
+
+    Stable version by Dougal J. Sutherland.
+    Params:
+    -- mu1 : Numpy array containing the activations of the pool_3 layer of the
+             inception net ( like returned by the function 'get_predictions')
+             for generated samples.
+    -- mu2   : The sample mean over activations of the pool_3 layer, precalcualted
+               on an representive data set.
+    -- sigma1: The covariance matrix over activations of the pool_3 layer for
+               generated samples.
+    -- sigma2: The covariance matrix over activations of the pool_3 layer,
+               precalcualted on an representive data set.
+    Returns:
+    --   : The Frechet Distance.
+    """
+
+    mu1 = np.atleast_1d(mu1)
+    mu2 = np.atleast_1d(mu2)
+
+    sigma1 = np.atleast_2d(sigma1)
+    sigma2 = np.atleast_2d(sigma2)
+
+    assert mu1.shape == mu2.shape, "Training and test mean vectors have different lengths"
+    assert sigma1.shape == sigma2.shape, "Training and test covariances have different dimensions"
+
+    diff = mu1 - mu2
+
+    # product might be almost singular
+    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
+    if not np.isfinite(covmean).all():
+        msg = "fid calculation produces singular product; adding %s to diagonal of cov estimates" % eps
+        warnings.warn(msg)
+        offset = np.eye(sigma1.shape[0]) * eps
+        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
+
+    # numerical error might give slight imaginary component
+    if np.iscomplexobj(covmean):
+        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
+            m = np.max(np.abs(covmean.imag))
+            raise ValueError("Imaginary component {}".format(m))
+        covmean = covmean.real
+
+    tr_covmean = np.trace(covmean)
+
+    return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean
+
+
+# -------------------------------------------------------------------------------
+
+
+def calculate_activation_statistics(images, sess, batch_size=50, verbose=False):
+    """Calculation of the statistics used by the FID.
+    Params:
+    -- images      : Numpy array of dimension (n_images, hi, wi, 3). The values
+                     must lie between 0 and 255.
+    -- sess        : current session
+    -- batch_size  : the images numpy array is split into batches with batch size
+                     batch_size. A reasonable batch size depends on the available hardware.
+    -- verbose     : If set to True and parameter out_step is given, the number of calculated
+                     batches is reported.
+    Returns:
+    -- mu    : The mean over samples of the activations of the pool_3 layer of
+               the inception model.
+    -- sigma : The covariance matrix of the activations of the pool_3 layer of
+               the inception model.
+    """
+    act = get_activations(images, sess, batch_size, verbose)
+    mu = np.mean(act, axis=0)
+    sigma = np.cov(act, rowvar=False)
+    return mu, sigma
+
+
+# ------------------
+# The following methods are implemented to obtain a batched version of the activations.
+# This has the advantage to reduce memory requirements, at the cost of slightly reduced efficiency.
+# - Pyrestone
+# ------------------
+
+
+def load_image_batch(files):
+    """Convenience method for batch-loading images
+    Params:
+    -- files    : list of paths to image files. Images need to have same dimensions for all files.
+    Returns:
+    -- A numpy array of dimensions (num_images,hi, wi, 3) representing the image pixel values.
+    """
+    return np.array([imread(str(fn)).astype(np.float32) for fn in files])
+
+
+def get_activations_from_files(files, sess, batch_size=50, verbose=False):
+    """Calculates the activations of the pool_3 layer for all images.
+    Params:
+    -- files      : list of paths to image files. Images need to have same dimensions for all files.
+    -- sess       : current session
+    -- batch_size : the images numpy array is split into batches with batch size
+                     batch_size. A reasonable batch size depends on the disposable hardware.
+    -- verbose    : If set to True and parameter out_step is given, the number of calculated
+                     batches is reported.
+    Returns:
+    -- A numpy array of dimension (num images, 2048) that contains the
+       activations of the given tensor when feeding inception with the query tensor.
+    """
+    inception_layer = _get_inception_layer(sess)
+    d0 = len(files)
+    if batch_size > d0:
+        print("warning: batch size is bigger than the data size. setting batch size to data size")
+        batch_size = d0
+    n_batches = d0 // batch_size
+    n_used_imgs = n_batches * batch_size
+    pred_arr = np.empty((n_used_imgs, 2048))
+    for i in range(n_batches):
+        if verbose:
+            print("\rPropagating batch %d/%d" % (i + 1, n_batches))
+        start = i * batch_size
+        end = start + batch_size
+        batch = load_image_batch(files[start:end])
+        pred = sess.run(inception_layer, {'FID_Inception_Net/ExpandDims:0': batch})
+        pred_arr[start:end] = pred.reshape(batch_size, -1)
+        del batch  # clean up memory
+    if verbose:
+        print(" done")
+    return pred_arr
+
+
+def calculate_activation_statistics_from_files(files, sess, batch_size=50, verbose=False):
+    """Calculation of the statistics used by the FID.
+    Params:
+    -- files      : list of paths to image files. Images need to have same dimensions for all files.
+    -- sess        : current session
+    -- batch_size  : the images numpy array is split into batches with batch size
+                     batch_size. A reasonable batch size depends on the available hardware.
+    -- verbose     : If set to True and parameter out_step is given, the number of calculated
+                     batches is reported.
+    Returns:
+    -- mu    : The mean over samples of the activations of the pool_3 layer of
+               the inception model.
+    -- sigma : The covariance matrix of the activations of the pool_3 layer of
+               the inception model.
+    """
+    act = get_activations_from_files(files, sess, batch_size, verbose)
+    mu = np.mean(act, axis=0)
+    sigma = np.cov(act, rowvar=False)
+    return mu, sigma
+
+
+# -------------------------------------------------------------------------------
+
+
+# -------------------------------------------------------------------------------
+# The following functions aren't needed for calculating the FID
+# they're just here to make this module work as a stand-alone script
+# for calculating FID scores
+# -------------------------------------------------------------------------------
+def check_or_download_inception(inception_path):
+    ''' Checks if the path to the inception file is valid, or downloads
+        the file if it is not present. '''
+    INCEPTION_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
+    if inception_path is None:
+        inception_path = MODEL_DIR
+    inception_path = pathlib.Path(inception_path)
+    model_file = inception_path / 'classify_image_graph_def.pb'
+    if not model_file.exists():
+        print("Downloading Inception model")
+        from urllib import request
+        import tarfile
+        fn, _ = request.urlretrieve(INCEPTION_URL)
+        with tarfile.open(fn, mode='r') as f:
+            f.extract('classify_image_graph_def.pb', str(model_file.parent))
+    return str(model_file)
+
+
+def _handle_path(path, sess, low_profile=False):
+    if path.endswith('.npz'):
+        f = np.load(path)
+        m, s = f['mu'][:], f['sigma'][:]
+        f.close()
+    else:
+        path = pathlib.Path(path)
+        files = []
+        for ext in ('*.png', '*.jpg', '*.jpeg', '.bmp'):
+            files.extend( list(path.glob(ext)) )
+
+        if low_profile:
+            m, s = calculate_activation_statistics_from_files(files, sess)
+        else:
+            # x = np.array([scipy.misc.imresize(imread(str(fn), mode='RGB'), (299, 299), interp='bilinear').astype(np.float32) for fn in files])
+            x = np.array([imread(str(fn)).astype(np.float32) for fn in files])
+            m, s = calculate_activation_statistics(x, sess)
+            del x  # clean up memory
+    return m, s
+
+
+def calculate_fid_given_paths(paths, inception_path, low_profile=False):
+    ''' Calculates the FID of two paths. '''
+    inception_path = check_or_download_inception(inception_path)
+
+    for p in paths:
+        if not os.path.exists(p):
+            raise RuntimeError("Invalid path: %s" % p)
+
+    create_inception_graph(str(inception_path))
+    with tf.Session() as sess:
+        sess.run(tf.global_variables_initializer())
+        m1, s1 = _handle_path(paths[0], sess, low_profile=low_profile)
+        m2, s2 = _handle_path(paths[1], sess, low_profile=low_profile)
+        fid_value = calculate_frechet_distance(m1, s1, m2, s2)
+        return fid_value
+
+
+if __name__ == "__main__":
+    from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
+
+    parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
+    parser.add_argument("path", type=str, nargs=2,
+                        help='Path to the generated images or to .npz statistic files')
+    parser.add_argument("-i", "--inception", type=str, default=None,
+                        help='Path to Inception model (will be downloaded if not provided)')
+    parser.add_argument("--gpu", default="", type=str,
+                        help='GPU to use (leave blank for CPU only)')
+    parser.add_argument("--lowprofile", action="store_true",
+                        help='Keep only one batch of images in memory at a time. This reduces memory footprint, but may decrease speed slightly.')
+    args = parser.parse_args()
+    os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
+    fid_value = calculate_fid_given_paths(args.path, args.inception, low_profile=args.lowprofile)
+    print("FID: ", fid_value)
diff --git a/deepliif/stat/inception_score.py b/deepliif/stat/inception_score.py
new file mode 100644
index 0000000..586b163
--- /dev/null
+++ b/deepliif/stat/inception_score.py
@@ -0,0 +1,100 @@
+# calculate inception score with Keras
+import os
+import sys
+from math import floor
+
+import numpy as np
+from numpy import ones
+from numpy import expand_dims
+from numpy import log
+from numpy import mean
+from numpy import std
+from numpy import exp
+try:
+    from tensorflow.keras.applications.inception_v3 import InceptionV3
+    from tensorflow.keras.applications.inception_v3 import preprocess_input
+except:
+    from keras.applications.inception_v3 import InceptionV3
+    from keras.applications.inception_v3 import preprocess_input
+import cv2
+
+
+# assumes images have the shape 299x299x3, pixels in [0,255]
+def calculate_inception_score(images, n_split=10, eps=1E-16):
+    # load inception v3 model
+    model = InceptionV3()
+    # convert from uint8 to float32
+    processed = images.astype('float32')
+    # pre-process raw images for inception v3 model
+    processed = preprocess_input(processed)
+    # predict class probabilities for images
+    yhat = model.predict(processed)
+    # enumerate splits of images/predictions
+    scores = list()
+    n_part = floor(images.shape[0] / n_split)
+    for i in range(n_split):
+        # retrieve p(y|x)
+        ix_start, ix_end = i * n_part, i * n_part + n_part
+        p_yx = yhat[ix_start:ix_end]
+        # calculate p(y)
+        p_y = expand_dims(p_yx.mean(axis=0), 0)
+        # calculate KL divergence using log probabilities
+        kl_d = p_yx * (log(p_yx + eps) - log(p_y + eps))
+        # sum over classes
+        sum_kl_d = kl_d.sum(axis=1)
+        # average over images
+        avg_kl_d = mean(sum_kl_d)
+        # undo the log
+        is_score = exp(avg_kl_d)
+        # store
+        scores.append(is_score)
+    # average across images
+    is_avg, is_std = mean(scores), std(scores)
+    return is_avg, is_std
+
+# assumes images have the shape 299x299x3, pixels in [0,255]
+def calculate_inception_score(images, n_split=10, eps=1E-16):
+    # load inception v3 model
+    model = InceptionV3()
+    # convert from uint8 to float32
+    processed = images.astype('float32')
+    # pre-process raw images for inception v3 model
+    processed = preprocess_input(processed)
+    # predict class probabilities for images
+    yhat = model.predict(processed)
+    # enumerate splits of images/predictions
+    scores = list()
+    n_part = floor(images.shape[0] / n_split)
+    for i in range(n_split):
+        # retrieve p(y|x)
+        ix_start, ix_end = i * n_part, i * n_part + n_part
+        p_yx = yhat[ix_start:ix_end]
+        # calculate p(y)
+        p_y = expand_dims(p_yx.mean(axis=0), 0)
+        # calculate KL divergence using log probabilities
+        kl_d = p_yx * (log(p_yx + eps) - log(p_y + eps))
+        # sum over classes
+        sum_kl_d = kl_d.sum(axis=1)
+        # average over images
+        avg_kl_d = mean(sum_kl_d)
+        # undo the log
+        is_score = exp(avg_kl_d)
+        # store
+        scores.append(is_score)
+    # average across images
+    is_avg, is_std = mean(scores), std(scores)
+    return is_avg, is_std
+
+if __name__ == '__main__':
+    # input_dir = 'D:/Pathology/DMIT/datasets/IHC2DAPI/testB'
+    input_dir = sys.argv[1]
+    print(input_dir)
+    images = os.listdir(input_dir)
+    real_images_array = []
+    for img in images:
+        image = cv2.imread(os.path.join(input_dir, img))
+        image = cv2.resize(image, (299, 299))
+        real_images_array.append(image)
+    real_images_array = np.array(real_images_array)
+    is_avg, is_std = calculate_inception_score(real_images_array)
+    print('score', is_avg, is_std)
\ No newline at end of file
diff --git a/deepliif/stat/swd.py b/deepliif/stat/swd.py
new file mode 100644
index 0000000..bd6a8b8
--- /dev/null
+++ b/deepliif/stat/swd.py
@@ -0,0 +1,158 @@
+from PIL import Image
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torchvision
+
+# Gaussian blur kernel
+def get_gaussian_kernel(device="cpu"):
+    kernel = np.array([
+        [1, 4, 6, 4, 1],
+        [4, 16, 24, 16, 4],
+        [6, 24, 36, 24, 6],
+        [4, 16, 24, 16, 4],
+        [1, 4, 6, 4, 1]], np.float32) / 256.0
+    gaussian_k = torch.as_tensor(kernel.reshape(1, 1, 5, 5)).to(device)
+    return gaussian_k
+
+def pyramid_down(image, device="cpu"):
+    gaussian_k = get_gaussian_kernel(device=device)        
+    # channel-wise conv(important)
+    multiband = [F.conv2d(image[:, i:i + 1,:,:], gaussian_k, padding=2, stride=2) for i in range(3)]
+    down_image = torch.cat(multiband, dim=1)
+    return down_image
+
+def pyramid_up(image, device="cpu"):
+    gaussian_k = get_gaussian_kernel(device=device)
+    upsample = F.interpolate(image, scale_factor=2)
+    multiband = [F.conv2d(upsample[:, i:i + 1,:,:], gaussian_k, padding=2) for i in range(3)]
+    up_image = torch.cat(multiband, dim=1)
+    return up_image
+
+def gaussian_pyramid(original, n_pyramids, device="cpu"):
+    x = original
+    # pyramid down
+    pyramids = [original]
+    for i in range(n_pyramids):
+        x = pyramid_down(x, device=device)
+        pyramids.append(x)
+    return pyramids
+
+def laplacian_pyramid(original, n_pyramids, device="cpu"):
+    # create gaussian pyramid
+    pyramids = gaussian_pyramid(original, n_pyramids, device=device)
+
+    # pyramid up - diff
+    laplacian = []
+    for i in range(len(pyramids) - 1):
+        diff = pyramids[i] - pyramid_up(pyramids[i + 1], device=device)
+        laplacian.append(diff)
+    # Add last gaussian pyramid
+    laplacian.append(pyramids[len(pyramids) - 1])        
+    return laplacian
+
+def minibatch_laplacian_pyramid(image, n_pyramids, batch_size, device="cpu"):
+    n = image.size(0) // batch_size + np.sign(image.size(0) % batch_size)
+    pyramids = []
+    for i in range(n):
+        x = image[i * batch_size:(i + 1) * batch_size]
+        p = laplacian_pyramid(x.to(device), n_pyramids, device=device)
+        p = [x.cpu() for x in p]
+        pyramids.append(p)
+    del x
+    result = []
+    for i in range(n_pyramids + 1):
+        x = []
+        for j in range(n):
+            x.append(pyramids[j][i])
+        result.append(torch.cat(x, dim=0))
+    return result
+
+def extract_patches(pyramid_layer, slice_indices,
+                    slice_size=7, unfold_batch_size=128, device="cpu"):
+    assert pyramid_layer.ndim == 4
+    n = pyramid_layer.size(0) // unfold_batch_size + np.sign(pyramid_layer.size(0) % unfold_batch_size)
+    # random slice 7x7
+    p_slice = []
+    for i in range(n):
+        # [unfold_batch_size, ch, n_slices, slice_size, slice_size]
+        ind_start = i * unfold_batch_size
+        ind_end = min((i + 1) * unfold_batch_size, pyramid_layer.size(0))
+        x = pyramid_layer[ind_start:ind_end].unfold(
+                2, slice_size, 1).unfold(3, slice_size, 1).reshape(
+                ind_end - ind_start, pyramid_layer.size(1), -1, slice_size, slice_size)
+        # [unfold_batch_size, ch, n_descriptors, slice_size, slice_size]
+        x = x[:,:, slice_indices,:,:]
+        # [unfold_batch_size, n_descriptors, ch, slice_size, slice_size]
+        p_slice.append(x.permute([0, 2, 1, 3, 4]))
+    # sliced tensor per layer [batch, n_descriptors, ch, slice_size, slice_size]
+    x = torch.cat(p_slice, dim=0)
+    # normalize along ch
+    std, mean = torch.std_mean(x, dim=(0, 1, 3, 4), keepdim=True)
+    x = (x - mean) / (std + 1e-8)
+    # reshape to 2rank
+    x = x.reshape(-1, 3 * slice_size * slice_size)
+    return x
+        
+def swd(image1, image2, 
+        n_pyramids=None, slice_size=7, n_descriptors=128,
+        n_repeat_projection=128, proj_per_repeat=4, device="cpu", return_by_resolution=False,
+        pyramid_batchsize=128):
+    # n_repeat_projectton * proj_per_repeat = 512
+    # Please change these values according to memory usage.
+    # original = n_repeat_projection=4, proj_per_repeat=128    
+    assert image1.size() == image2.size()
+    assert image1.ndim == 4 and image2.ndim == 4
+
+    if n_pyramids is None:
+        n_pyramids = int(np.rint(np.log2(image1.size(2) // 16)))
+    with torch.no_grad():
+        # minibatch laplacian pyramid for cuda memory reasons
+        pyramid1 = minibatch_laplacian_pyramid(image1, n_pyramids, pyramid_batchsize, device=device)
+        pyramid2 = minibatch_laplacian_pyramid(image2, n_pyramids, pyramid_batchsize, device=device)
+        result = []
+
+        for i_pyramid in range(n_pyramids + 1):
+            # indices
+            n = (pyramid1[i_pyramid].size(2) - 6) * (pyramid1[i_pyramid].size(3) - 6)
+            indices = torch.randperm(n)[:n_descriptors]
+
+            # extract patches on CPU
+            # patch : 2rank (n_image*n_descriptors, slice_size**2*ch)
+            p1 = extract_patches(pyramid1[i_pyramid], indices,
+                            slice_size=slice_size, device="cpu")
+            p2 = extract_patches(pyramid2[i_pyramid], indices,
+                            slice_size=slice_size, device="cpu")
+
+            p1, p2 = p1.to(device), p2.to(device)
+
+            distances = []
+            for j in range(n_repeat_projection):
+                # random
+                rand = torch.randn(p1.size(1), proj_per_repeat).to(device)  # (slice_size**2*ch)
+                rand = rand / torch.std(rand, dim=0, keepdim=True)  # noramlize
+                # projection
+                proj1 = torch.matmul(p1, rand)
+                proj2 = torch.matmul(p2, rand)
+                proj1, _ = torch.sort(proj1, dim=0)
+                proj2, _ = torch.sort(proj2, dim=0)
+                d = torch.abs(proj1 - proj2)
+                distances.append(torch.mean(d))
+
+            # swd
+            result.append(torch.mean(torch.stack(distances)))
+        
+        # average over resolution
+        result = torch.stack(result) * 1e3
+        if return_by_resolution:
+            return result.cpu()
+        else:
+            return torch.mean(result).cpu()
+
+
+def compute_swd(orig_images, mask_images, device):
+    orig_images = torch.as_tensor(orig_images.transpose([0, 3, 1, 2]).astype(np.float32) / 255.0)
+    mask_images = torch.as_tensor(mask_images.transpose([0, 3, 1, 2]).astype(np.float32) / 255.0)
+
+    out = swd(orig_images, mask_images, device=device)  # Fast estimation if device="cuda"
+    return out
\ No newline at end of file

From 20ee9a0285f7fe2e44fc6f7ac2c795ae4b45a500 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 20 Mar 2024 18:20:24 +0000
Subject: [PATCH 09/41] added tv loss

---
 deepliif/models/SDG_model.py | 13 +++++++++++--
 deepliif/models/networks.py  | 12 ++++++++++++
 2 files changed, 23 insertions(+), 2 deletions(-)

diff --git a/deepliif/models/SDG_model.py b/deepliif/models/SDG_model.py
index 5798eed..308f15c 100644
--- a/deepliif/models/SDG_model.py
+++ b/deepliif/models/SDG_model.py
@@ -16,6 +16,9 @@ def __init__(self, opt):
 
         self.mod_gen_no = self.opt.modalities_no
         opt.resize_conv = 'resizeconv' in opt.checkpoints_dir or 'resizeconv' in opt.name
+        print('opt.resize_conv', opt.resize_conv)
+        opt.tv_loss = 'tv' in opt.checkpoints_dir or 'tv' in opt.name
+        print('opt.tv_loss', opt.tv_loss)
             
 
         # weights of the modalities in generating segmentation mask
@@ -38,7 +41,7 @@ def __init__(self, opt):
         self.visual_names = ['real_A']
         # specify the training losses you want to print out. The training/test scripts will call <BaseModel.get_current_losses>
         for i in range(1, self.mod_gen_no + 1):
-            self.loss_names.extend(['G_GAN_' + str(i), 'G_L1_' + str(i), 'D_real_' + str(i), 'D_fake_' + str(i)])
+            self.loss_names.extend(['G_GAN_' + str(i), 'G_L1_' + str(i), 'G_VGG_'+ str(i),'G_TV_'+ str(i),'D_real_' + str(i), 'D_fake_' + str(i)])
             self.visual_names.extend(['fake_B_' + str(i), 'real_B_' + str(i)])
 
         # specify the images you want to save/display. The training/test scripts will call <BaseModel.get_current_visuals>
@@ -78,6 +81,7 @@ def __init__(self, opt):
             self.criterionSmoothL1 = torch.nn.SmoothL1Loss()
 
             self.criterionVGG = networks.VGGLoss().to(self.device)
+            self.criterionTV = networks.TotalVariationLoss().to(self.device)
 
             # initialize optimizers; schedulers will be automatically created by function <BaseModel.setup>.
             params = []
@@ -164,12 +168,17 @@ def backward_G(self):
         self.loss_G_VGG = []
         for i in range(self.mod_gen_no):
             self.loss_G_VGG.append(self.criterionVGG(self.fake_B[i], self.real_B[i]) * self.opt.lambda_feat)
+        
+        self.loss_G_TV = []
+        for i in range(self.mod_gen_no):
+            self.loss_G_TV.append(self.criterionTV(self.fake_B[i]) * 0.02)
 
         # self.loss_G = (self.loss_G_GAN[0] + self.loss_G_L1[0]) * self.loss_G_weights[0]
         self.loss_G = torch.tensor(0., device=self.device)
         for i in range(0, self.mod_gen_no):
             #self.loss_G += (self.loss_G_GAN[i] + self.loss_G_L1[i]) * self.loss_G_weights[i]
-            self.loss_G += (self.loss_G_GAN[i] + self.loss_G_L1[i] + self.loss_G_VGG[i]) * self.loss_G_weights[i]
+            #self.loss_G += (self.loss_G_GAN[i] + self.loss_G_L1[i] + self.loss_G_VGG[i]) * self.loss_G_weights[i]
+            self.loss_G += (self.loss_G_GAN[i] + self.loss_G_L1[i] + self.loss_G_VGG[i] + self.loss_G_TV[i]) * self.loss_G_weights[i]
         self.loss_G.backward()
 
     def optimize_parameters(self):
diff --git a/deepliif/models/networks.py b/deepliif/models/networks.py
index 989639f..a5269f1 100644
--- a/deepliif/models/networks.py
+++ b/deepliif/models/networks.py
@@ -699,3 +699,15 @@ def forward(self, x, y):
         for i in range(len(x_vgg)):
             loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach())
         return loss
+
+
+class TotalVariationLoss(nn.Module):
+    """
+    Absolute difference for neighbouring pixels (i,j to i+1,j, then i,j to i,j+1), averaged on pixel level
+    """ 
+    def __init__(self):
+        super(TotalVariationLoss, self).__init__()
+    
+    def forward(self, x):
+        tv = torch.abs(x[:,:,1:,:]-x[:,:,:-1,:]).sum() + torch.abs(x[:,:,:,1:]-x[:,:,:,:-1]).sum()
+        return tv / torch.prod(torch.tensor(x.size()))

From 507c70cda7e0a5938e176b23467089dd5cd1ee1c Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Thu, 4 Apr 2024 20:46:48 +0000
Subject: [PATCH 10/41] disabled TV loss

---
 deepliif/models/SDG_model.py | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/deepliif/models/SDG_model.py b/deepliif/models/SDG_model.py
index 308f15c..f633a86 100644
--- a/deepliif/models/SDG_model.py
+++ b/deepliif/models/SDG_model.py
@@ -41,7 +41,9 @@ def __init__(self, opt):
         self.visual_names = ['real_A']
         # specify the training losses you want to print out. The training/test scripts will call <BaseModel.get_current_losses>
         for i in range(1, self.mod_gen_no + 1):
-            self.loss_names.extend(['G_GAN_' + str(i), 'G_L1_' + str(i), 'G_VGG_'+ str(i),'G_TV_'+ str(i),'D_real_' + str(i), 'D_fake_' + str(i)])
+            self.loss_names.extend(['G_GAN_' + str(i), 'G_L1_' + str(i), 'G_VGG_'+ str(i),
+                                    #'G_TV_'+ str(i),
+                                    'D_real_' + str(i), 'D_fake_' + str(i)])
             self.visual_names.extend(['fake_B_' + str(i), 'real_B_' + str(i)])
 
         # specify the images you want to save/display. The training/test scripts will call <BaseModel.get_current_visuals>
@@ -65,6 +67,8 @@ def __init__(self, opt):
             print(self.opt.input_nc, self.opt.output_nc, self.opt.ngf, self.opt.net_g, self.opt.norm,
                                              not self.opt.no_dropout, self.opt.init_type, self.opt.init_gain, self.opt.gpu_ids, self.opt.padding)
             print('***************************************')
+            if i==0:
+                print(self.netG[i])
 
         if self.is_train:  # define a discriminator; conditional GANs need to take both input and output images; Therefore, #channels for D is input_nc + output_nc
             self.netD = [None for _ in range(self.mod_gen_no)]
@@ -169,16 +173,16 @@ def backward_G(self):
         for i in range(self.mod_gen_no):
             self.loss_G_VGG.append(self.criterionVGG(self.fake_B[i], self.real_B[i]) * self.opt.lambda_feat)
         
-        self.loss_G_TV = []
-        for i in range(self.mod_gen_no):
-            self.loss_G_TV.append(self.criterionTV(self.fake_B[i]) * 0.02)
+        # self.loss_G_TV = []
+        # for i in range(self.mod_gen_no):
+        #     self.loss_G_TV.append(self.criterionTV(self.fake_B[i]) * 0.02)
 
         # self.loss_G = (self.loss_G_GAN[0] + self.loss_G_L1[0]) * self.loss_G_weights[0]
         self.loss_G = torch.tensor(0., device=self.device)
         for i in range(0, self.mod_gen_no):
             #self.loss_G += (self.loss_G_GAN[i] + self.loss_G_L1[i]) * self.loss_G_weights[i]
-            #self.loss_G += (self.loss_G_GAN[i] + self.loss_G_L1[i] + self.loss_G_VGG[i]) * self.loss_G_weights[i]
-            self.loss_G += (self.loss_G_GAN[i] + self.loss_G_L1[i] + self.loss_G_VGG[i] + self.loss_G_TV[i]) * self.loss_G_weights[i]
+            self.loss_G += (self.loss_G_GAN[i] + self.loss_G_L1[i] + self.loss_G_VGG[i]) * self.loss_G_weights[i]
+            #self.loss_G += (self.loss_G_GAN[i] + self.loss_G_L1[i] + self.loss_G_VGG[i] + self.loss_G_TV[i]) * self.loss_G_weights[i]
         self.loss_G.backward()
 
     def optimize_parameters(self):

From 08a767c099787c8d647bb617e00f81339903aff4 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Thu, 4 Apr 2024 20:47:55 +0000
Subject: [PATCH 11/41] added experimental model: attention unet

---
 deepliif/models/att_unet.py | 197 ++++++++++++++++++++++++++++++++++++
 deepliif/models/networks.py |   4 +-
 2 files changed, 200 insertions(+), 1 deletion(-)
 create mode 100644 deepliif/models/att_unet.py

diff --git a/deepliif/models/att_unet.py b/deepliif/models/att_unet.py
new file mode 100644
index 0000000..e1a46e3
--- /dev/null
+++ b/deepliif/models/att_unet.py
@@ -0,0 +1,197 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import init
+
+def init_weights(net, init_type='normal', gain=0.02):
+    def init_func(m):
+        classname = m.__class__.__name__
+        if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
+            if init_type == 'normal':
+                init.normal_(m.weight.data, 0.0, gain)
+            elif init_type == 'xavier':
+                init.xavier_normal_(m.weight.data, gain=gain)
+            elif init_type == 'kaiming':
+                init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
+            elif init_type == 'orthogonal':
+                init.orthogonal_(m.weight.data, gain=gain)
+            else:
+                raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
+            if hasattr(m, 'bias') and m.bias is not None:
+                init.constant_(m.bias.data, 0.0)
+        elif classname.find('BatchNorm2d') != -1:
+            init.normal_(m.weight.data, 1.0, gain)
+            init.constant_(m.bias.data, 0.0)
+
+    print('initialize network with %s' % init_type)
+    net.apply(init_func)
+
+class conv_block(nn.Module):
+    def __init__(self,ch_in,ch_out,innermost=False,outermost=False):
+        super(conv_block,self).__init__()
+        if outermost:
+            self.conv = nn.Sequential(
+                nn.Conv2d(ch_in, ch_out, kernel_size=4,stride=2,padding=1,bias=True),
+                nn.LeakyReLU(0.2, True),
+            )
+        elif innermost:
+            self.conv = nn.Sequential(
+                nn.Conv2d(ch_in, ch_out, kernel_size=4,stride=2,padding=1,bias=True),
+                nn.ReLU(inplace=True),
+            )
+        else:
+            self.conv = nn.Sequential(
+                nn.Conv2d(ch_in, ch_out, kernel_size=4,stride=2,padding=1,bias=True),
+                nn.BatchNorm2d(ch_out),
+                nn.LeakyReLU(0.2, True),
+            )
+
+    def forward(self,x):
+        x = self.conv(x)
+        return x
+
+class up_conv(nn.Module):
+    def __init__(self,ch_in,ch_out,innermost=False,outermost=False):
+        super(up_conv,self).__init__()
+        use_bias=False
+        if outermost:
+            self.up = nn.Sequential(
+                          nn.ConvTranspose2d(ch_in * 2, ch_out,
+                                        kernel_size=4, stride=2,
+                                        padding=1),
+                          nn.Tanh())
+        elif innermost:
+            self.up = nn.Sequential(
+                          nn.ConvTranspose2d(ch_in, ch_out,
+                                        kernel_size=4, stride=2,
+                                        padding=1, bias=use_bias),
+                          nn.BatchNorm2d(ch_out),
+                          nn.ReLU(True))
+        else:
+            self.up = nn.Sequential(
+                          nn.ConvTranspose2d(ch_in * 2, ch_out,
+                                        kernel_size=4, stride=2,
+                                        padding=1, bias=use_bias),
+                          nn.BatchNorm2d(ch_out),
+                          nn.ReLU(True))
+                                        
+
+    def forward(self,x):
+        x = self.up(x)
+        return x
+
+
+class Attention_block(nn.Module):
+    def __init__(self,F_g,F_l,F_int):
+        super(Attention_block,self).__init__()
+        self.W_g = nn.Sequential(
+            nn.Conv2d(F_g, F_int, kernel_size=1,stride=1,padding=0,bias=True),
+            nn.BatchNorm2d(F_int)
+            )
+        
+        self.W_x = nn.Sequential(
+            nn.Conv2d(F_l, F_int, kernel_size=1,stride=1,padding=0,bias=True),
+            nn.BatchNorm2d(F_int)
+        )
+
+        self.psi = nn.Sequential(
+            nn.Conv2d(F_int, 1, kernel_size=1,stride=1,padding=0,bias=True),
+            nn.BatchNorm2d(1),
+            nn.Sigmoid()
+        )
+        
+        self.relu = nn.ReLU(inplace=True)
+        
+    def forward(self,g,x):
+        g1 = self.W_g(g)
+        x1 = self.W_x(x)
+        psi = self.relu(g1+x1)
+        psi = self.psi(psi)
+
+        return x*psi
+
+
+
+class AttU_Net(nn.Module):
+    def __init__(self,img_ch=3,output_ch=1):
+        super(AttU_Net,self).__init__()
+        
+        self.Conv1 = conv_block(ch_in=img_ch,ch_out=64,outermost=True)
+        self.Conv2 = conv_block(ch_in=64,ch_out=128)
+        self.Conv3 = conv_block(ch_in=128,ch_out=256)
+        self.Conv4 = conv_block(ch_in=256,ch_out=512)
+        self.Conv5 = conv_block(ch_in=512,ch_out=512)
+        self.Conv6 = conv_block(ch_in=512,ch_out=512)
+        self.Conv7 = conv_block(ch_in=512,ch_out=512)
+        self.Conv8 = conv_block(ch_in=512,ch_out=512,innermost=True)
+        #self.Conv9 = conv_block(ch_in=512,ch_out=512,innermost=True)
+        
+        self.Up8 = up_conv(ch_in=512,ch_out=512,innermost=True)
+        self.Att8 = Attention_block(F_g=512,F_l=512,F_int=512)
+        
+        self.Up7 = up_conv(ch_in=512,ch_out=512)
+        self.Att7 = Attention_block(F_g=512,F_l=512,F_int=512)
+        
+        self.Up6 = up_conv(ch_in=512,ch_out=512)
+        self.Att6 = Attention_block(F_g=512,F_l=512,F_int=512)
+        
+        self.Up5 = up_conv(ch_in=512,ch_out=512)
+        self.Att5 = Attention_block(F_g=512,F_l=512,F_int=512)
+        
+        self.Up4 = up_conv(ch_in=512,ch_out=256)
+        self.Att4 = Attention_block(F_g=256,F_l=256,F_int=128)
+        
+        self.Up3 = up_conv(ch_in=256,ch_out=128)
+        self.Att3 = Attention_block(F_g=128,F_l=128,F_int=64)
+        
+        self.Up2 = up_conv(ch_in=128,ch_out=64)
+        self.Att2 = Attention_block(F_g=64,F_l=64,F_int=32)
+        
+        self.Up1 = up_conv(ch_in=64,ch_out=img_ch,outermost=True)
+
+    def forward(self,x):
+        # encoding path
+        x1 = self.Conv1(x)
+        x2 = self.Conv2(x1)
+        x3 = self.Conv3(x2)
+        x4 = self.Conv4(x3)
+        x5 = self.Conv5(x4)
+        x6 = self.Conv6(x5)
+        x7 = self.Conv7(x6)
+        x8 = self.Conv8(x7)
+        #x9 = self.Conv9(x8)
+        
+        #d9 = self.Up
+        d8 = self.Up8(x8)
+        x7 = self.Att8(g=d8,x=x7)
+        d8 = torch.cat((x7,d8),dim=1)
+
+        d7 = self.Up7(d8)
+        x6 = self.Att7(g=d7,x=x6)
+        d7 = torch.cat((x6,d7),dim=1)
+        
+        d6 = self.Up6(d7)
+        x5 = self.Att6(g=d6,x=x5)
+        d6 = torch.cat((x5,d6),dim=1)
+        
+        d5 = self.Up5(d6)
+        x4 = self.Att5(g=d5,x=x4)
+        d5 = torch.cat((x4,d5),dim=1)
+        
+        
+        d4 = self.Up4(d5) # x4: [2, 512, 4, 4], d4: [2, 256, 4, 4]
+        x3 = self.Att4(g=d4,x=x3)
+        d4 = torch.cat((x3,d4),dim=1)
+
+        d3 = self.Up3(d4)
+        x2 = self.Att3(g=d3,x=x2)
+        d3 = torch.cat((x2,d3),dim=1)
+
+        d2 = self.Up2(d3)
+        x1 = self.Att2(g=d2,x=x1)
+        d2 = torch.cat((x1,d2),dim=1)
+
+        d1 = self.Up1(d2)
+
+        return d1
+
diff --git a/deepliif/models/networks.py b/deepliif/models/networks.py
index a5269f1..0e3ad9d 100644
--- a/deepliif/models/networks.py
+++ b/deepliif/models/networks.py
@@ -6,7 +6,7 @@
 import os
 
 from torchvision import models
-
+from .att_unet import AttU_Net
 ###############################################################################
 # Helper Functions
 ###############################################################################
@@ -165,6 +165,8 @@ def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, in
         net = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
     elif netG == 'unet_512':
         net = UnetGenerator(input_nc, output_nc, 9, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
+    elif netG == 'unet_512_attention':
+        net = AttU_Net(img_ch=3,output_ch=3)
     else:
         raise NotImplementedError('Generator model name [%s] is not recognized' % netG)
     return init_net(net, init_type, init_gain, gpu_ids)

From aca2060f3ebb9b1badd6c2ae00f34fdf1b88cfd8 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 15 May 2024 20:48:46 +0000
Subject: [PATCH 12/41] allowed to change net_gs

---
 deepliif/models/DeepLIIF_model.py | 12 +++++++-----
 deepliif/options/__init__.py      |  4 ++--
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/deepliif/models/DeepLIIF_model.py b/deepliif/models/DeepLIIF_model.py
index aeb57f8..e450980 100644
--- a/deepliif/models/DeepLIIF_model.py
+++ b/deepliif/models/DeepLIIF_model.py
@@ -13,6 +13,8 @@ def __init__(self, opt):
             opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
         """
         BaseModel.__init__(self, opt)
+        if not hasattr(opt,'net_gs'):
+            opt.net_gs = 'unet_512'
 
         # weights of the modalities in generating segmentation mask
         self.seg_weights = [0.25, 0.15, 0.25, 0.1, 0.25]
@@ -60,15 +62,15 @@ def __init__(self, opt):
         self.netG4 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids, opt.padding)
 
-        self.netG51 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, 'unet_512', opt.norm,
+        self.netG51 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs, opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
-        self.netG52 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, 'unet_512', opt.norm,
+        self.netG52 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs, opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
-        self.netG53 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, 'unet_512', opt.norm,
+        self.netG53 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs, opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
-        self.netG54 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, 'unet_512', opt.norm,
+        self.netG54 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs, opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
-        self.netG55 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, 'unet_512', opt.norm,
+        self.netG55 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs, opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
 
 
diff --git a/deepliif/options/__init__.py b/deepliif/options/__init__.py
index 5e2ea6b..a350def 100644
--- a/deepliif/options/__init__.py
+++ b/deepliif/options/__init__.py
@@ -35,8 +35,8 @@ def __init__(self, d_params=None, path_file=None, mode='train'):
 
         if mode == 'train':
             self.is_train = True
-            self.netG = 'resnet_9blocks'
-            self.netD = 'n_layers'
+            self.netG = self.net_g #'resnet_9blocks'
+            self.netD = self.net_d #'n_layers'
             self.n_layers_D = 4
             self.lambda_L1 = 100
             self.lambda_feat = 100

From 7a9c30cf81bb496c5920869a0cea883e25c489cf Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 15 May 2024 21:37:24 +0000
Subject: [PATCH 13/41] added argument return_seg_intermediate

---
 deepliif/models/__init__.py | 44 +++++++++++++++++++++++++++++--------
 1 file changed, 35 insertions(+), 9 deletions(-)

diff --git a/deepliif/models/__init__.py b/deepliif/models/__init__.py
index 61845d6..7649690 100644
--- a/deepliif/models/__init__.py
+++ b/deepliif/models/__init__.py
@@ -115,7 +115,7 @@ def load_torchscript_model(model_pt_path, device):
 
 
 
-def load_eager_models(opt, devices):
+def load_eager_models(opt, devices=None):
     # create a model given model and other options
     model = create_model(opt)
     # regular setup: load and print networks; create schedulers
@@ -138,7 +138,8 @@ def load_eager_models(opt, devices):
                 net = net.module
 
             nets[name] = net
-            nets[name].to(devices[name])
+            if devices:
+                nets[name].to(devices[name])
             
     return nets
 
@@ -253,10 +254,19 @@ def forward(input, model):
         lazy_segs['G51'] = forward(ts, nets['G51']).to(torch.device('cpu'))
         segs = compute(lazy_segs)[0]
     
-        seg_weights = [0.25, 0.25, 0.25, 0, 0.25]
-        seg = torch.stack([torch.mul(n, w) for n, w in zip(segs.values(), seg_weights)]).sum(dim=0)
+        #seg_weights = [0.25, 0.25, 0.25, 0, 0.25]
+        #seg = torch.stack([torch.mul(n, w) for n, w in zip(segs.values(), seg_weights)]).sum(dim=0)
+        weights = {
+            'G51': 0.25, # IHC
+            'G52': 0.25, # Hema
+            'G53': 0.25, # DAPI
+            'G54': 0.00, # Lap2
+            'G55': 0.25, # Marker
+        }
+        seg = torch.stack([torch.mul(segs[k], weights[k]) for k in segs.keys()]).sum(dim=0)
     
         res = {k: tensor_to_pil(v) for k, v in gens.items()}
+        res.update({k: tensor_to_pil(v) for k, v in segs.items()})
         res['G5'] = tensor_to_pil(seg)
     
         return res
@@ -280,12 +290,19 @@ def forward(input, model):
     
 
 
-def is_empty(tile):
+def is_empty_old(tile):
     # return True if np.mean(np.array(tile) - np.array(mean_background_val)) < 40 else False
     if isinstance(tile, list): # for pair of tiles, only mark it as empty / no need for prediction if ALL tiles are empty
         return all([True if calculate_background_area(t) > 98 else False for t in tile])
     else:
         return True if calculate_background_area(tile) > 98 else False
+      
+      
+def is_empty(tile):
+    if isinstance(tile, list): # for pair of tiles, only mark it as empty / no need for prediction if ALL tiles are empty
+        return all([True if np.max(image_variance_rgb(tile)) < 15 else False for t in tile])
+    else:
+        return True if np.max(image_variance_rgb(tile)) < 15 else False
 
 
 def run_wrapper(tile, run_fn, model_path, eager_mode=False, opt=None):
@@ -358,7 +375,7 @@ def get_net_tiles(n):
 
 
 def inference(img, tile_size, overlap_size, model_path, use_torchserve=False, eager_mode=False,
-              color_dapi=False, color_marker=False, opt=None):
+              color_dapi=False, color_marker=False, opt=None, return_seg_intermediate=False):
     if not opt:
         opt = get_opt(model_path)
         #print_options(opt)
@@ -373,7 +390,14 @@ def inference(img, tile_size, overlap_size, model_path, use_torchserve=False, ea
                           'DAPI':'G2',
                           'Lap2':'G3',
                           'Marker':'G4',
-                          'Seg':'G5'}
+                          'Seg':'G5',
+                          }
+        if return_seg_intermediate:
+            d_modality2net.update({'IHC_s':'G51',
+                                  'Hema_s':'G52',
+                                  'DAPI_s':'G53',
+                                  'Lap2_s':'G54',
+                                  'Marker_s':'G55',})
         
         for k in d_modality2net.keys():
             images[k] = create_image_for_stitching(tile_size, rows, cols)
@@ -509,7 +533,8 @@ def postprocess(orig, images, tile_size, model, seg_thresh=150, size_thresh='aut
 
 
 def infer_modalities(img, tile_size, model_dir, eager_mode=False,
-                     color_dapi=False, color_marker=False, opt=None):
+                     color_dapi=False, color_marker=False, opt=None,
+                     return_seg_intermediate=False):
     """
     This function is used to infer modalities for the given image using a trained model.
     :param img: The input image.
@@ -539,7 +564,8 @@ def infer_modalities(img, tile_size, model_dir, eager_mode=False,
         eager_mode=eager_mode,
         color_dapi=color_dapi,
         color_marker=color_marker,
-        opt=opt
+        opt=opt,
+        return_seg_intermediate=return_seg_intermediate
     )
     
     if not hasattr(opt,'seg_gen') or (hasattr(opt,'seg_gen') and opt.seg_gen): # the first condition accounts for old settings of deepliif; the second refers to deepliifext models

From 9aebf568fcdee06276885f9bd2a807cd9d8c98d3 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 26 Jun 2024 18:04:14 +0000
Subject: [PATCH 14/41] allowed cli.py inference and test.py to return raw
 segmentation predictions

---
 deepliif/models/DeepLIIF_model.py | 22 ++++++++++++----------
 deepliif/models/__init__.py       |  9 +++++++--
 deepliif/models/base_model.py     | 10 +++++++---
 3 files changed, 26 insertions(+), 15 deletions(-)

diff --git a/deepliif/models/DeepLIIF_model.py b/deepliif/models/DeepLIIF_model.py
index e450980..f4af446 100644
--- a/deepliif/models/DeepLIIF_model.py
+++ b/deepliif/models/DeepLIIF_model.py
@@ -33,7 +33,7 @@ def __init__(self, opt):
         # specify the training losses you want to print out. The training/test scripts will call <BaseModel.get_current_losses>
         for i in range(1, self.opt.modalities_no + 1 + 1):
             self.loss_names.extend(['G_GAN_' + str(i), 'G_L1_' + str(i), 'D_real_' + str(i), 'D_fake_' + str(i)])
-            self.visual_names.extend(['fake_B_' + str(i), 'real_B_' + str(i)])
+            self.visual_names.extend(['fake_B_' + str(i), 'fake_B_5' + str(i), 'real_B_' + str(i)])
 
         # specify the images you want to save/display. The training/test scripts will call <BaseModel.get_current_visuals>
         # specify the models you want to save to the disk. The training/test scripts will call <BaseModel.save_networks> and <BaseModel.load_networks>
@@ -53,24 +53,26 @@ def __init__(self, opt):
                 self.model_names.extend(['G5' + str(i)])
 
         # define networks (both generator and discriminator)
-        self.netG1 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.norm,
+        opt.netG = [opt.netG] * 4
+        opt.net_gs = [opt.net_gs]*5
+        self.netG1 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG[0], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids, opt.padding)
-        self.netG2 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.norm,
+        self.netG2 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG[1], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids, opt.padding)
-        self.netG3 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.norm,
+        self.netG3 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG[2], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids, opt.padding)
-        self.netG4 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.norm,
+        self.netG4 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG[3], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids, opt.padding)
 
-        self.netG51 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs, opt.norm,
+        self.netG51 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[0], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
-        self.netG52 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs, opt.norm,
+        self.netG52 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[1], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
-        self.netG53 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs, opt.norm,
+        self.netG53 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[2], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
-        self.netG54 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs, opt.norm,
+        self.netG54 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[3], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
-        self.netG55 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs, opt.norm,
+        self.netG55 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[4], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
 
 
diff --git a/deepliif/models/__init__.py b/deepliif/models/__init__.py
index 7649690..1e4af1f 100644
--- a/deepliif/models/__init__.py
+++ b/deepliif/models/__init__.py
@@ -313,7 +313,12 @@ def run_wrapper(tile, run_fn, model_path, eager_mode=False, opt=None):
                 'G2': Image.new(mode='RGB', size=(512, 512), color=(10, 10, 10)),
                 'G3': Image.new(mode='RGB', size=(512, 512), color=(0, 0, 0)),
                 'G4': Image.new(mode='RGB', size=(512, 512), color=(10, 10, 10)),
-                'G5': Image.new(mode='RGB', size=(512, 512), color=(0, 0, 0))
+                'G5': Image.new(mode='RGB', size=(512, 512), color=(0, 0, 0)),
+                'G51': Image.new(mode='RGB', size=(512, 512), color=(0, 0, 0)),
+                'G52': Image.new(mode='RGB', size=(512, 512), color=(0, 0, 0)),
+                'G53': Image.new(mode='RGB', size=(512, 512), color=(0, 0, 0)),
+                'G54': Image.new(mode='RGB', size=(512, 512), color=(0, 0, 0)),
+                'G55': Image.new(mode='RGB', size=(512, 512), color=(0, 0, 0)),
             }
         else:
             return run_fn(tile, model_path, eager_mode, opt)
@@ -409,7 +414,7 @@ def inference(img, tile_size, overlap_size, model_path, use_torchserve=False, ea
                 
                 for modality_name, net_name in d_modality2net.items():
                     stitch_tile(images[modality_name], res[net_name], tile_size, overlap_size, i, j)
-        
+
         for modality_name, output_img in images.items():
             images[modality_name] = output_img.resize(img.size)
     
diff --git a/deepliif/models/base_model.py b/deepliif/models/base_model.py
index cad9295..e74ecd7 100644
--- a/deepliif/models/base_model.py
+++ b/deepliif/models/base_model.py
@@ -134,10 +134,14 @@ def get_current_visuals(self):
         for name in self.visual_names:
             if isinstance(name, str):
                 if not hasattr(self, name):
-                    if len(name.split('_')) == 2:
-                        visual_ret[name] = getattr(self, name.split('_')[0])[int(name.split('_')[-1]) -1]
+                    if len(name.split('_')) != 2:
+                        if self.opt.model == 'DeepLIIF':
+                            img_name = name[:-1] + '_' + name[-1]
+                            visual_ret[name] = getattr(self, img_name)
+                        else:
+                            visual_ret[name] = getattr(self, name.split('_')[0] + '_' + name.split('_')[1])[int(name.split('_')[-1]) - 1]
                     else:
-                        visual_ret[name] = getattr(self, name.split('_')[0] + '_' + name.split('_')[1])[int(name.split('_')[-1]) - 1]
+                        visual_ret[name] = getattr(self, name.split('_')[0])[int(name.split('_')[-1]) -1]
                 else:
                     visual_ret[name] = getattr(self, name)
         return visual_ret

From 4cde80446e0f8e0168a325eae239cb7ab85ff697 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Tue, 16 Jul 2024 20:08:53 +0000
Subject: [PATCH 15/41] allowed to specify modality-wise generator arch; added
 validation loss calculation

---
 cli.py                            | 106 +++++++++++++++++++++++--
 deepliif/models/DeepLIIF_model.py |  19 +++--
 deepliif/models/base_model.py     |  11 +++
 deepliif/util/visualizer.py       | 127 +++++++++++++++++++++++++-----
 4 files changed, 229 insertions(+), 34 deletions(-)

diff --git a/cli.py b/cli.py
index 604838c..e9e9fc4 100644
--- a/cli.py
+++ b/cli.py
@@ -8,9 +8,10 @@
 import torch
 import numpy as np
 from PIL import Image
+from torchvision.transforms import ToPILImage
 
 from deepliif.data import create_dataset, transform
-from deepliif.models import init_nets, infer_modalities, infer_results_for_wsi, create_model
+from deepliif.models import init_nets, infer_modalities, infer_results_for_wsi, create_model, postprocess
 from deepliif.util import allowed_file, Visualizer, get_information, test_diff_original_serialized, disable_batchnorm_tracking_stats
 from deepliif.util.util import mkdirs, check_multi_scale
 # from deepliif.util import infer_results_for_wsi
@@ -85,7 +86,7 @@ def cli():
               help='specify discriminator architecture [basic | n_layers | pixel]. The basic model is a 70x70 '
                    'PatchGAN. n_layers allows you to specify the layers in the discriminator')
 @click.option('--net-g', default='resnet_9blocks',
-              help='specify generator architecture [resnet_9blocks | resnet_6blocks | unet_512 | unet_256 | unet_128]')
+              help='specify generator architecture [resnet_9blocks | resnet_6blocks | unet_512 | unet_256 | unet_128 | unet_512_attention]; to specify different arch for generators, list arch for each generator separated by comma, e.g., --net-g=resnet_9blocks,resnet_9blocks,resnet_9blocks,unet_512_attention,unet_512_attention')
 @click.option('--n-layers-d', default=4, help='only used if netD==n_layers')
 @click.option('--norm', default='batch',
               help='instance normalization or batch normalization [instance | batch | none]')
@@ -164,7 +165,7 @@ def cli():
 @click.option('--net-ds', type=str, default='n_layers',
               help='specify discriminator architecture for segmentation task [basic | n_layers | pixel]. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator')
 @click.option('--net-gs', type=str, default='unet_512',
-              help='specify generator architecture for segmentation task [resnet_9blocks | resnet_6blocks | unet_512 | unet_256 | unet_128]')
+              help='specify generator architecture for segmentation task [resnet_9blocks | resnet_6blocks | unet_512 | unet_256 | unet_128 | unet_512_attention]; to specify different arch for generators, list arch for each generator separated by comma, e.g., --net-g=resnet_9blocks,resnet_9blocks,resnet_9blocks,unet_512_attention,unet_512_attention')
 @click.option('--gan-mode', type=str, default='vanilla',
               help='the type of GAN objective for translation task. [vanilla| lsgan | wgangp]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.')
 @click.option('--gan-mode-s', type=str, default='lsgan',
@@ -235,17 +236,34 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
     dir_data_train = dataroot + '/train'
     fns = os.listdir(dir_data_train)
     fns = [x for x in fns if x.endswith('.png')]
+    print(f'{len(fns)} images found')
     img = Image.open(f"{dir_data_train}/{fns[0]}")
+    print(f'image shape:',img.size)
     
     num_img = img.size[0] / img.size[1]
     assert int(num_img) == num_img, f'img size {img.size[0]} / {img.size[1]} = {num_img} is not an integer'
     num_img = int(num_img)
     
     input_no = num_img - modalities_no - seg_no
-    assert input_no > 0, f'inferred number of input images is {input_no}; should be greater than 0'
+    assert input_no > 0, f'inferred number of input images is {input_no} (modalities_no {modalities_no}, seg_no {seg_no}); should be greater than 0'
     d_params['input_no'] = input_no
     d_params['scale_size'] = img.size[1]
     d_params['gpu_ids'] = gpu_ids
+    
+    # update generator arch
+    net_g = net_g.split(',')
+    assert len(net_g) in [1,modalities_no], f'net_g should contain either 1 architecture for all translation generators or the same number of architectures as the number of translation generators ({modalities_no})'
+    if len(net_g) == 1:
+      net_g = net_g*modalities_no
+      
+    
+    net_gs = net_gs.split(',')
+    assert len(net_gs) in [1,seg_no], f'net_gs should contain either 1 architecture for all segmentation generators or the same number of architectures as the number of segmentation generators ({seg_no})'
+    if len(net_gs) == 1:
+      net_gs = net_gs*seg_no
+    
+    d_params['net_g'] = net_g
+    d_params['net_gs'] = net_gs
 
     # create a dataset given dataset_mode and other options
     # dataset = AlignedDataset(opt)
@@ -253,9 +271,16 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
     opt = Options(d_params=d_params)
     print_options(opt, save=True)
     
+    # set dir for train and val
     dataset = create_dataset(opt)
+    dataset_val = create_dataset(opt,phase='val')
+    data_val = [batch for batch in dataset_val]
+    metrics_val = json.load(open(os.path.join(dataset_val.dataset.dir_AB,'metrics.json')))
+
     # get the number of images in the dataset.
     click.echo('The number of training images = %d' % len(dataset))
+    click.echo('The number of validation images = %d' % len(dataset_val))
+    click.echo('The number of validation images = %d' % len(data_val))
 
     # create a model given model and other options
     model = create_model(opt)
@@ -285,6 +310,7 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
 
         # inner loop within one epoch
         for i, data in enumerate(dataset):
+            
             # timer for computation per iteration
             iter_start_time = time.time()
             if total_iters % print_freq == 0:
@@ -301,15 +327,15 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
             if total_iters % display_freq == 0:
                 save_result = total_iters % update_html_freq == 0
                 model.compute_visuals()
-                visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
+                visualizer.display_current_results({**model.get_current_visuals()}, epoch, save_result)
 
             # print training losses and save logging information to the disk
             if total_iters % print_freq == 0:
-                losses = model.get_current_losses()
+                losses = model.get_current_losses() # get training losses
                 t_comp = (time.time() - iter_start_time) / batch_size
-                visualizer.print_current_losses(epoch, epoch_iter, losses, t_comp, t_data)
+                visualizer.print_current_losses(epoch, epoch_iter, {**losses}, t_comp, t_data)
                 if display_id > 0:
-                    visualizer.plot_current_losses(epoch, float(epoch_iter) / len(dataset), losses)
+                    visualizer.plot_current_losses(epoch, float(epoch_iter) / len(dataset), {**losses})
 
             # cache our latest model every <save_latest_freq> iterations
             if total_iters % save_latest_freq == 0:
@@ -325,6 +351,70 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
             model.save_networks('latest')
             model.save_networks(epoch)
 
+        
+        
+        # validation loss and metrics calculation
+        losses = model.get_current_losses() # get training losses to print
+        
+        model.eval()
+        l_losses_val = []
+        l_metrics_val = []
+        
+        # for each val image, calculate validation loss and cell count metrics
+        for j, data_val_batch in enumerate(data_val):
+            # batch size is effectively 1 for validation
+            model.set_input(data_val_batch)
+            model.calculate_losses() # this does not optimize parameters
+            visuals = model.get_current_visuals()  # get image results
+            
+            # val losses
+            losses_val_batch = model.get_current_losses()
+            l_losses_val += [(k,v) for k,v in losses_val_batch.items()]
+            
+            # calculate cell count metrics
+            l_seg_names = ['fake_B_5']
+            assert l_seg_names[0] in visuals.keys(), f'Cannot find {l_seg_names[0]} in generated image names ({list(visuals.keys())})'
+            seg_mod_suffix = l_seg_names[0].split('_')[-1]
+            l_seg_names += [x for x in visuals.keys() if x.startswith('fake') and x.split('_')[-1].startswith(seg_mod_suffix) and x != l_seg_names[0]]
+            # print(f'Running postprocess for {len(l_seg_names)} generated images ({l_seg_names})')
+
+            img_name_current = data_val_batch['A_paths'][0].split('/')[-1][:-4] # remove .png
+            metrics_gt = metrics_val[img_name_current]
+            
+            for seg_name in l_seg_names:
+                images = {'Seg':ToPILImage()((visuals[seg_name][0].cpu()+1)/2),
+                          'Marker':ToPILImage()((visuals['fake_B_4'][0].cpu()+1)/2)}
+                _, scoring = postprocess(ToPILImage()((data['A'][0]+1)/2), images, opt.scale_size, opt.model)
+                
+                for k,v in scoring.items():
+                    if k.startswith('num') or k.startswith('percent'):
+                        # to calculate the rmse, here we calculate (x_pred - x_true) ** 2
+                        l_metrics_val.append((k+'_'+seg_name,(v - metrics_gt[k])**2))
+                
+        d_losses_val = {k+'_val':0 for k in losses_val_batch.keys()}
+        for k,v in l_losses_val:
+            d_losses_val[k+'_val'] += v
+        for k in d_losses_val:
+            d_losses_val[k] = d_losses_val[k] / len(data_val)
+        
+        d_metrics_val = {}
+        for k,v in l_metrics_val:
+            try:
+                d_metrics_val[k] += v
+            except:
+                d_metrics_val[k] = v
+        for k in d_metrics_val:
+            # to calculate the rmse, this is the second part, where d_metrics_val[k] now represents sum((x_pred - x_true) ** 2)
+            d_metrics_val[k] = np.sqrt(d_metrics_val[k] / len(data_val))
+        
+        
+        model.train()
+        t_comp = (time.time() - iter_start_time) / batch_size
+        visualizer.print_current_losses(epoch, epoch_iter, {**losses,**d_losses_val, **d_metrics_val}, t_comp, t_data)
+        if display_id > 0:
+            visualizer.plot_current_losses(epoch, float(epoch_iter) / len(dataset), {**losses,**d_losses_val,**d_metrics_val})
+
+
         print('End of epoch %d / %d \t Time Taken: %d sec' % (
             epoch, n_epochs + n_epochs_decay, time.time() - epoch_start_time))
         # update learning rates at the end of every epoch.
diff --git a/deepliif/models/DeepLIIF_model.py b/deepliif/models/DeepLIIF_model.py
index f4af446..f851022 100644
--- a/deepliif/models/DeepLIIF_model.py
+++ b/deepliif/models/DeepLIIF_model.py
@@ -17,7 +17,7 @@ def __init__(self, opt):
             opt.net_gs = 'unet_512'
 
         # weights of the modalities in generating segmentation mask
-        self.seg_weights = [0.25, 0.15, 0.25, 0.1, 0.25]
+        self.seg_weights = [0.25, 0.25, 0.25, 0.0, 0.25]
 
         # loss weights in calculating the final loss
         self.loss_G_weights = [0.2, 0.2, 0.2, 0.2, 0.2]
@@ -53,8 +53,12 @@ def __init__(self, opt):
                 self.model_names.extend(['G5' + str(i)])
 
         # define networks (both generator and discriminator)
-        opt.netG = [opt.netG] * 4
-        opt.net_gs = [opt.net_gs]*5
+        print(opt.netG)
+        if isinstance(opt.netG, str):
+            opt.netG = [opt.netG] * 4
+        if isinstance(opt.net_gs, str):
+            opt.net_gs = [opt.net_gs]*5
+            
         self.netG1 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG[0], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids, opt.padding)
         self.netG2 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG[1], opt.norm,
@@ -64,15 +68,16 @@ def __init__(self, opt):
         self.netG4 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG[3], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids, opt.padding)
 
+        # DeepLIIF model currently uses one gs arch because there is only one explicit seg mod output
         self.netG51 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[0], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
-        self.netG52 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[1], opt.norm,
+        self.netG52 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[0], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
-        self.netG53 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[2], opt.norm,
+        self.netG53 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[0], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
-        self.netG54 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[3], opt.norm,
+        self.netG54 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[0], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
-        self.netG55 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[4], opt.norm,
+        self.netG55 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[0], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
 
 
diff --git a/deepliif/models/base_model.py b/deepliif/models/base_model.py
index e74ecd7..20fa424 100644
--- a/deepliif/models/base_model.py
+++ b/deepliif/models/base_model.py
@@ -88,6 +88,16 @@ def setup(self, opt):
             self.load_networks(load_suffix)
         self.print_networks(opt.verbose)
 
+    def train(self):
+        """Make models train mode """
+        for name in self.model_names:
+            if isinstance(name, str):
+                if '_' in name:
+                    net = getattr(self, 'net' + name.split('_')[0])[int(name.split('_')[-1]) - 1]
+                else:
+                    net = getattr(self, 'net' + name)
+                net.train()
+
     def eval(self):
         """Make models eval mode during test time"""
         for name in self.model_names:
@@ -244,6 +254,7 @@ def load_networks(self, epoch):
             epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
         """
         for name in self.model_names:
+            
             if isinstance(name, str):
                 load_filename = '%s_net_%s.pth' % (epoch, name)
                 load_path = os.path.join(self.save_dir, load_filename)
diff --git a/deepliif/util/visualizer.py b/deepliif/util/visualizer.py
index 0856b51..f1c327c 100644
--- a/deepliif/util/visualizer.py
+++ b/deepliif/util/visualizer.py
@@ -165,6 +165,7 @@ def display_current_results(self, visuals, epoch, save_result, **kwargs):
                 if ncols > 0:        # show all the images in one visdom panel
                     ncols = min(ncols, len(visuals))
                     h, w = next(iter(visuals.values())).shape[:2]
+                    print(1)
                     table_css = """<style>
                             table {border-collapse: separate; border-spacing: 4px; white-space: nowrap; text-align: center}
                             table td {width: % dpx; height: % dpx; padding: 4px; outline: 4px solid black}
@@ -176,13 +177,17 @@ def display_current_results(self, visuals, epoch, save_result, **kwargs):
                     images = []
                     idx = 0
                     for label, image in visuals.items():
-                        image_numpy = util.tensor2im(image)
-                        label_html_row += '<td>%s</td>' % label
-                        images.append(image_numpy.transpose([2, 0, 1]))
-                        idx += 1
-                        if idx % ncols == 0:
-                            label_html += '<tr>%s</tr>' % label_html_row
-                            label_html_row = ''
+                        if image.shape[1] != 3:
+                            pass
+                        else:
+                            image_numpy = util.tensor2im(image)
+                            label_html_row += '<td>%s</td>' % label
+                            images.append(image_numpy.transpose([2, 0, 1]))
+                            idx += 1
+                            if idx % ncols == 0:
+                                label_html += '<tr>%s</tr>' % label_html_row
+                                label_html_row = ''
+                    print(2)
 
                     white_image = np.ones_like(image_numpy.transpose([2, 0, 1])) * 255
                     while idx % ncols != 0:
@@ -191,10 +196,12 @@ def display_current_results(self, visuals, epoch, save_result, **kwargs):
                         idx += 1
                     if label_html_row != '':
                         label_html += '<tr>%s</tr>' % label_html_row
+                    
 
                     try:
                         self.vis.images(images, nrow=ncols, win=self.display_id + 1,
                                         padding=2, opts=dict(title=title + ' images'))
+                        print(4)
                         label_html = '<table>%s</table>' % label_html
                         self.vis.text(table_css + label_html, win=self.display_id + 2,
                                       opts=dict(title=title + ' labels'))
@@ -248,6 +255,10 @@ def plot_current_losses(self, epoch, counter_ratio, losses):
         # if having 2 processes, each process obtains 50% of the data (effective dataset_size divided by half), the effective counter ratio shall multiply by 2 to compensate that
         n_proc = int(os.getenv('WORLD_SIZE',1))
         counter_ratio = counter_ratio * n_proc
+
+        self.plot_data_update_train = False
+        self.plot_data_update_val = False
+        self.plot_data_update_metrics = False
         
         if self.remote:
             fn = 'plot_current_losses.pickle'
@@ -263,20 +274,98 @@ def plot_current_losses(self, epoch, counter_ratio, losses):
                     exec(f'{self.remote_transfer_cmd_function}("{path_source}")')
         else:
             if not hasattr(self, 'plot_data'):
-                self.plot_data = {'X': [], 'Y': [], 'legend': list(losses.keys())}
-            self.plot_data['X'].append(epoch + counter_ratio)
-            self.plot_data['Y'].append([losses[k] for k in self.plot_data['legend']])
+                self.plot_data = {'X': [], 'X_val':[], 'X_metrics':[],
+                                  'Y': [], 'Y_val':[], 'Y_metrics':[],
+                                  'legend': [], 'legend_val': [], 'legend_metrics':[]}
+                for k in list(losses.keys()):
+                    if k.endswith('_val'):
+                        self.plot_data['legend_val'].append(k)
+                    elif k.startswith(('G_','D_')):
+                        self.plot_data['legend'].append(k)
+                    else:
+                        self.plot_data['legend_metrics'].append(k)
+            
+            # check if all names in losses dict have been seen
+            # currently we assume the three types of metrics (train loss, val loss, other metrics) can come into the losses dict
+            # at any step, but each type will join or leave the dict as a whole (i.e., train loss metrics will either all appear or all be missing)
+            for k in list(losses.keys()):
+                    if k.endswith('_val'):
+                        if k not in self.plot_data['legend_val']:
+                            self.plot_data['legend_val'].append(k)
+                    elif k.startswith(('G_','D_')):
+                        if k not in self.plot_data['legend']:
+                            self.plot_data['legend'].append(k)
+                    else:
+                        if k not in self.plot_data['legend_metrics']:
+                            self.plot_data['legend_metrics'].append(k)
+
+            # update training loss
+            print('update training loss')
+            if len(self.plot_data['legend']) > 0:
+                if self.plot_data['legend'][0] in losses:
+                    self.plot_data['X'].append(epoch + counter_ratio)
+                    self.plot_data['Y'].append([losses[k] for k in self.plot_data['legend']])
+                    self.plot_data_update_train = True
+
+            # update validation loss
+            print('update validation loss')
+            if len(self.plot_data['legend_val']) > 0:
+                if self.plot_data['legend_val'][0] in losses:
+                    self.plot_data['X_val'].append(epoch + counter_ratio)
+                    self.plot_data['Y_val'].append([losses[k] for k in self.plot_data['legend_val']])
+                    self.plot_data_update_val = True
+
+            # update other calculated metrics
+            print('update other metrics')
+            if len(self.plot_data['legend_metrics']) > 0:
+                if self.plot_data['legend_metrics'][0] in losses:
+                    self.plot_data['X_metrics'].append(epoch + counter_ratio)
+                    self.plot_data['Y_metrics'].append([losses[k] for k in self.plot_data['legend_metrics']])
+                    self.plot_data_update_metrics = True
 
             try:
-                self.vis.line(
-                    X=np.stack([np.array(self.plot_data['X'])] * len(self.plot_data['legend']), 1),
-                    Y=np.array(self.plot_data['Y']),
-                    opts={
-                        'title': self.name + ' loss over time',
-                        'legend': self.plot_data['legend'],
-                        'xlabel': 'epoch',
-                        'ylabel': 'loss'},
-                    win=self.display_id)
+                if self.plot_data_update_train:
+                    print('plotting train loss')
+                    self.vis.line(
+                        X=np.stack([np.array(self.plot_data['X'])] * len(self.plot_data['legend']), 1),
+                        Y=np.array(self.plot_data['Y']),
+                        opts={
+                            'title': self.name + ' train loss over time',
+                            'legend': self.plot_data['legend'],
+                            'xlabel': 'epoch',
+                            'ylabel': 'loss'},
+                        win = 'train',
+                        #env=self.display_id
+                        )
+                
+                if self.plot_data_update_val:
+                    print('plotting val loss')
+                    self.vis.line(
+                        X=np.stack([np.array(self.plot_data['X_val'])] * len(self.plot_data['legend_val']), 1),
+                        Y=np.array(self.plot_data['Y_val']),
+                        opts={
+                            'title': self.name + ' val loss over time',
+                            'legend': self.plot_data['legend_val'],
+                            'xlabel': 'epoch',
+                            'ylabel': 'loss'},
+                        win = 'val',
+                        #env=self.display_id
+                        )
+                
+                if self.plot_data_update_metrics:
+                    print('plotting other metrics')
+                    self.vis.line(
+                        X=np.stack([np.array(self.plot_data['X_metrics'])] * len(self.plot_data['legend_metrics']), 1),
+                        Y=np.array(self.plot_data['Y_metrics']),
+                        opts={
+                            'title': self.name + ' metrics over time',
+                            'legend': self.plot_data['legend_metrics'],
+                            'xlabel': 'epoch',
+                            'ylabel': 'metrics'},
+                        win = 'metrics',
+                        #env=self.display_id
+                        )
+                
             except VisdomExceptionBase:
                 self.create_visdom_connections()
             

From 6d9ca5635348bbc03600991a394ed45e135e51a3 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Thu, 25 Jul 2024 18:37:18 +0000
Subject: [PATCH 16/41] added schedule-free adamw optimizer

---
 deepliif/models/DeepLIIF_model.py   |  45 +++++-
 deepliif/util/adamw_schedulefree.py | 208 ++++++++++++++++++++++++++++
 2 files changed, 251 insertions(+), 2 deletions(-)
 create mode 100644 deepliif/util/adamw_schedulefree.py

diff --git a/deepliif/models/DeepLIIF_model.py b/deepliif/models/DeepLIIF_model.py
index f851022..77467fc 100644
--- a/deepliif/models/DeepLIIF_model.py
+++ b/deepliif/models/DeepLIIF_model.py
@@ -1,6 +1,7 @@
 import torch
 from .base_model import BaseModel
 from . import networks
+from ..util.adamw_schedulefree import AdamWScheduleFree
 
 
 class DeepLIIFModel(BaseModel):
@@ -110,10 +111,12 @@ def __init__(self, opt):
 
             # initialize optimizers; schedulers will be automatically created by function <BaseModel.setup>.
             params = list(self.netG1.parameters()) + list(self.netG2.parameters()) + list(self.netG3.parameters()) + list(self.netG4.parameters()) + list(self.netG51.parameters()) + list(self.netG52.parameters()) + list(self.netG53.parameters()) + list(self.netG54.parameters()) + list(self.netG55.parameters())
-            self.optimizer_G = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+            # self.optimizer_G = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+            self.optimizer_G = AdamWScheduleFree(params, lr=opt.lr, betas=(opt.beta1, 0.999))
 
             params = list(self.netD1.parameters()) + list(self.netD2.parameters()) + list(self.netD3.parameters()) + list(self.netD4.parameters()) + list(self.netD51.parameters()) + list(self.netD52.parameters()) + list(self.netD53.parameters()) + list(self.netD54.parameters()) + list(self.netD55.parameters())
-            self.optimizer_D = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+            # self.optimizer_D = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+            self.optimizer_D = AdamWScheduleFree(params, lr=opt.lr, betas=(opt.beta1, 0.999))
 
             self.optimizers.append(self.optimizer_G)
             self.optimizers.append(self.optimizer_D)
@@ -331,3 +334,41 @@ def optimize_parameters(self):
         self.optimizer_G.zero_grad()        # set G's gradients to zero
         self.backward_G()                   # calculate graidents for G
         self.optimizer_G.step()             # udpate G's weights
+    
+    def calculate_losses(self):
+        """
+        Calculate losses but do not optimize parameters. Used in validation loss calculation during training.
+        """
+        # for eval/val, schedule free optimizers need to be set to eval mode: https://github.com/facebookresearch/schedule_free/tree/main?tab=readme-ov-file#how-to-use
+        self.optimizer_D.eval()
+        self.optimizer_G.eval()
+        
+        self.forward()                   # compute fake images: G(A)
+        # update D
+        self.set_requires_grad(self.netD1, True)  # enable backprop for D1
+        self.set_requires_grad(self.netD2, True)  # enable backprop for D2
+        self.set_requires_grad(self.netD3, True)  # enable backprop for D3
+        self.set_requires_grad(self.netD4, True)  # enable backprop for D4
+        self.set_requires_grad(self.netD51, True)  # enable backprop for D51
+        self.set_requires_grad(self.netD52, True)  # enable backprop for D52
+        self.set_requires_grad(self.netD53, True)  # enable backprop for D53
+        self.set_requires_grad(self.netD54, True)  # enable backprop for D54
+        self.set_requires_grad(self.netD55, True)  # enable backprop for D54
+
+        self.backward_D()                # calculate gradients for D
+
+        # update G
+        self.set_requires_grad(self.netD1, False)  # D1 requires no gradients when optimizing G1
+        self.set_requires_grad(self.netD2, False)  # D2 requires no gradients when optimizing G2
+        self.set_requires_grad(self.netD3, False)  # D3 requires no gradients when optimizing G3
+        self.set_requires_grad(self.netD4, False)  # D4 requires no gradients when optimizing G4
+        self.set_requires_grad(self.netD51, False)  # D51 requires no gradients when optimizing G51
+        self.set_requires_grad(self.netD52, False)  # D52 requires no gradients when optimizing G52
+        self.set_requires_grad(self.netD53, False)  # D53 requires no gradients when optimizing G53
+        self.set_requires_grad(self.netD54, False)  # D54 requires no gradients when optimizing G54
+        self.set_requires_grad(self.netD55, False)  # D54 requires no gradients when optimizing G54
+
+        self.backward_G()                   # calculate graidents for G
+        self.optimizer_D.train()
+        self.optimizer_G.train()
+        
diff --git a/deepliif/util/adamw_schedulefree.py b/deepliif/util/adamw_schedulefree.py
new file mode 100644
index 0000000..fc5915b
--- /dev/null
+++ b/deepliif/util/adamw_schedulefree.py
@@ -0,0 +1,208 @@
+# Schedule-Free Optimizer: AdamW
+# https://github.com/facebookresearch/schedule_free/blob/main/schedulefree/adamw_schedulefree.py
+
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+# 
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+from typing import Tuple, Union, Optional, Iterable, Dict, Any
+from typing_extensions import TypeAlias
+import torch
+import torch.optim
+try:
+    from torch.optim.optimizer import ParamsT
+except ImportError:
+    ParamsT : TypeAlias = Union[Iterable[torch.Tensor], Iterable[Dict[str, Any]]]
+import math
+
+class AdamWScheduleFree(torch.optim.Optimizer):
+    r"""
+    Schedule-Free AdamW
+    As the name suggests, no scheduler is needed with this optimizer. 
+    To add warmup, rather than using a learning rate schedule you can just
+    set the warmup_steps parameter.
+    
+    This optimizer requires that .train() and .eval() be called before the
+    beginning of training and evaluation respectively. The optimizer should
+    also be placed in eval mode when saving checkpoints.
+    
+    Arguments:
+        params (iterable): 
+            Iterable of parameters to optimize or dicts defining 
+            parameter groups.
+        lr (float): 
+            Learning rate parameter (default 0.0025)
+        betas (Tuple[float, float], optional): coefficients used for computing
+            running averages of gradient and its square (default: (0.9, 0.999)).
+        eps (float): 
+            Term added to the denominator outside of the root operation to 
+            improve numerical stability. (default: 1e-8).
+        weight_decay (float): 
+            Weight decay, i.e. a L2 penalty (default: 0).
+        warmup_steps (int): Enables a linear learning rate warmup (default 0).
+        r (float): Use polynomial weighting in the average 
+            with power r (default 0).
+        weight_lr_power (float): During warmup, the weights in the average will
+            be equal to lr raised to this power. Set to 0 for no weighting
+            (default 2.0).
+        foreach (bool): Use a foreach-backed implementation of the optimizer.
+            Should be significantly faster, but will have higher peak memory
+            usage (default True if supported in your PyTorch version).
+    """
+    def __init__(self,
+                 params: ParamsT,
+                 lr: Union[float, torch.Tensor] = 0.0025,
+                 betas: Tuple[float, float] = (0.9, 0.999),
+                 eps: float = 1e-8,
+                 weight_decay: float = 0,
+                 warmup_steps: int = 0,
+                 r: float = 0.0,
+                 weight_lr_power: float = 2.0,
+                 foreach: Optional[bool] = hasattr(torch, "_foreach_mul_")
+                 ):
+
+        defaults = dict(lr=lr, 
+                        betas=betas, 
+                        eps=eps,
+                        r=r,
+                        k=0,
+                        warmup_steps=warmup_steps,
+                        train_mode=True,
+                        weight_sum=0.0,
+                        lr_max=-1.0,
+                        weight_lr_power=weight_lr_power,
+                        weight_decay=weight_decay,
+                        foreach=foreach)
+        super().__init__(params, defaults)
+    
+    def eval(self):
+        for group in self.param_groups:
+            train_mode = group['train_mode']
+            beta1, _ = group['betas']
+            if train_mode:
+                for p in group['params']:
+                    state = self.state[p]
+                    if 'z' in state:
+                        # Set p.data to x
+                        p.data.lerp_(end=state['z'], weight=1-1/beta1)
+                group['train_mode'] = False
+
+    def train(self):
+        for group in self.param_groups:
+            train_mode = group['train_mode']
+            beta1, _ = group['betas']
+            if not train_mode:
+                for p in group['params']:
+                    state = self.state[p]
+                    if 'z' in state:
+                        # Set p.data to y
+                        p.data.lerp_(end=state['z'], weight=1-beta1)
+                group['train_mode'] = True
+
+    def step(self, closure=None):
+        """Performs a single optimization step.
+
+        Arguments:
+            closure (callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+
+        loss = None
+        if closure is not None:
+            loss = closure()
+        
+        for group in self.param_groups:
+            eps = group['eps']
+            beta1, beta2 = group['betas']
+            decay = group['weight_decay']
+            k = group['k']
+            r = group['r']
+            warmup_steps = group['warmup_steps']
+            weight_lr_power = group['weight_lr_power']
+            
+            if k < warmup_steps:
+              sched = (k+1) / warmup_steps
+            else:
+              sched = 1.0
+            
+            bias_correction2 = 1 - beta2 ** (k+1)
+            lr = group['lr']*sched*math.sqrt(bias_correction2)
+            
+            lr_max = group['lr_max'] = max(lr, group['lr_max'])
+            
+            weight = ((k+1)**r) * (lr_max**weight_lr_power)
+            weight_sum = group['weight_sum'] = group['weight_sum'] + weight
+
+            try:
+                ckp1 = weight/weight_sum
+            except ZeroDivisionError:
+                ckp1 = 0
+
+            if not group['train_mode']:
+                raise Exception("Not in train mode!")
+
+            active_p = [p for p in group['params'] if p.grad is not None]
+            
+            for p in active_p:
+                if 'z' not in self.state[p]:
+                    self.state[p]['z'] = torch.clone(p.data)
+                    self.state[p]['exp_avg_sq'] = torch.zeros_like(p.data)
+
+            if group['foreach'] and len(active_p) > 0:
+                y, grad, exp_avg_sq, z = zip(*[(p.data, 
+                                                p.grad, 
+                                                self.state[p]['exp_avg_sq'], 
+                                                self.state[p]['z']) 
+                                                for p in active_p])
+
+                # Decay the first and second moment running average coefficient
+                torch._foreach_mul_(exp_avg_sq, beta2)
+                torch._foreach_addcmul_(exp_avg_sq, grad, grad, value=1-beta2)
+                denom = torch._foreach_sqrt(exp_avg_sq)
+                torch._foreach_add_(denom, eps)
+
+                # Normalize grad in-place for memory efficiency
+                torch._foreach_div_(grad, denom)
+
+                # Weight decay calculated at y
+                if decay != 0:
+                    torch._foreach_add_(grad, y, alpha=decay)
+
+                # These operations update y in-place,
+                # without computing x explicitly.
+                torch._foreach_lerp_(y, z, weight=ckp1)
+                torch._foreach_add_(y, grad, alpha=lr*(beta1*(1-ckp1)-1))
+
+                # z step
+                torch._foreach_sub_(z, grad, alpha=lr)
+            else:
+                for p in active_p:
+                    y = p.data # Notation to match theory
+                    grad = p.grad.data
+
+                    state = self.state[p]
+
+                    z = state['z']
+                    exp_avg_sq = state['exp_avg_sq']
+
+                    exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1-beta2)
+                    denom = exp_avg_sq.sqrt().add_(eps)
+
+                    # Reuse grad buffer for memory efficiency
+                    grad_normalized = grad.div_(denom)
+
+                    # Weight decay calculated at y
+                    if decay != 0:
+                        grad_normalized.add_(y, alpha=decay)
+
+                    # These operations update y in-place,
+                    # without computing x explicitly.
+                    y.lerp_(end=z, weight=ckp1)
+                    y.add_(grad_normalized, alpha=lr*(beta1*(1-ckp1)-1))
+
+                    # z step
+                    z.sub_(grad_normalized, alpha=lr)
+
+            group['k'] = k+1
+        return loss

From 952a66032337f21c6adf607c2a69f55068f20723 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 31 Jul 2024 10:02:25 +0000
Subject: [PATCH 17/41] fixed schedule-free optimizer setup (to not use
 scheduler wrapper); add --optimizer in cli.py train

---
 cli.py                            |  6 +++++-
 deepliif/models/DeepLIIF_model.py | 20 ++++++++++----------
 deepliif/models/base_model.py     |  6 +++++-
 deepliif/models/networks.py       | 10 ++++++++--
 deepliif/options/__init__.py      |  1 +
 5 files changed, 29 insertions(+), 14 deletions(-)

diff --git a/cli.py b/cli.py
index e9e9fc4..90eadd4 100644
--- a/cli.py
+++ b/cli.py
@@ -129,6 +129,8 @@ def cli():
               help='number of epochs with the initial learning rate')
 @click.option('--n-epochs-decay', type=int, default=100,
               help='number of epochs to linearly decay learning rate to zero')
+@click.option('--optimizer', type=str, default='adam',
+              help='optimizer to use, applied to both generators and discriminators [adam | adamw-schedulefree]')
 @click.option('--beta1', default=0.5, help='momentum term of adam')
 @click.option('--lr', default=0.0002, help='initial learning rate for adam')
 @click.option('--lr-policy', default='linear',
@@ -177,7 +179,7 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
           batch_size, load_size, crop_size, max_dataset_size, preprocess, no_flip, display_winsize, epoch, load_iter,
           verbose, lambda_l1, is_train, display_freq, display_ncols, display_id, display_server, display_env,
           display_port, update_html_freq, print_freq, no_html, save_latest_freq, save_epoch_freq, save_by_iter,
-          continue_train, epoch_count, phase, lr_policy, n_epochs, n_epochs_decay, beta1, lr, lr_decay_iters,
+          continue_train, epoch_count, phase, lr_policy, n_epochs, n_epochs_decay, optimizer, beta1, lr, lr_decay_iters,
           remote, remote_transfer_cmd, seed, dataset_mode, padding, model, 
           modalities_no, seg_gen, net_ds, net_gs, gan_mode, gan_mode_s, local_rank):
     """General-purpose training script for multi-task image-to-image translation.
@@ -203,6 +205,8 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
         seg_no = 0
         seg_gen = False
     
+    assert optimizer in ['adam', 'adamw-schedulefree'], f'optimizer {optimizer} is not implemented'
+    
     d_params = locals()
 
     if gpu_ids and gpu_ids[0] == -1:
diff --git a/deepliif/models/DeepLIIF_model.py b/deepliif/models/DeepLIIF_model.py
index 77467fc..ea5226d 100644
--- a/deepliif/models/DeepLIIF_model.py
+++ b/deepliif/models/DeepLIIF_model.py
@@ -1,7 +1,7 @@
 import torch
 from .base_model import BaseModel
 from . import networks
-from ..util.adamw_schedulefree import AdamWScheduleFree
+from .networks import get_optimizer
 
 
 class DeepLIIFModel(BaseModel):
@@ -111,12 +111,10 @@ def __init__(self, opt):
 
             # initialize optimizers; schedulers will be automatically created by function <BaseModel.setup>.
             params = list(self.netG1.parameters()) + list(self.netG2.parameters()) + list(self.netG3.parameters()) + list(self.netG4.parameters()) + list(self.netG51.parameters()) + list(self.netG52.parameters()) + list(self.netG53.parameters()) + list(self.netG54.parameters()) + list(self.netG55.parameters())
-            # self.optimizer_G = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
-            self.optimizer_G = AdamWScheduleFree(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+            self.optimizer_G = get_optimizer(opt.optimizer)(params, lr=opt.lr, betas=(opt.beta1, 0.999))
 
             params = list(self.netD1.parameters()) + list(self.netD2.parameters()) + list(self.netD3.parameters()) + list(self.netD4.parameters()) + list(self.netD51.parameters()) + list(self.netD52.parameters()) + list(self.netD53.parameters()) + list(self.netD54.parameters()) + list(self.netD55.parameters())
-            # self.optimizer_D = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
-            self.optimizer_D = AdamWScheduleFree(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+            self.optimizer_D = get_optimizer(opt.optimizer)(params, lr=opt.lr, betas=(opt.beta1, 0.999))
 
             self.optimizers.append(self.optimizer_G)
             self.optimizers.append(self.optimizer_D)
@@ -340,8 +338,9 @@ def calculate_losses(self):
         Calculate losses but do not optimize parameters. Used in validation loss calculation during training.
         """
         # for eval/val, schedule free optimizers need to be set to eval mode: https://github.com/facebookresearch/schedule_free/tree/main?tab=readme-ov-file#how-to-use
-        self.optimizer_D.eval()
-        self.optimizer_G.eval()
+        if 'schedulefree' in self.opt.optimizer:
+            self.optimizer_D.eval()
+            self.optimizer_G.eval()
         
         self.forward()                   # compute fake images: G(A)
         # update D
@@ -369,6 +368,7 @@ def calculate_losses(self):
         self.set_requires_grad(self.netD55, False)  # D54 requires no gradients when optimizing G54
 
         self.backward_G()                   # calculate graidents for G
-        self.optimizer_D.train()
-        self.optimizer_G.train()
-        
+        if 'schedulefree' in self.opt.optimizer:
+            self.optimizer_D.train()
+            self.optimizer_G.train()
+            
diff --git a/deepliif/models/base_model.py b/deepliif/models/base_model.py
index 20fa424..39bdf3f 100644
--- a/deepliif/models/base_model.py
+++ b/deepliif/models/base_model.py
@@ -6,6 +6,7 @@
 from ..util import disable_batchnorm_tracking_stats
 from deepliif.util import *
 import itertools
+from ..util.adamw_schedulefree import AdamWScheduleFree
 
 
 class BaseModel(ABC):
@@ -82,7 +83,10 @@ def setup(self, opt):
             opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
         """
         if self.is_train:
-            self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers]
+            if isinstance(self.optimizers[0],AdamWScheduleFree):
+                self.schedulers = self.optimizers # schedulers are not needed in schedule-free optimizers
+            else:
+                self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers]
         if not self.is_train or opt.continue_train:
             load_suffix = 'iter_%d' % opt.load_iter if opt.load_iter > 0 else opt.epoch
             self.load_networks(load_suffix)
diff --git a/deepliif/models/networks.py b/deepliif/models/networks.py
index 0e3ad9d..e61bdef 100644
--- a/deepliif/models/networks.py
+++ b/deepliif/models/networks.py
@@ -2,7 +2,9 @@
 import torch.nn as nn
 from torch.nn import init
 import functools
-from torch.optim import lr_scheduler
+from torch.optim import lr_scheduler, Adam 
+from ..util.adamw_schedulefree import AdamWScheduleFree
+
 import os
 
 from torchvision import models
@@ -12,7 +14,6 @@
 ###############################################################################
 from deepliif.util import util
 
-
 class Identity(nn.Module):
     def forward(self, x):
         return x
@@ -37,6 +38,11 @@ def norm_layer(x): return Identity()
         raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
     return norm_layer
 
+def get_optimizer(optimizer_name):
+    if optimizer_name == 'adam':
+        return Adam
+    elif optimizer_name == 'adamw-schedulefree':
+        return AdamWScheduleFree
 
 def get_scheduler(optimizer, opt):
     """Return a learning rate scheduler
diff --git a/deepliif/options/__init__.py b/deepliif/options/__init__.py
index a350def..73b3987 100644
--- a/deepliif/options/__init__.py
+++ b/deepliif/options/__init__.py
@@ -40,6 +40,7 @@ def __init__(self, d_params=None, path_file=None, mode='train'):
             self.n_layers_D = 4
             self.lambda_L1 = 100
             self.lambda_feat = 100
+            self.optimizer = 'adam' if not hasattr(self,'optimizer') else self.optimizer
         else:
             self.phase = 'test'
             self.is_train = False

From 351f542fd0651b09590a93fa300514f47e1c03fd Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 7 Aug 2024 15:15:14 +0000
Subject: [PATCH 18/41] added enable_batchnorm_tracking_stats to be used for
 validation loss calculation during training (after which the stats needs to
 be re-enabled); added debug mode for cli.py train; added a flag in cli.py
 train to enable validation loss calculation; allowed to specify epoch in
 cli.py serialize

---
 cli.py                        | 138 +++++++++++++++++++---------------
 deepliif/models/base_model.py |   3 +-
 2 files changed, 80 insertions(+), 61 deletions(-)

diff --git a/cli.py b/cli.py
index 90eadd4..fbcc20e 100644
--- a/cli.py
+++ b/cli.py
@@ -174,6 +174,12 @@ def cli():
               help='the type of GAN objective for segmentation task. [vanilla| lsgan | wgangp]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.')
 # DDP related arguments
 @click.option('--local-rank', type=int, default=None, help='placeholder argument for torchrun, no need for manual setup')
+# Others
+@click.option('--with-val', is_flag=True,
+              help='use validation set to evaluate model performance at the end of each epoch')
+@click.option('--debug', is_flag=True,
+              help='debug mode, limits the number of data points per epoch to a small value')
+@click.option('--debug-data-size', default=10, type=int, help='data size per epoch used in debug mode; due to batch size, the epoch will be passed once the completed no. data points is greater than this value (e.g., for batch size 3, debug data size 10, the effective size used in training will be 12)')
 def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, ndf, net_d, net_g,
           n_layers_d, norm, init_type, init_gain, no_dropout, direction, serial_batches, num_threads,
           batch_size, load_size, crop_size, max_dataset_size, preprocess, no_flip, display_winsize, epoch, load_iter,
@@ -181,7 +187,7 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
           display_port, update_html_freq, print_freq, no_html, save_latest_freq, save_epoch_freq, save_by_iter,
           continue_train, epoch_count, phase, lr_policy, n_epochs, n_epochs_decay, optimizer, beta1, lr, lr_decay_iters,
           remote, remote_transfer_cmd, seed, dataset_mode, padding, model, 
-          modalities_no, seg_gen, net_ds, net_gs, gan_mode, gan_mode_s, local_rank):
+          modalities_no, seg_gen, net_ds, net_gs, gan_mode, gan_mode_s, local_rank, with_val, debug, debug_data_size):
     """General-purpose training script for multi-task image-to-image translation.
 
     This script works for various models (with option '--model': e.g., DeepLIIF) and
@@ -347,7 +353,11 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
                 save_suffix = 'iter_%d' % total_iters if save_by_iter else 'latest'
                 model.save_networks(save_suffix)
 
+
             iter_data_time = time.time()
+            if debug and epoch_iter >= debug_data_size:
+                print(f'debug mode, epoch {epoch} stopped at epoch iter {epoch_iter} (>= {debug_data_size})')
+                break
 
         # cache our model every <save_epoch_freq> epochs
         if epoch % save_epoch_freq == 0:
@@ -358,65 +368,70 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
         
         
         # validation loss and metrics calculation
-        losses = model.get_current_losses() # get training losses to print
-        
-        model.eval()
-        l_losses_val = []
-        l_metrics_val = []
-        
-        # for each val image, calculate validation loss and cell count metrics
-        for j, data_val_batch in enumerate(data_val):
-            # batch size is effectively 1 for validation
-            model.set_input(data_val_batch)
-            model.calculate_losses() # this does not optimize parameters
-            visuals = model.get_current_visuals()  # get image results
-            
-            # val losses
-            losses_val_batch = model.get_current_losses()
-            l_losses_val += [(k,v) for k,v in losses_val_batch.items()]
+        if with_val:
+            losses = model.get_current_losses() # get training losses to print
             
-            # calculate cell count metrics
-            l_seg_names = ['fake_B_5']
-            assert l_seg_names[0] in visuals.keys(), f'Cannot find {l_seg_names[0]} in generated image names ({list(visuals.keys())})'
-            seg_mod_suffix = l_seg_names[0].split('_')[-1]
-            l_seg_names += [x for x in visuals.keys() if x.startswith('fake') and x.split('_')[-1].startswith(seg_mod_suffix) and x != l_seg_names[0]]
-            # print(f'Running postprocess for {len(l_seg_names)} generated images ({l_seg_names})')
-
-            img_name_current = data_val_batch['A_paths'][0].split('/')[-1][:-4] # remove .png
-            metrics_gt = metrics_val[img_name_current]
+            model.eval()
+            l_losses_val = []
+            l_metrics_val = []
             
-            for seg_name in l_seg_names:
-                images = {'Seg':ToPILImage()((visuals[seg_name][0].cpu()+1)/2),
-                          'Marker':ToPILImage()((visuals['fake_B_4'][0].cpu()+1)/2)}
-                _, scoring = postprocess(ToPILImage()((data['A'][0]+1)/2), images, opt.scale_size, opt.model)
+            # for each val image, calculate validation loss and cell count metrics
+            for j, data_val_batch in enumerate(data_val):
+                # batch size is effectively 1 for validation
+                model.set_input(data_val_batch)
+                model.calculate_losses() # this does not optimize parameters
+                visuals = model.get_current_visuals()  # get image results
                 
-                for k,v in scoring.items():
-                    if k.startswith('num') or k.startswith('percent'):
-                        # to calculate the rmse, here we calculate (x_pred - x_true) ** 2
-                        l_metrics_val.append((k+'_'+seg_name,(v - metrics_gt[k])**2))
+                # val losses
+                losses_val_batch = model.get_current_losses()
+                l_losses_val += [(k,v) for k,v in losses_val_batch.items()]
                 
-        d_losses_val = {k+'_val':0 for k in losses_val_batch.keys()}
-        for k,v in l_losses_val:
-            d_losses_val[k+'_val'] += v
-        for k in d_losses_val:
-            d_losses_val[k] = d_losses_val[k] / len(data_val)
-        
-        d_metrics_val = {}
-        for k,v in l_metrics_val:
-            try:
-                d_metrics_val[k] += v
-            except:
-                d_metrics_val[k] = v
-        for k in d_metrics_val:
-            # to calculate the rmse, this is the second part, where d_metrics_val[k] now represents sum((x_pred - x_true) ** 2)
-            d_metrics_val[k] = np.sqrt(d_metrics_val[k] / len(data_val))
-        
-        
-        model.train()
-        t_comp = (time.time() - iter_start_time) / batch_size
-        visualizer.print_current_losses(epoch, epoch_iter, {**losses,**d_losses_val, **d_metrics_val}, t_comp, t_data)
-        if display_id > 0:
-            visualizer.plot_current_losses(epoch, float(epoch_iter) / len(dataset), {**losses,**d_losses_val,**d_metrics_val})
+                # calculate cell count metrics
+                l_seg_names = ['fake_B_5']
+                assert l_seg_names[0] in visuals.keys(), f'Cannot find {l_seg_names[0]} in generated image names ({list(visuals.keys())})'
+                seg_mod_suffix = l_seg_names[0].split('_')[-1]
+                l_seg_names += [x for x in visuals.keys() if x.startswith('fake') and x.split('_')[-1].startswith(seg_mod_suffix) and x != l_seg_names[0]]
+                # print(f'Running postprocess for {len(l_seg_names)} generated images ({l_seg_names})')
+    
+                img_name_current = data_val_batch['A_paths'][0].split('/')[-1][:-4] # remove .png
+                metrics_gt = metrics_val[img_name_current]
+                
+                for seg_name in l_seg_names:
+                    images = {'Seg':ToPILImage()((visuals[seg_name][0].cpu()+1)/2),
+                              'Marker':ToPILImage()((visuals['fake_B_4'][0].cpu()+1)/2)}
+                    _, scoring = postprocess(ToPILImage()((data['A'][0]+1)/2), images, opt.scale_size, opt.model)
+                    
+                    for k,v in scoring.items():
+                        if k.startswith('num') or k.startswith('percent'):
+                            # to calculate the rmse, here we calculate (x_pred - x_true) ** 2
+                            l_metrics_val.append((k+'_'+seg_name,(v - metrics_gt[k])**2))
+                
+                if debug and epoch_iter >= debug_data_size:
+                    print(f'debug mode, epoch {epoch} stopped at epoch iter {epoch_iter} (>= {debug_data_size})')
+                    break
+                    
+            d_losses_val = {k+'_val':0 for k in losses_val_batch.keys()}
+            for k,v in l_losses_val:
+                d_losses_val[k+'_val'] += v
+            for k in d_losses_val:
+                d_losses_val[k] = d_losses_val[k] / len(data_val)
+            
+            d_metrics_val = {}
+            for k,v in l_metrics_val:
+                try:
+                    d_metrics_val[k] += v
+                except:
+                    d_metrics_val[k] = v
+            for k in d_metrics_val:
+                # to calculate the rmse, this is the second part, where d_metrics_val[k] now represents sum((x_pred - x_true) ** 2)
+                d_metrics_val[k] = np.sqrt(d_metrics_val[k] / len(data_val))
+            
+            
+            model.train()
+            t_comp = (time.time() - iter_start_time) / batch_size
+            visualizer.print_current_losses(epoch, epoch_iter, {**losses,**d_losses_val, **d_metrics_val}, t_comp, t_data)
+            if display_id > 0:
+                visualizer.plot_current_losses(epoch, float(epoch_iter) / len(dataset), {**losses,**d_losses_val,**d_metrics_val})
 
 
         print('End of epoch %d / %d \t Time Taken: %d sec' % (
@@ -601,8 +616,9 @@ def trainlaunch(**kwargs):
 @click.option('--output-dir', help='saves results here.')
 #@click.option('--tile-size', type=int, default=None, help='tile size')
 @click.option('--device', default='cpu', type=str, help='device to load model for the similarity test, either cpu or gpu')
+@click.option('--epoch', default='latest', type=str, help='epoch to load and serialize')
 @click.option('--verbose', default=0, type=int,help='saves results here.')
-def serialize(model_dir, output_dir, device, verbose):
+def serialize(model_dir, output_dir, device, epoch, verbose):
     """Serialize DeepLIIF models using Torchscript
     """
     #if tile_size is None:
@@ -616,11 +632,13 @@ def serialize(model_dir, output_dir, device, verbose):
         shutil.copy(f'{model_dir}/train_opt.txt',f'{output_dir}/train_opt.txt')
     
     opt = Options(path_file=os.path.join(model_dir,'train_opt.txt'), mode='test')
+    opt.epoch = epoch
+    print_options(opt)
     sample = transform(Image.new('RGB', (opt.scale_size, opt.scale_size)))
     sample = torch.cat([sample]*opt.input_no, 1)
     
     with click.progressbar(
-            init_nets(model_dir, eager_mode=True, phase='test').items(),
+            init_nets(model_dir, eager_mode=True, opt=opt, phase='test').items(),
             label='Tracing nets',
             item_show_func=lambda n: n[0] if n else n
     ) as bar:
@@ -639,8 +657,8 @@ def serialize(model_dir, output_dir, device, verbose):
     
     # test: whether the original and the serialized model produces highly similar predictions
     print('testing similarity between prediction from original vs serialized models...')
-    models_original = init_nets(model_dir,eager_mode=True,phase='test')
-    models_serialized = init_nets(output_dir,eager_mode=False,phase='test')
+    models_original = init_nets(model_dir,eager_mode=True,opt=opt,phase='test')
+    models_serialized = init_nets(output_dir,eager_mode=False,opt=opt,phase='test')
     if device == 'gpu':
         sample = sample.cuda()
     else:
diff --git a/deepliif/models/base_model.py b/deepliif/models/base_model.py
index 39bdf3f..37c5295 100644
--- a/deepliif/models/base_model.py
+++ b/deepliif/models/base_model.py
@@ -3,7 +3,7 @@
 from collections import OrderedDict
 from abc import ABC, abstractmethod
 from . import networks
-from ..util import disable_batchnorm_tracking_stats
+from ..util import disable_batchnorm_tracking_stats, enable_batchnorm_tracking_stats
 from deepliif.util import *
 import itertools
 from ..util.adamw_schedulefree import AdamWScheduleFree
@@ -101,6 +101,7 @@ def train(self):
                 else:
                     net = getattr(self, 'net' + name)
                 net.train()
+                net = enable_batchnorm_tracking_stats(net)
 
     def eval(self):
         """Make models eval mode during test time"""

From 70a04567ee6c77a1ab9950e1b144bdfcb54a65f3 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 7 Aug 2024 15:15:54 +0000
Subject: [PATCH 19/41] minor fixes for options

---
 deepliif/models/__init__.py  |  3 +--
 deepliif/options/__init__.py | 17 ++++++++++++++---
 2 files changed, 15 insertions(+), 5 deletions(-)

diff --git a/deepliif/models/__init__.py b/deepliif/models/__init__.py
index 1e4af1f..27cfa1d 100644
--- a/deepliif/models/__init__.py
+++ b/deepliif/models/__init__.py
@@ -155,8 +155,7 @@ def init_nets(model_dir, eager_mode=False, opt=None, phase='test'):
     """ 
     if opt is None:
         opt = get_opt(model_dir, mode=phase)
-        opt.use_dp = False
-        #print_options(opt)
+    opt.use_dp = False
     
     if opt.model == 'DeepLIIF':
         net_groups = [
diff --git a/deepliif/options/__init__.py b/deepliif/options/__init__.py
index 73b3987..4382040 100644
--- a/deepliif/options/__init__.py
+++ b/deepliif/options/__init__.py
@@ -11,8 +11,17 @@ def read_model_params(file_addr):
     for line in lines:
         if ':' in line:
             key = line.split(':')[0].strip()
-            val = line.split(':')[1].split('[')[0].strip()
-            param_dict[key] = val
+            val = line.split(':')[1].strip()
+            #val = line.split(':')[1].split('[')[0].strip()
+            try:
+                param_dict[key] = eval(val)
+                #print(f'value of {key} is converted to {type(param_dict[key]).__name__}')
+            except:
+                param_dict[key] = val
+            
+            if isinstance(param_dict[key],list):
+                param_dict[key] = param_dict[key][0]
+            
     return param_dict
 
 class Options:
@@ -33,6 +42,8 @@ def __init__(self, d_params=None, path_file=None, mode='train'):
             except:
                 setattr(self,k,v)            
 
+        self.optimizer = 'adam' if not hasattr(self,'optimizer') else self.optimizer
+        
         if mode == 'train':
             self.is_train = True
             self.netG = self.net_g #'resnet_9blocks'
@@ -40,7 +51,7 @@ def __init__(self, d_params=None, path_file=None, mode='train'):
             self.n_layers_D = 4
             self.lambda_L1 = 100
             self.lambda_feat = 100
-            self.optimizer = 'adam' if not hasattr(self,'optimizer') else self.optimizer
+            
         else:
             self.phase = 'test'
             self.is_train = False

From 0051acfa3eced3dbff62e2bc1b521de6782755d2 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 7 Aug 2024 17:12:17 +0000
Subject: [PATCH 20/41] forced cli.py serialize to load models to gpu0 if
 device=gpu to fix inconsistent inference results

---
 cli.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/cli.py b/cli.py
index fbcc20e..5b2260d 100644
--- a/cli.py
+++ b/cli.py
@@ -615,7 +615,7 @@ def trainlaunch(**kwargs):
 @click.option('--model-dir', default='./model-server/DeepLIIF_Latest_Model', help='reads models from here')
 @click.option('--output-dir', help='saves results here.')
 #@click.option('--tile-size', type=int, default=None, help='tile size')
-@click.option('--device', default='cpu', type=str, help='device to load model for the similarity test, either cpu or gpu')
+@click.option('--device', default='cpu', type=str, help='device to run serialization as well as load model for the similarity test, either cpu or gpu')
 @click.option('--epoch', default='latest', type=str, help='epoch to load and serialize')
 @click.option('--verbose', default=0, type=int,help='saves results here.')
 def serialize(model_dir, output_dir, device, epoch, verbose):
@@ -631,8 +631,14 @@ def serialize(model_dir, output_dir, device, epoch, verbose):
     if model_dir != output_dir:
         shutil.copy(f'{model_dir}/train_opt.txt',f'{output_dir}/train_opt.txt')
     
+    # load and update opt for serialization
     opt = Options(path_file=os.path.join(model_dir,'train_opt.txt'), mode='test')
     opt.epoch = epoch
+    if device == 'gpu':
+        opt.gpu_ids = (0,) # use gpu 0, in case training was done on larger machines
+    else:
+        opt.gpu_ids = (-1,) # use cpu
+    
     print_options(opt)
     sample = transform(Image.new('RGB', (opt.scale_size, opt.scale_size)))
     sample = torch.cat([sample]*opt.input_no, 1)
@@ -659,6 +665,7 @@ def serialize(model_dir, output_dir, device, epoch, verbose):
     print('testing similarity between prediction from original vs serialized models...')
     models_original = init_nets(model_dir,eager_mode=True,opt=opt,phase='test')
     models_serialized = init_nets(output_dir,eager_mode=False,opt=opt,phase='test')
+    
     if device == 'gpu':
         sample = sample.cuda()
     else:
@@ -666,7 +673,7 @@ def serialize(model_dir, output_dir, device, epoch, verbose):
     for name in models_serialized.keys():
         print(name,':')
         model_original = models_original[name].cuda().eval() if device=='gpu' else models_original[name].cpu().eval()
-        model_serialized = models_serialized[name].cuda() if device=='gpu' else models_serialized[name].cpu().eval()
+        model_serialized = models_serialized[name].cuda().eval() if device=='gpu' else models_serialized[name].cpu().eval()
         if name.startswith('GS'):
             test_diff_original_serialized(model_original,model_serialized,torch.cat([sample, sample, sample], 1),verbose)
         else:

From f9a811499f28be31cfe9c2ab8026e3bac414c2aa Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 7 Aug 2024 18:29:39 +0000
Subject: [PATCH 21/41] updated batchnorm disable and enable functions (missed
 in the previous commits); moved functions used only for tiff file to the
 bottom

---
 deepliif/util/__init__.py | 175 ++++++++++++++++++++++++--------------
 1 file changed, 109 insertions(+), 66 deletions(-)

diff --git a/deepliif/util/__init__.py b/deepliif/util/__init__.py
index c2c2ae7..690a455 100644
--- a/deepliif/util/__init__.py
+++ b/deepliif/util/__init__.py
@@ -14,11 +14,6 @@
 from ..postprocessing import imadjust
 import cv2
 
-import bioformats
-import javabridge
-import bioformats.omexml as ome
-import tifffile as tf
-
 import pickle
 import sys
 
@@ -213,6 +208,115 @@ def get_information(filename):
     return size_x, size_y, size_z, size_c, size_t, pixel_type
 
 
+
+
+def write_results_to_pickle_file(output_addr, results):
+    """
+    This function writes data into the pickle file.
+    :param output_addr: The address of the pickle file to write data into.
+    :param results: The data to be written into the pickle file.
+    :return:
+    """
+    pickle_obj = open(output_addr, "wb")
+    pickle.dump(results, pickle_obj)
+    pickle_obj.close()
+
+
+def read_results_from_pickle_file(input_addr):
+    """
+    This function reads data from a pickle file and returns it.
+    :param input_addr: The address to the pickle file.
+    :return: The data inside pickle file.
+    """
+    pickle_obj = open(input_addr, "rb")
+    results = pickle.load(pickle_obj)
+    pickle_obj.close()
+    return results
+
+def test_diff_original_serialized(model_original,model_serialized,example,verbose=0):
+    threshold = 10
+
+    orig_res = model_original(example)
+    if verbose > 0:
+        print('Original:')
+        print(orig_res.shape)
+        print(orig_res[0, 0:10])
+        print('min abs value:{}'.format(torch.min(torch.abs(orig_res))))
+
+    ts_res = model_serialized(example)
+    if verbose > 0:
+        print('Torchscript:')
+        print(ts_res.shape)
+        print(ts_res[0, 0:10])
+        print('min abs value:{}'.format(torch.min(torch.abs(ts_res))))
+
+    abs_diff = torch.abs(orig_res-ts_res)
+    if verbose > 0:
+        print('Dif sum:')
+        print(torch.sum(abs_diff))
+        print('max dif:{}'.format(torch.max(abs_diff)))
+
+    assert torch.sum(abs_diff) <= threshold, f"Sum of difference in predicted values {torch.sum(abs_diff)} is larger than threshold {threshold}"
+
+def disable_batchnorm_tracking_stats(model):
+    # https://discuss.pytorch.org/t/performance-highly-degraded-when-eval-is-activated-in-the-test-phase/3323/16
+    # https://discuss.pytorch.org/t/performance-highly-degraded-when-eval-is-activated-in-the-test-phase/3323/67
+    # https://github.com/pytorch/pytorch/blob/ca39c5b04e30a67512589cafbd9d063cc17168a5/torch/nn/modules/batchnorm.py#L158
+    for m in model.modules():
+        for child in m.children():
+            if type(child) == torch.nn.BatchNorm2d:
+                child.track_running_stats = False
+                child.running_mean_backup = child.running_mean
+                child.running_mean = None
+                child.running_var_backup = child.running_var
+                child.running_var = None
+    return model
+
+def enable_batchnorm_tracking_stats(model):
+    """
+    This is needed during training when val set loss/metrics calculation is enabled.
+    In this case, we need to switch to eval mode for inference, which triggers
+    disable_batchnorm_tracking_stats(). After the evaluation, the model should be
+    set back to train mode, where running stats are restored for batchnorm layers.
+    """
+    for m in model.modules():
+        for child in m.children():
+            if type(child) == torch.nn.BatchNorm2d:
+                child.track_running_stats = True
+                assert hasattr(child, 'running_mean_backup') and hasattr(child, 'running_var_backup'), 'enable_batchnorm_tracking_stats() is supposed to be executed after disable_batchnorm_tracking_stats() is applied'
+                child.running_mean = child.running_mean_backup
+                child.running_var = child.running_var_backup
+    return model
+    
+  
+def image_variance_gray(img):
+    px = np.asarray(img) if img.mode == 'L' else np.asarray(img.convert('L'))
+    idx = np.logical_and(px != 255, px != 0)
+    val = px[idx]
+    if val.shape[0] == 0:
+        return 0
+    var = np.var(val)
+    return var
+
+
+def image_variance_rgb(img):
+    px = np.asarray(img) if img.mode == 'RGB' else np.asarray(img.convert('RGB'))
+    nonwhite = np.any(px != [255, 255, 255], axis=-1)
+    nonblack = np.any(px != [0, 0, 0], axis=-1)
+    idx = np.logical_and(nonwhite, nonblack)
+    val = px[idx]
+    if val.shape[0] == 0:
+        return [0, 0, 0]
+    var = np.var(val, axis=0)
+    return var
+
+
+
+import bioformats
+import javabridge
+import bioformats.omexml as ome
+import tifffile as tf
+
 def write_big_tiff_file(output_addr, img, tile_size):
     """
     This function write the image into a big tiff file using the tiling and compression.
@@ -354,64 +458,3 @@ def write_ome_tiff_file_array(results_array, output_addr, size_t, size_z, size_c
                         output_addr,
                         SizeT=size_t, SizeZ=size_z, SizeC=len(channel_names), SizeX=size_x, SizeY=size_y,
                         channel_names=channel_names)
-
-
-def write_results_to_pickle_file(output_addr, results):
-    """
-    This function writes data into the pickle file.
-    :param output_addr: The address of the pickle file to write data into.
-    :param results: The data to be written into the pickle file.
-    :return:
-    """
-    pickle_obj = open(output_addr, "wb")
-    pickle.dump(results, pickle_obj)
-    pickle_obj.close()
-
-
-def read_results_from_pickle_file(input_addr):
-    """
-    This function reads data from a pickle file and returns it.
-    :param input_addr: The address to the pickle file.
-    :return: The data inside pickle file.
-    """
-    pickle_obj = open(input_addr, "rb")
-    results = pickle.load(pickle_obj)
-    pickle_obj.close()
-    return results
-
-def test_diff_original_serialized(model_original,model_serialized,example,verbose=0):
-    threshold = 10
-
-    orig_res = model_original(example)
-    if verbose > 0:
-        print('Original:')
-        print(orig_res.shape)
-        print(orig_res[0, 0:10])
-        print('min abs value:{}'.format(torch.min(torch.abs(orig_res))))
-
-    ts_res = model_serialized(example)
-    if verbose > 0:
-        print('Torchscript:')
-        print(ts_res.shape)
-        print(ts_res[0, 0:10])
-        print('min abs value:{}'.format(torch.min(torch.abs(ts_res))))
-
-    abs_diff = torch.abs(orig_res-ts_res)
-    if verbose > 0:
-        print('Dif sum:')
-        print(torch.sum(abs_diff))
-        print('max dif:{}'.format(torch.max(abs_diff)))
-
-    assert torch.sum(abs_diff) <= threshold, f"Sum of difference in predicted values {torch.sum(abs_diff)} is larger than threshold {threshold}"
-
-def disable_batchnorm_tracking_stats(model):
-    # https://discuss.pytorch.org/t/performance-highly-degraded-when-eval-is-activated-in-the-test-phase/3323/16
-    # https://discuss.pytorch.org/t/performance-highly-degraded-when-eval-is-activated-in-the-test-phase/3323/67
-    # https://github.com/pytorch/pytorch/blob/ca39c5b04e30a67512589cafbd9d063cc17168a5/torch/nn/modules/batchnorm.py#L158
-    for m in model.modules():
-        for child in m.children():
-            if type(child) == torch.nn.BatchNorm2d:
-                child.track_running_stats = False
-                child.running_mean = None
-                child.running_var = None
-    return model

From a2119cd1d71a810f6f1e4aad2145d44660aa9eaa Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 7 Aug 2024 18:35:59 +0000
Subject: [PATCH 22/41] removed changes related to stats calculation

---
 DeepLIIF_Statistics/ComputeStatistics.py     |  16 +-
 DeepLIIF_Statistics/Segmentation_Metrics.py  |  10 +-
 deepliif/stat/ComputeStatistics.py           | 268 --------
 deepliif/stat/Create_Loss_Diagram.py         |  81 ---
 deepliif/stat/HelperFunctions.py             | 667 -------------------
 deepliif/stat/PostProcessSegmentationMask.py | 171 -----
 deepliif/stat/Segmentation_Metrics.py        | 237 -------
 deepliif/stat/__init__.py                    | 323 ---------
 deepliif/stat/fid.py                         | 334 ----------
 deepliif/stat/fid_official_tf.py             | 370 ----------
 deepliif/stat/inception_score.py             | 100 ---
 deepliif/stat/swd.py                         | 158 -----
 12 files changed, 9 insertions(+), 2726 deletions(-)
 delete mode 100644 deepliif/stat/ComputeStatistics.py
 delete mode 100644 deepliif/stat/Create_Loss_Diagram.py
 delete mode 100644 deepliif/stat/HelperFunctions.py
 delete mode 100644 deepliif/stat/PostProcessSegmentationMask.py
 delete mode 100644 deepliif/stat/Segmentation_Metrics.py
 delete mode 100644 deepliif/stat/__init__.py
 delete mode 100644 deepliif/stat/fid.py
 delete mode 100644 deepliif/stat/fid_official_tf.py
 delete mode 100644 deepliif/stat/inception_score.py
 delete mode 100644 deepliif/stat/swd.py

diff --git a/DeepLIIF_Statistics/ComputeStatistics.py b/DeepLIIF_Statistics/ComputeStatistics.py
index 7b35192..804945e 100644
--- a/DeepLIIF_Statistics/ComputeStatistics.py
+++ b/DeepLIIF_Statistics/ComputeStatistics.py
@@ -21,7 +21,7 @@
 parser.add_argument('--gt_path', type=str, required=True)
 parser.add_argument('--model_path', type=str, required=True)
 parser.add_argument('--output_path', type=str, required=True)
-parser.add_argument('--model_name', type=str, required=False, default='')
+parser.add_argument('--model_name', type=str, required=False, default='DeepLIIF')
 parser.add_argument('--mode', type=str, default='Segmentation',
                     help='Mode of the statistics computation including Segmentation, ImageSynthesis, All')
 parser.add_argument('--raw_segmentation', action='store_true')
@@ -30,8 +30,7 @@
                     help='Batch size to use')
 parser.add_argument('--num_workers', type=int, default=8,
                     help='Number of processes to use for data loading')
-parser.add_argument('--image_types', type=str, default='Hema,DAPI,Lap2,Marker', help='These are non-seg modalities to be evaluated.')
-parser.add_argument('--seg_type', type=str, default='Seg', help='This is the seg modality to be evaluated.')
+parser.add_argument('--image_types', type=str, default='Hema,DAPI,Lap2,Marker')
 
 
 class Statistics:
@@ -46,7 +45,6 @@ def __init__(self, args):
         self.num_workers = args.num_workers
         self.device = args.device
         self.image_types = args.image_types.replace(' ', '').split(',')
-        self.seg_type = args.seg_type
 
         # Image Similarity Metrics
         self.inception_avg = collections.defaultdict(float)
@@ -186,9 +184,8 @@ def compute_segmentation_metrics(self):
         thresh = 100
         boundary_thresh = 100
         noise_size = 50
-        
-        self.segmentation_info, self.segmentation_metrics = compute_segmentation_metrics(self.gt_path, self.model_path, self.model_name, image_size=512, thresh=thresh, boundary_thresh=boundary_thresh, small_object_size=noise_size, raw_segmentation=self.raw_segmentation, suffix_seg=self.seg_type)
-        assert len(self.segmentation_info) > 0, 'No segmentation results returned; one typical cause is a wrong --seg-type and/or --image-types that cannot be found in any image filename'
+        print(thresh, noise_size)
+        self.segmentation_info, self.segmentation_metrics = compute_segmentation_metrics(self.gt_path, self.model_path, self.model_name, image_size=512, thresh=thresh, boundary_thresh=boundary_thresh, small_object_size=noise_size, raw_segmentation=self.raw_segmentation)
         self.write_list_to_csv(self.segmentation_info, self.segmentation_info[0].keys(),
                                filename='segmentation_info_' + self.mode + '_' + self.model_name + '_' + str(thresh) + '_' + str(noise_size) + '.csv')
         for key in self.segmentation_metrics:
@@ -205,14 +202,14 @@ def compute_statistics(self):
         self.create_all_info()
 
     def write_dict_to_csv(self, info_dict, csv_columns, filename='info.csv'):
-        print('Writing in csv',os.path.join(self.output_path, filename))
+        print('Writing in csv')
         info_csv = open(os.path.join(self.output_path, filename), 'w')
         writer = csv.DictWriter(info_csv, fieldnames=csv_columns)
         writer.writeheader()
         writer.writerow(info_dict)
 
     def write_list_to_csv(self, info_dict, csv_columns, filename='info.csv'):
-        print('Writing in csv',os.path.join(self.output_path, filename))
+        print('Writing in csv')
         info_csv = open(os.path.join(self.output_path, filename), 'w')
         writer = csv.DictWriter(info_csv, fieldnames=csv_columns)
         writer.writeheader()
@@ -231,4 +228,3 @@ def write_list_to_csv(self, info_dict, csv_columns, filename='info.csv'):
         stat.compute_segmentation_metrics()
     elif stat.mode == 'ImageSynthesis':
         stat.compute_image_similarity_metrics()
-    stat.create_all_info()
diff --git a/DeepLIIF_Statistics/Segmentation_Metrics.py b/DeepLIIF_Statistics/Segmentation_Metrics.py
index 4e2c50a..65a62f2 100644
--- a/DeepLIIF_Statistics/Segmentation_Metrics.py
+++ b/DeepLIIF_Statistics/Segmentation_Metrics.py
@@ -102,16 +102,12 @@ def compute_aji(gt_image, mask_image):
     return aji
 
 
-def compute_segmentation_metrics(gt_dir, model_dir, model_name, image_size=512, thresh=100, boundary_thresh=100, small_object_size=20, raw_segmentation=True, suffix_seg=None):
+def compute_segmentation_metrics(gt_dir, model_dir, model_name, image_size=512, thresh=100, boundary_thresh=100, small_object_size=20, raw_segmentation=True):
     info_dict = []
     metrics = collections.defaultdict(float)
     images = os.listdir(model_dir)
-
     counter = 0
-    if suffix_seg is not None:
-        postfix = f'_{suffix_seg}.png'
-    else:
-        postfix = '_Seg.png' if raw_segmentation else '_SegRefined.png'
+    postfix = '_Seg.png' if raw_segmentation else '_SegRefined.png'
     for mask_name in images:
         if postfix in mask_name:
             counter += 1
@@ -127,7 +123,7 @@ def compute_segmentation_metrics(gt_dir, model_dir, model_name, image_size=512,
             positive_mask[positive_mask > 0] = 1
             negative_mask[negative_mask > 0] = 1
 
-            gt_img = cv2.cvtColor(cv2.imread(os.path.join(gt_dir, mask_name)), cv2.COLOR_BGR2RGB)
+            gt_img = cv2.cvtColor(cv2.imread(os.path.join(gt_dir, mask_name.replace(postfix, '.png'))), cv2.COLOR_BGR2RGB)
             gt_img = cv2.resize(gt_img, (image_size, image_size))
 
             positive_gt = gt_img[:, :, 0]
diff --git a/deepliif/stat/ComputeStatistics.py b/deepliif/stat/ComputeStatistics.py
deleted file mode 100644
index ca471fb..0000000
--- a/deepliif/stat/ComputeStatistics.py
+++ /dev/null
@@ -1,268 +0,0 @@
-import os
-import cv2
-import numpy as np
-import csv
-from numba import cuda
-import time
-
-from .Segmentation_Metrics import compute_segmentation_metrics
-#from fid_official_tf import calculate_fid_given_paths
-from .fid import calculate_fid_given_paths
-from .inception_score import calculate_inception_score
-
-from skimage.metrics import structural_similarity as ssim
-from skimage.metrics import peak_signal_noise_ratio as psnr
-from skimage.metrics import mean_squared_error
-from skimage import img_as_float, io, measure
-from skimage.color import rgb2gray
-import collections
-from .swd import compute_swd
-
-
-"""
-params:
-  gt_path
-  model_path
-  output_path
-  model_name
-  mode: Mode of the statistics computation including Segmentation, ImageSynthesis, All
-  raw_segmentation
-  device: Device to use. Like cuda, cuda:0 or cpu
-  batch_size: Batch size to use
-  num_workers: Number of processes to use for data loading
-  image_types: These are non-seg modalities to be evaluated.
-  seg_type: This is the seg modality to be evaluated.
-"""
-
-class Statistics:
-    def __init__(self, gt_path, model_path, output_path, model_name='', mode='Segmentation',
-                 raw_segmentation=False, device='cuda', batch_size=50, num_workers=8, 
-                 image_types='Hema,DAPI,Lap2,Marker', seg_type='Seg'):
-        self.gt_path = gt_path
-        self.model_path = model_path
-        self.output_path = output_path
-        self.model_name = model_name
-        self.mode = mode
-        self.raw_segmentation = raw_segmentation
-        self.batch_size = batch_size
-        self.num_workers = num_workers
-        self.device = device
-        self.image_types = image_types.replace(' ', '').split(',')
-        self.seg_type = seg_type
-
-        # Image Similarity Metrics
-        self.inception_avg = collections.defaultdict(float)
-        self.inception_std = collections.defaultdict(float)
-
-        self.mse_avg = collections.defaultdict(float)
-        self.mse_std = collections.defaultdict(float)
-
-        self.ssim_avg = collections.defaultdict(float)
-        self.ssim_std = collections.defaultdict(float)
-
-        self.psnr_avg = collections.defaultdict(float)
-        self.psnr_std = collections.defaultdict(float)
-        
-        self.fid_value = collections.defaultdict(float)
-        self.swd_value = collections.defaultdict(float)
-
-        self.all_info = {}
-        self.all_info['Model'] = self.model_name
-
-        # Segmentation Metrics
-        self.segmentation_metrics = collections.defaultdict(float)
-        self.segmentation_info = None
-
-        if not os.path.exists(self.output_path):
-            os.makedirs(self.output_path)
-
-    def compute_mse_ssim_scores(self):
-        for img_type in self.image_types:
-            images = os.listdir(self.model_path)
-            mse_arr = []
-            ssim_arr = []
-            # mse_info = []
-            for img_name in images:
-                if img_type in img_name:
-                    orig_img = img_as_float(rgb2gray(io.imread(os.path.join(self.gt_path, img_name))))
-                    mask_img = img_as_float(rgb2gray(io.imread(os.path.join(self.model_path, img_name))))
-
-                    mse_mask = mean_squared_error(orig_img, mask_img)
-                    ssim_mask = ssim(orig_img, mask_img, multichannel=True, gaussian_weights=True, sigma=1.5, use_sample_covariance=False, data_range=1)
-
-                    mse_arr.append(mse_mask)
-                    ssim_arr.append(ssim_mask)
-                    
-                    # mse_info.append({'image_name': img_name, 'image_type':img_type, 'mse': mse_mask, 'ssim': ssim_mask})
-            # self.write_list_to_csv(mse_info, mse_info[0].keys(),
-            #                        filename='inference_info_' + img_type + '_' + self.model_name + '.csv')
-            self.mse_avg[img_type], self.mse_std[img_type] = np.mean(mse_arr), np.std(mse_arr)
-            self.ssim_avg[img_type], self.ssim_std[img_type] = np.mean(ssim_arr), np.std(ssim_arr)
-        self.all_info['mse_avg'] = self.mse_avg
-        self.all_info['mse_std'] = self.mse_std
-        self.all_info['ssim_avg'] = self.ssim_avg
-        self.all_info['ssim_std'] = self.ssim_std
-    
-    def compute_psnr_scores(self):
-        """
-        Peak signal-to-noise ratio
-        """
-        for img_type in self.image_types:
-            images = os.listdir(self.model_path)
-            mse_arr = []
-            score_arr = []
-            # mse_info = []
-            for img_name in images:
-                if img_type in img_name:
-                    orig_img = img_as_float(rgb2gray(io.imread(os.path.join(self.gt_path, img_name))))
-                    mask_img = img_as_float(rgb2gray(io.imread(os.path.join(self.model_path, img_name))))
-                    #print(orig_img)
-
-                    score_arr.append(psnr(orig_img, mask_img, data_range=1))
-            self.psnr_avg[img_type], self.psnr_std[img_type] = np.mean(score_arr), np.std(score_arr)
-        self.all_info['psnr_avg'] = self.psnr_avg
-        self.all_info['psnr_std'] = self.psnr_std
-
-    def compute_inception_score(self):
-        for img_type in self.image_types:
-            images = os.listdir(self.model_path)
-            real_images_array = []
-            for img in images:
-                if img_type in img:
-                    image = cv2.imread(os.path.join(self.model_path, img))
-                    image = cv2.resize(image, (299, 299))
-                    real_images_array.append(image)
-            real_images_array = np.array(real_images_array)
-            self.inception_avg[img_type], self.inception_std[img_type] = calculate_inception_score(real_images_array)
-
-    def compute_fid_score(self):
-        for img_type in self.image_types:
-            os.environ['CUDA_VISIBLE_DEVICES'] = self.gpu
-            self.fid_value[img_type] = calculate_fid_given_paths([self.gt_path, self.model_path], None, low_profile=False)
-            print("FID: ", self.fid_value[img_type])
-            # self.fid_value[img_type] = calculate_fid_given_paths(paths=[self.gt_path, self.model_path], batch_size=self.batch_size, dims=self.fid_dims, num_workers=self.num_workers, mod_type='_' + img_type)
-
-            device = cuda.get_current_device()
-            device.reset()
-
-    def compute_swd(self):
-        for img_type in self.image_types:
-            orig_images = []
-            mask_images = []
-            images = os.listdir(self.model_path)
-            for img_name in images:
-                if img_type in img_name:
-                    orig_img = cv2.cvtColor(cv2.imread(os.path.join(self.gt_path, img_name)), cv2.COLOR_BGR2RGB)
-                    mask_img = cv2.cvtColor(cv2.imread(os.path.join(self.model_path, img_name)), cv2.COLOR_BGR2RGB)
-                    orig_images.append(orig_img)
-                    mask_images.append(mask_img)
-
-            self.swd_value[img_type] = compute_swd(np.array(orig_images), np.array(mask_images), self.device)
-
-    def compute_image_similarity_metrics(self):
-        self.compute_mse_ssim_scores()
-        print('SSIM Computed')
-        self.compute_inception_score()
-        print('inception Computed')
-        self.compute_fid_score()
-        print('fid Computed')
-        self.compute_swd()
-        print('swd Computed')
-
-        for key in self.mse_avg:
-            self.all_info[key + '_' + 'MSE_avg'] = self.mse_avg[key]
-            self.all_info[key + '_' + 'MSE_std'] = self.mse_std[key]
-            self.all_info[key + '_' + 'ssim_avg'] = self.ssim_avg[key]
-            self.all_info[key + '_' + 'ssim_std'] = self.ssim_std[key]
-            self.all_info[key + '_' + 'inception_avg'] = self.inception_avg[key]
-            self.all_info[key + '_' + 'inception_std'] = self.inception_std[key]
-            self.all_info[key + '_' + 'fid_value'] = self.fid_value[key]
-            self.all_info[key + '_' + 'swd_value'] = self.swd_value[key]
-
-    def compute_IHC_scoring(self):
-        images = os.listdir(self.gt_path)
-        IHC_info = []
-        for img in images:
-            gt_image = cv2.cvtColor(cv2.imread(os.path.join(self.gt_path, img)), cv2.COLOR_BGR2RGB)
-            if 'DeepLIIF' in self.model_name:
-                mask_image = cv2.cvtColor(cv2.imread(os.path.join(self.model_path, img.replace('_Seg', '_Seg_Refined'))), cv2.COLOR_BGR2RGB)
-            else:
-                mask_image = cv2.cvtColor(cv2.imread(os.path.join(self.model_path, img)), cv2.COLOR_BGR2RGB)
-            gt_image[gt_image < 10] = 0
-            label_image_red_gt = measure.label(gt_image[:, :, 0], background=0)
-            label_image_blue_gt = measure.label(gt_image[:, :, 2], background=0)
-            number_of_positive_cells_gt = (len(np.unique(label_image_red_gt)) - 1)
-            number_of_negative_cells_gt = (len(np.unique(label_image_blue_gt)) - 1)
-            number_of_all_cells_gt = number_of_positive_cells_gt + number_of_negative_cells_gt
-            gt_IHC_score = number_of_positive_cells_gt / number_of_all_cells_gt if number_of_all_cells_gt > 0 else 0
-
-            mask_image[mask_image < 10] = 0
-            label_image_red_mask = measure.label(mask_image[:, :, 0], background=0)
-            label_image_blue_mask = measure.label(mask_image[:, :, 2], background=0)
-            number_of_positive_cells_mask = (len(np.unique(label_image_red_mask)) - 1)
-            number_of_negative_cells_mask = (len(np.unique(label_image_blue_mask)) - 1)
-            number_of_all_cells_mask = number_of_positive_cells_mask + number_of_negative_cells_mask
-            mask_IHC_score = number_of_positive_cells_mask / number_of_all_cells_mask if number_of_all_cells_mask > 0 else 0
-            diff = abs(gt_IHC_score * 100 - mask_IHC_score * 100)
-            IHC_info.append({'Model': self.model_name, 'Sample': img, 'Diff_IHC_Score': diff})
-        self.write_list_to_csv(IHC_info, IHC_info[0].keys(),
-                               filename='IHC_Scoring_info_' + self.mode + '_' + self.model_name + '.csv')
-
-    def compute_segmentation_metrics(self):
-        # max_dice = [0, 0, 0]
-        # max_AJI = [0, 0, 0]
-        # for thresh in range(60, 150, 10):
-        #     for noise_size in range(10, 80, 20):
-        thresh = 100
-        boundary_thresh = 100
-        noise_size = 50
-        
-        self.segmentation_info, self.segmentation_metrics = compute_segmentation_metrics(self.gt_path, self.model_path, self.model_name, image_size=512, thresh=thresh, boundary_thresh=boundary_thresh, small_object_size=noise_size, raw_segmentation=self.raw_segmentation, suffix_seg=self.seg_type)
-        assert len(self.segmentation_info) > 0, 'No segmentation results returned; one typical cause is a wrong --seg-type and/or --image-types that cannot be found in any image filename'
-        self.write_list_to_csv(self.segmentation_info, self.segmentation_info[0].keys(),
-                               filename='segmentation_info_' + self.mode + '_' + self.model_name + '_' + str(thresh) + '_' + str(noise_size) + '.csv')
-        for key in self.segmentation_metrics:
-            self.all_info[key] = self.segmentation_metrics[key]
-            print(key, self.all_info[key])
-        print('-------------------------------------------------------')
-
-    def create_all_info(self):
-        self.write_dict_to_csv(self.all_info, list(self.all_info.keys()), filename='metrics_' + self.mode + '_' + self.model_name + '.csv')
-
-    def compute_statistics(self):
-        self.compute_image_similarity_metrics()
-        self.compute_segmentation_metrics()
-        self.create_all_info()
-
-    def write_dict_to_csv(self, info_dict, csv_columns, filename='info.csv'):
-        print('Writing in csv',os.path.join(self.output_path, filename))
-        info_csv = open(os.path.join(self.output_path, filename), 'w')
-        writer = csv.DictWriter(info_csv, fieldnames=csv_columns)
-        writer.writeheader()
-        writer.writerow(info_dict)
-
-    def write_list_to_csv(self, info_dict, csv_columns, filename='info.csv'):
-        print('Writing in csv',os.path.join(self.output_path, filename))
-        info_csv = open(os.path.join(self.output_path, filename), 'w')
-        writer = csv.DictWriter(info_csv, fieldnames=csv_columns)
-        writer.writeheader()
-        for data in info_dict:
-            writer.writerow(data)
-    
-    def run(self,write_to_csv=False):
-        if self.mode == 'All':
-            self.compute_statistics()
-            self.compute_IHC_scoring()
-        elif self.mode == 'Segmentation':
-            self.compute_segmentation_metrics()
-        elif self.mode == 'ImageSynthesis':
-            self.compute_image_similarity_metrics()
-        elif self.mode == 'SSIM':
-            self.compute_mse_ssim_scores()
-        elif self.mode == 'Upscaling':
-            self.compute_mse_ssim_scores()
-            self.compute_psnr_scores()
-        if write_to_csv:
-            self.create_all_info()
-        return self.all_info
-    
diff --git a/deepliif/stat/Create_Loss_Diagram.py b/deepliif/stat/Create_Loss_Diagram.py
deleted file mode 100644
index 586489e..0000000
--- a/deepliif/stat/Create_Loss_Diagram.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import collections
-import numpy as np
-import matplotlib.pyplot as plt
-
-def isfloat(num):
-    try:
-        float(num)
-        return True
-    except ValueError:
-        return False
-
-
-def read_losses(file_name):
-    losses = {}
-    with open(file_name) as f:
-        lines = f.readlines()
-    for line in lines:
-        if line.startswith('(epoch'):
-            line = line.replace(',', '').replace('(', '').replace(')', '').replace(':', '').strip()
-            values = line.split(' ')
-            current_losses = {}
-            epoch_number = -1
-            for i in range(len(values)):
-                if values[i] == 'epoch':
-                    epoch_number = int(values[i + 1])
-                else:
-                    if not isfloat(values[i]) and values[i] != 'time' and values[i] != 'iters' and values[i] != 'data':
-                        print(values[i], values[i + 1])
-                        current_losses[values[i]] = float(values[i + 1])
-
-            losses[epoch_number] = current_losses
-    loss_values = collections.defaultdict(list)
-    for key in losses:
-        loss_values['epoch'].append(key)
-        for k in losses[key].keys():
-            loss_values[k].append(losses[key][k])
-
-    return loss_values
-
-
-def create_loss_diagram(file_name):
-    loss_values = read_losses(file_name)
-    x = loss_values['epoch']
-    plt.figure(figsize=(12, 4))
-    for i in range(1, 6):
-        plt.plot(x, loss_values['G_GAN_' + str(i)], label='G_GAN_' + str(i))
-    # plt.legend()
-    # plt.show()
-
-    # plt.figure()
-    for i in range(1, 6):
-        plt.plot(x, loss_values['G_L1_' + str(i)], label='G_L1_' + str(i))
-    # plt.legend()
-    # plt.show()
-
-    # plt.figure()
-    for i in range(1, 6):
-        plt.plot(x, loss_values['D_real_' + str(i)], label='D_real_' + str(i))
-    # plt.legend()
-    # plt.show()
-
-    # plt.figure()
-    for i in range(1, 6):
-        plt.plot(x, loss_values['D_fake_' + str(i)], label='D_fake_' + str(i))
-    # plt.legend()
-    # plt.gca().legend(loc='center left', bbox_to_anchor=(1, 0.5), ncol=5)
-    plt.legend(ncol=5)
-    plt.show()
-    # plt.scatter(x, y)
-    # plt.plot(x, y)
-    # plt.title("Connected Scatterplot points with line")
-    # plt.xlabel("epoch")
-    # plt.ylabel("G_GAN_1")
-    # plt.show()
-    # figure.tight_layout()
-    # print(loss_values)
-
-
-# create_loss_diagram('D://DeepLIIF//checkpoints//DeepLIIF_Empty_500_Model//loss_log.txt')
-# create_loss_diagram('C://Users//localadmin//Desktop//loss_log_SpectralNorm_SYN.txt')
-create_loss_diagram('C://Users//localadmin//Desktop//loss_log_DeepLIIF.txt')
\ No newline at end of file
diff --git a/deepliif/stat/HelperFunctions.py b/deepliif/stat/HelperFunctions.py
deleted file mode 100644
index 11ed519..0000000
--- a/deepliif/stat/HelperFunctions.py
+++ /dev/null
@@ -1,667 +0,0 @@
-import csv
-import json
-import math
-import os
-import h5py
-import numpy as np
-# import staintools
-from matplotlib.colors import LinearSegmentedColormap
-from numba import jit
-from scipy import ndimage
-import cv2
-import random
-from skimage.feature import peak_local_max
-import skimage.segmentation
-from skimage.morphology import watershed as ws, remove_small_objects
-import matplotlib.pyplot as plt
-
-def remove_small_objects_from_image(red_channel, min_size=100):
-    red_channel_copy = red_channel.copy()
-    red_channel_copy[red_channel > 0] = 1
-    red_channel_copy = red_channel_copy.astype(np.bool)
-    removed_red_channel = remove_small_objects(red_channel_copy, min_size=min_size).astype(np.uint8)
-    red_channel[removed_red_channel == 0] = 0
-    return red_channel
-
-
-def read_BC_detection_mask(img_name, data_type='test'):
-    img_type = img_name.split('.')[-1]
-    base_dir = '/home/parmida/Downloads/BCData'
-    annotations_dir = os.path.join(base_dir, 'annotations')
-    images_dir = os.path.join(base_dir, 'images')
-    negative_dir = os.path.join(annotations_dir, data_type, 'negative')
-    positive_dir = os.path.join(annotations_dir, data_type, 'positive')
-    images_dir = os.path.join(images_dir, data_type)
-    print(os.path.join(negative_dir, img_name.replace('.png', '.h5')))
-    gt_file_negative = h5py.File(os.path.join(negative_dir, img_name.replace('.' + img_type, '.h5')))
-    coordinates_negative = np.asarray(gt_file_negative['coordinates'])
-    gt_file_positive = h5py.File(os.path.join(positive_dir, img_name.replace('.' + img_type, '.h5')))
-    coordinates_positive = np.asarray(gt_file_positive['coordinates'])
-
-    positive_mask = np.zeros((640, 640), dtype=np.uint8)
-    negative_mask = np.zeros((640, 640), dtype=np.uint8)
-    for coord in coordinates_positive:
-        positive_mask[coord[1], coord[0]] = 255
-
-    for coord in coordinates_negative:
-        negative_mask[coord[1], coord[0]] = 255
-
-    return positive_mask, negative_mask
-
-def read_BC_detection_point(img_name, data_type='test'):
-    img_type = img_name.split('.')[-1]
-    base_dir = '/home/parmida/Downloads/BCData'
-    annotations_dir = os.path.join(base_dir, 'annotations')
-    images_dir = os.path.join(base_dir, 'images')
-    negative_dir = os.path.join(annotations_dir, data_type, 'negative')
-    positive_dir = os.path.join(annotations_dir, data_type, 'positive')
-    images_dir = os.path.join(images_dir, data_type)
-    print(os.path.join(negative_dir, img_name.replace('.png', '.h5')))
-    gt_file_negative = h5py.File(os.path.join(negative_dir, img_name.replace('.' + img_type, '.h5')))
-    coordinates_negative = np.asarray(gt_file_negative['coordinates'])
-    gt_file_positive = h5py.File(os.path.join(positive_dir, img_name.replace('.' + img_type, '.h5')))
-    coordinates_positive = np.asarray(gt_file_positive['coordinates'])
-
-    return coordinates_positive, coordinates_negative
-
-
-
-def compute_TP_FP_of_each_class(image, marked_class):
-    labeled, nr_objects = ndimage.label(image > 0)
-    TP = 0
-    FP = 0
-    for c in range(1, nr_objects):
-        component = np.zeros_like(image)
-        component[labeled == c] = image[labeled == c]
-        component = cv2.morphologyEx(component, cv2.MORPH_DILATE, kernel=np.ones((5, 5)), iterations=1)
-        TP, FP = compute_component_TP_FP(component, marked_class, TP, FP)
-    return TP, FP
-
-
-@jit(nopython=True)
-def compute_component_TP_FP(component, marked_class, TP, FP):
-    indices = np.nonzero(component)
-    cell_flag = False
-    for i in range(len(indices[0])):
-        if marked_class[indices[0][i], indices[1][i]] > 0:
-                TP += 1
-                cell_flag = True
-    if not cell_flag:
-        FP += 1
-    return TP, FP
-
-
-def compute_precision_recall_f1(TP, FP, FN):
-    precision = TP / (TP + FP) if (TP + FP) > 0 else 1
-    recall = TP / (TP + FN) if (TP + FN) > 0 else 1
-    F1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0
-    return precision, recall, F1
-
-
-def mark_Shiraz_image_with_markers(image, immunopositive, immunonegative, immunoTIL):
-    marked_image = image.copy()
-    positive = cv2.morphologyEx(immunopositive, cv2.MORPH_DILATE, kernel=np.ones((5,5)))
-    negative = cv2.morphologyEx(immunonegative, cv2.MORPH_DILATE, kernel=np.ones((5,5)))
-    TIL = cv2.morphologyEx(immunoTIL, cv2.MORPH_DILATE, kernel=np.ones((5,5)))
-    marked_image[positive > 0] = (0,0,255)
-    marked_image[negative > 0] = (255,0,0)
-    marked_image[TIL > 0] = (0,255,0)
-    return marked_image
-
-def read_NuClick_mask(img_name, dir_type='Train'):
-    # image_dir = '/home/parmida/Pathology/IHC_Nuclick/images/Train'
-    mask_dir = '/home/parmida/Pathology/IHC_Nuclick/masks/' + dir_type
-    mask = np.load(os.path.join(mask_dir, img_name.replace('.png', '.npy')))
-    labeled_mask = np.zeros((mask.shape[0], mask.shape[1], 3), dtype=np.uint8)
-    final_mask = np.zeros((mask.shape[0], mask.shape[1], 3), dtype=np.uint8)
-    labels_no = np.max(mask) + 1
-    color_dict = {}
-    color_dict[0] = (0, 0, 0)
-    for i in range(1, labels_no):
-        color_dict[i] = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
-    for i in range(mask.shape[0]):
-        for j in range(mask.shape[1]):
-            labeled_mask[i, j] = color_dict[mask[i, j]]
-            final_mask[i, j] = (0, 0, 0)
-    boundaries = cv2.Canny(labeled_mask, 100, 200)
-    # boundaries = cv2.dilate(boundaries, kernel=np.ones((3, 3), np.uint8))
-    labeled_mask_bw = cv2.cvtColor(labeled_mask, cv2.COLOR_RGB2GRAY)
-    final_mask[labeled_mask_bw > 0] = (0, 0, 255)
-    # final_mask[labeled_mask_bw == 0] = (0, 0, 255)
-    # final_mask[boundaries > 0] = (255, 255, 255)
-
-    contours, hierarchy = cv2.findContours(boundaries,
-                                           cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
-    cv2.drawContours(final_mask, contours, -1, (255, 255, 255), 2)
-    # cv2.imshow('labeled_mask', labeled_mask)
-    # cv2.imshow('boundaries', boundaries)
-    # cv2.imshow('final', final_mask)
-    # cv2.waitKey(0)
-    boundaries[boundaries > 0] = 255
-    return final_mask
-
-
-def get_detection_points(seg_img):
-    seg_img = cv2.resize(seg_img, (640, 640))
-    det_img = np.zeros_like(seg_img)
-    thresh = 50
-    det_img[np.logical_and(seg_img[:, :, 2] > thresh, seg_img[:, :, 2] > seg_img[:, :, 0] + 50)] = (0, 0, 255)
-    det_img[np.logical_and(seg_img[:, :, 0] > thresh, seg_img[:, :, 0] >= seg_img[:, :, 2])] = (255, 0, 0)
-    det_img[seg_img[:, :, 1] > thresh] = 0
-    det_img[:, :, 0] = remove_small_objects_from_image(det_img[:, :, 0], 80)
-    det_img[:, :, 2] = remove_small_objects_from_image(det_img[:, :, 2], 80)
-    det_img[:, :, 0] = ndimage.binary_fill_holes(det_img[:, :, 0]).astype(np.uint8) * 255
-    det_img[:, :, 2] = ndimage.binary_fill_holes(det_img[:, :, 2]).astype(np.uint8) * 255
-    # det_img[:, :, 0] = cv2.morphologyEx(det_img[:, :, 0], cv2.MORPH_ERODE, kernel=np.ones((3, 3)), iterations=2)
-    # det_img[:, :, 2] = cv2.morphologyEx(det_img[:, :, 2], cv2.MORPH_ERODE, kernel=np.ones((3, 3)), iterations=2)
-    # cv2.imshow('det_img', det_img)
-    det_img = np.squeeze(det_img).astype(np.uint8)
-    cells = watershed(det_img)
-    final_cells = []
-    positive_points = []
-    negative_points = []
-    seen = np.zeros((seg_img.shape[0], seg_img.shape[1]), dtype=np.uint8)
-    for i in range(len(cells)):
-        p1 = cells[i]
-        x1, y1, c1 = int(p1[1]), int(p1[0]), int(p1[2])
-        flag = False
-        seen[x1][y1] = 1
-        for j in range(len(cells)):
-            p2 = cells[j]
-            x2, y2, c2 = int(p2[1]), int(p2[0]), int(p2[2])
-            if seen[x2][y2] == 0:
-                if abs(x1 - x2) < 20 and abs(y1 - y2) < 20:
-                    flag = True
-                    # new_cell = int((x1 + x2) / 2), int((y1 + y2) / 2), int((c1 + c2)/2)
-                    # final_cells.append(new_cell)
-        if not flag:
-            final_cells.append(p1)
-            if c1 == 2:
-                positive_points.append((x1, y1))
-            elif c1 == 0:
-                negative_points.append((x1, y1))
-    return final_cells, positive_points, negative_points
-
-def detect_circles(component, output):
-    gray_blurred = cv2.blur(component, (3, 3))
-
-    # Apply Hough transform on the blurred image.
-    detected_circles = cv2.HoughCircles(gray_blurred,
-                                        cv2.HOUGH_GRADIENT, 1, 20, param1=100,
-                                        param2=20, minRadius=1, maxRadius=40)
-    # circles = cv2.HoughCircles(component, cv2.HOUGH_GRADIENT, 1, 10)
-    # ensure at least some circles were found
-    if detected_circles is not None:
-
-        # Convert the circle parameters a, b and r to integers.
-        detected_circles = np.uint16(np.around(detected_circles))
-
-        for pt in detected_circles[0, :]:
-            a, b, r = pt[0], pt[1], pt[2]
-
-            # Draw the circumference of the circle.
-            cv2.circle(output, (a, b), r, (0, 255, 0), 2)
-
-            # Draw a small circle (of radius 1) to show the center.
-            # cv2.circle(output, (a, b), 1, (0, 0, 255), 3)
-            # cv2.imshow("Detected Circle", output)
-            # cv2.waitKey(0)
-            # cv2.imshow('component', component)
-
-def watershed(pred):
-    cells=[]
-    for ch in range(3):
-        gray=pred[:,:,ch]
-        D = ndimage.distance_transform_edt(gray)
-        localMax = peak_local_max(D, indices=False, min_distance=10,exclude_border=False,labels=gray)
-        markers = ndimage.label(localMax, structure=np.ones((3, 3)))[0]
-        labels = ws(-D, markers, mask=gray)
-        for label in np.unique(labels):
-            if label == 0:
-                continue
-            mask = np.zeros(gray.shape, dtype="uint8")
-            mask[labels == label] = 255
-            cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[-2]
-            c = max(cnts, key=cv2.contourArea)
-            ((x, y), _) = cv2.minEnclosingCircle(c)
-            cells.append([x,y,ch])
-    return np.array(cells)
-
-def read_PathoNet_data(img_addr):
-    print(img_addr)
-    points = np.loadtxt(img_addr.replace('.jpg', '_points.txt'))
-    image = cv2.imread(img_addr)
-    # positive_mask = np.zeros((640, 640), dtype=np.uint8)
-    # negative_mask = np.zeros((640, 640), dtype=np.uint8)
-    positive_points = []
-    negative_points = []
-    for p in points:
-        if int(p[2]) == 1:
-            image[int(p[1]), int(p[0])] = (255, 0, 255)
-            negative_points.append((int(p[1]), int(p[0])))
-        else:
-            image[int(p[1]), int(p[0])] = (0, 255, 255)
-            positive_points.append((int(p[1]), int(p[0])))
-    # cv2.imshow('image', image)
-    # cv2.waitKey(0)
-    return positive_points, negative_points
-
-
-def crop_modalities(input_dir, img_name, img_types, location, size, output_dir):
-    for img_type in img_types:
-        image = cv2.imread(os.path.join(input_dir, img_name + img_type + '.png'))
-        crop = image[location[0]:location[0] + size[0], location[1]: location[1] + size[1]]
-        cv2.imwrite(os.path.join(output_dir, 'MYC_' + img_type + '.png'), crop)
-
-
-def read_mask_rcnn_segmentation_masks(input_dir, image_size):
-    images = os.listdir(input_dir)
-    masks = {}
-    for img in images:
-        if '.png' in img and len(img.split('_')) > 5:
-            print(img)
-            splitted = img.split('_')
-            image_name = ''
-            for i in range(0, len(splitted) - 3):
-                image_name += splitted[i] + '_'
-            image_name += splitted[-3]
-            cell_type = 'positive' if splitted[-2] == 1 else 'negative'
-            image = cv2.imread(os.path.join(input_dir, img))
-            image = cv2.resize(image, (image_size, image_size))
-            image_bw = np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)
-            image_bw[image[:,:,0] > 250] = 1
-            image_bw[image[:,:,1] > 250] = 1
-            image_bw[image[:,:,2] > 250] = 1
-            if image_name not in masks.keys():
-                masks[image_name] = {'positive': np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8), 'negative': np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8), 'binary': np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)}
-            masks[image_name][cell_type][image_bw > 0] = 1
-            masks[image_name]['binary'][image_bw > 0] = 1
-    return masks
-
-
-def read_mask_rcnn_detection_masks(input_dir, image_size):
-    images = os.listdir(input_dir)
-    masks = {}
-    for img in images:
-        if '_' in img and '.png' in img:
-            splitted = img.split('_')
-            image_name = ''
-            for i in range(0, len(splitted) - 3):
-                image_name += splitted[i] + '_'
-            image_name += splitted[-3]
-            cell_type = 'positive' if splitted[-2] == '1' else 'negative'
-            image = cv2.imread(os.path.join(input_dir, img))
-            image = cv2.resize(image, (image_size, image_size))
-            image_bw = np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)
-            image_bw[image[:,:,0] > 250] = 1
-            image_bw[image[:,:,1] > 250] = 1
-            image_bw[image[:,:,2] > 250] = 1
-            points = np.nonzero(image_bw)
-            x = points[0]
-            y = points[1]
-            bounding_box = [np.min(x), np.min(y), np.max(x), np.max(y)]
-            center = (int((bounding_box[0] + bounding_box[2]) / 2), int((bounding_box[1] + bounding_box[3]) / 2))
-            if image_name not in masks.keys():
-                masks[image_name] = {'positive': [], 'negative': [], 'binary': []}
-            masks[image_name][cell_type].append(center)
-            masks[image_name]['binary'].append(center)
-    return masks
-
-
-def read_unetplusplus_unet(input_npy, npy_path):
-    # results = np.load(input_dir)
-    imgs = np.load(input_npy)
-    with open(npy_path + 'dict_name.txt', 'r') as f:
-        names = json.load(f)
-    res_path = os.path.dirname(input_npy)
-    for i in range(imgs.shape[0]):
-        img = imgs[i]
-        print(img.shape)
-        plt.imsave(os.path.join(res_path, names[str(i)] + '.png'), (img * 255).astype(np.uint8))
-
-
-def read_Unet_plusplus_detection_masks(input_dir, image_size):
-    images = os.listdir(input_dir)
-    masks = {}
-    for img in images:
-        if '.png' in img:
-            image = cv2.imread(os.path.join(input_dir, img))
-            image = cv2.resize(image, (image_size, image_size))
-            # cv2.imshow('image1', image)
-            img = img.replace('.png', '')
-            new_image = np.zeros_like(image)
-            new_image[image[:,:,0] > 150] = (255, 0, 0)
-            new_image[image[:,:,2] > 150] = (0, 0, 255)
-            # cv2.imshow('image2', new_image)
-            det_img = np.squeeze(new_image).astype(np.uint8)
-            cells = watershed(det_img)
-            final_cells = []
-            positive_points = []
-            negative_points = []
-            seen = np.zeros((new_image.shape[0], new_image.shape[1]), dtype=np.uint8)
-            for i in range(len(cells)):
-                p1 = cells[i]
-                x1, y1, c1 = int(p1[1]), int(p1[0]), int(p1[2])
-                flag = False
-                seen[x1][y1] = 1
-                for j in range(len(cells)):
-                    p2 = cells[j]
-                    x2, y2, c2 = int(p2[1]), int(p2[0]), int(p2[2])
-                    if seen[x2][y2] == 0:
-                        if abs(x1 - x2) < 20 and abs(y1 - y2) < 20:
-                            flag = True
-                            # new_cell = int((x1 + x2) / 2), int((y1 + y2) / 2), int((c1 + c2)/2)
-                            # final_cells.append(new_cell)
-                if not flag:
-                    final_cells.append(p1)
-                    if c1 == 2:
-                        positive_points.append((x1, y1))
-                    elif c1 == 0:
-                        negative_points.append((x1, y1))
-
-            # for p in positive_points:
-            #     new_image[p[0]-5:p[0]+5, p[1]-5:p[1]+5] = (255,0,255)
-            # for p in negative_points:
-            #     new_image[p[0]-5:p[0]+5, p[1]-5:p[1]+5] = (0,255,255)
-
-            # cv2.imshow('image3', new_image)
-            # cv2.waitKey(0)
-            masks[img] = {'positive': [], 'negative': []}
-            masks[img]['positive'] = positive_points
-            masks[img]['negative'] = negative_points
-            # masks[img]['binary'].append(center)
-    return masks
-
-
-def read_Unet_plusplus_segmentation_masks(input_dir, image_size):
-    images = os.listdir(input_dir)
-    masks = {}
-    for img in images:
-        if '.png' in img:
-            image = cv2.imread(os.path.join(input_dir, img))
-            image = cv2.resize(image, (image_size, image_size))
-            img = img.replace('.png', '')
-            new_image = np.zeros_like(image)
-            new_image[image[:,:,0] > 0] = (255, 0, 0)
-            new_image[image[:,:,2] > 0] = (0, 0, 255)
-            masks[img] = {'positive': np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8), 'negative': np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8), 'binary': np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)}
-            masks[img]['positive'][image[:,:,0] > 0] = 1
-            masks[img]['negative'][image[:,:,2] > 0] = 1
-            masks[img]['binary'][image[:,:,0] > 0] = 1
-            masks[img]['binary'][image[:,:,2] > 0] = 1
-    return masks
-
-def read_DeepLIIF_segmentation_masks(input_dir, image_size, thresh=100):
-    images = os.listdir(input_dir)
-    masks = {}
-    for img in images:
-        if '_fake_B_5.png' in img:
-            image = cv2.imread(os.path.join(input_dir, img))
-            image = cv2.resize(image, (image_size, image_size))
-            img = img.replace('_fake_B_5.png', '')
-            new_image = np.zeros_like(image)
-            new_image[np.logical_and(image[:,:,0] > thresh, image[:,:,0] > image[:,:,2])] = (255, 0, 0)
-            new_image[np.logical_and(image[:,:,2] > thresh, image[:,:,2] >= image[:,:,0])] = (0, 0, 255)
-            # new_image[image[:,:,1] > thresh] = 0
-            # new_image[image[:,:,0] > 100] = (255, 0, 0)
-            # new_image[image[:,:,2] > 100] = (0, 0, 255)
-            # cv2.imshow('image', image)
-            # cv2.imshow('new_image', new_image)
-            # cv2.waitKey(0)
-
-            positive_mask = new_image[:, :, 0]
-            negative_mask = new_image[:, :, 2]
-            positive_mask = ndimage.binary_fill_holes(positive_mask, structure=np.ones((5, 5))).astype(np.uint8)
-            negative_mask = ndimage.binary_fill_holes(negative_mask, structure=np.ones((5, 5))).astype(np.uint8)
-            positive_mask = remove_small_objects_from_image(positive_mask, 50)
-            negative_mask = remove_small_objects_from_image(negative_mask, 50)
-            positive_mask[negative_mask > 0] = 0
-            masks[img] = {'positive': np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8), 'negative': np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8), 'binary': np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)}
-            # masks[img]['positive'][image[:,:,0] > thresh] = 1
-            # masks[img]['negative'][image[:,:,2] > thresh] = 1
-            # masks[img]['binary'][image[:,:,0] > thresh] = 1
-            # masks[img]['binary'][image[:,:,2] > thresh] = 1
-            masks[img]['positive'][positive_mask > 0] = 1
-            masks[img]['negative'][negative_mask > 0] = 1
-            masks[img]['binary'][positive_mask > 0] = 1
-            masks[img]['binary'][negative_mask > 0] = 1
-    return masks
-
-
-def read_ki67_detection_points(img):
-    input_dir = '/media/parmida/Work/DetectionDataset/test_Ki67'
-    image_size = 512
-    image = cv2.imread(os.path.join(input_dir, img))
-    image = image[:,5*512:]
-    image = cv2.resize(image, (image_size, image_size))
-    cv2.imshow('mask', image)
-    # cv2.waitKey(0)
-    positive_image = np.zeros((image.shape[0], image.shape[1]))
-    positive_image[image[:, :, 2] > 0] = 1
-    positive_mask = get_centers_of_objects(positive_image)
-
-    negative_image = np.zeros((image.shape[0], image.shape[1]))
-    negative_image[image[:, :, 0] > 0] = 1
-    negative_mask = get_centers_of_objects(negative_image)
-
-    return positive_mask, negative_mask
-
-
-def get_centers_of_objects(image):
-    mask = np.zeros((image.shape[0], image.shape[1]))
-    labeled, nr_objects = ndimage.label(image > 0)
-    for c in range(1, nr_objects):
-        component = np.zeros_like(image)
-        component[labeled == c] = image[labeled == c]
-
-        points = np.nonzero(component)
-        x = points[0]
-        y = points[1]
-        bounding_box = [np.min(x), np.min(y), np.max(x), np.max(y)]
-        center = (int((bounding_box[0] + bounding_box[2]) / 2), int((bounding_box[1] + bounding_box[3]) / 2))
-        mask[center[0], center[1]] = 255
-    return mask
-
-
-def read_Unet_plusplus_boundary_mask_image(img_addr, image_size):
-    print(img_addr)
-    image = cv2.cvtColor(cv2.imread(img_addr), cv2.COLOR_BGR2RGB)
-    image = cv2.resize(image, (image_size, image_size))
-    new_image = np.zeros_like(image)
-    res_image = np.zeros_like(image)
-    new_image[image[:,:,0] > 100] = (255, 0, 0)
-    new_image[image[:,:,2] > 100] = (0, 0, 255)
-    positive_mask = new_image[:,:,0]
-    negative_mask = new_image[:,:,2]
-    contours, hierarchy = cv2.findContours(positive_mask,
-                                           cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
-    cv2.drawContours(res_image, contours, -1, (255, 0, 0), 2)
-    contours, hierarchy = cv2.findContours(negative_mask,
-                                           cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
-    cv2.drawContours(res_image, contours, -1, (0, 0, 255), 2)
-    cv2.imwrite(img_addr.replace('.png', '_Seg.png'), cv2.cvtColor(new_image, cv2.COLOR_BGR2RGB))
-    return res_image, new_image
-
-def read_DeepLIIF_boundary_mask_image(img_addr, image_size, thresh=120):
-    image = cv2.cvtColor(cv2.imread(img_addr), cv2.COLOR_BGR2RGB)
-    # cv2.imshow('image', image)
-    image = cv2.resize(image, (image_size, image_size))
-    new_image = np.zeros_like(image)
-
-    new_image[np.logical_and(image[:, :, 0] > thresh, image[:, :, 0] > image[:, :, 2])] = (255, 0, 0)
-    new_image[np.logical_and(image[:, :, 2] > thresh, image[:, :, 2] >= image[:, :, 0])] = (0, 0, 255)
-
-    new_image[image[:,:,1] > 80] = 0
-    # new_image[image[:,:,0] > 100] = (255, 0, 0)
-    # new_image[image[:,:,2] > 100] = (0, 0, 255)
-    # cv2.imshow('image', image)
-    # cv2.imshow('new_image', new_image)
-    # cv2.waitKey(0)
-
-    positive_mask = new_image[:, :, 0]
-    negative_mask = new_image[:, :, 2]
-    positive_mask = ndimage.binary_fill_holes(positive_mask, structure=np.ones((5, 5))).astype(np.uint8)
-    negative_mask = ndimage.binary_fill_holes(negative_mask, structure=np.ones((5, 5))).astype(np.uint8)
-    positive_mask = remove_small_objects_from_image(positive_mask, 50)
-    negative_mask = remove_small_objects_from_image(negative_mask, 50)
-    # positive_mask[negative_mask > 0] = 0
-    negative_mask[positive_mask > 0] = 0
-
-    # new_image[np.logical_and(image[:,:,0] > thresh, image[:,:,0] > image[:,:,2])] = (255, 0, 0)
-    # new_image[np.logical_and(image[:,:,2] > thresh, image[:,:,2] >= image[:,:,0])] = (0, 0, 255)
-    # new_image[image[:,:,1] > thresh] = 0
-    #
-    res_image = np.zeros_like(image)
-    # positive_mask = new_image[:,:,0]
-    # negative_mask = new_image[:,:,2]
-    # # positive_mask = cv2.morphologyEx(positive_mask, cv2.MORPH_DILATE, kernel=np.ones((3,3)))
-    # # negative_mask = cv2.morphologyEx(negative_mask, cv2.MORPH_DILATE, kernel=np.ones((3,3)))
-    # positive_mask = ndimage.binary_fill_holes(positive_mask, structure=np.ones((5,5))).astype(np.uint8)
-    # negative_mask = ndimage.binary_fill_holes(negative_mask, structure=np.ones((5,5))).astype(np.uint8)
-    # positive_mask = remove_small_objects_from_image(positive_mask, 50)
-    # negative_mask = remove_small_objects_from_image(negative_mask, 50)
-    # # positive_mask[negative_mask > 0] = 0
-    # negative_mask[positive_mask > 0] = 0
-    # negative_mask = mask.copy()
-    new_image = np.zeros_like(image)
-    new_image[positive_mask > 0] = (255,0,0)
-    new_image[negative_mask > 0] = (0,0,255)
-    # cv2.imshow('positive_mask', positive_mask*255)
-    # cv2.imshow('negative_mask', negative_mask*255)
-    # cv2.waitKey(0)
-
-    contours, hierarchy = cv2.findContours(positive_mask,
-                                           cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
-    cv2.drawContours(res_image, contours, -1, (255, 0, 0), 2)
-    contours, hierarchy = cv2.findContours(negative_mask,
-                                           cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
-    cv2.drawContours(res_image, contours, -1, (0, 0, 255), 2)
-    return res_image, new_image
-
-
-
-def create_log_area_mask_cell_type(predicted_mask, gt_mask, index=0, colormap='bwr'):
-    smooth = 0.0001
-    predicted = predicted_mask[:,:,index]
-    gt = gt_mask[:,:,index]
-    # gt[gt_mask[:,:,1] > 0]=0
-    final_mask = np.zeros((predicted.shape[0], predicted.shape[1]))
-
-    labeled, nr_objects = ndimage.label(predicted > 0)
-    labeled_gt, nr_objects_gt = ndimage.label(gt > 0)
-    for c in range(1, nr_objects):
-        component = np.zeros_like(predicted)
-        component[labeled == c] = predicted[labeled == c]
-        nonzeros = np.nonzero(component)
-        component_gt_size = 0
-        # cv2.imshow('component', component)
-        for i in range(len(nonzeros[0])):
-            if gt[nonzeros[0][i], nonzeros[1][i]] > 0 and labeled_gt[nonzeros[0][i], nonzeros[1][i]] > 0:
-                label = labeled_gt[nonzeros[0][i], nonzeros[1][i]]
-                component_gt = np.zeros_like(gt)
-                component_gt[labeled_gt == label] = gt[labeled_gt == label]
-                nonzeros_gt = np.nonzero(component_gt)
-                component_gt_size = len(nonzeros_gt[0])
-                # print(component_gt_size)
-                break
-        # if component_gt_size > 0:
-        component_size = len(nonzeros[0])
-        # print('component_gt_size:', component_gt_size)
-        # print('component_size:', component_size)
-        if component_gt_size == 0:
-            value = 5
-            # print('yes')
-        else:
-            value = np.log2((component_size+smooth)/(component_gt_size+smooth))
-            value = min(value, 2) if value >= 0 else max(value, -2)
-        # print((len(nonzeros[0])+smooth)/(component_gt_size+smooth))
-        # if value < 0:
-        #     print(value)
-        #     print(min(value, 2) if value >= 0 else max(value, -2))
-
-        final_mask[component > 0] = value
-        # cv2.waitKey(0)
-        # final_mask[predicted == 0] = 0
-    # color_range = np.max(final_mask)
-    image_log = np.zeros_like(gt_mask)
-    for i in range(0, final_mask.shape[0]):
-        for j in range(0, final_mask.shape[1]):
-            value = final_mask[i][j]
-            if value == 5:
-                # print('yes!!!!!!!')
-                image_log[i, j] = (255, 255, 0)
-            elif -0.5 <= value <= 0.5:
-                image_log[i, j] = (255, 0, 0) if colormap == 'positive' else (0, 0, 255)
-            elif value > 0.5:
-                image_log[i, j] = (int(127.5/value), 0, 0) if colormap == 'positive' else (0, 0, int(127.5/value))
-            elif value < -0.5:
-                image_log[i, j] = (255, 255 - int(127.5/abs(value)), 255 - int(127.5/abs(value))) if colormap == 'positive' else (255 - int(127.5/abs(value)), 255 - int(127.5/abs(value)), 255)
-                # print((255, 255 - int(127.5/abs(value)), 255 - int(127.5/abs(value))))
-    # plt.tight_layout()
-    # plt.savefig('temp.png', bbox_inches='tight', pad_inches=0)
-    #
-    # image_log = plt.imread('temp.png')
-    # image_log = cv2.resize(image_log, (512, 512))
-    # image_log[predicted_mask[:, :, index] == 0] = 0
-    return image_log
-
-
-def create_log_area_mask(predicted_mask, gt_mask):
-    log_positive = create_log_area_mask_cell_type(predicted_mask, gt_mask, index=0, colormap='positive')
-    log_negative = create_log_area_mask_cell_type(predicted_mask, gt_mask, index=2, colormap='negative')
-    log_positive = (log_positive[:,:,:3] * 255).astype(np.uint8)
-    log_negative = (log_negative[:,:,:3] * 255).astype(np.uint8)
-    log_image = np.zeros_like(predicted_mask)
-    log_image[predicted_mask[:,:,0] > 0] = log_positive[predicted_mask[:,:,0] > 0]
-    log_image[predicted_mask[:,:,2] > 0] = log_negative[predicted_mask[:,:,2] > 0]
-    return log_image
-
-
-
-def create_color_map_image(colormap):
-    image = np.zeros((400, 100))
-    for i in range(image.shape[1]):
-        for j in range(image.shape[0]):
-            image[j, i] = (j - 200) / 100
-    colormap_image = np.zeros((400, 100, 3), dtype=np.uint8)
-    for i in range(0, image.shape[0]):
-        for j in range(0, image.shape[1]):
-            value = image[i][j]
-            if -0.5 <= value <= 0.5:
-                colormap_image[i, j] = (255, 0, 0) if colormap == 'positive' else (0, 0, 255)
-            elif value > 0.5:
-                colormap_image[i, j] = (int(127.5/value), 0, 0) if colormap == 'positive' else (0, 0, int(127.5/value))
-            elif value < -0.5:
-                colormap_image[i, j] = (255, 255 - int(127.5/abs(value)), 255 - int(127.5/abs(value))) if colormap == 'positive' else (255 - int(127.5/abs(value)), 255 - int(127.5/abs(value)), 255)
-    colormap_image = cv2.rotate(colormap_image, cv2.ROTATE_90_COUNTERCLOCKWISE)
-    return colormap_image
-
-
-def read_image_write_crop_parts(input_dir, img_name, location, crop_size, output_dir):
-    types = ['', '_DAPI', '_DAPILap2', '_Hema', '_Ki67', '_Seg_Aligned_Bound']
-    for image_type in types:
-        image = cv2.imread(os.path.join(input_dir, img_name + image_type + '.png'))
-        crop = image[location[0]: location[0] + crop_size[0], location[1]: location[1] + crop_size[1]]
-        cv2.imwrite(os.path.join(output_dir, img_name + image_type + '.png'), crop)
-
-
-def overlay_ki67_on_DAPI(input_DAPI, input_ki67):
-    # overlaid_image = np.zeros((input_DAPI.shape[0], input_DAPI.shape[1], 3), dtype=np.uint8)
-    # overlaid_image[:,:,2] = input_ki67
-    # overlaid_image[:,:,0] = input_DAPI
-    # overlaid_image[:,:,1] = np.floor((input_DAPI + input_ki67) / 2)
-    # overlaid_image[:,:,1] = input_DAPI
-
-    # overlaid_image = cv2.addWeighted(input_DAPI, 0.6, input_Lap2, 0.4, 1)
-    # overlaid_image = cv2.addWeighted(input_DAPI, 0.9, input_ki67, 0.1, 1)
-    overlaid_image = input_DAPI.copy()
-    overlaid_image[input_ki67[:,:,2] >= 30] = input_ki67[input_ki67[:,:,2] >= 30]
-    # overlaid_image[input_ki67[:,:,2] < 30] = input_ki67[input_ki67[:,:,2] < 30] * 0.2 + overlaid_image[input_ki67[:,:,2]] * 0.8
-    return overlaid_image
-
-
-def count_cell_number(image, channel=1, thresh=0):
-    mask = image[:, :, channel]
-    labeled, nr_objects = ndimage.label(mask > thresh)
-    return nr_objects
-
diff --git a/deepliif/stat/PostProcessSegmentationMask.py b/deepliif/stat/PostProcessSegmentationMask.py
deleted file mode 100644
index 07e1681..0000000
--- a/deepliif/stat/PostProcessSegmentationMask.py
+++ /dev/null
@@ -1,171 +0,0 @@
-import os.path
-import cv2
-import numpy as np
-from scipy import ndimage
-from numba import jit
-from skimage import measure, feature
-
-
-def get_average_cell_size(image):
-    label_image = measure.label(image, background=0)
-    labels = np.unique(label_image)
-    average_cell_size = 0
-    for _label in range(1, len(labels)):
-        indices = np.where(label_image == _label)
-        pixel_count = np.count_nonzero(image[indices])
-        average_cell_size += pixel_count
-    average_cell_size /= len(labels)
-    return average_cell_size
-
-
-@jit(nopython=True)
-def get_average_cell_size_gpu(label_image, image_size, labels_no):
-    average_cell_size = 0
-    for _label in labels_no:
-        if _label == 0:
-            continue
-        pixel_count = 0
-        for index_x in range(image_size[0]):
-            for index_y in range(image_size[1]):
-                if label_image[index_x, index_y] == _label:
-                    pixel_count += 1
-        average_cell_size += pixel_count
-    average_cell_size /= len(labels_no)
-    return average_cell_size
-
-
-@jit(nopython=True)
-def compute_cell_mapping(new_mapping, image_size, small_object_size=20):
-    marked = [[False for _ in range(image_size[1])] for _ in range(image_size[0])]
-    for i in range(image_size[0]):
-        for j in range(image_size[1]):
-            if marked[i][j] is False and (new_mapping[i, j, 0] > 0 or new_mapping[i, j, 2] > 0):
-                cluster_red_no, cluster_blue_no = 0, 0
-                pixels = [(i, j)]
-                cluster = [(i, j)]
-                marked[i][j] = True
-                while len(pixels) > 0:
-                    pixel = pixels.pop()
-                    if new_mapping[pixel[0], pixel[1], 0] > 0:
-                        cluster_red_no += 1
-                    if new_mapping[pixel[0], pixel[1], 2] > 0:
-                        cluster_blue_no += 1
-                    for neigh_i in range(-1, 2):
-                        for neigh_j in range(-1, 2):
-                            neigh_pixel = (pixel[0] + neigh_i, pixel[1] + neigh_j)
-                            if 0 <= neigh_pixel[0] < image_size[0] and 0 <= neigh_pixel[1] < image_size[1] and marked[neigh_pixel[0]][neigh_pixel[1]] is False and (new_mapping[neigh_pixel[0], neigh_pixel[1], 0] > 0 or new_mapping[neigh_pixel[0], neigh_pixel[1], 2] > 0):
-                                cluster.append(neigh_pixel)
-                                pixels.append(neigh_pixel)
-                                marked[neigh_pixel[0]][neigh_pixel[1]] = True
-                cluster_value = None
-                if cluster_red_no < cluster_blue_no:
-                    cluster_value = (0, 0, 255)
-                else:
-                    cluster_value = (255, 0, 0)
-                if len(cluster) < small_object_size:
-                    cluster_value = (0, 0, 0)
-                if cluster_value is not None:
-                    for node in cluster:
-                        new_mapping[node[0], node[1]] = cluster_value
-    return new_mapping
-
-
-@jit(nopython=True)
-def remove_noises(channel, image_size, small_object_size=20):
-    marked = [[False for _ in range(image_size[1])] for _ in range(image_size[0])]
-    for i in range(image_size[0]):
-        for j in range(image_size[1]):
-            if marked[i][j] is False and channel[i, j] > 0:
-                pixels = [(i, j)]
-                cluster = [(i, j)]
-                marked[i][j] = True
-                while len(pixels) > 0:
-                    pixel = pixels.pop()
-                    for neigh_i in range(-1, 2):
-                        for neigh_j in range(-1, 2):
-                            neigh_pixel = (pixel[0] + neigh_i, pixel[1] + neigh_j)
-                            if 0 <= neigh_pixel[0] < image_size[0] and 0 <= neigh_pixel[1] < image_size[1] and marked[neigh_pixel[0]][neigh_pixel[1]] is False and channel[neigh_pixel[0], neigh_pixel[1]] > 0:
-                                cluster.append(neigh_pixel)
-                                pixels.append(neigh_pixel)
-                                marked[neigh_pixel[0]][neigh_pixel[1]] = True
-
-                cluster_value = None
-                if len(cluster) < small_object_size:
-                    cluster_value = 0
-                if cluster_value is not None:
-                    for node in cluster:
-                        channel[node[0], node[1]] = cluster_value
-    return channel
-
-
-def remove_noises_fill_empty_holes(label_img, size=200):
-    inverse_img = 255 - label_img
-    inverse_img_removed = remove_noises(inverse_img, inverse_img.shape, small_object_size=size)
-    label_img[inverse_img_removed == 0] = 255
-    return label_img
-
-
-def positive_negative_masks(mask, thresh=100, boundary_thresh=100, noise_objects_size=50):
-    positive_mask = np.zeros((mask.shape[0], mask.shape[1]), dtype=np.uint8)
-    negative_mask = np.zeros((mask.shape[0], mask.shape[1]), dtype=np.uint8)
-
-    red = mask[:, :, 0]
-    blue = mask[:, :, 2]
-    boundary = mask[:, :, 1]
-
-    # Filtering boundary pixels
-    boundary[boundary < boundary_thresh] = 0
-
-    positive_mask[red > thresh] = 255
-    positive_mask[boundary > 0] = 0
-    positive_mask[blue > red] = 0
-
-    negative_mask[blue > thresh] = 255
-    negative_mask[boundary > 0] = 0
-    negative_mask[red >= blue] = 0
-
-    cell_mapping = np.zeros_like(mask)
-    cell_mapping[:, :, 0] = positive_mask
-    cell_mapping[:, :, 2] = negative_mask
-
-    compute_cell_mapping(cell_mapping, mask.shape, small_object_size=noise_objects_size)
-    cell_mapping[cell_mapping > 0] = 255
-
-    positive_mask = cell_mapping[:, :, 0]
-    negative_mask = cell_mapping[:, :, 2]
-
-    # return remove_noises_fill_empty_holes(positive_mask, noise_objects_size), remove_noises_fill_empty_holes(negative_mask, noise_objects_size)
-    return positive_mask, negative_mask
-
-
-def create_final_segmentation_mask_with_boundaries(positive_mask, negative_mask):
-    refined_mask = np.zeros((positive_mask.shape[0], positive_mask.shape[1], 3), dtype=np.uint8)
-
-    refined_mask[positive_mask > 0] = (255, 0, 0)
-    refined_mask[negative_mask > 0] = (0, 0, 255)
-
-    edges = feature.canny(refined_mask[:,:,0], sigma=3).astype(np.uint8)
-    contours, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
-    cv2.drawContours(refined_mask, contours, -1, (0, 255, 0), 2)
-
-    edges = feature.canny(refined_mask[:,:,2], sigma=3).astype(np.uint8)
-    contours, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
-    cv2.drawContours(refined_mask, contours, -1, (0, 255, 0), 2)
-
-    return refined_mask
-
-
-def count_number_of_cells(input_dir):
-    images = os.listdir(input_dir)
-    total_red = 0
-    total_blue = 0
-    for img in images:
-        image = cv2.cvtColor(cv2.imread(os.path.join(input_dir, img)), cv2.COLOR_BGR2RGB)
-        image = image[:,5*512:]
-        red = image[:,:,0]
-        blue = image[:,:,2]
-        labeled_red, nr_objects_red = ndimage.label(red > 0)
-        labeled_blue, nr_objects_blue = ndimage.label(blue > 0)
-        total_red += nr_objects_red
-        total_blue += nr_objects_blue
-    return total_red, total_blue
diff --git a/deepliif/stat/Segmentation_Metrics.py b/deepliif/stat/Segmentation_Metrics.py
deleted file mode 100644
index afb2aac..0000000
--- a/deepliif/stat/Segmentation_Metrics.py
+++ /dev/null
@@ -1,237 +0,0 @@
-import collections
-
-import numpy as np
-import cv2
-import os
-from numba import jit
-from skimage import measure
-import time
-from .PostProcessSegmentationMask import positive_negative_masks
-
-
-@jit(nopython=True)
-def compute_metrics_gpu(mask_img, gt_img, image_size):
-    TP, FP, FN, TN = 0, 0, 0, 0
-    for i in range(image_size[0]):
-        for j in range(image_size[1]):
-            if mask_img[i, j] > 0 and gt_img[i, j] > 0:
-                TP += 1
-            elif mask_img[i, j] > 0 and gt_img[i, j] == 0:
-                FP += 1
-            elif mask_img[i, j] == 0 and gt_img[i, j] > 0:
-                FN += 1
-            elif mask_img[i, j] == 0 and gt_img[i, j] == 0:
-                TN += 1
-
-    smooth = 0.0001
-    if TP == 0:
-        if np.count_nonzero(gt_img) > 0 or FP > 0:
-            IOU, precision, recall, Dice, f1, pixAcc = 0, 0, 0, 0, 0, 0
-        else:
-            IOU, precision, recall, Dice, f1, pixAcc = 1, 1, 1, 1, 1, 1
-    else:
-        IOU = (TP) / (TP + FP + FN)
-        precision = (TP) / (TP + FP)
-        recall = (TP) / (TP + FN)
-        f1 = 2 * precision * recall / (precision + recall)
-        Dice = (2 * TP) / (2 * TP + FP + FN)
-        pixAcc = (TP + TN) / (TP + TN + FP + FN)
-    return IOU, precision, recall, f1, Dice, pixAcc
-
-
-def compute_metrics(mask_img, gt_img):
-    smooth = 0.0001
-    intesection_TP = np.logical_and(gt_img, mask_img)
-    intesection_FN = np.logical_and(gt_img, 1 - mask_img)
-    intesection_FP = np.logical_and(1 - gt_img, mask_img)
-    intesection_TN = np.logical_and(1 - gt_img, 1 - mask_img)
-    union = np.logical_or(gt_img, mask_img)
-
-    iou_score = (np.sum(intesection_TP) + smooth) / (np.sum(union) + smooth)
-    precision_score = (np.sum(intesection_TP) + smooth) / (np.sum(intesection_TP) + np.sum(intesection_FP) + smooth)
-    recall_score = (np.sum(intesection_TP) + smooth) / (np.sum(intesection_TP) + np.sum(intesection_FN) + smooth)
-    f1_score = 2 * (precision_score * recall_score) / (precision_score + recall_score)
-    dice_score = (2 * np.sum(intesection_TP) + smooth) / (2 * np.sum(intesection_TP) + np.sum(intesection_FN) + np.sum(intesection_FP) + smooth)
-    pix_acc_score = (np.sum(intesection_TP) + np.sum(intesection_TN) + smooth) / (np.sum(intesection_TP) + np.sum(intesection_TN) + np.sum(intesection_FN) + np.sum(intesection_FP) + smooth)
-    return iou_score, precision_score, recall_score, f1_score, dice_score, pix_acc_score
-
-
-def compute_jaccard_index(set_1, set_2):
-    n = len(set_1.intersection(set_2))
-    return n / float(len(set_1) + len(set_2) - n)
-
-
-def compute_aji(gt_image, mask_image):
-    label_image_gt = measure.label(gt_image, background=0)
-    label_image_mask = measure.label(mask_image, background=0)
-    gt_labels = np.unique(label_image_gt)
-    mask_labels = np.unique(label_image_mask)
-    mask_components = []
-    mask_marked = []
-    for mask_label in mask_labels:
-        if mask_label == 0:
-            continue
-        comp = np.zeros((gt_image.shape[0], gt_image.shape[1]), dtype=np.uint8)
-        comp[label_image_mask == mask_label] = 1
-        mask_components.append(comp)
-        mask_marked.append(False)
-
-    total_intersection = 0
-    total_union = 0
-    total_U = 0
-    for gt_label in gt_labels:
-        if gt_label == 0:
-            continue
-        comp = np.zeros((gt_image.shape[0], gt_image.shape[1]), dtype=np.uint8)
-        comp[label_image_gt == gt_label] = 1
-        intersection = [0, 0, 0]    # index, intersection, union
-        for i in range(len(mask_components)):
-            if not mask_marked[i]:
-                comp_intersection = np.sum(np.logical_and(comp, mask_components[i]))
-                if comp_intersection > intersection[1]:
-                    union = np.sum(np.logical_or(comp, mask_components[i]))
-                    intersection = [i, comp_intersection, union]
-        if intersection[1] > 0:
-            mask_marked[intersection[0]] = True
-            total_intersection += intersection[1]
-            total_union += intersection[2]
-    for i in range(len(mask_marked)):
-        if not mask_marked[i]:
-            total_U += np.sum(mask_components[i])
-    aji = total_intersection / (total_union + total_U) if (total_union + total_U) > 0 else 0
-    return aji
-
-
-def compute_segmentation_metrics(gt_dir, model_dir, model_name, image_size=512, thresh=100, boundary_thresh=100, small_object_size=20, raw_segmentation=True, suffix_seg=None):
-    info_dict = []
-    metrics = collections.defaultdict(float)
-    images = os.listdir(model_dir)
-
-    counter = 0
-    if suffix_seg is not None:
-        postfix = f'_{suffix_seg}.png'
-    else:
-        postfix = '_Seg.png' if raw_segmentation else '_SegRefined.png'
-    for mask_name in images:
-        if postfix in mask_name:
-            counter += 1
-
-            mask_image = cv2.cvtColor(cv2.imread(os.path.join(model_dir, mask_name)), cv2.COLOR_BGR2RGB)
-            mask_image = cv2.resize(mask_image, (image_size, image_size))
-            if not raw_segmentation:
-                positive_mask = mask_image[:, :, 0]
-                negative_mask = mask_image[:, :, 2]
-            else:
-                positive_mask, negative_mask = positive_negative_masks(mask_image, thresh, boundary_thresh, small_object_size)
-
-            positive_mask[positive_mask > 0] = 1
-            negative_mask[negative_mask > 0] = 1
-
-            gt_img = cv2.cvtColor(cv2.imread(os.path.join(gt_dir, mask_name)), cv2.COLOR_BGR2RGB)
-            gt_img = cv2.resize(gt_img, (image_size, image_size))
-
-            positive_gt = gt_img[:, :, 0]
-            negative_gt = gt_img[:, :, 2]
-
-            positive_gt[positive_gt > 0] = 1
-            negative_gt[negative_gt > 0] = 1
-
-            # AJI_positive = compute_aji(positive_gt, positive_mask)
-            # start = time.time()
-            IOU_positive, precision_positive, recall_positive, f1_positive, Dice_positive, pixAcc_positive = compute_metrics_gpu(positive_mask, positive_gt, gt_img.shape)
-            # end = time.time()
-            # print(end - start)
-            # print('GPU: ', IOU_positive, precision_positive, recall_positive, f1_positive, Dice_positive, pixAcc_positive)
-            # start = time.time()
-            # IOU_positive, precision_positive, recall_positive, f1_positive, Dice_positive, pixAcc_positive = compute_metrics(positive_mask, positive_gt)
-            # end = time.time()
-            # print(end - start)
-            # print('CPU: ', end - start, IOU_positive, precision_positive, recall_positive, f1_positive, Dice_positive, pixAcc_positive)
-
-            # AJI_negative = compute_aji(negative_gt, negative_mask)
-            # start = time.time()
-            IOU_negative, precision_negative, recall_negative, f1_negative, Dice_negative, pixAcc_negative = compute_metrics_gpu(negative_mask, negative_gt, gt_img.shape)
-            # end = time.time()
-            # print('GPU: ', IOU_negative, precision_negative, recall_negative, f1_negative, Dice_negative, pixAcc_negative)
-            # start = time.time()
-            # IOU_negative, precision_negative, recall_negative, f1_negative, Dice_negative, pixAcc_negative = compute_metrics(negative_mask, negative_gt)
-            # end = time.time()
-            # print('CPU: ', end - start, IOU_negative, precision_negative, recall_negative, f1_negative, Dice_negative, pixAcc_negative)
-
-            info_dict.append({'Model': model_name,
-                              'image_name': mask_name,
-                              'cell_type': 'Positive',
-                              'precision': precision_positive * 100,
-                              'recall': recall_positive * 100,
-                              'f1': f1_positive * 100,
-                              'Dice': Dice_positive * 100,
-                              'IOU': IOU_positive * 100,
-                              'PixAcc': pixAcc_positive * 100
-                              # 'AJI': AJI_positive * 100
-                              })
-
-            info_dict.append({'Model': model_name,
-                              'image_name': mask_name,
-                              'cell_type': 'Negative',
-                              'precision': precision_negative * 100,
-                              'recall': recall_negative * 100,
-                              'f1': f1_negative * 100,
-                              'Dice': Dice_negative * 100,
-                              'IOU': IOU_negative * 100,
-                              'PixAcc': pixAcc_negative * 100
-                              # 'AJI': AJI_negative * 100
-                              })
-
-            precision = (precision_positive * 100 + precision_negative * 100) / 2
-            recall = (recall_positive * 100 + recall_negative * 100) / 2
-            f1 = (f1_positive * 100 + f1_negative * 100) / 2
-            Dice = (Dice_positive * 100 + Dice_negative * 100) / 2
-            IOU = (IOU_positive * 100 + IOU_negative * 100) / 2
-            pixAcc = (pixAcc_positive * 100 + pixAcc_negative * 100) / 2
-            # AJI = (AJI_positive * 100 + AJI_negative * 100) / 2
-
-            info_dict.append({'Model': model_name,
-                              'image_name': mask_name,
-                              'cell_type': 'Mean',
-                              'precision': precision,
-                              'recall': recall,
-                              'f1': f1,
-                              'Dice': Dice,
-                              'IOU': IOU,
-                              'PixAcc': pixAcc,
-                              # 'AJI': AJI
-                              })
-
-            metrics['precision'] += precision
-            metrics['precision_positive'] += precision_positive
-            metrics['precision_negative'] += precision_negative
-
-            metrics['recall'] += recall
-            metrics['recall_positive'] += recall_positive
-            metrics['recall_negative'] += recall_negative
-
-            metrics['f1'] += f1
-            metrics['f1_positive'] += f1_positive
-            metrics['f1_negative'] += f1_negative
-
-            metrics['Dice'] += Dice
-            metrics['Dice_positive'] += Dice_positive
-            metrics['Dice_negative'] += Dice_negative
-
-            metrics['IOU'] += IOU
-            metrics['IOU_positive'] += IOU_positive
-            metrics['IOU_negative'] += IOU_negative
-
-            metrics['PixAcc'] += pixAcc
-            metrics['PixAcc_positive'] += pixAcc_positive
-            metrics['PixAcc_negative'] += pixAcc_negative
-
-            # metrics['AJI'] += AJI
-            # metrics['AJI_positive'] += AJI_positive
-            # metrics['AJI_negative'] += AJI_negative
-
-    for key in metrics:
-        metrics[key] /= counter
-
-    return info_dict, metrics
-
diff --git a/deepliif/stat/__init__.py b/deepliif/stat/__init__.py
deleted file mode 100644
index 7daac75..0000000
--- a/deepliif/stat/__init__.py
+++ /dev/null
@@ -1,323 +0,0 @@
-
-
-import os
-import subprocess
-import shutil
-import time
-from .ComputeStatistics import Statistics
-
-from ..options.test_options import TestOptions
-from ..options import read_model_params, Options, print_options
-from ..data import create_dataset
-from ..models import create_model, init_nets, infer_modalities, infer_results_for_wsi
-from ..util.visualizer import save_images
-from ..util import html, allowed_file
-import torch
-import click
-
-from PIL import Image
-import json
-
-def ensure_exists(d):
-    if not os.path.exists(d):
-        os.makedirs(d)
-
-
-def format_file_structure(output_dir,source_folder='images',target_folder={'gt':'_real_B', 'pred':'_fake_B', 'input':'_real_A'}):
-    """
-    target_folder: keys are new folder names, values are unique token with which to filter the needed images for this folder
-        1) if segmentation metrics are needed and no seg images are generated from the main model, folder name "input" can be
-        specified when you want to run segmentation (using the seg model) on the original input as well
-        2) if you want to also run metrics on the original input (e.g., SSIM/PSNR for upscaling task), then you should also specify
-        "input""
-        otherwise only "gt" and "pred" are used
-    """
-    dir_source = os.path.join(output_dir,source_folder)
-    
-    for folder_name, unique_token in target_folder.items():
-        # create gt_dir and pred_dir
-        dir_folder = os.path.join(output_dir, folder_name)
-        if os.path.exists(dir_folder) and os.path.isdir(dir_folder):
-            shutil.rmtree(dir_folder)
-        os.makedirs(dir_folder)
-
-        # separate the images
-        subprocess.run(f"cp {dir_source}/*{unique_token}* {dir_folder}", shell=True, check=True)
-        
-        # rename files
-        fns = os.listdir(dir_folder)
-        for fn in fns:
-            os.rename(f'{dir_folder}/{fn}',f"{dir_folder}/{fn.replace(f'{unique_token}','_')}")
-
-
-def generate_predictions(dataroot, results_dir, checkpoints_dir, name='', num_test=10000,
-                         phase='val', gpu_ids=(-1,), batch_size=None, epoch='latest'):
-    """
-    a function version of cli.py test / test.py, to be used with evaluate()
-    params:
-      dataroot: reads images from here; expected to have a subfolder
-      results_dir: saves results here.
-      checkpoints_dir: load models from here.
-      name: name of the experiment, used as a subfolder under results_dir
-      num_test: only run test for num_test images
-      phase: this effectively refers to the subfolder name from where to load the images
-      gpu_ids: gpu-ids 0 gpu-ids 1 or gpu-ids -1 for CPU
-      batch_size: input batch size
-    """
-    # retrieve options used in training setting, similar to cli.py test
-    model_dir = os.path.join(checkpoints_dir, name)
-    opt = Options(path_file=os.path.join(model_dir,'train_opt.txt'), mode='test')
-    
-    if gpu_ids and gpu_ids[0] == -1:
-        gpu_ids = []
-    
-    # overwrite/supply unseen options using the values from the options provided in the command
-    setattr(opt,'checkpoints_dir',checkpoints_dir)
-    setattr(opt,'dataroot',dataroot)
-    setattr(opt,'name',name)
-    setattr(opt,'results_dir',results_dir)
-    setattr(opt,'num_test',num_test)
-    setattr(opt,'phase',phase)
-    setattr(opt,'gpu_ids',gpu_ids)
-    setattr(opt,'batch_size',batch_size)
-    setattr(opt,'epoch',str(epoch))
-        
-    if not hasattr(opt,'seg_gen'): # old settings for DeepLIIF models
-        opt.seg_gen = True
-    
-    # hard-code some parameters for test.py
-    opt.aspect_ratio = 1.0 # from previous default setting
-    opt.display_winsize = 512 # from previous default setting
-    opt.use_dp = True # whether to initialize model in DataParallel setting (all models to one gpu, then pytorch controls the usage of specified set of GPUs for inference)
-    opt.num_threads = 0   # test code only supports num_threads = 1
-    opt.batch_size = 1    # test code only supports batch_size = 1
-    opt.serial_batches = True  # disable data shuffling; comment this line if results on randomly chosen images are needed.
-    opt.no_flip = True    # no flip; comment this line if results on flipped images are needed.
-    opt.display_id = -1   # no visdom display; the test code saves the results to a HTML file.
-    print_options(opt)
-    
-    batch_size = batch_size if batch_size else opt.batch_size
-    dataset = create_dataset(opt, phase=phase, batch_size=batch_size)  # create a dataset given opt.dataset_mode and other options
-    model = create_model(opt)      # create a model given opt.model and other options
-    model.setup(opt)               # regular setup: load and print networks; create schedulers
-    torch.backends.cudnn.benchmark = False
-    # create a website
-    web_dir = os.path.join(opt.results_dir, opt.name, '{}_{}'.format(opt.phase, opt.epoch))  # define the website directory
-    if opt.load_iter > 0:  # load_iter is 0 by default
-        web_dir = '{:s}_iter{:d}'.format(web_dir, opt.load_iter)
-    print('creating web directory', web_dir)
-    webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.epoch))
-    # test with eval mode. This only affects layers like batchnorm and dropout.
-    # For [pix2pix]: we use batchnorm and dropout in the original pix2pix. You can experiment it with and without eval() mode.
-    # For [CycleGAN]: It should not affect CycleGAN as CycleGAN uses instancenorm without dropout.
-    model.eval()
-    # if opt.eval:
-    #     model.eval()
-
-    _start_time = time.time()
-
-    for i, data in enumerate(dataset):
-        if i >= opt.num_test:  # only apply our model to opt.num_test images.
-            break
-        
-        model.set_input(data)  # unpack data from data loader
-        model.test()           # run inference
-        visuals = model.get_current_visuals()  # get image results
-        img_path = model.get_image_paths()     # get image paths
-        if i % 5 == 0:  # save images to an HTML file
-            print('processing (%04d)-th image... %s (batch size %s)' % (i*batch_size, img_path[-1], batch_size))
-        save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize)
-
-    t_sec = round(time.time() - _start_time)
-    (t_min, t_sec) = divmod(t_sec, 60)
-    (t_hour, t_min) = divmod(t_min, 60)
-    print('Time passed: {}hour:{}min:{}sec'.format(t_hour, t_min, t_sec))
-    webpage.save()  # save the HTML
-
-
-def generate_predictions_inference(input_dir, output_dir, tile_size=None, model_dir='./model-server/DeepLIIF_Latest_Model/', 
-                                   gpu_ids=(-1,), region_size=20000, eager_mode=True, color_dapi=False, color_marker=False,
-                                   save_type_pattern='Seg'):
-    output_dir = output_dir or input_dir
-    ensure_exists(output_dir)
-
-    image_files = sorted([fn for fn in os.listdir(input_dir) if allowed_file(fn)])
-    print(len(image_files),'images found')
-    files = os.listdir(model_dir)
-    assert 'train_opt.txt' in files, f'file train_opt.txt is missing from model directory {model_dir}'
-    opt = Options(path_file=os.path.join(model_dir,'train_opt.txt'), mode='test')
-    opt.use_dp = False
-    
-    number_of_gpus_all = torch.cuda.device_count()
-    if number_of_gpus_all < len(gpu_ids) and -1 not in gpu_ids:
-        number_of_gpus = 0
-        gpu_ids = [-1]
-        print(f'Specified to use GPU {opt.gpu_ids} for inference, but there are only {number_of_gpus_all} GPU devices. Switched to CPU inference.')
-
-    if len(gpu_ids) > 0 and gpu_ids[0] == -1:
-        gpu_ids = []
-    elif len(gpu_ids) == 0:
-        gpu_ids = list(range(number_of_gpus_all))
-    
-    opt.gpu_ids = gpu_ids # overwrite gpu_ids; for test command, default gpu_ids at first is [] which will be translated to a list of all gpus
-    
-    # fix opt from old settings
-    if not hasattr(opt,'modalities_no') and hasattr(opt,'targets_no'):
-        opt.modalities_no = opt.targets_no - 1
-        del opt.targets_no
-    print_options(opt)
-
-    count = 0
-    with click.progressbar(
-            image_files,
-            label=f'Processing {len(image_files)} images',
-            item_show_func=lambda fn: fn
-    ) as bar:
-        for filename in bar:
-            if '.svs' in filename:
-                start_time = time.time()
-                infer_results_for_wsi(input_dir, filename, output_dir, model_dir, tile_size, region_size)
-                print(time.time() - start_time)
-            else:
-                img = Image.open(os.path.join(input_dir, filename)).convert('RGB')
-                images, scoring = infer_modalities(img, tile_size, model_dir, eager_mode, color_dapi, color_marker, opt)
-
-                for name, i in images.items():
-                    if save_type_pattern and save_type_pattern in name:
-                        i.save(os.path.join(
-                            output_dir,
-                            filename.replace('.' + filename.split('.')[-1], f'_{name}.png')
-                        ))
-
-                with open(os.path.join(
-                        output_dir,
-                        filename.replace('.' + filename.split('.')[-1], f'.json')
-                ), 'w') as f:
-                    json.dump(scoring, f, indent=2)
-                
-            count += 1
-            if count % 100 == 0 or count == len(image_files):
-                print(f'Done {count}/{len(image_files)}')
-    
-
-def evaluate(model_dir, input_dir, output_dir, seg_model_dir=None, phase='val', subfolder=None, image_types='B_1,B_2,B_3,B_4', seg_type='B_5', 
-         seg_gen=False, epoch='latest',mode='Segmentation', seg_on_input=False, include_input_metrics=False, batch_size=None, gpu_ids=(-1,),overwrite=False, verbose=False):
-    """
-    params:
-      model_dir: directory of the trained model
-      input_dir: input directory of the whole dataset, which should contain multiple subfolders like train and test
-      output_dir: path to the trained model
-      seg_model_dir: only used when seg_gen=False; this is the model to generate segmentation mask for both gt and pred images
-      phase: this effectively is the subfolder name under model_dir in which is the input data
-      subfolder: this effectively is the subfolder name under output_dir in which is the folder for predictions "./images" locates
-      image_types: unique marker in filename for each modality; in model class DeepLIIFExt and SDG, it could be a string like B_1,B_2,B_3
-      seg_type: unique marker in filename for segmentation, if exists; in model class DeepLIIFExt and SDG, it could be a string like B_4
-      seg_gen: True (Translation and Segmentation), False (Only Translation).
-      epoch: the name of epoch to load, default to "latest"
-      mode: Mode of the statistics computation including Segmentation, ImageSynthesis, All, SSIM, Upscaling
-      seg_on_input: a flag to indicate whether to run the seg model on the input image; this applies only to the situation where 
-            seg_gen=False, mode includes segmentation metrics, and seg_model_dir is provided; if True, 2 sets of segmentation metrics will
-            be returned (seg on fake vs seg on real, seg on input vs seg on real)
-      batch_size: batch size for inference; if not set, default is opt.batch_size
-      overwrite: overwrite results; otherwise, if output_dir already exists, prediction generation will be skipped
-      verbose: print more info if True
-    """
-    if seg_gen == False and mode in ['Segmentation','All']:
-        if not seg_model_dir:
-            mode = 'SSIM'
-            print('seg_gen is False, cannot run segmentation metrics; mode is changed to SSIM')
-    
-    d_res = {'elapsed_time':[]}
-    if verbose:
-        params = locals()
-        for k,v in params:
-            print(k,v)
-    
-    if not subfolder:
-        subfolder = f'{phase}_{epoch}'
-    
-    print('Generating predictions...')
-    time_s = time.time()
-    if os.path.exists(os.path.join(output_dir, subfolder)):
-        print(f'Folder {os.path.join(output_dir, subfolder)} already exists')
-        print(f'overwrite: {overwrite}')
-        if not overwrite:
-            print('Skip prediction generation')
-        else:
-            print('Deleting folder and regenerating predictions...')
-            shutil.rmtree(output_dir)
-            generate_predictions(checkpoints_dir=model_dir, name='.', dataroot=input_dir,
-                         results_dir=output_dir, gpu_ids=gpu_ids, phase=phase, 
-                         batch_size=batch_size, epoch=epoch)
-    else:
-        generate_predictions(checkpoints_dir=model_dir, name='.', dataroot=input_dir,
-                         results_dir=output_dir, gpu_ids=gpu_ids, phase=phase,
-                         batch_size=batch_size, epoch=epoch)
-    d_res['elapsed_time'].append(time.time() - time_s)
-    
-    if seg_gen == False and seg_model_dir:
-        print('Generating segmentation mask...')
-        time_s = time.time()
-        seg_output_dir = os.path.join(output_dir, subfolder, 'seg')
-        if os.path.exists(seg_output_dir):
-            print(f'Folder {os.path.join(output_dir, subfolder, "seg")} already exists')
-            print(f'overwrite: {overwrite}')
-            if not overwrite:
-                print('Skip segmentation mask generation')
-            else:
-                print('Deleting folder and regenerating segmentation mask...')
-                shutil.rmtree(os.path.join(output_dir, subfolder, 'seg'))
-                generate_predictions_inference(input_dir=os.path.join(output_dir,subfolder,'images'),
-                                               output_dir=seg_output_dir, tile_size=512, model_dir=seg_model_dir,
-                                               gpu_ids=gpu_ids, eager_mode=True)
-        else:
-            generate_predictions_inference(input_dir=os.path.join(output_dir,subfolder,'images'),
-                                               output_dir=seg_output_dir, tile_size=512, model_dir=seg_model_dir,
-                                               gpu_ids=gpu_ids, eager_mode=True)
-        d_res['elapsed_time'].append(time.time() - time_s)
-
-    print('Preparing folder structure and formating filenames...')
-    time_s = time.time()
-    format_file_structure(os.path.join(output_dir,subfolder)) # the folder name val_latest
-    d_res['elapsed_time'].append(time.time() - time_s)
-
-    if seg_gen == False and seg_model_dir:
-        print('Preparing folder structure and formating filenames...')
-        time_s = time.time()
-        format_file_structure(os.path.join(output_dir,subfolder),source_folder='seg',target_folder={'gt_seg':'_real_B_1_SegRefined','pred_seg':'_fake_B_1_SegRefined', 'input_seg':'_real_A_SegRefined'}) # the folder name val_latest
-        d_res['elapsed_time'].append(time.time() - time_s)
-
-    print('Starting ComputeStatistics.py...')
-
-    time_s = time.time()
-    if seg_gen == False and seg_model_dir and mode in ['Segmentation', 'All']:
-        gt_dir = os.path.join(output_dir, subfolder, 'gt_seg')
-        pred_dir = os.path.join(output_dir, subfolder, 'pred_seg')
-        stats = Statistics(gt_path=gt_dir, model_path=pred_dir, output_path=output_dir,
-                           image_types=image_types, seg_type=seg_type, mode='Segmentation')
-        d_stat = stats.run()
-
-        if mode == 'All':
-            gt_dir = os.path.join(output_dir, subfolder, 'gt')
-            pred_dir = os.path.join(output_dir, subfolder, 'pred')
-            stats = Statistics(gt_path=gt_dir, model_path=pred_dir, output_path=output_dir,
-                               image_types=image_types, seg_type=seg_type, mode='ImageSynthesis')
-            d_stat_imagesynthesis = stats.run()
-            d_stat = {**d_stat, **d_stat_imagesynthesis}
-    else:
-        gt_dir = os.path.join(output_dir, subfolder, 'gt')
-        pred_dir = os.path.join(output_dir, subfolder, 'pred')
-        stats = Statistics(gt_path=gt_dir, model_path=pred_dir, output_path=output_dir,
-                           image_types=image_types, seg_type=seg_type, mode=mode)
-        d_stat = stats.run()
-
-        if include_input_metrics:
-            input_dir = os.path.join(output_dir, subfolder, 'input')
-            stats = Statistics(gt_path=gt_dir, model_path=input_dir, output_path=output_dir,
-                           image_types=image_types, seg_type=seg_type, mode=mode)
-    d_res['elapsed_time'].append(time.time() - time_s)
-
-
-    return {**d_stat, **d_res}
-
-
diff --git a/deepliif/stat/fid.py b/deepliif/stat/fid.py
deleted file mode 100644
index d57a50a..0000000
--- a/deepliif/stat/fid.py
+++ /dev/null
@@ -1,334 +0,0 @@
-#!/usr/bin/env python3
-''' Calculates the Frechet Inception Distance (FID) to evalulate GANs.
-
-The FID metric calculates the distance between two distributions of images.
-Typically, we have summary statistics (mean & covariance matrix) of one
-of these distributions, while the 2nd distribution is given by a GAN.
-
-When run as a stand-alone program, it compares the distribution of
-images that are stored as PNG/JPEG at a specified location with a
-distribution given by summary statistics (in pickle format).
-
-The FID is calculated by assuming that X_1 and X_2 are the activations of
-the pool_3 layer of the inception net for generated samples and real world
-samples respectivly.
-
-See --help to see further details.
-'''
-
-from __future__ import absolute_import, division, print_function
-import numpy as np
-import os
-import gzip, pickle
-import tensorflow as tf
-from imageio import imread
-from scipy import linalg
-import pathlib
-import urllib
-import warnings
-
-class InvalidFIDException(Exception):
-    pass
-
-
-def create_inception_graph(pth):
-    """Creates a graph from saved GraphDef file."""
-    # Creates graph from saved graph_def.pb.
-    with tf.io.gfile.GFile( pth, 'rb') as f:
-        graph_def = tf.compat.v1.GraphDef()
-        graph_def.ParseFromString( f.read())
-        _ = tf.import_graph_def( graph_def, name='FID_Inception_Net')
-#-------------------------------------------------------------------------------
-
-
-# code for handling inception net derived from
-#   https://github.com/openai/improved-gan/blob/master/inception_score/model.py
-def _get_inception_layer(sess):
-    """Prepares inception net for batched usage and returns pool_3 layer. """
-    layername = 'FID_Inception_Net/pool_3:0'
-    pool3 = sess.graph.get_tensor_by_name(layername)
-    ops = pool3.graph.get_operations()
-    for op_idx, op in enumerate(ops):
-        for o in op.outputs:
-            shape = o.get_shape()
-            if shape._dims is not None:
-              #shape = [s.value for s in shape] TF 1.x
-              shape = [s for s in shape] #TF 2.x
-              new_shape = []
-              for j, s in enumerate(shape):
-                if s == 1 and j == 0:
-                  new_shape.append(None)
-                else:
-                  new_shape.append(s)
-              o.__dict__['_shape_val'] = tf.TensorShape(new_shape)
-    return pool3
-#-------------------------------------------------------------------------------
-
-
-def get_activations(images, sess, batch_size=50, verbose=False):
-    """Calculates the activations of the pool_3 layer for all images.
-
-    Params:
-    -- images      : Numpy array of dimension (n_images, hi, wi, 3). The values
-                     must lie between 0 and 256.
-    -- sess        : current session
-    -- batch_size  : the images numpy array is split into batches with batch size
-                     batch_size. A reasonable batch size depends on the disposable hardware.
-    -- verbose    : If set to True and parameter out_step is given, the number of calculated
-                     batches is reported.
-    Returns:
-    -- A numpy array of dimension (num images, 2048) that contains the
-       activations of the given tensor when feeding inception with the query tensor.
-    """
-    inception_layer = _get_inception_layer(sess)
-    n_images = images.shape[0]
-    if batch_size > n_images:
-        print("warning: batch size is bigger than the data size. setting batch size to data size")
-        batch_size = n_images
-    n_batches = n_images//batch_size # drops the last batch if < batch_size
-    pred_arr = np.empty((n_batches * batch_size,2048))
-    for i in range(n_batches):
-        if verbose:
-            print("\rPropagating batch %d/%d" % (i+1, n_batches), end="", flush=True)
-        start = i*batch_size
-        
-        if start+batch_size < n_images:
-            end = start+batch_size
-        else:
-            end = n_images
-        
-        batch = images[start:end]
-        pred = sess.run(inception_layer, {'FID_Inception_Net/ExpandDims:0': batch})
-        pred_arr[start:end] = pred.reshape(batch.shape[0],-1)
-    if verbose:
-        print(" done")
-    return pred_arr
-#-------------------------------------------------------------------------------
-
-
-def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
-    """Numpy implementation of the Frechet Distance.
-    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
-    and X_2 ~ N(mu_2, C_2) is
-            d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
-            
-    Stable version by Dougal J. Sutherland.
-
-    Params:
-    -- mu1 : Numpy array containing the activations of the pool_3 layer of the
-             inception net ( like returned by the function 'get_predictions')
-             for generated samples.
-    -- mu2   : The sample mean over activations of the pool_3 layer, precalcualted
-               on an representive data set.
-    -- sigma1: The covariance matrix over activations of the pool_3 layer for
-               generated samples.
-    -- sigma2: The covariance matrix over activations of the pool_3 layer,
-               precalcualted on an representive data set.
-
-    Returns:
-    --   : The Frechet Distance.
-    """
-
-    mu1 = np.atleast_1d(mu1)
-    mu2 = np.atleast_1d(mu2)
-
-    sigma1 = np.atleast_2d(sigma1)
-    sigma2 = np.atleast_2d(sigma2)
-
-    assert mu1.shape == mu2.shape, "Training and test mean vectors have different lengths"
-    assert sigma1.shape == sigma2.shape, "Training and test covariances have different dimensions"
-
-    diff = mu1 - mu2
-
-    # product might be almost singular
-    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
-    if not np.isfinite(covmean).all():
-        msg = "fid calculation produces singular product; adding %s to diagonal of cov estimates" % eps
-        warnings.warn(msg)
-        offset = np.eye(sigma1.shape[0]) * eps
-        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
-
-    # numerical error might give slight imaginary component
-    if np.iscomplexobj(covmean):
-        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
-            m = np.max(np.abs(covmean.imag))
-            raise ValueError("Imaginary component {}".format(m))
-        covmean = covmean.real
-
-    tr_covmean = np.trace(covmean)
-
-    return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean
-#-------------------------------------------------------------------------------
-
-
-def calculate_activation_statistics(images, sess, batch_size=50, verbose=False):
-    """Calculation of the statistics used by the FID.
-    Params:
-    -- images      : Numpy array of dimension (n_images, hi, wi, 3). The values
-                     must lie between 0 and 255.
-    -- sess        : current session
-    -- batch_size  : the images numpy array is split into batches with batch size
-                     batch_size. A reasonable batch size depends on the available hardware.
-    -- verbose     : If set to True and parameter out_step is given, the number of calculated
-                     batches is reported.
-    Returns:
-    -- mu    : The mean over samples of the activations of the pool_3 layer of
-               the incption model.
-    -- sigma : The covariance matrix of the activations of the pool_3 layer of
-               the incption model.
-    """
-    act = get_activations(images, sess, batch_size, verbose)
-    mu = np.mean(act, axis=0)
-    sigma = np.cov(act, rowvar=False)
-    return mu, sigma
-    
-
-#------------------
-# The following methods are implemented to obtain a batched version of the activations.
-# This has the advantage to reduce memory requirements, at the cost of slightly reduced efficiency.
-# - Pyrestone
-#------------------
-
-
-def load_image_batch(files):
-    """Convenience method for batch-loading images
-    Params:
-    -- files    : list of paths to image files. Images need to have same dimensions for all files.
-    Returns:
-    -- A numpy array of dimensions (num_images,hi, wi, 3) representing the image pixel values.
-    """
-    return np.array([imread(str(fn)).astype(np.float32) for fn in files])
-
-def get_activations_from_files(files, sess, batch_size=50, verbose=False):
-    """Calculates the activations of the pool_3 layer for all images.
-
-    Params:
-    -- files      : list of paths to image files. Images need to have same dimensions for all files.
-    -- sess        : current session
-    -- batch_size  : the images numpy array is split into batches with batch size
-                     batch_size. A reasonable batch size depends on the disposable hardware.
-    -- verbose    : If set to True and parameter out_step is given, the number of calculated
-                     batches is reported.
-    Returns:
-    -- A numpy array of dimension (num images, 2048) that contains the
-       activations of the given tensor when feeding inception with the query tensor.
-    """
-    inception_layer = _get_inception_layer(sess)
-    n_imgs = len(files)
-    if batch_size > n_imgs:
-        print("warning: batch size is bigger than the data size. setting batch size to data size")
-        batch_size = n_imgs
-    n_batches = n_imgs//batch_size + 1
-    pred_arr = np.empty((n_imgs,2048))
-    for i in range(n_batches):
-        if verbose:
-            print("\rPropagating batch %d/%d" % (i+1, n_batches), end="", flush=True)
-        start = i*batch_size
-        if start+batch_size < n_imgs:
-            end = start+batch_size
-        else:
-            end = n_imgs
-        
-        batch = load_image_batch(files[start:end])
-        pred = sess.run(inception_layer, {'FID_Inception_Net/ExpandDims:0': batch})
-        pred_arr[start:end] = pred.reshape(batch_size,-1)
-        del batch #clean up memory
-    if verbose:
-        print(" done")
-    return pred_arr
-    
-def calculate_activation_statistics_from_files(files, sess, batch_size=50, verbose=False):
-    """Calculation of the statistics used by the FID.
-    Params:
-    -- files      : list of paths to image files. Images need to have same dimensions for all files.
-    -- sess        : current session
-    -- batch_size  : the images numpy array is split into batches with batch size
-                     batch_size. A reasonable batch size depends on the available hardware.
-    -- verbose     : If set to True and parameter out_step is given, the number of calculated
-                     batches is reported.
-    Returns:
-    -- mu    : The mean over samples of the activations of the pool_3 layer of
-               the incption model.
-    -- sigma : The covariance matrix of the activations of the pool_3 layer of
-               the incption model.
-    """
-    act = get_activations_from_files(files, sess, batch_size, verbose)
-    mu = np.mean(act, axis=0)
-    sigma = np.cov(act, rowvar=False)
-    return mu, sigma
-    
-#-------------------------------------------------------------------------------
-
-
-#-------------------------------------------------------------------------------
-# The following functions aren't needed for calculating the FID
-# they're just here to make this module work as a stand-alone script
-# for calculating FID scores
-#-------------------------------------------------------------------------------
-def check_or_download_inception(inception_path):
-    ''' Checks if the path to the inception file is valid, or downloads
-        the file if it is not present. '''
-    INCEPTION_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
-    if inception_path is None:
-        inception_path = '/tmp'
-    inception_path = pathlib.Path(inception_path)
-    model_file = inception_path / 'classify_image_graph_def.pb'
-    if not model_file.exists():
-        print("Downloading Inception model")
-        from urllib import request
-        import tarfile
-        fn, _ = request.urlretrieve(INCEPTION_URL)
-        with tarfile.open(fn, mode='r') as f:
-            f.extract('classify_image_graph_def.pb', str(model_file.parent))
-    return str(model_file)
-
-
-def _handle_path(path, sess, low_profile=False):
-    if path.endswith('.npz'):
-        f = np.load(path)
-        m, s = f['mu'][:], f['sigma'][:]
-        f.close()
-    else:
-        path = pathlib.Path(path)
-        files = list(path.glob('*.jpg')) + list(path.glob('*.png'))
-        if low_profile:
-            m, s = calculate_activation_statistics_from_files(files, sess)
-        else:
-            x = np.array([imread(str(fn)).astype(np.float32) for fn in files])
-            m, s = calculate_activation_statistics(x, sess)
-            del x #clean up memory
-    return m, s
-
-
-def calculate_fid_given_paths(paths, inception_path, low_profile=False):
-    ''' Calculates the FID of two paths. '''
-    inception_path = check_or_download_inception(inception_path)
-
-    for p in paths:
-        if not os.path.exists(p):
-            raise RuntimeError("Invalid path: %s" % p)
-
-    create_inception_graph(str(inception_path))
-    with tf.Session() as sess:
-        sess.run(tf.global_variables_initializer())
-        m1, s1 = _handle_path(paths[0], sess, low_profile=low_profile)
-        m2, s2 = _handle_path(paths[1], sess, low_profile=low_profile)
-        fid_value = calculate_frechet_distance(m1, s1, m2, s2)
-        return fid_value
-
-
-if __name__ == "__main__":
-    from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
-    parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
-    parser.add_argument("path", type=str, nargs=2,
-        help='Path to the generated images or to .npz statistic files')
-    parser.add_argument("-i", "--inception", type=str, default=None,
-        help='Path to Inception model (will be downloaded if not provided)')
-    parser.add_argument("--gpu", default="", type=str,
-        help='GPU to use (leave blank for CPU only)')
-    parser.add_argument("--lowprofile", action="store_true",
-        help='Keep only one batch of images in memory at a time. This reduces memory footprint, but may decrease speed slightly.')
-    args = parser.parse_args()
-    os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
-    fid_value = calculate_fid_given_paths(args.path, args.inception, low_profile=args.lowprofile)
-    print("FID: ", fid_value)
diff --git a/deepliif/stat/fid_official_tf.py b/deepliif/stat/fid_official_tf.py
deleted file mode 100644
index ab67ce5..0000000
--- a/deepliif/stat/fid_official_tf.py
+++ /dev/null
@@ -1,370 +0,0 @@
-"""
-@Brief:
-    Tensorflow implementation of FID score, should be the same as the official one
-    modified from official inception score implementation
-    [bioinf-jku/TTUR](https://github.com/bioinf-jku/TTUR)
-@Author: lzhbrian (https://lzhbrian.me)
-@Date: 2019.4.7
-@Usage:
-    # CMD
-        # from 2 precalculated stats
-        python fid_official_tf.py res/stats_tf/fid_stats_imagenet_valid.npz res/stats_tf/fid_stats_imagenet_train.npz --gpu 0
-
-        # from 1 precalculated stats, 1 image foldername/
-        python fid_official_tf.py res/stats_tf/fid_stats_imagenet_valid.npz /path/to/image/foldername/ --gpu 0
-
-        # from 2 image foldername/
-        python fid_official_tf.py /path/to/image/foldername1/ /path/to/image/foldername2/ --gpu 0
-
-    # used in code
-        ```
-        import tensorflow as tf
-
-        # load from precalculated
-        f = np.load('res/stats_tf/fid_stats_imagenet_train.npz')
-        mu1, sigma1 = f['mu'][:], f['sigma'][:]
-        f.close()
-
-        # calc from image ndarray
-        # images should be Numpy array of dimension (N, H, W, C). images should be in 0~255
-        with tf.Session() as sess:
-            sess.run(tf.global_variables_initializer())
-            mu2, sigma2 = fid_official_tf.calculate_activation_statistics(images, sess, batch_size=100)
-        fid_score = calculate_frechet_distance(mu1, sigma1, mu2, sigma2)
-        ```
-
-@Note:
-    Need to first download stats_tf of datasets in stats_tf/, see README.md
-
-    also, the same as inception_score_official_tf.py, the inception model used
-    contains resize and normalization layers
-    so the input of our images should be 0~255, and arbitrary HxW size
-
-    For calculating mu and sigma for foldername/, see precalc_stats_official_tf.py
-"""
-
-import numpy as np
-import os
-import tensorflow as tf
-import scipy.misc
-from scipy.misc import imread
-from scipy import linalg
-import pathlib
-import urllib
-import warnings
-from tqdm import tqdm
-
-
-cur_dirname = os.path.dirname(os.path.abspath(__file__))
-
-MODEL_DIR = '%s/res/' % cur_dirname
-
-class InvalidFIDException(Exception):
-    pass
-
-
-def create_inception_graph(pth):
-    """Creates a graph from saved GraphDef file."""
-    # Creates graph from saved graph_def.pb.
-    with tf.gfile.FastGFile(pth, 'rb') as f:
-        graph_def = tf.GraphDef()
-        graph_def.ParseFromString(f.read())
-        _ = tf.import_graph_def(graph_def, name='FID_Inception_Net')
-
-
-# -------------------------------------------------------------------------------
-
-
-# code for handling inception net derived from
-#   https://github.com/openai/improved-gan/blob/master/inception_score/model.py
-def _get_inception_layer(sess):
-    """Prepares inception net for batched usage and returns pool_3 layer. """
-    layername = 'FID_Inception_Net/pool_3:0'
-    pool3 = sess.graph.get_tensor_by_name(layername)
-    ops = pool3.graph.get_operations()
-    for op_idx, op in enumerate(ops):
-        for o in op.outputs:
-            shape = o.get_shape()
-            if shape._dims != []:
-                shape = [s.value for s in shape]
-                new_shape = []
-                for j, s in enumerate(shape):
-                    if s == 1 and j == 0:
-                        new_shape.append(None)
-                    else:
-                        new_shape.append(s)
-                o.__dict__['_shape_val'] = tf.TensorShape(new_shape)
-    return pool3
-
-
-# -------------------------------------------------------------------------------
-
-
-def get_activations(images, sess, batch_size=50, verbose=False):
-    """Calculates the activations of the pool_3 layer for all images.
-    Params:
-    -- images      : Numpy array of dimension (n_images, hi, wi, 3). The values
-                     must lie between 0 and 256.
-    -- sess        : current session
-    -- batch_size  : the images numpy array is split into batches with batch size
-                     batch_size. A reasonable batch size depends on the disposable hardware.
-    -- verbose    : If set to True and parameter out_step is given, the number of calculated
-                     batches is reported.
-    Returns:
-    -- A numpy array of dimension (num images, 2048) that contains the
-       activations of the given tensor when feeding inception with the query tensor.
-    """
-    inception_layer = _get_inception_layer(sess)
-    d0 = images.shape[0]
-    if batch_size > d0:
-        print("warning: batch size is bigger than the data size. setting batch size to data size")
-        batch_size = d0
-    n_batches = d0 // batch_size
-    n_used_imgs = n_batches * batch_size
-    pred_arr = np.empty((n_used_imgs, 2048))
-    for i in tqdm(range(n_batches)):
-        if verbose:
-            print("\rPropagating batch %d/%d" % (i + 1, n_batches))
-        start = i * batch_size
-        end = start + batch_size
-        batch = images[start:end]
-        pred = sess.run(inception_layer, {'FID_Inception_Net/ExpandDims:0': batch})
-        pred_arr[start:end] = pred.reshape(batch_size, -1)
-    if verbose:
-        print(" done")
-    return pred_arr
-
-
-# -------------------------------------------------------------------------------
-
-
-def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
-    """Numpy implementation of the Frechet Distance.
-    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
-    and X_2 ~ N(mu_2, C_2) is
-            d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
-
-    Stable version by Dougal J. Sutherland.
-    Params:
-    -- mu1 : Numpy array containing the activations of the pool_3 layer of the
-             inception net ( like returned by the function 'get_predictions')
-             for generated samples.
-    -- mu2   : The sample mean over activations of the pool_3 layer, precalcualted
-               on an representive data set.
-    -- sigma1: The covariance matrix over activations of the pool_3 layer for
-               generated samples.
-    -- sigma2: The covariance matrix over activations of the pool_3 layer,
-               precalcualted on an representive data set.
-    Returns:
-    --   : The Frechet Distance.
-    """
-
-    mu1 = np.atleast_1d(mu1)
-    mu2 = np.atleast_1d(mu2)
-
-    sigma1 = np.atleast_2d(sigma1)
-    sigma2 = np.atleast_2d(sigma2)
-
-    assert mu1.shape == mu2.shape, "Training and test mean vectors have different lengths"
-    assert sigma1.shape == sigma2.shape, "Training and test covariances have different dimensions"
-
-    diff = mu1 - mu2
-
-    # product might be almost singular
-    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
-    if not np.isfinite(covmean).all():
-        msg = "fid calculation produces singular product; adding %s to diagonal of cov estimates" % eps
-        warnings.warn(msg)
-        offset = np.eye(sigma1.shape[0]) * eps
-        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
-
-    # numerical error might give slight imaginary component
-    if np.iscomplexobj(covmean):
-        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
-            m = np.max(np.abs(covmean.imag))
-            raise ValueError("Imaginary component {}".format(m))
-        covmean = covmean.real
-
-    tr_covmean = np.trace(covmean)
-
-    return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean
-
-
-# -------------------------------------------------------------------------------
-
-
-def calculate_activation_statistics(images, sess, batch_size=50, verbose=False):
-    """Calculation of the statistics used by the FID.
-    Params:
-    -- images      : Numpy array of dimension (n_images, hi, wi, 3). The values
-                     must lie between 0 and 255.
-    -- sess        : current session
-    -- batch_size  : the images numpy array is split into batches with batch size
-                     batch_size. A reasonable batch size depends on the available hardware.
-    -- verbose     : If set to True and parameter out_step is given, the number of calculated
-                     batches is reported.
-    Returns:
-    -- mu    : The mean over samples of the activations of the pool_3 layer of
-               the inception model.
-    -- sigma : The covariance matrix of the activations of the pool_3 layer of
-               the inception model.
-    """
-    act = get_activations(images, sess, batch_size, verbose)
-    mu = np.mean(act, axis=0)
-    sigma = np.cov(act, rowvar=False)
-    return mu, sigma
-
-
-# ------------------
-# The following methods are implemented to obtain a batched version of the activations.
-# This has the advantage to reduce memory requirements, at the cost of slightly reduced efficiency.
-# - Pyrestone
-# ------------------
-
-
-def load_image_batch(files):
-    """Convenience method for batch-loading images
-    Params:
-    -- files    : list of paths to image files. Images need to have same dimensions for all files.
-    Returns:
-    -- A numpy array of dimensions (num_images,hi, wi, 3) representing the image pixel values.
-    """
-    return np.array([imread(str(fn)).astype(np.float32) for fn in files])
-
-
-def get_activations_from_files(files, sess, batch_size=50, verbose=False):
-    """Calculates the activations of the pool_3 layer for all images.
-    Params:
-    -- files      : list of paths to image files. Images need to have same dimensions for all files.
-    -- sess       : current session
-    -- batch_size : the images numpy array is split into batches with batch size
-                     batch_size. A reasonable batch size depends on the disposable hardware.
-    -- verbose    : If set to True and parameter out_step is given, the number of calculated
-                     batches is reported.
-    Returns:
-    -- A numpy array of dimension (num images, 2048) that contains the
-       activations of the given tensor when feeding inception with the query tensor.
-    """
-    inception_layer = _get_inception_layer(sess)
-    d0 = len(files)
-    if batch_size > d0:
-        print("warning: batch size is bigger than the data size. setting batch size to data size")
-        batch_size = d0
-    n_batches = d0 // batch_size
-    n_used_imgs = n_batches * batch_size
-    pred_arr = np.empty((n_used_imgs, 2048))
-    for i in range(n_batches):
-        if verbose:
-            print("\rPropagating batch %d/%d" % (i + 1, n_batches))
-        start = i * batch_size
-        end = start + batch_size
-        batch = load_image_batch(files[start:end])
-        pred = sess.run(inception_layer, {'FID_Inception_Net/ExpandDims:0': batch})
-        pred_arr[start:end] = pred.reshape(batch_size, -1)
-        del batch  # clean up memory
-    if verbose:
-        print(" done")
-    return pred_arr
-
-
-def calculate_activation_statistics_from_files(files, sess, batch_size=50, verbose=False):
-    """Calculation of the statistics used by the FID.
-    Params:
-    -- files      : list of paths to image files. Images need to have same dimensions for all files.
-    -- sess        : current session
-    -- batch_size  : the images numpy array is split into batches with batch size
-                     batch_size. A reasonable batch size depends on the available hardware.
-    -- verbose     : If set to True and parameter out_step is given, the number of calculated
-                     batches is reported.
-    Returns:
-    -- mu    : The mean over samples of the activations of the pool_3 layer of
-               the inception model.
-    -- sigma : The covariance matrix of the activations of the pool_3 layer of
-               the inception model.
-    """
-    act = get_activations_from_files(files, sess, batch_size, verbose)
-    mu = np.mean(act, axis=0)
-    sigma = np.cov(act, rowvar=False)
-    return mu, sigma
-
-
-# -------------------------------------------------------------------------------
-
-
-# -------------------------------------------------------------------------------
-# The following functions aren't needed for calculating the FID
-# they're just here to make this module work as a stand-alone script
-# for calculating FID scores
-# -------------------------------------------------------------------------------
-def check_or_download_inception(inception_path):
-    ''' Checks if the path to the inception file is valid, or downloads
-        the file if it is not present. '''
-    INCEPTION_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
-    if inception_path is None:
-        inception_path = MODEL_DIR
-    inception_path = pathlib.Path(inception_path)
-    model_file = inception_path / 'classify_image_graph_def.pb'
-    if not model_file.exists():
-        print("Downloading Inception model")
-        from urllib import request
-        import tarfile
-        fn, _ = request.urlretrieve(INCEPTION_URL)
-        with tarfile.open(fn, mode='r') as f:
-            f.extract('classify_image_graph_def.pb', str(model_file.parent))
-    return str(model_file)
-
-
-def _handle_path(path, sess, low_profile=False):
-    if path.endswith('.npz'):
-        f = np.load(path)
-        m, s = f['mu'][:], f['sigma'][:]
-        f.close()
-    else:
-        path = pathlib.Path(path)
-        files = []
-        for ext in ('*.png', '*.jpg', '*.jpeg', '.bmp'):
-            files.extend( list(path.glob(ext)) )
-
-        if low_profile:
-            m, s = calculate_activation_statistics_from_files(files, sess)
-        else:
-            # x = np.array([scipy.misc.imresize(imread(str(fn), mode='RGB'), (299, 299), interp='bilinear').astype(np.float32) for fn in files])
-            x = np.array([imread(str(fn)).astype(np.float32) for fn in files])
-            m, s = calculate_activation_statistics(x, sess)
-            del x  # clean up memory
-    return m, s
-
-
-def calculate_fid_given_paths(paths, inception_path, low_profile=False):
-    ''' Calculates the FID of two paths. '''
-    inception_path = check_or_download_inception(inception_path)
-
-    for p in paths:
-        if not os.path.exists(p):
-            raise RuntimeError("Invalid path: %s" % p)
-
-    create_inception_graph(str(inception_path))
-    with tf.Session() as sess:
-        sess.run(tf.global_variables_initializer())
-        m1, s1 = _handle_path(paths[0], sess, low_profile=low_profile)
-        m2, s2 = _handle_path(paths[1], sess, low_profile=low_profile)
-        fid_value = calculate_frechet_distance(m1, s1, m2, s2)
-        return fid_value
-
-
-if __name__ == "__main__":
-    from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
-
-    parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
-    parser.add_argument("path", type=str, nargs=2,
-                        help='Path to the generated images or to .npz statistic files')
-    parser.add_argument("-i", "--inception", type=str, default=None,
-                        help='Path to Inception model (will be downloaded if not provided)')
-    parser.add_argument("--gpu", default="", type=str,
-                        help='GPU to use (leave blank for CPU only)')
-    parser.add_argument("--lowprofile", action="store_true",
-                        help='Keep only one batch of images in memory at a time. This reduces memory footprint, but may decrease speed slightly.')
-    args = parser.parse_args()
-    os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
-    fid_value = calculate_fid_given_paths(args.path, args.inception, low_profile=args.lowprofile)
-    print("FID: ", fid_value)
diff --git a/deepliif/stat/inception_score.py b/deepliif/stat/inception_score.py
deleted file mode 100644
index 586b163..0000000
--- a/deepliif/stat/inception_score.py
+++ /dev/null
@@ -1,100 +0,0 @@
-# calculate inception score with Keras
-import os
-import sys
-from math import floor
-
-import numpy as np
-from numpy import ones
-from numpy import expand_dims
-from numpy import log
-from numpy import mean
-from numpy import std
-from numpy import exp
-try:
-    from tensorflow.keras.applications.inception_v3 import InceptionV3
-    from tensorflow.keras.applications.inception_v3 import preprocess_input
-except:
-    from keras.applications.inception_v3 import InceptionV3
-    from keras.applications.inception_v3 import preprocess_input
-import cv2
-
-
-# assumes images have the shape 299x299x3, pixels in [0,255]
-def calculate_inception_score(images, n_split=10, eps=1E-16):
-    # load inception v3 model
-    model = InceptionV3()
-    # convert from uint8 to float32
-    processed = images.astype('float32')
-    # pre-process raw images for inception v3 model
-    processed = preprocess_input(processed)
-    # predict class probabilities for images
-    yhat = model.predict(processed)
-    # enumerate splits of images/predictions
-    scores = list()
-    n_part = floor(images.shape[0] / n_split)
-    for i in range(n_split):
-        # retrieve p(y|x)
-        ix_start, ix_end = i * n_part, i * n_part + n_part
-        p_yx = yhat[ix_start:ix_end]
-        # calculate p(y)
-        p_y = expand_dims(p_yx.mean(axis=0), 0)
-        # calculate KL divergence using log probabilities
-        kl_d = p_yx * (log(p_yx + eps) - log(p_y + eps))
-        # sum over classes
-        sum_kl_d = kl_d.sum(axis=1)
-        # average over images
-        avg_kl_d = mean(sum_kl_d)
-        # undo the log
-        is_score = exp(avg_kl_d)
-        # store
-        scores.append(is_score)
-    # average across images
-    is_avg, is_std = mean(scores), std(scores)
-    return is_avg, is_std
-
-# assumes images have the shape 299x299x3, pixels in [0,255]
-def calculate_inception_score(images, n_split=10, eps=1E-16):
-    # load inception v3 model
-    model = InceptionV3()
-    # convert from uint8 to float32
-    processed = images.astype('float32')
-    # pre-process raw images for inception v3 model
-    processed = preprocess_input(processed)
-    # predict class probabilities for images
-    yhat = model.predict(processed)
-    # enumerate splits of images/predictions
-    scores = list()
-    n_part = floor(images.shape[0] / n_split)
-    for i in range(n_split):
-        # retrieve p(y|x)
-        ix_start, ix_end = i * n_part, i * n_part + n_part
-        p_yx = yhat[ix_start:ix_end]
-        # calculate p(y)
-        p_y = expand_dims(p_yx.mean(axis=0), 0)
-        # calculate KL divergence using log probabilities
-        kl_d = p_yx * (log(p_yx + eps) - log(p_y + eps))
-        # sum over classes
-        sum_kl_d = kl_d.sum(axis=1)
-        # average over images
-        avg_kl_d = mean(sum_kl_d)
-        # undo the log
-        is_score = exp(avg_kl_d)
-        # store
-        scores.append(is_score)
-    # average across images
-    is_avg, is_std = mean(scores), std(scores)
-    return is_avg, is_std
-
-if __name__ == '__main__':
-    # input_dir = 'D:/Pathology/DMIT/datasets/IHC2DAPI/testB'
-    input_dir = sys.argv[1]
-    print(input_dir)
-    images = os.listdir(input_dir)
-    real_images_array = []
-    for img in images:
-        image = cv2.imread(os.path.join(input_dir, img))
-        image = cv2.resize(image, (299, 299))
-        real_images_array.append(image)
-    real_images_array = np.array(real_images_array)
-    is_avg, is_std = calculate_inception_score(real_images_array)
-    print('score', is_avg, is_std)
\ No newline at end of file
diff --git a/deepliif/stat/swd.py b/deepliif/stat/swd.py
deleted file mode 100644
index bd6a8b8..0000000
--- a/deepliif/stat/swd.py
+++ /dev/null
@@ -1,158 +0,0 @@
-from PIL import Image
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torchvision
-
-# Gaussian blur kernel
-def get_gaussian_kernel(device="cpu"):
-    kernel = np.array([
-        [1, 4, 6, 4, 1],
-        [4, 16, 24, 16, 4],
-        [6, 24, 36, 24, 6],
-        [4, 16, 24, 16, 4],
-        [1, 4, 6, 4, 1]], np.float32) / 256.0
-    gaussian_k = torch.as_tensor(kernel.reshape(1, 1, 5, 5)).to(device)
-    return gaussian_k
-
-def pyramid_down(image, device="cpu"):
-    gaussian_k = get_gaussian_kernel(device=device)        
-    # channel-wise conv(important)
-    multiband = [F.conv2d(image[:, i:i + 1,:,:], gaussian_k, padding=2, stride=2) for i in range(3)]
-    down_image = torch.cat(multiband, dim=1)
-    return down_image
-
-def pyramid_up(image, device="cpu"):
-    gaussian_k = get_gaussian_kernel(device=device)
-    upsample = F.interpolate(image, scale_factor=2)
-    multiband = [F.conv2d(upsample[:, i:i + 1,:,:], gaussian_k, padding=2) for i in range(3)]
-    up_image = torch.cat(multiband, dim=1)
-    return up_image
-
-def gaussian_pyramid(original, n_pyramids, device="cpu"):
-    x = original
-    # pyramid down
-    pyramids = [original]
-    for i in range(n_pyramids):
-        x = pyramid_down(x, device=device)
-        pyramids.append(x)
-    return pyramids
-
-def laplacian_pyramid(original, n_pyramids, device="cpu"):
-    # create gaussian pyramid
-    pyramids = gaussian_pyramid(original, n_pyramids, device=device)
-
-    # pyramid up - diff
-    laplacian = []
-    for i in range(len(pyramids) - 1):
-        diff = pyramids[i] - pyramid_up(pyramids[i + 1], device=device)
-        laplacian.append(diff)
-    # Add last gaussian pyramid
-    laplacian.append(pyramids[len(pyramids) - 1])        
-    return laplacian
-
-def minibatch_laplacian_pyramid(image, n_pyramids, batch_size, device="cpu"):
-    n = image.size(0) // batch_size + np.sign(image.size(0) % batch_size)
-    pyramids = []
-    for i in range(n):
-        x = image[i * batch_size:(i + 1) * batch_size]
-        p = laplacian_pyramid(x.to(device), n_pyramids, device=device)
-        p = [x.cpu() for x in p]
-        pyramids.append(p)
-    del x
-    result = []
-    for i in range(n_pyramids + 1):
-        x = []
-        for j in range(n):
-            x.append(pyramids[j][i])
-        result.append(torch.cat(x, dim=0))
-    return result
-
-def extract_patches(pyramid_layer, slice_indices,
-                    slice_size=7, unfold_batch_size=128, device="cpu"):
-    assert pyramid_layer.ndim == 4
-    n = pyramid_layer.size(0) // unfold_batch_size + np.sign(pyramid_layer.size(0) % unfold_batch_size)
-    # random slice 7x7
-    p_slice = []
-    for i in range(n):
-        # [unfold_batch_size, ch, n_slices, slice_size, slice_size]
-        ind_start = i * unfold_batch_size
-        ind_end = min((i + 1) * unfold_batch_size, pyramid_layer.size(0))
-        x = pyramid_layer[ind_start:ind_end].unfold(
-                2, slice_size, 1).unfold(3, slice_size, 1).reshape(
-                ind_end - ind_start, pyramid_layer.size(1), -1, slice_size, slice_size)
-        # [unfold_batch_size, ch, n_descriptors, slice_size, slice_size]
-        x = x[:,:, slice_indices,:,:]
-        # [unfold_batch_size, n_descriptors, ch, slice_size, slice_size]
-        p_slice.append(x.permute([0, 2, 1, 3, 4]))
-    # sliced tensor per layer [batch, n_descriptors, ch, slice_size, slice_size]
-    x = torch.cat(p_slice, dim=0)
-    # normalize along ch
-    std, mean = torch.std_mean(x, dim=(0, 1, 3, 4), keepdim=True)
-    x = (x - mean) / (std + 1e-8)
-    # reshape to 2rank
-    x = x.reshape(-1, 3 * slice_size * slice_size)
-    return x
-        
-def swd(image1, image2, 
-        n_pyramids=None, slice_size=7, n_descriptors=128,
-        n_repeat_projection=128, proj_per_repeat=4, device="cpu", return_by_resolution=False,
-        pyramid_batchsize=128):
-    # n_repeat_projectton * proj_per_repeat = 512
-    # Please change these values according to memory usage.
-    # original = n_repeat_projection=4, proj_per_repeat=128    
-    assert image1.size() == image2.size()
-    assert image1.ndim == 4 and image2.ndim == 4
-
-    if n_pyramids is None:
-        n_pyramids = int(np.rint(np.log2(image1.size(2) // 16)))
-    with torch.no_grad():
-        # minibatch laplacian pyramid for cuda memory reasons
-        pyramid1 = minibatch_laplacian_pyramid(image1, n_pyramids, pyramid_batchsize, device=device)
-        pyramid2 = minibatch_laplacian_pyramid(image2, n_pyramids, pyramid_batchsize, device=device)
-        result = []
-
-        for i_pyramid in range(n_pyramids + 1):
-            # indices
-            n = (pyramid1[i_pyramid].size(2) - 6) * (pyramid1[i_pyramid].size(3) - 6)
-            indices = torch.randperm(n)[:n_descriptors]
-
-            # extract patches on CPU
-            # patch : 2rank (n_image*n_descriptors, slice_size**2*ch)
-            p1 = extract_patches(pyramid1[i_pyramid], indices,
-                            slice_size=slice_size, device="cpu")
-            p2 = extract_patches(pyramid2[i_pyramid], indices,
-                            slice_size=slice_size, device="cpu")
-
-            p1, p2 = p1.to(device), p2.to(device)
-
-            distances = []
-            for j in range(n_repeat_projection):
-                # random
-                rand = torch.randn(p1.size(1), proj_per_repeat).to(device)  # (slice_size**2*ch)
-                rand = rand / torch.std(rand, dim=0, keepdim=True)  # noramlize
-                # projection
-                proj1 = torch.matmul(p1, rand)
-                proj2 = torch.matmul(p2, rand)
-                proj1, _ = torch.sort(proj1, dim=0)
-                proj2, _ = torch.sort(proj2, dim=0)
-                d = torch.abs(proj1 - proj2)
-                distances.append(torch.mean(d))
-
-            # swd
-            result.append(torch.mean(torch.stack(distances)))
-        
-        # average over resolution
-        result = torch.stack(result) * 1e3
-        if return_by_resolution:
-            return result.cpu()
-        else:
-            return torch.mean(result).cpu()
-
-
-def compute_swd(orig_images, mask_images, device):
-    orig_images = torch.as_tensor(orig_images.transpose([0, 3, 1, 2]).astype(np.float32) / 255.0)
-    mask_images = torch.as_tensor(mask_images.transpose([0, 3, 1, 2]).astype(np.float32) / 255.0)
-
-    out = swd(orig_images, mask_images, device=device)  # Fast estimation if device="cuda"
-    return out
\ No newline at end of file

From e43728345d7fdca3d79b5fc7d3cbbea9ee233f58 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 7 Aug 2024 19:21:26 +0000
Subject: [PATCH 23/41] removed schedule-free optimizers; allowed --optimizer
 to use any optimizer class supported in torch.optim

---
 cli.py                               |   5 +-
 deepliif/models/DeepLIIFExt_model.py |  12 +-
 deepliif/models/DeepLIIF_model.py    |  13 +-
 deepliif/models/SDG_model.py         |  12 +-
 deepliif/models/base_model.py        |   6 +-
 deepliif/models/networks.py          |  18 ++-
 deepliif/util/adamw_schedulefree.py  | 208 ---------------------------
 7 files changed, 46 insertions(+), 228 deletions(-)
 delete mode 100644 deepliif/util/adamw_schedulefree.py

diff --git a/cli.py b/cli.py
index 9d7406b..f502ed2 100644
--- a/cli.py
+++ b/cli.py
@@ -130,7 +130,7 @@ def cli():
 @click.option('--n-epochs-decay', type=int, default=100,
               help='number of epochs to linearly decay learning rate to zero')
 @click.option('--optimizer', type=str, default='adam',
-              help='optimizer to use, applied to both generators and discriminators [adam | adamw-schedulefree]')
+              help='optimizer from torch.optim to use, applied to both generators and discriminators [adam | sgd | adamw | ...]; the current parameters however are set up for adam, so other optimziers may encounter issue')
 @click.option('--beta1', default=0.5, help='momentum term of adam')
 @click.option('--lr', default=0.0002, help='initial learning rate for adam')
 @click.option('--lr-policy', default='linear',
@@ -211,7 +211,8 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
         seg_no = 0
         seg_gen = False
     
-    assert optimizer in ['adam', 'adamw-schedulefree'], f'optimizer {optimizer} is not implemented'
+    if optimizer != 'adam':
+        print(f'Optimizer torch.optim.{optimizer} is not tested. Be careful about the parameters of the optimizer.')
     
     d_params = locals()
 
diff --git a/deepliif/models/DeepLIIFExt_model.py b/deepliif/models/DeepLIIFExt_model.py
index de7b0e6..c967ca7 100644
--- a/deepliif/models/DeepLIIFExt_model.py
+++ b/deepliif/models/DeepLIIFExt_model.py
@@ -125,7 +125,11 @@ def __init__(self, opt):
             for i in range(len(self.netGS)):
                 if self.netGS[i]:
                     params += list(self.netGS[i].parameters())
-            self.optimizer_G = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+            try:
+                self.optimizer_G = get_optimizer(opt.optimizer)(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+            except:
+                print(f'lr and betas are not used for optimizer torch.optim.{opt.optimizer} in generators')
+                self.optimizer_G = get_optimizer(opt.optimizer)(params)
 
             params = []
             for i in range(len(self.netD)):
@@ -133,7 +137,11 @@ def __init__(self, opt):
             for i in range(len(self.netDS)):
                 if self.netDS[i]:
                     params += list(self.netDS[i].parameters())
-            self.optimizer_D = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+            try:
+                self.optimizer_D = get_optimizer(opt.optimizer)(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+            except:
+                print(f'lr and betas are not used for optimizer torch.optim.{opt.optimizer} in discriminators')
+                self.optimizer_D = get_optimizer(opt.optimizer)(params)
 
             self.optimizers.append(self.optimizer_G)
             self.optimizers.append(self.optimizer_D)
diff --git a/deepliif/models/DeepLIIF_model.py b/deepliif/models/DeepLIIF_model.py
index ea5226d..14b6522 100644
--- a/deepliif/models/DeepLIIF_model.py
+++ b/deepliif/models/DeepLIIF_model.py
@@ -54,7 +54,6 @@ def __init__(self, opt):
                 self.model_names.extend(['G5' + str(i)])
 
         # define networks (both generator and discriminator)
-        print(opt.netG)
         if isinstance(opt.netG, str):
             opt.netG = [opt.netG] * 4
         if isinstance(opt.net_gs, str):
@@ -111,10 +110,18 @@ def __init__(self, opt):
 
             # initialize optimizers; schedulers will be automatically created by function <BaseModel.setup>.
             params = list(self.netG1.parameters()) + list(self.netG2.parameters()) + list(self.netG3.parameters()) + list(self.netG4.parameters()) + list(self.netG51.parameters()) + list(self.netG52.parameters()) + list(self.netG53.parameters()) + list(self.netG54.parameters()) + list(self.netG55.parameters())
-            self.optimizer_G = get_optimizer(opt.optimizer)(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+            try:
+                self.optimizer_G = get_optimizer(opt.optimizer)(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+            except:
+                print(f'lr and betas are not used for optimizer torch.optim.{opt.optimizer} in generators')
+                self.optimizer_G = get_optimizer(opt.optimizer)(params)
 
             params = list(self.netD1.parameters()) + list(self.netD2.parameters()) + list(self.netD3.parameters()) + list(self.netD4.parameters()) + list(self.netD51.parameters()) + list(self.netD52.parameters()) + list(self.netD53.parameters()) + list(self.netD54.parameters()) + list(self.netD55.parameters())
-            self.optimizer_D = get_optimizer(opt.optimizer)(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+            try:
+                self.optimizer_D = get_optimizer(opt.optimizer)(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+            except:
+                print(f'lr and betas are not used for optimizer torch.optim.{opt.optimizer} in discriminators')
+                self.optimizer_D = get_optimizer(opt.optimizer)(params)
 
             self.optimizers.append(self.optimizer_G)
             self.optimizers.append(self.optimizer_D)
diff --git a/deepliif/models/SDG_model.py b/deepliif/models/SDG_model.py
index f633a86..797200a 100644
--- a/deepliif/models/SDG_model.py
+++ b/deepliif/models/SDG_model.py
@@ -91,12 +91,20 @@ def __init__(self, opt):
             params = []
             for i in range(len(self.netG)):
                 params += list(self.netG[i].parameters())
-            self.optimizer_G = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+            try:
+                self.optimizer_G = get_optimizer(opt.optimizer)(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+            except:
+                print(f'lr and betas are not used for optimizer torch.optim.{opt.optimizer} in generators')
+                self.optimizer_G = get_optimizer(opt.optimizer)(params)
 
             params = []
             for i in range(len(self.netD)):
                 params += list(self.netD[i].parameters())
-            self.optimizer_D = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+            try:
+                self.optimizer_D = get_optimizer(opt.optimizer)(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+            except:
+                print(f'lr and betas are not used for optimizer torch.optim.{opt.optimizer} in discriminators')
+                self.optimizer_D = get_optimizer(opt.optimizer)(params)
 
             self.optimizers.append(self.optimizer_G)
             self.optimizers.append(self.optimizer_D)
diff --git a/deepliif/models/base_model.py b/deepliif/models/base_model.py
index 37c5295..76fd9d2 100644
--- a/deepliif/models/base_model.py
+++ b/deepliif/models/base_model.py
@@ -6,7 +6,6 @@
 from ..util import disable_batchnorm_tracking_stats, enable_batchnorm_tracking_stats
 from deepliif.util import *
 import itertools
-from ..util.adamw_schedulefree import AdamWScheduleFree
 
 
 class BaseModel(ABC):
@@ -83,10 +82,7 @@ def setup(self, opt):
             opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
         """
         if self.is_train:
-            if isinstance(self.optimizers[0],AdamWScheduleFree):
-                self.schedulers = self.optimizers # schedulers are not needed in schedule-free optimizers
-            else:
-                self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers]
+            self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers]
         if not self.is_train or opt.continue_train:
             load_suffix = 'iter_%d' % opt.load_iter if opt.load_iter > 0 else opt.epoch
             self.load_networks(load_suffix)
diff --git a/deepliif/models/networks.py b/deepliif/models/networks.py
index e61bdef..28c152a 100644
--- a/deepliif/models/networks.py
+++ b/deepliif/models/networks.py
@@ -2,8 +2,7 @@
 import torch.nn as nn
 from torch.nn import init
 import functools
-from torch.optim import lr_scheduler, Adam 
-from ..util.adamw_schedulefree import AdamWScheduleFree
+from torch.optim import lr_scheduler
 
 import os
 
@@ -14,6 +13,10 @@
 ###############################################################################
 from deepliif.util import util
 
+# as of pytorch 2.4, all optimizers start with an uppercase letter
+OPTIMIZER_MAPPING = {optimizer_name.lower():optimizer_name for optimizer_name in dir(torch.optim) if optimizer_name[0].isupper()} 
+
+
 class Identity(nn.Module):
     def forward(self, x):
         return x
@@ -39,10 +42,13 @@ def norm_layer(x): return Identity()
     return norm_layer
 
 def get_optimizer(optimizer_name):
-    if optimizer_name == 'adam':
-        return Adam
-    elif optimizer_name == 'adamw-schedulefree':
-        return AdamWScheduleFree
+    try:
+        return getattr(torch.optim, optimizer_name)
+    except:
+        try:
+            return getattr(torch.optim, OPTIMIZER_MAPPING[optimizer_name])
+        except:
+            raise NotImplementedError('optimizer [%s] is not found' % optimizer_name)
 
 def get_scheduler(optimizer, opt):
     """Return a learning rate scheduler
diff --git a/deepliif/util/adamw_schedulefree.py b/deepliif/util/adamw_schedulefree.py
deleted file mode 100644
index fc5915b..0000000
--- a/deepliif/util/adamw_schedulefree.py
+++ /dev/null
@@ -1,208 +0,0 @@
-# Schedule-Free Optimizer: AdamW
-# https://github.com/facebookresearch/schedule_free/blob/main/schedulefree/adamw_schedulefree.py
-
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-# 
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-from typing import Tuple, Union, Optional, Iterable, Dict, Any
-from typing_extensions import TypeAlias
-import torch
-import torch.optim
-try:
-    from torch.optim.optimizer import ParamsT
-except ImportError:
-    ParamsT : TypeAlias = Union[Iterable[torch.Tensor], Iterable[Dict[str, Any]]]
-import math
-
-class AdamWScheduleFree(torch.optim.Optimizer):
-    r"""
-    Schedule-Free AdamW
-    As the name suggests, no scheduler is needed with this optimizer. 
-    To add warmup, rather than using a learning rate schedule you can just
-    set the warmup_steps parameter.
-    
-    This optimizer requires that .train() and .eval() be called before the
-    beginning of training and evaluation respectively. The optimizer should
-    also be placed in eval mode when saving checkpoints.
-    
-    Arguments:
-        params (iterable): 
-            Iterable of parameters to optimize or dicts defining 
-            parameter groups.
-        lr (float): 
-            Learning rate parameter (default 0.0025)
-        betas (Tuple[float, float], optional): coefficients used for computing
-            running averages of gradient and its square (default: (0.9, 0.999)).
-        eps (float): 
-            Term added to the denominator outside of the root operation to 
-            improve numerical stability. (default: 1e-8).
-        weight_decay (float): 
-            Weight decay, i.e. a L2 penalty (default: 0).
-        warmup_steps (int): Enables a linear learning rate warmup (default 0).
-        r (float): Use polynomial weighting in the average 
-            with power r (default 0).
-        weight_lr_power (float): During warmup, the weights in the average will
-            be equal to lr raised to this power. Set to 0 for no weighting
-            (default 2.0).
-        foreach (bool): Use a foreach-backed implementation of the optimizer.
-            Should be significantly faster, but will have higher peak memory
-            usage (default True if supported in your PyTorch version).
-    """
-    def __init__(self,
-                 params: ParamsT,
-                 lr: Union[float, torch.Tensor] = 0.0025,
-                 betas: Tuple[float, float] = (0.9, 0.999),
-                 eps: float = 1e-8,
-                 weight_decay: float = 0,
-                 warmup_steps: int = 0,
-                 r: float = 0.0,
-                 weight_lr_power: float = 2.0,
-                 foreach: Optional[bool] = hasattr(torch, "_foreach_mul_")
-                 ):
-
-        defaults = dict(lr=lr, 
-                        betas=betas, 
-                        eps=eps,
-                        r=r,
-                        k=0,
-                        warmup_steps=warmup_steps,
-                        train_mode=True,
-                        weight_sum=0.0,
-                        lr_max=-1.0,
-                        weight_lr_power=weight_lr_power,
-                        weight_decay=weight_decay,
-                        foreach=foreach)
-        super().__init__(params, defaults)
-    
-    def eval(self):
-        for group in self.param_groups:
-            train_mode = group['train_mode']
-            beta1, _ = group['betas']
-            if train_mode:
-                for p in group['params']:
-                    state = self.state[p]
-                    if 'z' in state:
-                        # Set p.data to x
-                        p.data.lerp_(end=state['z'], weight=1-1/beta1)
-                group['train_mode'] = False
-
-    def train(self):
-        for group in self.param_groups:
-            train_mode = group['train_mode']
-            beta1, _ = group['betas']
-            if not train_mode:
-                for p in group['params']:
-                    state = self.state[p]
-                    if 'z' in state:
-                        # Set p.data to y
-                        p.data.lerp_(end=state['z'], weight=1-beta1)
-                group['train_mode'] = True
-
-    def step(self, closure=None):
-        """Performs a single optimization step.
-
-        Arguments:
-            closure (callable, optional): A closure that reevaluates the model
-                and returns the loss.
-        """
-
-        loss = None
-        if closure is not None:
-            loss = closure()
-        
-        for group in self.param_groups:
-            eps = group['eps']
-            beta1, beta2 = group['betas']
-            decay = group['weight_decay']
-            k = group['k']
-            r = group['r']
-            warmup_steps = group['warmup_steps']
-            weight_lr_power = group['weight_lr_power']
-            
-            if k < warmup_steps:
-              sched = (k+1) / warmup_steps
-            else:
-              sched = 1.0
-            
-            bias_correction2 = 1 - beta2 ** (k+1)
-            lr = group['lr']*sched*math.sqrt(bias_correction2)
-            
-            lr_max = group['lr_max'] = max(lr, group['lr_max'])
-            
-            weight = ((k+1)**r) * (lr_max**weight_lr_power)
-            weight_sum = group['weight_sum'] = group['weight_sum'] + weight
-
-            try:
-                ckp1 = weight/weight_sum
-            except ZeroDivisionError:
-                ckp1 = 0
-
-            if not group['train_mode']:
-                raise Exception("Not in train mode!")
-
-            active_p = [p for p in group['params'] if p.grad is not None]
-            
-            for p in active_p:
-                if 'z' not in self.state[p]:
-                    self.state[p]['z'] = torch.clone(p.data)
-                    self.state[p]['exp_avg_sq'] = torch.zeros_like(p.data)
-
-            if group['foreach'] and len(active_p) > 0:
-                y, grad, exp_avg_sq, z = zip(*[(p.data, 
-                                                p.grad, 
-                                                self.state[p]['exp_avg_sq'], 
-                                                self.state[p]['z']) 
-                                                for p in active_p])
-
-                # Decay the first and second moment running average coefficient
-                torch._foreach_mul_(exp_avg_sq, beta2)
-                torch._foreach_addcmul_(exp_avg_sq, grad, grad, value=1-beta2)
-                denom = torch._foreach_sqrt(exp_avg_sq)
-                torch._foreach_add_(denom, eps)
-
-                # Normalize grad in-place for memory efficiency
-                torch._foreach_div_(grad, denom)
-
-                # Weight decay calculated at y
-                if decay != 0:
-                    torch._foreach_add_(grad, y, alpha=decay)
-
-                # These operations update y in-place,
-                # without computing x explicitly.
-                torch._foreach_lerp_(y, z, weight=ckp1)
-                torch._foreach_add_(y, grad, alpha=lr*(beta1*(1-ckp1)-1))
-
-                # z step
-                torch._foreach_sub_(z, grad, alpha=lr)
-            else:
-                for p in active_p:
-                    y = p.data # Notation to match theory
-                    grad = p.grad.data
-
-                    state = self.state[p]
-
-                    z = state['z']
-                    exp_avg_sq = state['exp_avg_sq']
-
-                    exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1-beta2)
-                    denom = exp_avg_sq.sqrt().add_(eps)
-
-                    # Reuse grad buffer for memory efficiency
-                    grad_normalized = grad.div_(denom)
-
-                    # Weight decay calculated at y
-                    if decay != 0:
-                        grad_normalized.add_(y, alpha=decay)
-
-                    # These operations update y in-place,
-                    # without computing x explicitly.
-                    y.lerp_(end=z, weight=ckp1)
-                    y.add_(grad_normalized, alpha=lr*(beta1*(1-ckp1)-1))
-
-                    # z step
-                    z.sub_(grad_normalized, alpha=lr)
-
-            group['k'] = k+1
-        return loss

From bcf69bbd353d026672cbd1d7b667c08883948b87 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 7 Aug 2024 19:21:54 +0000
Subject: [PATCH 24/41] resolved merge conflict

---
 deepliif/models/__init__.py | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/deepliif/models/__init__.py b/deepliif/models/__init__.py
index 76343cd..c92bdce 100644
--- a/deepliif/models/__init__.py
+++ b/deepliif/models/__init__.py
@@ -382,13 +382,8 @@ def get_net_tiles(n):
     return images
 
 
-<<<<<<< HEAD
 def inference_old2(img, tile_size, overlap_size, model_path, use_torchserve=False, eager_mode=False,
                    color_dapi=False, color_marker=False, opt=None):
-=======
-def inference(img, tile_size, overlap_size, model_path, use_torchserve=False, eager_mode=False,
-              color_dapi=False, color_marker=False, opt=None, return_seg_intermediate=False):
->>>>>>> main-update-training
     if not opt:
         opt = get_opt(model_path)
         #print_options(opt)

From da6ebc833d931993c4d1f1cebfa523c462a08da7 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Thu, 8 Aug 2024 14:04:18 +0000
Subject: [PATCH 25/41] added code lines missed during merge

---
 deepliif/models/__init__.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/deepliif/models/__init__.py b/deepliif/models/__init__.py
index c92bdce..ffddf10 100644
--- a/deepliif/models/__init__.py
+++ b/deepliif/models/__init__.py
@@ -175,7 +175,6 @@ def init_nets(model_dir, eager_mode=False, opt=None, phase='test'):
 
     number_of_gpus_all = torch.cuda.device_count()
     number_of_gpus = len(opt.gpu_ids)
-    #print(number_of_gpus)
 
     if number_of_gpus > 0:
         mapping_gpu_ids = {i:idx for i,idx in enumerate(opt.gpu_ids)}
@@ -510,7 +509,8 @@ def get_net_tiles(n):
 
 
 def inference(img, tile_size, overlap_size, model_path, use_torchserve=False,
-              eager_mode=False, color_dapi=False, color_marker=False, opt=None):
+              eager_mode=False, color_dapi=False, color_marker=False, opt=None,
+              return_seg_intermediate=False):
     if not opt:
         opt = get_opt(model_path)
         #print_options(opt)
@@ -539,6 +539,14 @@ def inference(img, tile_size, overlap_size, model_path, use_torchserve=False,
             'Marker': results['G4'],
             'Seg': results['G5'],
         }
+        
+        if return_seg_intermediate:
+            images({'IHC_s':results['G51'],
+                    'Hema_s':results['G52'],
+                    'DAPI_s':results['G53'],
+                    'Lap2_s':results['G54'],
+                    'Marker_s':results['G55'],})
+        
         if color_dapi:
             matrix = (       0,        0,        0, 0,
                       299/1000, 587/1000, 114/1000, 0,

From 661bbb263f28e9c657ccb73ce67610b193eb515f Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Thu, 8 Aug 2024 14:07:24 +0000
Subject: [PATCH 26/41] changed netg and netgs configuration from list to tuple
 to avoid parsing issue; moved val data loading to with_val condition

---
 cli.py                       | 18 ++++++++++--------
 deepliif/options/__init__.py |  3 +--
 2 files changed, 11 insertions(+), 10 deletions(-)

diff --git a/cli.py b/cli.py
index f502ed2..635759e 100644
--- a/cli.py
+++ b/cli.py
@@ -262,13 +262,13 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
     d_params['gpu_ids'] = gpu_ids
     
     # update generator arch
-    net_g = net_g.split(',')
+    net_g = tuple(net_g.split(',')) # avoid using list which will cause problem in option parsing later on
     assert len(net_g) in [1,modalities_no], f'net_g should contain either 1 architecture for all translation generators or the same number of architectures as the number of translation generators ({modalities_no})'
     if len(net_g) == 1:
       net_g = net_g*modalities_no
       
     
-    net_gs = net_gs.split(',')
+    net_gs = tuple(net_gs.split(','))
     assert len(net_gs) in [1,seg_no], f'net_gs should contain either 1 architecture for all segmentation generators or the same number of architectures as the number of segmentation generators ({seg_no})'
     if len(net_gs) == 1:
       net_gs = net_gs*seg_no
@@ -284,14 +284,16 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
     
     # set dir for train and val
     dataset = create_dataset(opt)
-    dataset_val = create_dataset(opt,phase='val')
-    data_val = [batch for batch in dataset_val]
-    metrics_val = json.load(open(os.path.join(dataset_val.dataset.dir_AB,'metrics.json')))
 
     # get the number of images in the dataset.
     click.echo('The number of training images = %d' % len(dataset))
-    click.echo('The number of validation images = %d' % len(dataset_val))
-    click.echo('The number of validation images = %d' % len(data_val))
+    
+    if with_val:
+        dataset_val = create_dataset(opt,phase='val')
+        data_val = [batch for batch in dataset_val]
+        metrics_val = json.load(open(os.path.join(dataset_val.dataset.dir_AB,'metrics.json')))
+        click.echo('The number of validation images = %d' % len(dataset_val))
+        click.echo('The number of validation images = %d' % len(data_val))
 
     # create a model given model and other options
     model = create_model(opt)
@@ -638,7 +640,7 @@ def serialize(model_dir, output_dir, device, epoch, verbose):
     if device == 'gpu':
         opt.gpu_ids = (0,) # use gpu 0, in case training was done on larger machines
     else:
-        opt.gpu_ids = (-1,) # use cpu
+        opt.gpu_ids = () # use cpu
     
     print_options(opt)
     sample = transform(Image.new('RGB', (opt.scale_size, opt.scale_size)))
diff --git a/deepliif/options/__init__.py b/deepliif/options/__init__.py
index 4382040..f37a40d 100644
--- a/deepliif/options/__init__.py
+++ b/deepliif/options/__init__.py
@@ -11,8 +11,7 @@ def read_model_params(file_addr):
     for line in lines:
         if ':' in line:
             key = line.split(':')[0].strip()
-            val = line.split(':')[1].strip()
-            #val = line.split(':')[1].split('[')[0].strip()
+            val = line.split(':')[1].split('[')[0].strip()
             try:
                 param_dict[key] = eval(val)
                 #print(f'value of {key} is converted to {type(param_dict[key]).__name__}')

From cbf8c818fb8e92630e39f6c8f70c9a114db455ff Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Tue, 13 Aug 2024 15:59:37 +0000
Subject: [PATCH 27/41] changed to use list in config to standardize

---
 cli.py                       |  8 ++++----
 deepliif/options/__init__.py | 14 +++++++++++++-
 2 files changed, 17 insertions(+), 5 deletions(-)

diff --git a/cli.py b/cli.py
index 635759e..65c79b0 100644
--- a/cli.py
+++ b/cli.py
@@ -262,13 +262,13 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
     d_params['gpu_ids'] = gpu_ids
     
     # update generator arch
-    net_g = tuple(net_g.split(',')) # avoid using list which will cause problem in option parsing later on
+    net_g = net_g.split(',')
     assert len(net_g) in [1,modalities_no], f'net_g should contain either 1 architecture for all translation generators or the same number of architectures as the number of translation generators ({modalities_no})'
     if len(net_g) == 1:
       net_g = net_g*modalities_no
       
     
-    net_gs = tuple(net_gs.split(','))
+    net_gs = net_gs.split(',')
     assert len(net_gs) in [1,seg_no], f'net_gs should contain either 1 architecture for all segmentation generators or the same number of architectures as the number of segmentation generators ({seg_no})'
     if len(net_gs) == 1:
       net_gs = net_gs*seg_no
@@ -638,9 +638,9 @@ def serialize(model_dir, output_dir, device, epoch, verbose):
     opt = Options(path_file=os.path.join(model_dir,'train_opt.txt'), mode='test')
     opt.epoch = epoch
     if device == 'gpu':
-        opt.gpu_ids = (0,) # use gpu 0, in case training was done on larger machines
+        opt.gpu_ids = [0] # use gpu 0, in case training was done on larger machines
     else:
-        opt.gpu_ids = () # use cpu
+        opt.gpu_ids = [] # use cpu
     
     print_options(opt)
     sample = transform(Image.new('RGB', (opt.scale_size, opt.scale_size)))
diff --git a/deepliif/options/__init__.py b/deepliif/options/__init__.py
index f37a40d..5a734c0 100644
--- a/deepliif/options/__init__.py
+++ b/deepliif/options/__init__.py
@@ -3,6 +3,7 @@
 from pathlib import Path
 import os
 from ..util.util import mkdirs
+import re
 
 def read_model_params(file_addr):
     with open(file_addr) as f:
@@ -11,7 +12,18 @@ def read_model_params(file_addr):
     for line in lines:
         if ':' in line:
             key = line.split(':')[0].strip()
-            val = line.split(':')[1].split('[')[0].strip()
+            val = ':'.join(line.split(':')[1:])
+            
+            # drop default value
+            str_default = [x for x in re.findall(r"\[.+?\]", val) if x.startswith('[default')]
+            if len(str_default) > 1:
+                raise Exception('train_opt.txt should not contain multiple possible default keys in one line:',str_default)
+            elif len(str_default) == 1:
+                str_default = str_default[0]
+                val = val.replace(str_default,'')
+            val = val.strip()
+            
+            # val = line.split(':')[1].split('[')[0].strip()
             try:
                 param_dict[key] = eval(val)
                 #print(f'value of {key} is converted to {type(param_dict[key]).__name__}')

From 7f4a3cfef8e6822c091a3f5e3b8b6c03532df3c8 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Tue, 13 Aug 2024 16:01:24 +0000
Subject: [PATCH 28/41] allowed SDG to use the same postprocess steps as
 DeepLIIFExt

---
 deepliif/models/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/deepliif/models/__init__.py b/deepliif/models/__init__.py
index ffddf10..8d7a7c3 100644
--- a/deepliif/models/__init__.py
+++ b/deepliif/models/__init__.py
@@ -585,7 +585,7 @@ def postprocess(orig, images, tile_size, model, seg_thresh=150, size_thresh='aut
         processed_images['SegRefined'] = Image.fromarray(refined)
         return processed_images, scoring
 
-    elif model == 'DeepLIIFExt':
+    elif model in ['DeepLIIFExt','SDG']:
         resolution = '40x' if tile_size > 768 else ('20x' if tile_size > 384 else '10x')
         processed_images = {}
         scoring = {}

From fb75f20e5bc89689c4ba825efba56243d2f762e1 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 14 Aug 2024 16:34:28 +0000
Subject: [PATCH 29/41] completed validation implementation for deepliifext and
 sdg, with added calculate_losses method; always use opt.lr in optimizers; set
 cell count metrics in validation only for deepliif

---
 cli.py                               | 37 ++++++++---------
 deepliif/models/DeepLIIFExt_model.py | 59 ++++++++++++++++++----------
 deepliif/models/DeepLIIF_model.py    | 17 +++-----
 deepliif/models/SDG_model.py         | 52 ++++++++++++++++++------
 4 files changed, 102 insertions(+), 63 deletions(-)

diff --git a/cli.py b/cli.py
index 65c79b0..1078f36 100644
--- a/cli.py
+++ b/cli.py
@@ -390,25 +390,26 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
                 l_losses_val += [(k,v) for k,v in losses_val_batch.items()]
                 
                 # calculate cell count metrics
-                l_seg_names = ['fake_B_5']
-                assert l_seg_names[0] in visuals.keys(), f'Cannot find {l_seg_names[0]} in generated image names ({list(visuals.keys())})'
-                seg_mod_suffix = l_seg_names[0].split('_')[-1]
-                l_seg_names += [x for x in visuals.keys() if x.startswith('fake') and x.split('_')[-1].startswith(seg_mod_suffix) and x != l_seg_names[0]]
-                # print(f'Running postprocess for {len(l_seg_names)} generated images ({l_seg_names})')
-    
-                img_name_current = data_val_batch['A_paths'][0].split('/')[-1][:-4] # remove .png
-                metrics_gt = metrics_val[img_name_current]
-                
-                for seg_name in l_seg_names:
-                    images = {'Seg':ToPILImage()((visuals[seg_name][0].cpu()+1)/2),
-                              'Marker':ToPILImage()((visuals['fake_B_4'][0].cpu()+1)/2)}
-                    _, scoring = postprocess(ToPILImage()((data['A'][0]+1)/2), images, opt.scale_size, opt.model)
+                if type(model).__name__ == 'DeepLIIFModel':
+                    l_seg_names = ['fake_B_5']
+                    assert l_seg_names[0] in visuals.keys(), f'Cannot find {l_seg_names[0]} in generated image names ({list(visuals.keys())})'
+                    seg_mod_suffix = l_seg_names[0].split('_')[-1]
+                    l_seg_names += [x for x in visuals.keys() if x.startswith('fake') and x.split('_')[-1].startswith(seg_mod_suffix) and x != l_seg_names[0]]
+                    # print(f'Running postprocess for {len(l_seg_names)} generated images ({l_seg_names})')
+        
+                    img_name_current = data_val_batch['A_paths'][0].split('/')[-1][:-4] # remove .png
+                    metrics_gt = metrics_val[img_name_current]
+                    
+                    for seg_name in l_seg_names:
+                        images = {'Seg':ToPILImage()((visuals[seg_name][0].cpu()+1)/2),
+                                  'Marker':ToPILImage()((visuals['fake_B_4'][0].cpu()+1)/2)}
+                        _, scoring = postprocess(ToPILImage()((data['A'][0]+1)/2), images, opt.scale_size, opt.model)
+                        
+                        for k,v in scoring.items():
+                            if k.startswith('num') or k.startswith('percent'):
+                                # to calculate the rmse, here we calculate (x_pred - x_true) ** 2
+                                l_metrics_val.append((k+'_'+seg_name,(v - metrics_gt[k])**2))
                     
-                    for k,v in scoring.items():
-                        if k.startswith('num') or k.startswith('percent'):
-                            # to calculate the rmse, here we calculate (x_pred - x_true) ** 2
-                            l_metrics_val.append((k+'_'+seg_name,(v - metrics_gt[k])**2))
-                
                 if debug and epoch_iter >= debug_data_size:
                     print(f'debug mode, epoch {epoch} stopped at epoch iter {epoch_iter} (>= {debug_data_size})')
                     break
diff --git a/deepliif/models/DeepLIIFExt_model.py b/deepliif/models/DeepLIIFExt_model.py
index c967ca7..0522f74 100644
--- a/deepliif/models/DeepLIIFExt_model.py
+++ b/deepliif/models/DeepLIIFExt_model.py
@@ -1,6 +1,7 @@
 import torch
 from .base_model import BaseModel
 from . import networks
+from .networks import get_optimizer
 
 
 class DeepLIIFExtModel(BaseModel):
@@ -72,22 +73,19 @@ def __init__(self, opt):
                     self.model_names.extend(['GS_' + str(i)])
 
         # define networks (both generator and discriminator)
+        if isinstance(opt.net_g, str):
+            self.opt.net_g = [self.opt.net_g] * self.mod_gen_no
+        if isinstance(opt.net_gs, str):
+            self.opt.net_gs = [self.opt.net_gs]*self.mod_gen_no
         self.netG = [None for _ in range(self.mod_gen_no)]
         self.netGS = [None for _ in range(self.mod_gen_no)]
         for i in range(self.mod_gen_no):
-            self.netG[i] = networks.define_G(self.opt.input_nc, self.opt.output_nc, self.opt.ngf, self.opt.net_g, self.opt.norm,
+            self.netG[i] = networks.define_G(self.opt.input_nc, self.opt.output_nc, self.opt.ngf, self.opt.net_g[i], self.opt.norm,
                                              not self.opt.no_dropout, self.opt.init_type, self.opt.init_gain, self.gpu_ids, self.opt.padding)
-            print('***************************************')
-            print(self.opt.input_nc, self.opt.output_nc, self.opt.ngf, self.opt.net_g, self.opt.norm,
-                                             not self.opt.no_dropout, self.opt.init_type, self.opt.init_gain, self.gpu_ids, self.opt.padding)
-            print('***************************************')
+
         for i in range(self.mod_gen_no):
             if self.opt.seg_gen:
-                # if i == 0:
-                #     self.netGS[i] = networks.define_G(self.opt.input_nc, self.opt.output_nc, self.opt.ngf, self.opt.net_gs, self.opt.norm,
-                #                                       not self.opt.no_dropout, self.opt.init_type, self.opt.init_gain, self.gpu_ids)
-                # else:
-                self.netGS[i] = networks.define_G(self.opt.input_nc * 3, self.opt.output_nc, self.opt.ngf, self.opt.net_gs, self.opt.norm,
+                self.netGS[i] = networks.define_G(self.opt.input_nc * 3, self.opt.output_nc, self.opt.ngf, self.opt.net_gs[i], self.opt.norm,
                                                   not self.opt.no_dropout, self.opt.init_type, self.opt.init_gain, self.gpu_ids)
 
         if self.is_train:  # define a discriminator; conditional GANs need to take both input and output images; Therefore, #channels for D is input_nc + output_nc
@@ -99,11 +97,6 @@ def __init__(self, opt):
                                                  self.gpu_ids)
             for i in range(self.mod_gen_no):
                 if self.opt.seg_gen:
-                    # if i == 0:
-                    #     self.netDS[i] = networks.define_D(self.opt.input_nc + self.opt.output_nc, self.opt.ndf, self.opt.net_ds,
-                    #                                       self.opt.n_layers_D, self.opt.norm, self.opt.init_type, self.opt.init_gain,
-                    #                                       self.gpu_ids)
-                    # else:
                     self.netDS[i] = networks.define_D(self.opt.input_nc * 3 + self.opt.output_nc, self.opt.ndf, self.opt.net_ds,
                                                       self.opt.n_layers_D, self.opt.norm, self.opt.init_type, self.opt.init_gain,
                                                       self.gpu_ids)
@@ -113,9 +106,7 @@ def __init__(self, opt):
             # define loss functions
             self.criterionGAN_mod = networks.GANLoss(self.opt.gan_mode).to(self.device)
             self.criterionGAN_seg = networks.GANLoss(self.opt.gan_mode_s).to(self.device)
-
             self.criterionSmoothL1 = torch.nn.SmoothL1Loss()
-
             self.criterionVGG = networks.VGGLoss().to(self.device)
 
             # initialize optimizers; schedulers will be automatically created by function <BaseModel.setup>.
@@ -128,8 +119,8 @@ def __init__(self, opt):
             try:
                 self.optimizer_G = get_optimizer(opt.optimizer)(params, lr=opt.lr, betas=(opt.beta1, 0.999))
             except:
-                print(f'lr and betas are not used for optimizer torch.optim.{opt.optimizer} in generators')
-                self.optimizer_G = get_optimizer(opt.optimizer)(params)
+                print(f'betas are not used for optimizer torch.optim.{opt.optimizer} in generators')
+                self.optimizer_G = get_optimizer(opt.optimizer)(params, lr=opt.lr)
 
             params = []
             for i in range(len(self.netD)):
@@ -140,8 +131,8 @@ def __init__(self, opt):
             try:
                 self.optimizer_D = get_optimizer(opt.optimizer)(params, lr=opt.lr, betas=(opt.beta1, 0.999))
             except:
-                print(f'lr and betas are not used for optimizer torch.optim.{opt.optimizer} in discriminators')
-                self.optimizer_D = get_optimizer(opt.optimizer)(params)
+                print(f'betas are not used for optimizer torch.optim.{opt.optimizer} in discriminators')
+                self.optimizer_D = get_optimizer(opt.optimizer)(params, lr=opt.lr)
 
             self.optimizers.append(self.optimizer_G)
             self.optimizers.append(self.optimizer_D)
@@ -303,3 +294,29 @@ def optimize_parameters(self):
         self.optimizer_G.zero_grad()  # set G's gradients to zero
         self.backward_G()  # calculate graidents for G
         self.optimizer_G.step()  # udpate G's weights
+    
+    def calculate_losses(self):
+        """
+        Calculate losses but do not optimize parameters. Used in validation loss calculation during training.
+        """
+        self.forward()  # compute fake images: G(A)
+        # update D
+        for i in range(self.mod_gen_no):
+            self.set_requires_grad(self.netD[i], True)  # enable backprop for D1
+        for i in range(self.mod_gen_no):
+            if self.netDS[i]:
+                self.set_requires_grad(self.netDS[i], True)
+
+        self.optimizer_D.zero_grad()  # set D's gradients to zero
+        self.backward_D()  # calculate gradients for D
+
+        # update G
+        for i in range(self.mod_gen_no):
+            self.set_requires_grad(self.netD[i], False)
+        for i in range(self.mod_gen_no):
+            if self.netDS[i]:
+                self.set_requires_grad(self.netDS[i], False)
+
+        self.optimizer_G.zero_grad()  # set G's gradients to zero
+        self.backward_G()  # calculate graidents for G
+
diff --git a/deepliif/models/DeepLIIF_model.py b/deepliif/models/DeepLIIF_model.py
index 14b6522..adea246 100644
--- a/deepliif/models/DeepLIIF_model.py
+++ b/deepliif/models/DeepLIIF_model.py
@@ -113,15 +113,15 @@ def __init__(self, opt):
             try:
                 self.optimizer_G = get_optimizer(opt.optimizer)(params, lr=opt.lr, betas=(opt.beta1, 0.999))
             except:
-                print(f'lr and betas are not used for optimizer torch.optim.{opt.optimizer} in generators')
-                self.optimizer_G = get_optimizer(opt.optimizer)(params)
+                print(f'betas are not used for optimizer torch.optim.{opt.optimizer} in generators')
+                self.optimizer_G = get_optimizer(opt.optimizer)(params, lr=opt.lr)
 
             params = list(self.netD1.parameters()) + list(self.netD2.parameters()) + list(self.netD3.parameters()) + list(self.netD4.parameters()) + list(self.netD51.parameters()) + list(self.netD52.parameters()) + list(self.netD53.parameters()) + list(self.netD54.parameters()) + list(self.netD55.parameters())
             try:
                 self.optimizer_D = get_optimizer(opt.optimizer)(params, lr=opt.lr, betas=(opt.beta1, 0.999))
             except:
-                print(f'lr and betas are not used for optimizer torch.optim.{opt.optimizer} in discriminators')
-                self.optimizer_D = get_optimizer(opt.optimizer)(params)
+                print(f'betas are not used for optimizer torch.optim.{opt.optimizer} in discriminators')
+                self.optimizer_D = get_optimizer(opt.optimizer)(params, lr=opt.lr)
 
             self.optimizers.append(self.optimizer_G)
             self.optimizers.append(self.optimizer_D)
@@ -344,10 +344,6 @@ def calculate_losses(self):
         """
         Calculate losses but do not optimize parameters. Used in validation loss calculation during training.
         """
-        # for eval/val, schedule free optimizers need to be set to eval mode: https://github.com/facebookresearch/schedule_free/tree/main?tab=readme-ov-file#how-to-use
-        if 'schedulefree' in self.opt.optimizer:
-            self.optimizer_D.eval()
-            self.optimizer_G.eval()
         
         self.forward()                   # compute fake images: G(A)
         # update D
@@ -361,6 +357,7 @@ def calculate_losses(self):
         self.set_requires_grad(self.netD54, True)  # enable backprop for D54
         self.set_requires_grad(self.netD55, True)  # enable backprop for D54
 
+        self.optimizer_D.zero_grad()        # set D's gradients to zero
         self.backward_D()                # calculate gradients for D
 
         # update G
@@ -374,8 +371,6 @@ def calculate_losses(self):
         self.set_requires_grad(self.netD54, False)  # D54 requires no gradients when optimizing G54
         self.set_requires_grad(self.netD55, False)  # D54 requires no gradients when optimizing G54
 
+        self.optimizer_G.zero_grad()        # set G's gradients to zero
         self.backward_G()                   # calculate graidents for G
-        if 'schedulefree' in self.opt.optimizer:
-            self.optimizer_D.train()
-            self.optimizer_G.train()
             
diff --git a/deepliif/models/SDG_model.py b/deepliif/models/SDG_model.py
index 797200a..2906d30 100644
--- a/deepliif/models/SDG_model.py
+++ b/deepliif/models/SDG_model.py
@@ -1,6 +1,7 @@
 import torch
 from .base_model import BaseModel
 from . import networks
+from .networks import get_optimizer
 
 
 class SDGModel(BaseModel):
@@ -37,6 +38,13 @@ def __init__(self, opt):
         self.loss_D_weights = [1 / self.mod_gen_no] * self.mod_gen_no
         self.loss_DS_weights = [1 / self.mod_gen_no] * self.mod_gen_no
 
+        # self.gpu_ids is a possibly modifed one for model initialization
+        # self.opt.gpu_ids is the original one received in the command
+        if not opt.is_train:
+            self.gpu_ids = [] # avoid the models being loaded as DP
+        else:
+            self.gpu_ids = opt.gpu_ids
+
         self.loss_names = []
         self.visual_names = ['real_A']
         # specify the training losses you want to print out. The training/test scripts will call <BaseModel.get_current_losses>
@@ -59,23 +67,22 @@ def __init__(self, opt):
                 self.model_names.extend(['G_' + str(i)])
 
         # define networks (both generator and discriminator)
+        if isinstance(opt.net_g, str):
+            self.opt.net_g = [self.opt.net_g] * self.mod_gen_no
+        if isinstance(opt.net_gs, str):
+            self.opt.net_gs = [self.opt.net_gs]*self.mod_gen_no
         self.netG = [None for _ in range(self.mod_gen_no)]
+        self.netGS = [None for _ in range(self.mod_gen_no)]
         for i in range(self.mod_gen_no):
-            self.netG[i] = networks.define_G(self.opt.input_nc * self.opt.input_no, self.opt.output_nc, self.opt.ngf, self.opt.net_g, self.opt.norm,
-                                             not self.opt.no_dropout, self.opt.init_type, self.opt.init_gain, self.opt.gpu_ids, self.opt.padding, resize_conv=opt.resize_conv)
-            print('***************************************')
-            print(self.opt.input_nc, self.opt.output_nc, self.opt.ngf, self.opt.net_g, self.opt.norm,
-                                             not self.opt.no_dropout, self.opt.init_type, self.opt.init_gain, self.opt.gpu_ids, self.opt.padding)
-            print('***************************************')
-            if i==0:
-                print(self.netG[i])
+            self.netG[i] = networks.define_G(self.opt.input_nc * self.opt.input_no, self.opt.output_nc, self.opt.ngf, self.opt.net_g[i], self.opt.norm,
+                                             not self.opt.no_dropout, self.opt.init_type, self.opt.init_gain, self.gpu_ids, self.opt.padding, resize_conv=opt.resize_conv)
 
         if self.is_train:  # define a discriminator; conditional GANs need to take both input and output images; Therefore, #channels for D is input_nc + output_nc
             self.netD = [None for _ in range(self.mod_gen_no)]
             for i in range(self.mod_gen_no):
                 self.netD[i] = networks.define_D(self.opt.input_nc * self.opt.input_no + self.opt.output_nc, self.opt.ndf, self.opt.net_d,
                                                  self.opt.n_layers_D, self.opt.norm, self.opt.init_type, self.opt.init_gain,
-                                                 self.opt.gpu_ids)
+                                                 self.gpu_ids)
 
         if self.is_train:
             # define loss functions
@@ -94,8 +101,8 @@ def __init__(self, opt):
             try:
                 self.optimizer_G = get_optimizer(opt.optimizer)(params, lr=opt.lr, betas=(opt.beta1, 0.999))
             except:
-                print(f'lr and betas are not used for optimizer torch.optim.{opt.optimizer} in generators')
-                self.optimizer_G = get_optimizer(opt.optimizer)(params)
+                print(f'betas are not used for optimizer torch.optim.{opt.optimizer} in generators')
+                self.optimizer_G = get_optimizer(opt.optimizer)(params, lr=opt.lr)
 
             params = []
             for i in range(len(self.netD)):
@@ -103,8 +110,8 @@ def __init__(self, opt):
             try:
                 self.optimizer_D = get_optimizer(opt.optimizer)(params, lr=opt.lr, betas=(opt.beta1, 0.999))
             except:
-                print(f'lr and betas are not used for optimizer torch.optim.{opt.optimizer} in discriminators')
-                self.optimizer_D = get_optimizer(opt.optimizer)(params)
+                print(f'betas are not used for optimizer torch.optim.{opt.optimizer} in discriminators')
+                self.optimizer_D = get_optimizer(opt.optimizer)(params, lr=opt.lr)
 
             self.optimizers.append(self.optimizer_G)
             self.optimizers.append(self.optimizer_D)
@@ -210,3 +217,22 @@ def optimize_parameters(self):
         self.optimizer_G.zero_grad()  # set G's gradients to zero
         self.backward_G()  # calculate graidents for G
         self.optimizer_G.step()  # udpate G's weights
+    
+    def calculate_losses(self):
+        """
+        Calculate losses but do not optimize parameters. Used in validation loss calculation during training.
+        """
+        self.forward()  # compute fake images: G(A)
+        # update D
+        for i in range(self.mod_gen_no):
+            self.set_requires_grad(self.netD[i], True)  # enable backprop for D1
+
+        self.optimizer_D.zero_grad()  # set D's gradients to zero
+        self.backward_D()  # calculate gradients for D
+
+        # update G
+        for i in range(self.mod_gen_no):
+            self.set_requires_grad(self.netD[i], False)
+
+        self.optimizer_G.zero_grad()  # set G's gradients to zero
+        self.backward_G()  # calculate graidents for G

From 8cc668bb99740f0b2a3e1b443c994a9ebf7e613b Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 14 Aug 2024 16:35:58 +0000
Subject: [PATCH 30/41] added test cases for training

---
 tests/test_cli_train.py | 106 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 106 insertions(+)

diff --git a/tests/test_cli_train.py b/tests/test_cli_train.py
index 66df855..4baac45 100644
--- a/tests/test_cli_train.py
+++ b/tests/test_cli_train.py
@@ -1,3 +1,14 @@
+# Tests for training
+# hardware config: cpu / single gpu / multi gpu
+# tests (cpu, multi gpu):
+#   - basic training
+# tests (sinlge gpu):
+#   - basic training
+#   - optimizer
+#   - net-g
+#   - net-gs
+#   - with-val
+
 import subprocess
 import os
 import torch
@@ -5,6 +16,9 @@
 
 available_gpus = torch.cuda.device_count()
 
+#----------------------
+#---- cpu-based -------
+#----------------------
 def test_cli_train(tmp_path, model_info):
     torch.cuda.nvtx.range_push("test_cli_train")
     dirs_input = model_info['dir_input_train']
@@ -22,6 +36,10 @@ def test_cli_train(tmp_path, model_info):
     torch.cuda.nvtx.range_pop()
 
 
+
+#----------------------
+#---- gpu: single -----
+#----------------------
 def test_cli_train_single_gpu(tmp_path, model_info):
     if available_gpus > 0:
         torch.cuda.nvtx.range_push("test_cli_train_single_gpu")
@@ -42,6 +60,94 @@ def test_cli_train_single_gpu(tmp_path, model_info):
         pytest.skip(f'Detected {available_gpus} (< 1) available GPUs. Skip.')
 
 
+def test_cli_train_single_gpu_optimizer(tmp_path, model_info):
+    if available_gpus > 0:
+        torch.cuda.nvtx.range_push("test_cli_train_single_gpu_optimizer")
+        dirs_input = model_info['dir_input_train']
+        for i in range(len(dirs_input)):
+            torch.cuda.nvtx.range_push(f"test_cli_train_single_gpu {dirs_input[i]}")
+            dir_save = tmp_path
+            
+            fns_input = [f for f in os.listdir(dirs_input[i] + '/train') if os.path.isfile(os.path.join(dirs_input[i] + '/train', f)) and f.endswith('png')]
+            num_input = len(fns_input)
+            assert num_input > 0
+    
+            res = subprocess.run(f'python cli.py train --model {model_info["model"]} --dataroot {dirs_input[i]} --name test_local --modalities-no {model_info["modalities_no"][i]} --seg-gen {model_info["seg_gen"][i]} --batch-size 1 --num-threads 0 --checkpoints-dir {dir_save} --remote True --n-epochs 1 --n-epochs-decay 1 --gpu-ids 0 --optimizer sgd',shell=True)
+            assert res.returncode == 0
+            torch.cuda.nvtx.range_pop()
+        torch.cuda.nvtx.range_pop()
+    else:
+        pytest.skip(f'Detected {available_gpus} (< 1) available GPUs. Skip.')
+
+
+def test_cli_train_single_gpu_netg(tmp_path, model_info):
+    if available_gpus > 0:
+        torch.cuda.nvtx.range_push("test_cli_train_single_gpu_netg")
+        dirs_input = model_info['dir_input_train']
+        for i in range(len(dirs_input)):
+            torch.cuda.nvtx.range_push(f"test_cli_train_single_gpu {dirs_input[i]}")
+            dir_save = tmp_path
+            
+            fns_input = [f for f in os.listdir(dirs_input[i] + '/train') if os.path.isfile(os.path.join(dirs_input[i] + '/train', f)) and f.endswith('png')]
+            num_input = len(fns_input)
+            assert num_input > 0
+    
+            res = subprocess.run(f'python cli.py train --model {model_info["model"]} --dataroot {dirs_input[i]} --name test_local --modalities-no {model_info["modalities_no"][i]} --seg-gen {model_info["seg_gen"][i]} --batch-size 1 --num-threads 0 --checkpoints-dir {dir_save} --remote True --n-epochs 1 --n-epochs-decay 1 --gpu-ids 0 --net-g unet_512',shell=True)
+            assert res.returncode == 0
+            
+            res = subprocess.run(f'python cli.py train --model {model_info["model"]} --dataroot {dirs_input[i]} --name test_local --modalities-no {model_info["modalities_no"][i]} --seg-gen {model_info["seg_gen"][i]} --batch-size 1 --num-threads 0 --checkpoints-dir {dir_save} --remote True --n-epochs 1 --n-epochs-decay 1 --gpu-ids 0 --net-g unet_512_attention',shell=True)
+            assert res.returncode == 0
+            torch.cuda.nvtx.range_pop()
+        torch.cuda.nvtx.range_pop()
+    else:
+        pytest.skip(f'Detected {available_gpus} (< 1) available GPUs. Skip.')
+        
+
+def test_cli_train_single_gpu_netgs(tmp_path, model_info):
+    if available_gpus > 0:
+        torch.cuda.nvtx.range_push("test_cli_train_single_gpu_netgs")
+        dirs_input = model_info['dir_input_train']
+        for i in range(len(dirs_input)):
+            torch.cuda.nvtx.range_push(f"test_cli_train_single_gpu {dirs_input[i]}")
+            dir_save = tmp_path
+            
+            fns_input = [f for f in os.listdir(dirs_input[i] + '/train') if os.path.isfile(os.path.join(dirs_input[i] + '/train', f)) and f.endswith('png')]
+            num_input = len(fns_input)
+            assert num_input > 0
+    
+            res = subprocess.run(f'python cli.py train --model {model_info["model"]} --dataroot {dirs_input[i]} --name test_local --modalities-no {model_info["modalities_no"][i]} --seg-gen {model_info["seg_gen"][i]} --batch-size 1 --num-threads 0 --checkpoints-dir {dir_save} --remote True --n-epochs 1 --n-epochs-decay 1 --gpu-ids 0 --net-gs unet_512',shell=True)
+            assert res.returncode == 0
+            
+            res = subprocess.run(f'python cli.py train --model {model_info["model"]} --dataroot {dirs_input[i]} --name test_local --modalities-no {model_info["modalities_no"][i]} --seg-gen {model_info["seg_gen"][i]} --batch-size 1 --num-threads 0 --checkpoints-dir {dir_save} --remote True --n-epochs 1 --n-epochs-decay 1 --gpu-ids 0 --net-gs unet_512_attention',shell=True)
+            assert res.returncode == 0
+            torch.cuda.nvtx.range_pop()
+        torch.cuda.nvtx.range_pop()
+    else:
+        pytest.skip(f'Detected {available_gpus} (< 1) available GPUs. Skip.')
+
+
+def test_cli_train_single_gpu_withval(tmp_path, model_info):
+    if available_gpus > 0:
+        torch.cuda.nvtx.range_push("test_cli_train_single_gpu_withval")
+        dirs_input = model_info['dir_input_train']
+        for i in range(len(dirs_input)):
+            torch.cuda.nvtx.range_push(f"test_cli_train_single_gpu {dirs_input[i]}")
+            dir_save = tmp_path
+            
+            fns_input = [f for f in os.listdir(dirs_input[i] + '/train') if os.path.isfile(os.path.join(dirs_input[i] + '/train', f)) and f.endswith('png')]
+            num_input = len(fns_input)
+            assert num_input > 0
+    
+            res = subprocess.run(f'python cli.py train --model {model_info["model"]} --dataroot {dirs_input[i]} --name test_local --modalities-no {model_info["modalities_no"][i]} --seg-gen {model_info["seg_gen"][i]} --batch-size 1 --num-threads 0 --checkpoints-dir {dir_save} --remote True --n-epochs 1 --n-epochs-decay 1 --gpu-ids 0 --with-val',shell=True)
+            assert res.returncode == 0
+            torch.cuda.nvtx.range_pop()
+        torch.cuda.nvtx.range_pop()
+    else:
+        pytest.skip(f'Detected {available_gpus} (< 1) available GPUs. Skip.')
+
+#----------------------
+#---- gpu: multi ------
+#----------------------
 def test_cli_train_multi_gpu_dp(tmp_path, model_info):
     if available_gpus > 1:
         torch.cuda.nvtx.range_push("test_cli_train_multi_gpu_dp")

From 5a277ed88ee75753ade22d44f806e6667f2354ac Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 14 Aug 2024 17:09:15 +0000
Subject: [PATCH 31/41] added fake validation set (same as training set :))

---
 Datasets/Sample_Dataset/val/Bladder1.json |  10 ++++++++++
 Datasets/Sample_Dataset/val/Bladder1.png  | Bin 0 -> 1974688 bytes
 Datasets/Sample_Dataset/val/Lung1.json    |  10 ++++++++++
 Datasets/Sample_Dataset/val/Lung1.png     | Bin 0 -> 1749802 bytes
 Datasets/Sample_Dataset/val/metrics.json  |  22 ++++++++++++++++++++++
 5 files changed, 42 insertions(+)
 create mode 100644 Datasets/Sample_Dataset/val/Bladder1.json
 create mode 100644 Datasets/Sample_Dataset/val/Bladder1.png
 create mode 100644 Datasets/Sample_Dataset/val/Lung1.json
 create mode 100644 Datasets/Sample_Dataset/val/Lung1.png
 create mode 100644 Datasets/Sample_Dataset/val/metrics.json

diff --git a/Datasets/Sample_Dataset/val/Bladder1.json b/Datasets/Sample_Dataset/val/Bladder1.json
new file mode 100644
index 0000000..f10b59f
--- /dev/null
+++ b/Datasets/Sample_Dataset/val/Bladder1.json
@@ -0,0 +1,10 @@
+{
+  "num_total": 183,
+  "num_pos": 15,
+  "num_neg": 168,
+  "percent_pos": 8.2,
+  "prob_thresh": 150,
+  "size_thresh": 30,
+  "size_thresh_upper": null,
+  "marker_thresh": null
+}
\ No newline at end of file
diff --git a/Datasets/Sample_Dataset/val/Bladder1.png b/Datasets/Sample_Dataset/val/Bladder1.png
new file mode 100644
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diff --git a/Datasets/Sample_Dataset/val/Lung1.json b/Datasets/Sample_Dataset/val/Lung1.json
new file mode 100644
index 0000000..90fb847
--- /dev/null
+++ b/Datasets/Sample_Dataset/val/Lung1.json
@@ -0,0 +1,10 @@
+{
+  "num_total": 60,
+  "num_pos": 11,
+  "num_neg": 49,
+  "percent_pos": 18.3,
+  "prob_thresh": 150,
+  "size_thresh": 78,
+  "size_thresh_upper": null,
+  "marker_thresh": null
+}
\ No newline at end of file
diff --git a/Datasets/Sample_Dataset/val/Lung1.png b/Datasets/Sample_Dataset/val/Lung1.png
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zca!iVZyO8pug^UDm&Zzo_v62O6z*kB_((ag#t1}B7V#wTxa)lg%Z7l>#hDQvSUKQB
z@n19ya3p>;%L$_ftN5idLDRJW9z1rPEU;Ms9VJ9$CV*OlY9+AXUwmo<tNtRu6Rfs2
zVF(X_(+tpbNfUdp4p0=}J;ArxUqG-4e7gn+!25yJlpMM$AnE`H!~++BbeaIFPBB4v
zEDU@WH-It|<jLHI;3U{#e%uKKp)6DBU@68rKu8O(p5VF4LyoQ<rLXrZC<v6@`y~s&
zU4d(rDNuu52COkxrU2;d23L0^9P4rU3s}g&c{xund`W(=SgC^!^T$IP3d{xhxIRPx
z)ox@sgTou>jOV|l|2%&i+B1Po@eMe$tDtzV!%7H6pYr5Dn=P<+%7YZ-YIq#9s9}(i
zQpW>C;6Pgrcq!1UegvUt$TG+R`Iy;#!r8S8FZ$*60Nf0_*f_zP2^^l_zIA^*hu+9M
z&yG!nd7HM((|o|7&y#a5uu%B#J!SKG+}8Mv3ia@WtOZ2LXx-SNUeIVh$Cu6)pOCV^
RMd5)z%8%6_6)Tv&|35s-+GhX&

literal 0
HcmV?d00001

diff --git a/Datasets/Sample_Dataset/val/metrics.json b/Datasets/Sample_Dataset/val/metrics.json
new file mode 100644
index 0000000..cdc25dd
--- /dev/null
+++ b/Datasets/Sample_Dataset/val/metrics.json
@@ -0,0 +1,22 @@
+{
+  "Lung1": {
+    "num_total": 60,
+    "num_pos": 11,
+    "num_neg": 49,
+    "percent_pos": 18.3,
+    "prob_thresh": 150,
+    "size_thresh": 78,
+    "size_thresh_upper": null,
+    "marker_thresh": null
+  },
+  "Bladder1": {
+    "num_total": 183,
+    "num_pos": 15,
+    "num_neg": 168,
+    "percent_pos": 8.2,
+    "prob_thresh": 150,
+    "size_thresh": 30,
+    "size_thresh_upper": null,
+    "marker_thresh": null
+  }
+}
\ No newline at end of file

From edab58dd0368948d49c0d7ece38b7edf49d76b00 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 14 Aug 2024 17:10:16 +0000
Subject: [PATCH 32/41] added method to calculate the cell count metrics for
 the ground truth data

---
 deepliif/stat/__init__.py | 83 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 83 insertions(+)
 create mode 100644 deepliif/stat/__init__.py

diff --git a/deepliif/stat/__init__.py b/deepliif/stat/__init__.py
new file mode 100644
index 0000000..559db4f
--- /dev/null
+++ b/deepliif/stat/__init__.py
@@ -0,0 +1,83 @@
+import os
+import json
+from PIL import Image
+from ..models import postprocess
+import re
+
+def get_cell_count_metrics(dir_img, dir_save=None, model = None,
+                           tile_size=512, single_tile=False,
+                           use_marker = False,
+                           save_individual = False):
+    """
+    Obtain cell count metrics through postprocess functions.
+    Currenlty implemented only for ground truth tiles.
+    
+    dir_img: directory to load images for calculation
+    dir_save: directory to save the results out
+    model: model type (DeepLIIF, DeepLIIFExt, SDG)
+    tile_size: tile size used for postprocess calculation
+    single_tile: True if the images are single-tile images; use False if the 
+                 images contain a row of multiple tiles like those used in
+                 training or validation
+    use_marker: whether to use the marker image (if Truem, assumes the marker
+                image is the second last tile (single_tile=False) or has a 
+                suffix of "_4.png" (single_tile=True))
+    """
+    dir_save = dir_save if dir_img is None else dir_save
+    
+    if model is None:
+        if 'sdg' in dir_img:
+            model = 'SDG'
+        elif 'ext' in dir_img:
+            model = 'DeepLIIFExt'
+        else:
+            model = 'DeepLIIF'
+    
+    if single_tile:
+        fns = [x for x in os.listdir(dir_img) if x.endswith('_5.png') or x.endswith('_4.png')]
+        fns = list(set([x[:-6] for x in fns])) # fns do not have extention
+    else:
+        fns = [x for x in os.listdir(dir_img) if x.endswith('.png')] # fns have extension
+    
+    d_metrics = {}
+    count = 0
+    for fn in fns:
+        if single_tile:
+            img_gt = Image.open(os.path.join(dir_img,fn+'_5.png'))
+            img_marker = Image.open(os.path.join(dir_img,fn+'_4.png'))
+            img_input = Image.open(os.path.join(dir_img.replace('/gt','/input'),fn+'.png'))
+            k = fn
+        else:
+            img = Image.open(os.path.join(dir_img,fn))
+            w, h = img.size
+            
+            # assume in the row of tiles, the first is the input and the last is the ground truth
+            img_input = img.crop((0,0,h,h))
+            img_gt = img.crop((w-h,0,w,h))
+            img_marker = img.crop((w-h*2,0,w-h,h)) # the second last is marker, if marker is included
+            k = re.sub('\..*?$','',fn) # remove extension
+    
+        images = {'Seg':img_gt}
+        if use_marker:
+            images['Marker'] = img_marker
+        
+        post_images, scoring = postprocess(img_input, images, tile_size, model)
+        d_metrics[k] = scoring
+        
+        if save_individual:
+            with open(os.path.join(
+                dir_save,
+                k+'.json'
+            ), 'w') as f:
+                json.dump(scoring, f, indent=2)
+        
+        count += 1
+        
+        if count % 100 == 0 or count == len(fns):
+            print(count,'/',len(fns))
+
+    with open(os.path.join(
+                dir_save,
+                'metrics.json'
+            ), 'w') as f:
+                json.dump(d_metrics, f, indent=2)      

From 38a4f9e8bef5a372750684616e1ceaaef125572b Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 21 Aug 2024 14:22:16 +0000
Subject: [PATCH 33/41] minor fixes: disabled marker image in postprocess in
 cli.py train validation; removed duplicated function; fixed syntax error

---
 cli.py                      |  3 ++-
 deepliif/models/__init__.py | 19 ++++++-------------
 2 files changed, 8 insertions(+), 14 deletions(-)

diff --git a/cli.py b/cli.py
index 1078f36..6f65d90 100644
--- a/cli.py
+++ b/cli.py
@@ -402,7 +402,8 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
                     
                     for seg_name in l_seg_names:
                         images = {'Seg':ToPILImage()((visuals[seg_name][0].cpu()+1)/2),
-                                  'Marker':ToPILImage()((visuals['fake_B_4'][0].cpu()+1)/2)}
+                                  #'Marker':ToPILImage()((visuals['fake_B_4'][0].cpu()+1)/2)
+                                  }
                         _, scoring = postprocess(ToPILImage()((data['A'][0]+1)/2), images, opt.scale_size, opt.model)
                         
                         for k,v in scoring.items():
diff --git a/deepliif/models/__init__.py b/deepliif/models/__init__.py
index 8d7a7c3..3d3049d 100644
--- a/deepliif/models/__init__.py
+++ b/deepliif/models/__init__.py
@@ -174,7 +174,7 @@ def init_nets(model_dir, eager_mode=False, opt=None, phase='test'):
         raise Exception(f'init_nets() not implemented for model {opt.model}')
 
     number_of_gpus_all = torch.cuda.device_count()
-    number_of_gpus = len(opt.gpu_ids)
+    number_of_gpus = min(len(opt.gpu_ids),number_of_gpus_all)
 
     if number_of_gpus > 0:
         mapping_gpu_ids = {i:idx for i,idx in enumerate(opt.gpu_ids)}
@@ -291,13 +291,6 @@ def is_empty_old(tile):
     else:
         return True if calculate_background_area(tile) > 98 else False
       
-      
-def is_empty(tile):
-    if isinstance(tile, list): # for pair of tiles, only mark it as empty / no need for prediction if ALL tiles are empty
-        return all([True if np.max(image_variance_rgb(tile)) < 15 else False for t in tile])
-    else:
-        return True if np.max(image_variance_rgb(tile)) < 15 else False
-
 
 def is_empty(tile):
     thresh = 15
@@ -541,11 +534,11 @@ def inference(img, tile_size, overlap_size, model_path, use_torchserve=False,
         }
         
         if return_seg_intermediate:
-            images({'IHC_s':results['G51'],
-                    'Hema_s':results['G52'],
-                    'DAPI_s':results['G53'],
-                    'Lap2_s':results['G54'],
-                    'Marker_s':results['G55'],})
+            images.update({'IHC_s':results['G51'],
+                          'Hema_s':results['G52'],
+                          'DAPI_s':results['G53'],
+                          'Lap2_s':results['G54'],
+                          'Marker_s':results['G55'],})
         
         if color_dapi:
             matrix = (       0,        0,        0, 0,

From 0af813d724fe3d09c6af03ddec93b263b5ebaf36 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 21 Aug 2024 15:42:18 +0000
Subject: [PATCH 34/41] allowed attunet to use custom input output channel size
 for ext and sdg

---
 deepliif/models/att_unet.py | 4 +++-
 deepliif/models/networks.py | 2 +-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/deepliif/models/att_unet.py b/deepliif/models/att_unet.py
index e1a46e3..a7920ba 100644
--- a/deepliif/models/att_unet.py
+++ b/deepliif/models/att_unet.py
@@ -1,3 +1,5 @@
+# adapted from https://github.com/LeeJunHyun/Image_Segmentation/blob/master/network.py
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -147,7 +149,7 @@ def __init__(self,img_ch=3,output_ch=1):
         self.Up2 = up_conv(ch_in=128,ch_out=64)
         self.Att2 = Attention_block(F_g=64,F_l=64,F_int=32)
         
-        self.Up1 = up_conv(ch_in=64,ch_out=img_ch,outermost=True)
+        self.Up1 = up_conv(ch_in=64,ch_out=output_ch,outermost=True)
 
     def forward(self,x):
         # encoding path
diff --git a/deepliif/models/networks.py b/deepliif/models/networks.py
index 28c152a..6aef419 100644
--- a/deepliif/models/networks.py
+++ b/deepliif/models/networks.py
@@ -178,7 +178,7 @@ def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, in
     elif netG == 'unet_512':
         net = UnetGenerator(input_nc, output_nc, 9, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
     elif netG == 'unet_512_attention':
-        net = AttU_Net(img_ch=3,output_ch=3)
+        net = AttU_Net(img_ch=input_nc,output_ch=output_nc)
     else:
         raise NotImplementedError('Generator model name [%s] is not recognized' % netG)
     return init_net(net, init_type, init_gain, gpu_ids)

From b18e138d79b1f962ae9ebc0fb940fbcf7014c990 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 21 Aug 2024 15:45:58 +0000
Subject: [PATCH 35/41] minor fixes: removed print logs

---
 deepliif/util/visualizer.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/deepliif/util/visualizer.py b/deepliif/util/visualizer.py
index f1c327c..0bbf7cb 100644
--- a/deepliif/util/visualizer.py
+++ b/deepliif/util/visualizer.py
@@ -165,7 +165,7 @@ def display_current_results(self, visuals, epoch, save_result, **kwargs):
                 if ncols > 0:        # show all the images in one visdom panel
                     ncols = min(ncols, len(visuals))
                     h, w = next(iter(visuals.values())).shape[:2]
-                    print(1)
+
                     table_css = """<style>
                             table {border-collapse: separate; border-spacing: 4px; white-space: nowrap; text-align: center}
                             table td {width: % dpx; height: % dpx; padding: 4px; outline: 4px solid black}
@@ -187,7 +187,6 @@ def display_current_results(self, visuals, epoch, save_result, **kwargs):
                             if idx % ncols == 0:
                                 label_html += '<tr>%s</tr>' % label_html_row
                                 label_html_row = ''
-                    print(2)
 
                     white_image = np.ones_like(image_numpy.transpose([2, 0, 1])) * 255
                     while idx % ncols != 0:
@@ -201,7 +200,6 @@ def display_current_results(self, visuals, epoch, save_result, **kwargs):
                     try:
                         self.vis.images(images, nrow=ncols, win=self.display_id + 1,
                                         padding=2, opts=dict(title=title + ' images'))
-                        print(4)
                         label_html = '<table>%s</table>' % label_html
                         self.vis.text(table_css + label_html, win=self.display_id + 2,
                                       opts=dict(title=title + ' labels'))

From 7c5cb30eee5b17fc897b1289bdd580e2935401da Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 21 Aug 2024 16:47:48 +0000
Subject: [PATCH 36/41] updated train.py and trainlaunch; minor fixes: number
 of netgs for deepliif

---
 cli.py                            |  22 ++-
 deepliif/models/DeepLIIF_model.py |   9 +-
 deepliif/scripts/train.py         | 168 +++++++++++++++++----
 train.py                          | 236 ++++++++++++++++++++++--------
 4 files changed, 338 insertions(+), 97 deletions(-)

diff --git a/cli.py b/cli.py
index 6f65d90..8e796dd 100644
--- a/cli.py
+++ b/cli.py
@@ -265,13 +265,14 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
     net_g = net_g.split(',')
     assert len(net_g) in [1,modalities_no], f'net_g should contain either 1 architecture for all translation generators or the same number of architectures as the number of translation generators ({modalities_no})'
     if len(net_g) == 1:
-      net_g = net_g*modalities_no
-      
+        net_g = net_g*modalities_no
     
     net_gs = net_gs.split(',')
     assert len(net_gs) in [1,seg_no], f'net_gs should contain either 1 architecture for all segmentation generators or the same number of architectures as the number of segmentation generators ({seg_no})'
-    if len(net_gs) == 1:
-      net_gs = net_gs*seg_no
+    if len(net_gs) == 1 and model == 'DeepLIIF':
+        net_gs = net_gs*(modalities_no + seg_no)
+    elif len(net_gs) == 1:
+        net_gs = net_gs*seg_no
     
     d_params['net_g'] = net_g
     d_params['net_gs'] = net_gs
@@ -463,7 +464,7 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
               help='specify discriminator architecture [basic | n_layers | pixel]. The basic model is a 70x70 '
                    'PatchGAN. n_layers allows you to specify the layers in the discriminator')
 @click.option('--net-g', default='resnet_9blocks',
-              help='specify generator architecture [resnet_9blocks | resnet_6blocks | unet_512 | unet_256 | unet_128]')
+              help='specify generator architecture [resnet_9blocks | resnet_6blocks | unet_512 | unet_256 | unet_128 | unet_512_attention]; to specify different arch for generators, list arch for each generator separated by comma, e.g., --net-g=resnet_9blocks,resnet_9blocks,resnet_9blocks,unet_512_attention,unet_512_attention')
 @click.option('--n-layers-d', default=4, help='only used if netD==n_layers')
 @click.option('--norm', default='batch',
               help='instance normalization or batch normalization [instance | batch | none]')
@@ -506,6 +507,8 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
               help='number of epochs with the initial learning rate')
 @click.option('--n-epochs-decay', type=int, default=100,
               help='number of epochs to linearly decay learning rate to zero')
+@click.option('--optimizer', type=str, default='adam',
+              help='optimizer from torch.optim to use, applied to both generators and discriminators [adam | sgd | adamw | ...]; the current parameters however are set up for adam, so other optimziers may encounter issue')
 @click.option('--beta1', default=0.5, help='momentum term of adam')
 @click.option('--lr', default=0.0002, help='initial learning rate for adam')
 @click.option('--lr-policy', default='linear',
@@ -542,13 +545,20 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
 @click.option('--net-ds', type=str, default='n_layers',
               help='specify discriminator architecture for segmentation task [basic | n_layers | pixel]. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator')
 @click.option('--net-gs', type=str, default='unet_512',
-              help='specify generator architecture for segmentation task [resnet_9blocks | resnet_6blocks | unet_512 | unet_256 | unet_128]')
+              help='specify generator architecture for segmentation task [resnet_9blocks | resnet_6blocks | unet_512 | unet_256 | unet_128 | unet_512_attention]; to specify different arch for generators, list arch for each generator separated by comma, e.g., --net-g=resnet_9blocks,resnet_9blocks,resnet_9blocks,unet_512_attention,unet_512_attention')
 @click.option('--gan-mode', type=str, default='vanilla',
               help='the type of GAN objective for translation task. [vanilla| lsgan | wgangp]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.')
 @click.option('--gan-mode-s', type=str, default='lsgan',
               help='the type of GAN objective for segmentation task. [vanilla| lsgan | wgangp]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.')
 # DDP related arguments
 @click.option('--local-rank', type=int, default=None, help='placeholder argument for torchrun, no need for manual setup')
+# Others
+@click.option('--with-val', is_flag=True,
+              help='use validation set to evaluate model performance at the end of each epoch')
+@click.option('--debug', is_flag=True,
+              help='debug mode, limits the number of data points per epoch to a small value')
+@click.option('--debug-data-size', default=10, type=int, help='data size per epoch used in debug mode; due to batch size, the epoch will be passed once the completed no. data points is greater than this value (e.g., for batch size 3, debug data size 10, the effective size used in training will be 12)')
+# trainlaunch DDP related arguments
 @click.option('--use-torchrun', type=str, default=None, help='provide torchrun options, all in one string, for example "-t3 --log_dir ~/log/ --nproc_per_node 1"; if your pytorch version is older than 1.10, torch.distributed.launch will be called instead of torchrun')
 def trainlaunch(**kwargs):
     """
diff --git a/deepliif/models/DeepLIIF_model.py b/deepliif/models/DeepLIIF_model.py
index adea246..8279e61 100644
--- a/deepliif/models/DeepLIIF_model.py
+++ b/deepliif/models/DeepLIIF_model.py
@@ -58,6 +58,7 @@ def __init__(self, opt):
             opt.netG = [opt.netG] * 4
         if isinstance(opt.net_gs, str):
             opt.net_gs = [opt.net_gs]*5
+
             
         self.netG1 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG[0], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids, opt.padding)
@@ -71,13 +72,13 @@ def __init__(self, opt):
         # DeepLIIF model currently uses one gs arch because there is only one explicit seg mod output
         self.netG51 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[0], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
-        self.netG52 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[0], opt.norm,
+        self.netG52 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[1], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
-        self.netG53 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[0], opt.norm,
+        self.netG53 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[2], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
-        self.netG54 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[0], opt.norm,
+        self.netG54 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[3], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
-        self.netG55 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[0], opt.norm,
+        self.netG55 = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.net_gs[4], opt.norm,
                                       not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
 
 
diff --git a/deepliif/scripts/train.py b/deepliif/scripts/train.py
index 34eb5cd..ec2bcce 100644
--- a/deepliif/scripts/train.py
+++ b/deepliif/scripts/train.py
@@ -4,24 +4,23 @@
 They are EXACTLY THE SAME.
 """
 
-import os
-import json
 import time
-import random
-
-import click
-import cv2
-import torch
-import numpy as np
-from PIL import Image
-
+from deepliif.options.train_options import TrainOptions
 from deepliif.data import create_dataset
-from deepliif.models import create_model
-from deepliif.util import Visualizer
-from deepliif.options import Options, print_options
+from deepliif.models import create_model, postprocess
+from deepliif.options import read_model_params, Options, print_options
+from deepliif.util.visualizer import Visualizer
+from PIL import Image
+import os
 
+import numpy as np
+import random
+import json
+import torch
 import torch.distributed as dist
-    
+from torchvision.transforms import ToPILImage
+
+import click
 
 def set_seed(seed=0,rank=None):
     """
@@ -54,8 +53,6 @@ def set_seed(seed=0,rank=None):
         return False
 
 
-
-
 @click.command()
 @click.option('--dataroot', required=True, type=str,
               help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
@@ -74,7 +71,7 @@ def set_seed(seed=0,rank=None):
               help='specify discriminator architecture [basic | n_layers | pixel]. The basic model is a 70x70 '
                    'PatchGAN. n_layers allows you to specify the layers in the discriminator')
 @click.option('--net-g', default='resnet_9blocks',
-              help='specify generator architecture [resnet_9blocks | resnet_6blocks | unet_512 | unet_256 | unet_128]')
+              help='specify generator architecture [resnet_9blocks | resnet_6blocks | unet_512 | unet_256 | unet_128 | unet_512_attention]; to specify different arch for generators, list arch for each generator separated by comma, e.g., --net-g=resnet_9blocks,resnet_9blocks,resnet_9blocks,unet_512_attention,unet_512_attention')
 @click.option('--n-layers-d', default=4, help='only used if netD==n_layers')
 @click.option('--norm', default='batch',
               help='instance normalization or batch normalization [instance | batch | none]')
@@ -117,6 +114,8 @@ def set_seed(seed=0,rank=None):
               help='number of epochs with the initial learning rate')
 @click.option('--n-epochs-decay', type=int, default=100,
               help='number of epochs to linearly decay learning rate to zero')
+@click.option('--optimizer', type=str, default='adam',
+              help='optimizer from torch.optim to use, applied to both generators and discriminators [adam | sgd | adamw | ...]; the current parameters however are set up for adam, so other optimziers may encounter issue')
 @click.option('--beta1', default=0.5, help='momentum term of adam')
 @click.option('--lr', default=0.0002, help='initial learning rate for adam')
 @click.option('--lr-policy', default='linear',
@@ -153,21 +152,27 @@ def set_seed(seed=0,rank=None):
 @click.option('--net-ds', type=str, default='n_layers',
               help='specify discriminator architecture for segmentation task [basic | n_layers | pixel]. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator')
 @click.option('--net-gs', type=str, default='unet_512',
-              help='specify generator architecture for segmentation task [resnet_9blocks | resnet_6blocks | unet_512 | unet_256 | unet_128]')
+              help='specify generator architecture for segmentation task [resnet_9blocks | resnet_6blocks | unet_512 | unet_256 | unet_128 | unet_512_attention]; to specify different arch for generators, list arch for each generator separated by comma, e.g., --net-g=resnet_9blocks,resnet_9blocks,resnet_9blocks,unet_512_attention,unet_512_attention')
 @click.option('--gan-mode', type=str, default='vanilla',
               help='the type of GAN objective for translation task. [vanilla| lsgan | wgangp]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.')
 @click.option('--gan-mode-s', type=str, default='lsgan',
               help='the type of GAN objective for segmentation task. [vanilla| lsgan | wgangp]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.')
 # DDP related arguments
 @click.option('--local-rank', type=int, default=None, help='placeholder argument for torchrun, no need for manual setup')
+# Others
+@click.option('--with-val', is_flag=True,
+              help='use validation set to evaluate model performance at the end of each epoch')
+@click.option('--debug', is_flag=True,
+              help='debug mode, limits the number of data points per epoch to a small value')
+@click.option('--debug-data-size', default=10, type=int, help='data size per epoch used in debug mode; due to batch size, the epoch will be passed once the completed no. data points is greater than this value (e.g., for batch size 3, debug data size 10, the effective size used in training will be 12)')
 def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, ndf, net_d, net_g,
           n_layers_d, norm, init_type, init_gain, no_dropout, direction, serial_batches, num_threads,
           batch_size, load_size, crop_size, max_dataset_size, preprocess, no_flip, display_winsize, epoch, load_iter,
           verbose, lambda_l1, is_train, display_freq, display_ncols, display_id, display_server, display_env,
           display_port, update_html_freq, print_freq, no_html, save_latest_freq, save_epoch_freq, save_by_iter,
-          continue_train, epoch_count, phase, lr_policy, n_epochs, n_epochs_decay, beta1, lr, lr_decay_iters,
+          continue_train, epoch_count, phase, lr_policy, n_epochs, n_epochs_decay, optimizer, beta1, lr, lr_decay_iters,
           remote, remote_transfer_cmd, seed, dataset_mode, padding, model, 
-          modalities_no, seg_gen, net_ds, net_gs, gan_mode, gan_mode_s, local_rank):
+          modalities_no, seg_gen, net_ds, net_gs, gan_mode, gan_mode_s, local_rank, with_val, debug, debug_data_size):
     """General-purpose training script for multi-task image-to-image translation.
 
     This script works for various models (with option '--model': e.g., DeepLIIF) and
@@ -191,6 +196,9 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
         seg_no = 0
         seg_gen = False
     
+    if optimizer != 'adam':
+        print(f'Optimizer torch.optim.{optimizer} is not tested. Be careful about the parameters of the optimizer.')
+    
     d_params = locals()
 
     if gpu_ids and gpu_ids[0] == -1:
@@ -202,8 +210,8 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
     if len(gpu_ids) > 0:
         if local_rank is not None:
             local_rank = int(local_rank)
-            torch.cuda.set_device(local_rank)#gpu_ids[local_rank])
-            gpu_ids = [local_rank]
+            torch.cuda.set_device(gpu_ids[local_rank])
+            gpu_ids=[local_rank]
         else:
             torch.cuda.set_device(gpu_ids[0])
 
@@ -224,17 +232,36 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
     dir_data_train = dataroot + '/train'
     fns = os.listdir(dir_data_train)
     fns = [x for x in fns if x.endswith('.png')]
+    print(f'{len(fns)} images found')
     img = Image.open(f"{dir_data_train}/{fns[0]}")
+    print(f'image shape:',img.size)
     
     num_img = img.size[0] / img.size[1]
     assert int(num_img) == num_img, f'img size {img.size[0]} / {img.size[1]} = {num_img} is not an integer'
     num_img = int(num_img)
     
     input_no = num_img - modalities_no - seg_no
-    assert input_no > 0, f'inferred number of input images is {input_no}; should be greater than 0'
+    assert input_no > 0, f'inferred number of input images is {input_no} (modalities_no {modalities_no}, seg_no {seg_no}); should be greater than 0'
     d_params['input_no'] = input_no
     d_params['scale_size'] = img.size[1]
     d_params['gpu_ids'] = gpu_ids
+    
+    # update generator arch
+    net_g = net_g.split(',')
+    assert len(net_g) in [1,modalities_no], f'net_g should contain either 1 architecture for all translation generators or the same number of architectures as the number of translation generators ({modalities_no})'
+    if len(net_g) == 1:
+      net_g = net_g*modalities_no
+      
+    
+    net_gs = net_gs.split(',')
+    assert len(net_gs) in [1,seg_no], f'net_gs should contain either 1 architecture for all segmentation generators or the same number of architectures as the number of segmentation generators ({seg_no})'
+    if len(net_gs) == 1 and model == 'DeepLIIF':
+        net_gs = net_gs*(modalities_no + seg_no)
+    elif len(net_gs) == 1:
+        net_gs = net_gs*seg_no
+    
+    d_params['net_g'] = net_g
+    d_params['net_gs'] = net_gs
 
     # create a dataset given dataset_mode and other options
     # dataset = AlignedDataset(opt)
@@ -242,9 +269,18 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
     opt = Options(d_params=d_params)
     print_options(opt, save=True)
     
+    # set dir for train and val
     dataset = create_dataset(opt)
+
     # get the number of images in the dataset.
     click.echo('The number of training images = %d' % len(dataset))
+    
+    if with_val:
+        dataset_val = create_dataset(opt,phase='val')
+        data_val = [batch for batch in dataset_val]
+        metrics_val = json.load(open(os.path.join(dataset_val.dataset.dir_AB,'metrics.json')))
+        click.echo('The number of validation images = %d' % len(dataset_val))
+        click.echo('The number of validation images = %d' % len(data_val))
 
     # create a model given model and other options
     model = create_model(opt)
@@ -274,6 +310,7 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
 
         # inner loop within one epoch
         for i, data in enumerate(dataset):
+            
             # timer for computation per iteration
             iter_start_time = time.time()
             if total_iters % print_freq == 0:
@@ -290,15 +327,15 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
             if total_iters % display_freq == 0:
                 save_result = total_iters % update_html_freq == 0
                 model.compute_visuals()
-                visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
+                visualizer.display_current_results({**model.get_current_visuals()}, epoch, save_result)
 
             # print training losses and save logging information to the disk
             if total_iters % print_freq == 0:
-                losses = model.get_current_losses()
+                losses = model.get_current_losses() # get training losses
                 t_comp = (time.time() - iter_start_time) / batch_size
-                visualizer.print_current_losses(epoch, epoch_iter, losses, t_comp, t_data)
+                visualizer.print_current_losses(epoch, epoch_iter, {**losses}, t_comp, t_data)
                 if display_id > 0:
-                    visualizer.plot_current_losses(epoch, float(epoch_iter) / len(dataset), losses)
+                    visualizer.plot_current_losses(epoch, float(epoch_iter) / len(dataset), {**losses})
 
             # cache our latest model every <save_latest_freq> iterations
             if total_iters % save_latest_freq == 0:
@@ -306,7 +343,11 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
                 save_suffix = 'iter_%d' % total_iters if save_by_iter else 'latest'
                 model.save_networks(save_suffix)
 
+
             iter_data_time = time.time()
+            if debug and epoch_iter >= debug_data_size:
+                print(f'debug mode, epoch {epoch} stopped at epoch iter {epoch_iter} (>= {debug_data_size})')
+                break
 
         # cache our model every <save_epoch_freq> epochs
         if epoch % save_epoch_freq == 0:
@@ -314,6 +355,77 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
             model.save_networks('latest')
             model.save_networks(epoch)
 
+        
+        
+        # validation loss and metrics calculation
+        if with_val:
+            losses = model.get_current_losses() # get training losses to print
+            
+            model.eval()
+            l_losses_val = []
+            l_metrics_val = []
+            
+            # for each val image, calculate validation loss and cell count metrics
+            for j, data_val_batch in enumerate(data_val):
+                # batch size is effectively 1 for validation
+                model.set_input(data_val_batch)
+                model.calculate_losses() # this does not optimize parameters
+                visuals = model.get_current_visuals()  # get image results
+                
+                # val losses
+                losses_val_batch = model.get_current_losses()
+                l_losses_val += [(k,v) for k,v in losses_val_batch.items()]
+                
+                # calculate cell count metrics
+                if type(model).__name__ == 'DeepLIIFModel':
+                    l_seg_names = ['fake_B_5']
+                    assert l_seg_names[0] in visuals.keys(), f'Cannot find {l_seg_names[0]} in generated image names ({list(visuals.keys())})'
+                    seg_mod_suffix = l_seg_names[0].split('_')[-1]
+                    l_seg_names += [x for x in visuals.keys() if x.startswith('fake') and x.split('_')[-1].startswith(seg_mod_suffix) and x != l_seg_names[0]]
+                    # print(f'Running postprocess for {len(l_seg_names)} generated images ({l_seg_names})')
+        
+                    img_name_current = data_val_batch['A_paths'][0].split('/')[-1][:-4] # remove .png
+                    metrics_gt = metrics_val[img_name_current]
+                    
+                    for seg_name in l_seg_names:
+                        images = {'Seg':ToPILImage()((visuals[seg_name][0].cpu()+1)/2),
+                                  #'Marker':ToPILImage()((visuals['fake_B_4'][0].cpu()+1)/2)
+                                  }
+                        _, scoring = postprocess(ToPILImage()((data['A'][0]+1)/2), images, opt.scale_size, opt.model)
+                        
+                        for k,v in scoring.items():
+                            if k.startswith('num') or k.startswith('percent'):
+                                # to calculate the rmse, here we calculate (x_pred - x_true) ** 2
+                                l_metrics_val.append((k+'_'+seg_name,(v - metrics_gt[k])**2))
+                    
+                if debug and epoch_iter >= debug_data_size:
+                    print(f'debug mode, epoch {epoch} stopped at epoch iter {epoch_iter} (>= {debug_data_size})')
+                    break
+                    
+            d_losses_val = {k+'_val':0 for k in losses_val_batch.keys()}
+            for k,v in l_losses_val:
+                d_losses_val[k+'_val'] += v
+            for k in d_losses_val:
+                d_losses_val[k] = d_losses_val[k] / len(data_val)
+            
+            d_metrics_val = {}
+            for k,v in l_metrics_val:
+                try:
+                    d_metrics_val[k] += v
+                except:
+                    d_metrics_val[k] = v
+            for k in d_metrics_val:
+                # to calculate the rmse, this is the second part, where d_metrics_val[k] now represents sum((x_pred - x_true) ** 2)
+                d_metrics_val[k] = np.sqrt(d_metrics_val[k] / len(data_val))
+            
+            
+            model.train()
+            t_comp = (time.time() - iter_start_time) / batch_size
+            visualizer.print_current_losses(epoch, epoch_iter, {**losses,**d_losses_val, **d_metrics_val}, t_comp, t_data)
+            if display_id > 0:
+                visualizer.plot_current_losses(epoch, float(epoch_iter) / len(dataset), {**losses,**d_losses_val,**d_metrics_val})
+
+
         print('End of epoch %d / %d \t Time Taken: %d sec' % (
             epoch, n_epochs + n_epochs_decay, time.time() - epoch_start_time))
         # update learning rates at the end of every epoch.
@@ -321,4 +433,4 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
 
 
 if __name__ == '__main__':
-    train()
\ No newline at end of file
+    train()
diff --git a/train.py b/train.py
index d326a08..cc2baf9 100644
--- a/train.py
+++ b/train.py
@@ -11,7 +11,7 @@
 import time
 from deepliif.options.train_options import TrainOptions
 from deepliif.data import create_dataset
-from deepliif.models import create_model
+from deepliif.models import create_model, postprocess
 from deepliif.options import read_model_params, Options, print_options
 from deepliif.util.visualizer import Visualizer
 from PIL import Image
@@ -19,7 +19,10 @@
 
 import numpy as np
 import random
+import json
 import torch
+import torch.distributed as dist
+from torchvision.transforms import ToPILImage
 
 import click
 
@@ -59,113 +62,121 @@ def set_seed(seed=0,rank=None):
               help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
 @click.option('--name', default='experiment_name',
               help='name of the experiment. It decides where to store samples and models')
-@click.option('--gpu_ids', type=int, multiple=True, help='gpu-ids 0 gpu-ids 1 or gpu-ids -1 for CPU')
-@click.option('--checkpoints_dir', default='./checkpoints', help='models are saved here')
-@click.option('--modalities_no', default=4, type=int, help='number of targets')
+@click.option('--gpu-ids', type=int, multiple=True, help='gpu-ids 0 gpu-ids 1 or gpu-ids -1 for CPU')
+@click.option('--checkpoints-dir', default='./checkpoints', help='models are saved here')
+@click.option('--modalities-no', default=4, type=int, help='number of targets')
 # model parameters
 @click.option('--model', default='DeepLIIF', help='name of model class')
-@click.option('--input_nc', default=3, help='# of input image channels: 3 for RGB and 1 for grayscale')
-@click.option('--output_nc', default=3, help='# of output image channels: 3 for RGB and 1 for grayscale')
+@click.option('--input-nc', default=3, help='# of input image channels: 3 for RGB and 1 for grayscale')
+@click.option('--output-nc', default=3, help='# of output image channels: 3 for RGB and 1 for grayscale')
 @click.option('--ngf', default=64, help='# of gen filters in the last conv layer')
 @click.option('--ndf', default=64, help='# of discrim filters in the first conv layer')
-@click.option('--net_d', default='n_layers',
+@click.option('--net-d', default='n_layers',
               help='specify discriminator architecture [basic | n_layers | pixel]. The basic model is a 70x70 '
                    'PatchGAN. n_layers allows you to specify the layers in the discriminator')
-@click.option('--net_g', default='resnet_9blocks',
-              help='specify generator architecture [resnet_9blocks | resnet_6blocks | unet_512 | unet_256 | unet_128]')
-@click.option('--n_layers_d', default=4, help='only used if netD==n_layers')
+@click.option('--net-g', default='resnet_9blocks',
+              help='specify generator architecture [resnet_9blocks | resnet_6blocks | unet_512 | unet_256 | unet_128 | unet_512_attention]; to specify different arch for generators, list arch for each generator separated by comma, e.g., --net-g=resnet_9blocks,resnet_9blocks,resnet_9blocks,unet_512_attention,unet_512_attention')
+@click.option('--n-layers-d', default=4, help='only used if netD==n_layers')
 @click.option('--norm', default='batch',
               help='instance normalization or batch normalization [instance | batch | none]')
-@click.option('--init_type', default='normal',
+@click.option('--init-type', default='normal',
               help='network initialization [normal | xavier | kaiming | orthogonal]')
-@click.option('--init_gain', default=0.02, help='scaling factor for normal, xavier and orthogonal.')
-@click.option('--no_dropout', is_flag=True, help='no dropout for the generator')
+@click.option('--init-gain', default=0.02, help='scaling factor for normal, xavier and orthogonal.')
+@click.option('--no-dropout', is_flag=True, help='no dropout for the generator')
 # dataset parameters
 @click.option('--direction', default='AtoB', help='AtoB or BtoA')
-@click.option('--serial_batches', is_flag=True,
+@click.option('--serial-batches', is_flag=True,
               help='if true, takes images in order to make batches, otherwise takes them randomly')
-@click.option('--num_threads', default=4, help='# threads for loading data')
-@click.option('--batch_size', default=1, help='input batch size')
-@click.option('--load_size', default=512, help='scale images to this size')
-@click.option('--crop_size', default=512, help='then crop to this size')
-@click.option('--max_dataset_size', type=int,
+@click.option('--num-threads', default=4, help='# threads for loading data')
+@click.option('--batch-size', default=1, help='input batch size')
+@click.option('--load-size', default=512, help='scale images to this size')
+@click.option('--crop-size', default=512, help='then crop to this size')
+@click.option('--max-dataset-size', type=int,
               help='Maximum number of samples allowed per dataset. If the dataset directory contains more than '
                    'max_dataset_size, only a subset is loaded.')
 @click.option('--preprocess', type=str,
               help='scaling and cropping of images at load time [resize_and_crop | crop | scale_width | '
                    'scale_width_and_crop | none]')
-@click.option('--no_flip', is_flag=True,
+@click.option('--no-flip', is_flag=True,
               help='if specified, do not flip the images for data augmentation')
-@click.option('--display_winsize', default=512, help='display window size for both visdom and HTML')
+@click.option('--display-winsize', default=512, help='display window size for both visdom and HTML')
 # additional parameters
 @click.option('--epoch', default='latest',
               help='which epoch to load? set to latest to use latest cached model')
-@click.option('--load_iter', default=0,
+@click.option('--load-iter', default=0,
               help='which iteration to load? if load_iter > 0, the code will load models by iter_[load_iter]; '
                    'otherwise, the code will load models by [epoch]')
 @click.option('--verbose', is_flag=True, help='if specified, print more debugging information')
-@click.option('--lambda_L1', default=100.0, help='weight for L1 loss')
-@click.option('--is_train', is_flag=True, default=True)
-@click.option('--continue_train', is_flag=True, help='continue training: load the latest model')
-@click.option('--epoch_count', type=int, default=0,
+@click.option('--lambda-L1', default=100.0, help='weight for L1 loss')
+@click.option('--is-train', is_flag=True, default=True)
+@click.option('--continue-train', is_flag=True, help='continue training: load the latest model')
+@click.option('--epoch-count', type=int, default=0,
               help='the starting  epoch count, we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>')
 @click.option('--phase', default='train', help='train, val, test, etc')
 # training parameters
-@click.option('--n_epochs', type=int, default=100,
+@click.option('--n-epochs', type=int, default=100,
               help='number of epochs with the initial learning rate')
-@click.option('--n_epochs_decay', type=int, default=100,
+@click.option('--n-epochs-decay', type=int, default=100,
               help='number of epochs to linearly decay learning rate to zero')
+@click.option('--optimizer', type=str, default='adam',
+              help='optimizer from torch.optim to use, applied to both generators and discriminators [adam | sgd | adamw | ...]; the current parameters however are set up for adam, so other optimziers may encounter issue')
 @click.option('--beta1', default=0.5, help='momentum term of adam')
 @click.option('--lr', default=0.0002, help='initial learning rate for adam')
-@click.option('--lr_policy', default='linear',
+@click.option('--lr-policy', default='linear',
               help='learning rate policy. [linear | step | plateau | cosine]')
-@click.option('--lr_decay_iters', type=int, default=50,
+@click.option('--lr-decay-iters', type=int, default=50,
               help='multiply by a gamma every lr_decay_iters iterations')
 @click.option('--seed', type=int, default=None, help='basic seed to be used for deterministic training, default to None (non-deterministic)')
 # visdom and HTML visualization parameters
-@click.option('--display_freq', default=400, help='frequency of showing training results on screen')
-@click.option('--display_ncols', default=4,
+@click.option('--display-freq', default=400, help='frequency of showing training results on screen')
+@click.option('--display-ncols', default=4,
               help='if positive, display all images in a single visdom web panel with certain number of images per row.')
-@click.option('--display_id', default=1, help='window id of the web display')
-@click.option('--display_server', default="http://localhost", help='visdom server of the web display')
-@click.option('--display_env', default='main',
+@click.option('--display-id', default=1, help='window id of the web display')
+@click.option('--display-server', default="http://localhost", help='visdom server of the web display')
+@click.option('--display-env', default='main',
               help='visdom display environment name (default is "main")')
-@click.option('--display_port', default=8097, help='visdom port of the web display')
-@click.option('--update_html_freq', default=1000, help='frequency of saving training results to html')
-@click.option('--print_freq', default=100, help='frequency of showing training results on console')
-@click.option('--no_html', is_flag=True,
+@click.option('--display-port', default=8097, help='visdom port of the web display')
+@click.option('--update-html-freq', default=1000, help='frequency of saving training results to html')
+@click.option('--print-freq', default=100, help='frequency of showing training results on console')
+@click.option('--no-html', is_flag=True,
               help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/')
 # network saving and loading parameters
-@click.option('--save_latest_freq', default=500, help='frequency of saving the latest results')
-@click.option('--save_epoch_freq', default=100,
+@click.option('--save-latest-freq', default=500, help='frequency of saving the latest results')
+@click.option('--save-epoch-freq', default=100,
               help='frequency of saving checkpoints at the end of epochs')
-@click.option('--save_by_iter', is_flag=True, help='whether saves model by iteration')
+@click.option('--save-by-iter', is_flag=True, help='whether saves model by iteration')
 @click.option('--remote', type=bool, default=False, help='whether isolate visdom checkpoints or not; if False, you can run a separate visdom server anywhere that consumes the checkpoints')
-@click.option('--remote_transfer_cmd', type=str, default=None, help='module and function to be used to transfer remote files to target storage location, for example mymodule.myfunction')
-@click.option('--dataset_mode', type=str, default='aligned',
+@click.option('--remote-transfer-cmd', type=str, default=None, help='module and function to be used to transfer remote files to target storage location, for example mymodule.myfunction')
+@click.option('--dataset-mode', type=str, default='aligned',
               help='chooses how datasets are loaded. [unaligned | aligned | single | colorization]')
 @click.option('--padding', type=str, default='zero',
               help='chooses the type of padding used by resnet generator. [reflect | zero]')
 # DeepLIIFExt params
-@click.option('--seg_gen', type=bool, default=True, help='True (Translation and Segmentation), False (Only Translation).')
-@click.option('--net_ds', type=str, default='n_layers',
+@click.option('--seg-gen', type=bool, default=True, help='True (Translation and Segmentation), False (Only Translation).')
+@click.option('--net-ds', type=str, default='n_layers',
               help='specify discriminator architecture for segmentation task [basic | n_layers | pixel]. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator')
-@click.option('--net_gs', type=str, default='unet_512',
-              help='specify generator architecture for segmentation task [resnet_9blocks | resnet_6blocks | unet_512 | unet_256 | unet_128]')
-@click.option('--gan_mode', type=str, default='vanilla',
+@click.option('--net-gs', type=str, default='unet_512',
+              help='specify generator architecture for segmentation task [resnet_9blocks | resnet_6blocks | unet_512 | unet_256 | unet_128 | unet_512_attention]; to specify different arch for generators, list arch for each generator separated by comma, e.g., --net-g=resnet_9blocks,resnet_9blocks,resnet_9blocks,unet_512_attention,unet_512_attention')
+@click.option('--gan-mode', type=str, default='vanilla',
               help='the type of GAN objective for translation task. [vanilla| lsgan | wgangp]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.')
-@click.option('--gan_mode_s', type=str, default='lsgan',
+@click.option('--gan-mode-s', type=str, default='lsgan',
               help='the type of GAN objective for segmentation task. [vanilla| lsgan | wgangp]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.')
 # DDP related arguments
 @click.option('--local-rank', type=int, default=None, help='placeholder argument for torchrun, no need for manual setup')
+# Others
+@click.option('--with-val', is_flag=True,
+              help='use validation set to evaluate model performance at the end of each epoch')
+@click.option('--debug', is_flag=True,
+              help='debug mode, limits the number of data points per epoch to a small value')
+@click.option('--debug-data-size', default=10, type=int, help='data size per epoch used in debug mode; due to batch size, the epoch will be passed once the completed no. data points is greater than this value (e.g., for batch size 3, debug data size 10, the effective size used in training will be 12)')
 def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, ndf, net_d, net_g,
           n_layers_d, norm, init_type, init_gain, no_dropout, direction, serial_batches, num_threads,
           batch_size, load_size, crop_size, max_dataset_size, preprocess, no_flip, display_winsize, epoch, load_iter,
           verbose, lambda_l1, is_train, display_freq, display_ncols, display_id, display_server, display_env,
           display_port, update_html_freq, print_freq, no_html, save_latest_freq, save_epoch_freq, save_by_iter,
-          continue_train, epoch_count, phase, lr_policy, n_epochs, n_epochs_decay, beta1, lr, lr_decay_iters,
+          continue_train, epoch_count, phase, lr_policy, n_epochs, n_epochs_decay, optimizer, beta1, lr, lr_decay_iters,
           remote, remote_transfer_cmd, seed, dataset_mode, padding, model, 
-          modalities_no, seg_gen, net_ds, net_gs, gan_mode, gan_mode_s, local_rank):
+          modalities_no, seg_gen, net_ds, net_gs, gan_mode, gan_mode_s, local_rank, with_val, debug, debug_data_size):
     """General-purpose training script for multi-task image-to-image translation.
 
     This script works for various models (with option '--model': e.g., DeepLIIF) and
@@ -189,6 +200,9 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
         seg_no = 0
         seg_gen = False
     
+    if optimizer != 'adam':
+        print(f'Optimizer torch.optim.{optimizer} is not tested. Be careful about the parameters of the optimizer.')
+    
     d_params = locals()
 
     if gpu_ids and gpu_ids[0] == -1:
@@ -200,8 +214,8 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
     if len(gpu_ids) > 0:
         if local_rank is not None:
             local_rank = int(local_rank)
-            torch.cuda.set_device(local_rank)#gpu_ids[local_rank])
-            gpu_ids = [local_rank]
+            torch.cuda.set_device(gpu_ids[local_rank])
+            gpu_ids=[local_rank]
         else:
             torch.cuda.set_device(gpu_ids[0])
 
@@ -222,17 +236,36 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
     dir_data_train = dataroot + '/train'
     fns = os.listdir(dir_data_train)
     fns = [x for x in fns if x.endswith('.png')]
+    print(f'{len(fns)} images found')
     img = Image.open(f"{dir_data_train}/{fns[0]}")
+    print(f'image shape:',img.size)
     
     num_img = img.size[0] / img.size[1]
     assert int(num_img) == num_img, f'img size {img.size[0]} / {img.size[1]} = {num_img} is not an integer'
     num_img = int(num_img)
     
     input_no = num_img - modalities_no - seg_no
-    assert input_no > 0, f'inferred number of input images is {input_no}; should be greater than 0'
+    assert input_no > 0, f'inferred number of input images is {input_no} (modalities_no {modalities_no}, seg_no {seg_no}); should be greater than 0'
     d_params['input_no'] = input_no
     d_params['scale_size'] = img.size[1]
     d_params['gpu_ids'] = gpu_ids
+    
+    # update generator arch
+    net_g = net_g.split(',')
+    assert len(net_g) in [1,modalities_no], f'net_g should contain either 1 architecture for all translation generators or the same number of architectures as the number of translation generators ({modalities_no})'
+    if len(net_g) == 1:
+      net_g = net_g*modalities_no
+      
+    
+    net_gs = net_gs.split(',')
+    assert len(net_gs) in [1,seg_no], f'net_gs should contain either 1 architecture for all segmentation generators or the same number of architectures as the number of segmentation generators ({seg_no})'
+    if len(net_gs) == 1 and model == 'DeepLIIF':
+        net_gs = net_gs*(modalities_no + seg_no)
+    elif len(net_gs) == 1:
+        net_gs = net_gs*seg_no
+    
+    d_params['net_g'] = net_g
+    d_params['net_gs'] = net_gs
 
     # create a dataset given dataset_mode and other options
     # dataset = AlignedDataset(opt)
@@ -240,9 +273,18 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
     opt = Options(d_params=d_params)
     print_options(opt, save=True)
     
+    # set dir for train and val
     dataset = create_dataset(opt)
+
     # get the number of images in the dataset.
     click.echo('The number of training images = %d' % len(dataset))
+    
+    if with_val:
+        dataset_val = create_dataset(opt,phase='val')
+        data_val = [batch for batch in dataset_val]
+        metrics_val = json.load(open(os.path.join(dataset_val.dataset.dir_AB,'metrics.json')))
+        click.echo('The number of validation images = %d' % len(dataset_val))
+        click.echo('The number of validation images = %d' % len(data_val))
 
     # create a model given model and other options
     model = create_model(opt)
@@ -272,6 +314,7 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
 
         # inner loop within one epoch
         for i, data in enumerate(dataset):
+            
             # timer for computation per iteration
             iter_start_time = time.time()
             if total_iters % print_freq == 0:
@@ -288,15 +331,15 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
             if total_iters % display_freq == 0:
                 save_result = total_iters % update_html_freq == 0
                 model.compute_visuals()
-                visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
+                visualizer.display_current_results({**model.get_current_visuals()}, epoch, save_result)
 
             # print training losses and save logging information to the disk
             if total_iters % print_freq == 0:
-                losses = model.get_current_losses()
+                losses = model.get_current_losses() # get training losses
                 t_comp = (time.time() - iter_start_time) / batch_size
-                visualizer.print_current_losses(epoch, epoch_iter, losses, t_comp, t_data)
+                visualizer.print_current_losses(epoch, epoch_iter, {**losses}, t_comp, t_data)
                 if display_id > 0:
-                    visualizer.plot_current_losses(epoch, float(epoch_iter) / len(dataset), losses)
+                    visualizer.plot_current_losses(epoch, float(epoch_iter) / len(dataset), {**losses})
 
             # cache our latest model every <save_latest_freq> iterations
             if total_iters % save_latest_freq == 0:
@@ -304,7 +347,11 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
                 save_suffix = 'iter_%d' % total_iters if save_by_iter else 'latest'
                 model.save_networks(save_suffix)
 
+
             iter_data_time = time.time()
+            if debug and epoch_iter >= debug_data_size:
+                print(f'debug mode, epoch {epoch} stopped at epoch iter {epoch_iter} (>= {debug_data_size})')
+                break
 
         # cache our model every <save_epoch_freq> epochs
         if epoch % save_epoch_freq == 0:
@@ -312,6 +359,77 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
             model.save_networks('latest')
             model.save_networks(epoch)
 
+        
+        
+        # validation loss and metrics calculation
+        if with_val:
+            losses = model.get_current_losses() # get training losses to print
+            
+            model.eval()
+            l_losses_val = []
+            l_metrics_val = []
+            
+            # for each val image, calculate validation loss and cell count metrics
+            for j, data_val_batch in enumerate(data_val):
+                # batch size is effectively 1 for validation
+                model.set_input(data_val_batch)
+                model.calculate_losses() # this does not optimize parameters
+                visuals = model.get_current_visuals()  # get image results
+                
+                # val losses
+                losses_val_batch = model.get_current_losses()
+                l_losses_val += [(k,v) for k,v in losses_val_batch.items()]
+                
+                # calculate cell count metrics
+                if type(model).__name__ == 'DeepLIIFModel':
+                    l_seg_names = ['fake_B_5']
+                    assert l_seg_names[0] in visuals.keys(), f'Cannot find {l_seg_names[0]} in generated image names ({list(visuals.keys())})'
+                    seg_mod_suffix = l_seg_names[0].split('_')[-1]
+                    l_seg_names += [x for x in visuals.keys() if x.startswith('fake') and x.split('_')[-1].startswith(seg_mod_suffix) and x != l_seg_names[0]]
+                    # print(f'Running postprocess for {len(l_seg_names)} generated images ({l_seg_names})')
+        
+                    img_name_current = data_val_batch['A_paths'][0].split('/')[-1][:-4] # remove .png
+                    metrics_gt = metrics_val[img_name_current]
+                    
+                    for seg_name in l_seg_names:
+                        images = {'Seg':ToPILImage()((visuals[seg_name][0].cpu()+1)/2),
+                                  #'Marker':ToPILImage()((visuals['fake_B_4'][0].cpu()+1)/2)
+                                  }
+                        _, scoring = postprocess(ToPILImage()((data['A'][0]+1)/2), images, opt.scale_size, opt.model)
+                        
+                        for k,v in scoring.items():
+                            if k.startswith('num') or k.startswith('percent'):
+                                # to calculate the rmse, here we calculate (x_pred - x_true) ** 2
+                                l_metrics_val.append((k+'_'+seg_name,(v - metrics_gt[k])**2))
+                    
+                if debug and epoch_iter >= debug_data_size:
+                    print(f'debug mode, epoch {epoch} stopped at epoch iter {epoch_iter} (>= {debug_data_size})')
+                    break
+                    
+            d_losses_val = {k+'_val':0 for k in losses_val_batch.keys()}
+            for k,v in l_losses_val:
+                d_losses_val[k+'_val'] += v
+            for k in d_losses_val:
+                d_losses_val[k] = d_losses_val[k] / len(data_val)
+            
+            d_metrics_val = {}
+            for k,v in l_metrics_val:
+                try:
+                    d_metrics_val[k] += v
+                except:
+                    d_metrics_val[k] = v
+            for k in d_metrics_val:
+                # to calculate the rmse, this is the second part, where d_metrics_val[k] now represents sum((x_pred - x_true) ** 2)
+                d_metrics_val[k] = np.sqrt(d_metrics_val[k] / len(data_val))
+            
+            
+            model.train()
+            t_comp = (time.time() - iter_start_time) / batch_size
+            visualizer.print_current_losses(epoch, epoch_iter, {**losses,**d_losses_val, **d_metrics_val}, t_comp, t_data)
+            if display_id > 0:
+                visualizer.plot_current_losses(epoch, float(epoch_iter) / len(dataset), {**losses,**d_losses_val,**d_metrics_val})
+
+
         print('End of epoch %d / %d \t Time Taken: %d sec' % (
             epoch, n_epochs + n_epochs_decay, time.time() - epoch_start_time))
         # update learning rates at the end of every epoch.

From 0df24a8a8443763d67a5edaf5f78c57a9b576e7e Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 21 Aug 2024 16:52:28 +0000
Subject: [PATCH 37/41] added tests for trainlaunch; changed how the test
 commands are formed in training tests to highlight test params

---
 tests/test_cli_train.py       |  62 +++++++++--
 tests/test_cli_trainlaunch.py | 203 ++++++++++++++++++++++++++++++++++
 2 files changed, 256 insertions(+), 9 deletions(-)
 create mode 100644 tests/test_cli_trainlaunch.py

diff --git a/tests/test_cli_train.py b/tests/test_cli_train.py
index 4baac45..767a89e 100644
--- a/tests/test_cli_train.py
+++ b/tests/test_cli_train.py
@@ -15,6 +15,7 @@
 import pytest
 
 available_gpus = torch.cuda.device_count()
+CMD_BASIC = 'python cli.py train --model {model} --dataroot {dataroot} --name test_local --modalities-no {modalities_no} --seg-gen {seg_gen} --batch-size 1 --num-threads 0 --checkpoints-dir {dir_save} --remote True --n-epochs 1 --n-epochs-decay 1'
 
 #----------------------
 #---- cpu-based -------
@@ -30,7 +31,10 @@ def test_cli_train(tmp_path, model_info):
         num_input = len(fns_input)
         assert num_input > 0
         
-        res = subprocess.run(f'python cli.py train --model {model_info["model"]} --dataroot {dirs_input[i]} --name test_local --modalities-no {model_info["modalities_no"][i]} --seg-gen {model_info["seg_gen"][i]} --batch-size 1 --num-threads 0 --checkpoints-dir {dir_save} --remote True --n-epochs 1 --n-epochs-decay 1 --print-freq 1 --display-freq 1 --update-html-freq 1 --save-latest-freq 1 --save-epoch-freq 1',shell=True)
+        cmd = CMD_BASIC.format(model=model_info["model"], dataroot=dirs_input[i], 
+                               modalities_no=model_info["modalities_no"][i], 
+                               seg_gen=model_info["seg_gen"][i], dir_save=dir_save)
+        res = subprocess.run(cmd,shell=True)
         assert res.returncode == 0
         torch.cuda.nvtx.range_pop()
     torch.cuda.nvtx.range_pop()
@@ -52,7 +56,12 @@ def test_cli_train_single_gpu(tmp_path, model_info):
             num_input = len(fns_input)
             assert num_input > 0
     
-            res = subprocess.run(f'python cli.py train --model {model_info["model"]} --dataroot {dirs_input[i]} --name test_local --modalities-no {model_info["modalities_no"][i]} --seg-gen {model_info["seg_gen"][i]} --batch-size 1 --num-threads 0 --checkpoints-dir {dir_save} --remote True --n-epochs 1 --n-epochs-decay 1 --gpu-ids 0',shell=True)
+            test_param = '--gpu-ids 0'
+            cmd = CMD_BASIC.format(model=model_info["model"], dataroot=dirs_input[i], 
+                                   modalities_no=model_info["modalities_no"][i], 
+                                   seg_gen=model_info["seg_gen"][i], dir_save=dir_save)
+            cmd += f' {test_param}'
+            res = subprocess.run(cmd,shell=True)
             assert res.returncode == 0
             torch.cuda.nvtx.range_pop()
         torch.cuda.nvtx.range_pop()
@@ -72,7 +81,12 @@ def test_cli_train_single_gpu_optimizer(tmp_path, model_info):
             num_input = len(fns_input)
             assert num_input > 0
     
-            res = subprocess.run(f'python cli.py train --model {model_info["model"]} --dataroot {dirs_input[i]} --name test_local --modalities-no {model_info["modalities_no"][i]} --seg-gen {model_info["seg_gen"][i]} --batch-size 1 --num-threads 0 --checkpoints-dir {dir_save} --remote True --n-epochs 1 --n-epochs-decay 1 --gpu-ids 0 --optimizer sgd',shell=True)
+            test_param = '--gpu-ids 0 --optimizer sgd'
+            cmd = CMD_BASIC.format(model=model_info["model"], dataroot=dirs_input[i], 
+                                   modalities_no=model_info["modalities_no"][i], 
+                                   seg_gen=model_info["seg_gen"][i], dir_save=dir_save)
+            cmd += f' {test_param}'
+            res = subprocess.run(cmd,shell=True)
             assert res.returncode == 0
             torch.cuda.nvtx.range_pop()
         torch.cuda.nvtx.range_pop()
@@ -92,10 +106,20 @@ def test_cli_train_single_gpu_netg(tmp_path, model_info):
             num_input = len(fns_input)
             assert num_input > 0
     
-            res = subprocess.run(f'python cli.py train --model {model_info["model"]} --dataroot {dirs_input[i]} --name test_local --modalities-no {model_info["modalities_no"][i]} --seg-gen {model_info["seg_gen"][i]} --batch-size 1 --num-threads 0 --checkpoints-dir {dir_save} --remote True --n-epochs 1 --n-epochs-decay 1 --gpu-ids 0 --net-g unet_512',shell=True)
+            test_param = '--gpu-ids 0 --net-g unet_512'
+            cmd = CMD_BASIC.format(model=model_info["model"], dataroot=dirs_input[i], 
+                                   modalities_no=model_info["modalities_no"][i], 
+                                   seg_gen=model_info["seg_gen"][i], dir_save=dir_save)
+            cmd += f' {test_param}'
+            res = subprocess.run(cmd,shell=True)
             assert res.returncode == 0
             
-            res = subprocess.run(f'python cli.py train --model {model_info["model"]} --dataroot {dirs_input[i]} --name test_local --modalities-no {model_info["modalities_no"][i]} --seg-gen {model_info["seg_gen"][i]} --batch-size 1 --num-threads 0 --checkpoints-dir {dir_save} --remote True --n-epochs 1 --n-epochs-decay 1 --gpu-ids 0 --net-g unet_512_attention',shell=True)
+            test_param = '--gpu-ids 0 --net-g unet_512_attention'
+            cmd = CMD_BASIC.format(model=model_info["model"], dataroot=dirs_input[i], 
+                                   modalities_no=model_info["modalities_no"][i], 
+                                   seg_gen=model_info["seg_gen"][i], dir_save=dir_save)
+            cmd += f' {test_param}'
+            res = subprocess.run(cmd,shell=True)
             assert res.returncode == 0
             torch.cuda.nvtx.range_pop()
         torch.cuda.nvtx.range_pop()
@@ -115,10 +139,20 @@ def test_cli_train_single_gpu_netgs(tmp_path, model_info):
             num_input = len(fns_input)
             assert num_input > 0
     
-            res = subprocess.run(f'python cli.py train --model {model_info["model"]} --dataroot {dirs_input[i]} --name test_local --modalities-no {model_info["modalities_no"][i]} --seg-gen {model_info["seg_gen"][i]} --batch-size 1 --num-threads 0 --checkpoints-dir {dir_save} --remote True --n-epochs 1 --n-epochs-decay 1 --gpu-ids 0 --net-gs unet_512',shell=True)
+            test_param = '--gpu-ids 0 --net-gs unet_512'
+            cmd = CMD_BASIC.format(model=model_info["model"], dataroot=dirs_input[i], 
+                                   modalities_no=model_info["modalities_no"][i], 
+                                   seg_gen=model_info["seg_gen"][i], dir_save=dir_save)
+            cmd += f' {test_param}'
+            res = subprocess.run(cmd,shell=True)
             assert res.returncode == 0
             
-            res = subprocess.run(f'python cli.py train --model {model_info["model"]} --dataroot {dirs_input[i]} --name test_local --modalities-no {model_info["modalities_no"][i]} --seg-gen {model_info["seg_gen"][i]} --batch-size 1 --num-threads 0 --checkpoints-dir {dir_save} --remote True --n-epochs 1 --n-epochs-decay 1 --gpu-ids 0 --net-gs unet_512_attention',shell=True)
+            test_param = '--gpu-ids 0 --net-gs unet_512_attention'
+            cmd = CMD_BASIC.format(model=model_info["model"], dataroot=dirs_input[i], 
+                                   modalities_no=model_info["modalities_no"][i], 
+                                   seg_gen=model_info["seg_gen"][i], dir_save=dir_save)
+            cmd += f' {test_param}'
+            res = subprocess.run(cmd,shell=True)
             assert res.returncode == 0
             torch.cuda.nvtx.range_pop()
         torch.cuda.nvtx.range_pop()
@@ -138,7 +172,12 @@ def test_cli_train_single_gpu_withval(tmp_path, model_info):
             num_input = len(fns_input)
             assert num_input > 0
     
-            res = subprocess.run(f'python cli.py train --model {model_info["model"]} --dataroot {dirs_input[i]} --name test_local --modalities-no {model_info["modalities_no"][i]} --seg-gen {model_info["seg_gen"][i]} --batch-size 1 --num-threads 0 --checkpoints-dir {dir_save} --remote True --n-epochs 1 --n-epochs-decay 1 --gpu-ids 0 --with-val',shell=True)
+            test_param = '--gpu-ids 0 --with-val'
+            cmd = CMD_BASIC.format(model=model_info["model"], dataroot=dirs_input[i], 
+                                   modalities_no=model_info["modalities_no"][i], 
+                                   seg_gen=model_info["seg_gen"][i], dir_save=dir_save)
+            cmd += f' {test_param}'
+            res = subprocess.run(cmd,shell=True)
             assert res.returncode == 0
             torch.cuda.nvtx.range_pop()
         torch.cuda.nvtx.range_pop()
@@ -160,7 +199,12 @@ def test_cli_train_multi_gpu_dp(tmp_path, model_info):
             num_input = len(fns_input)
             assert num_input > 0
             
-            res = subprocess.run(f'python cli.py train --model {model_info["model"]} --dataroot {dirs_input[i]} --name test_local --modalities-no {model_info["modalities_no"][i]} --seg-gen {model_info["seg_gen"][i]} --batch-size 1 --num-threads 0 --checkpoints-dir {dir_save} --remote True --n-epochs 1 --n-epochs-decay 1 --gpu-ids 0 --gpu-ids 1',shell=True)
+            test_param = '--gpu-ids 0 --gpu-ids 1'
+            cmd = CMD_BASIC.format(model=model_info["model"], dataroot=dirs_input[i], 
+                                   modalities_no=model_info["modalities_no"][i], 
+                                   seg_gen=model_info["seg_gen"][i], dir_save=dir_save)
+            cmd += f' {test_param}'
+            res = subprocess.run(cmd,shell=True)
             assert res.returncode == 0
             torch.cuda.nvtx.range_pop()
         torch.cuda.nvtx.range_pop()
diff --git a/tests/test_cli_trainlaunch.py b/tests/test_cli_trainlaunch.py
new file mode 100644
index 0000000..ef8670c
--- /dev/null
+++ b/tests/test_cli_trainlaunch.py
@@ -0,0 +1,203 @@
+# Tests for training
+# hardware config: cpu / single gpu / multi gpu
+# tests (single gpu):
+#   - basic training
+#   - optimizer
+#   - net-g
+#   - net-gs
+#   - with-val
+# tests (multi gpu):
+#   - basic training
+
+import subprocess
+import os
+import torch
+import pytest
+
+available_gpus = torch.cuda.device_count()
+DDP_PARAM = '--use-torchrun "-t 3 --log_dir ../log/ --nproc_per_node 1"'
+CMD_BASIC = 'python cli.py trainlaunch --model {model} --dataroot {dataroot} --name test_local --modalities-no {modalities_no} --seg-gen {seg_gen} --batch-size 1 --num-threads 0 --checkpoints-dir {dir_save} --remote True --n-epochs 1 --n-epochs-decay 1'
+
+print('Building and installing deepliif locally to test trainlaunch..')
+res = subprocess.run('pip install -e . --no-dependencies',shell=True)
+assert res.returncode == 0
+
+#----------------------
+#---- gpu: single -----
+#----------------------
+def test_cli_trainlaunch_single_gpu(tmp_path, model_info):
+    if available_gpus > 0:
+        torch.cuda.nvtx.range_push("test_cli_trainlaunch_single_gpu")
+        dirs_input = model_info['dir_input_train']
+        
+        for i in range(len(dirs_input)):
+            torch.cuda.nvtx.range_push(f"test_cli_trainlaunch_single_gpu {dirs_input[i]}")
+            dir_save = tmp_path
+            
+            fns_input = [f for f in os.listdir(dirs_input[i] + '/train') if os.path.isfile(os.path.join(dirs_input[i] + '/train', f)) and f.endswith('png')]
+            num_input = len(fns_input)
+            assert num_input > 0
+    
+            test_param = '--gpu-ids 0'
+            cmd = CMD_BASIC.format(model=model_info["model"], dataroot=dirs_input[i], 
+                                   modalities_no=model_info["modalities_no"][i], 
+                                   seg_gen=model_info["seg_gen"][i], dir_save=dir_save)
+            cmd += f' {test_param}'
+            cmd += f' {DDP_PARAM}'
+            res = subprocess.run(cmd,shell=True)
+            assert res.returncode == 0
+            torch.cuda.nvtx.range_pop()
+        torch.cuda.nvtx.range_pop()
+    else:
+        pytest.skip(f'Detected {available_gpus} (< 1) available GPUs. Skip.')
+
+
+def test_cli_trainlaunch_single_gpu_optimizer(tmp_path, model_info):
+    if available_gpus > 0:
+        torch.cuda.nvtx.range_push("test_cli_trainlaunch_single_gpu_optimizer")
+        dirs_input = model_info['dir_input_train']
+        for i in range(len(dirs_input)):
+            torch.cuda.nvtx.range_push(f"test_cli_trainlaunch_single_gpu {dirs_input[i]}")
+            dir_save = tmp_path
+            
+            fns_input = [f for f in os.listdir(dirs_input[i] + '/train') if os.path.isfile(os.path.join(dirs_input[i] + '/train', f)) and f.endswith('png')]
+            num_input = len(fns_input)
+            assert num_input > 0
+    
+            test_param = '--gpu-ids 0 --optimizer sgd'
+            cmd = CMD_BASIC.format(model=model_info["model"], dataroot=dirs_input[i], 
+                                   modalities_no=model_info["modalities_no"][i], 
+                                   seg_gen=model_info["seg_gen"][i], dir_save=dir_save)
+            cmd += f' {test_param}'
+            cmd += f' {DDP_PARAM}'
+            res = subprocess.run(cmd,shell=True)
+            assert res.returncode == 0
+            torch.cuda.nvtx.range_pop()
+        torch.cuda.nvtx.range_pop()
+    else:
+        pytest.skip(f'Detected {available_gpus} (< 1) available GPUs. Skip.')
+
+
+def test_cli_trainlaunch_single_gpu_netg(tmp_path, model_info):
+    if available_gpus > 0:
+        torch.cuda.nvtx.range_push("test_cli_trainlaunch_single_gpu_netg")
+        dirs_input = model_info['dir_input_train']
+        for i in range(len(dirs_input)):
+            torch.cuda.nvtx.range_push(f"test_cli_trainlaunch_single_gpu {dirs_input[i]}")
+            dir_save = tmp_path
+            
+            fns_input = [f for f in os.listdir(dirs_input[i] + '/train') if os.path.isfile(os.path.join(dirs_input[i] + '/train', f)) and f.endswith('png')]
+            num_input = len(fns_input)
+            assert num_input > 0
+    
+            test_param = '--gpu-ids 0 --net-g unet_512'
+            cmd = CMD_BASIC.format(model=model_info["model"], dataroot=dirs_input[i], 
+                                   modalities_no=model_info["modalities_no"][i], 
+                                   seg_gen=model_info["seg_gen"][i], dir_save=dir_save)
+            cmd += f' {test_param}'
+            cmd += f' {DDP_PARAM}'
+            res = subprocess.run(cmd,shell=True)
+            assert res.returncode == 0
+            
+            test_param = '--gpu-ids 0 --net-g unet_512_attention'
+            cmd = CMD_BASIC.format(model=model_info["model"], dataroot=dirs_input[i], 
+                                   modalities_no=model_info["modalities_no"][i], 
+                                   seg_gen=model_info["seg_gen"][i], dir_save=dir_save)
+            cmd += f' {test_param}'
+            cmd += f' {DDP_PARAM}'
+            res = subprocess.run(cmd,shell=True)
+            assert res.returncode == 0
+            torch.cuda.nvtx.range_pop()
+        torch.cuda.nvtx.range_pop()
+    else:
+        pytest.skip(f'Detected {available_gpus} (< 1) available GPUs. Skip.')
+        
+
+def test_cli_trainlaunch_single_gpu_netgs(tmp_path, model_info):
+    if available_gpus > 0:
+        torch.cuda.nvtx.range_push("test_cli_trainlaunch_single_gpu_netgs")
+        dirs_input = model_info['dir_input_train']
+        for i in range(len(dirs_input)):
+            torch.cuda.nvtx.range_push(f"test_cli_trainlaunch_single_gpu {dirs_input[i]}")
+            dir_save = tmp_path
+            
+            fns_input = [f for f in os.listdir(dirs_input[i] + '/train') if os.path.isfile(os.path.join(dirs_input[i] + '/train', f)) and f.endswith('png')]
+            num_input = len(fns_input)
+            assert num_input > 0
+    
+            test_param = '--gpu-ids 0 --net-gs unet_512'
+            cmd = CMD_BASIC.format(model=model_info["model"], dataroot=dirs_input[i], 
+                                   modalities_no=model_info["modalities_no"][i], 
+                                   seg_gen=model_info["seg_gen"][i], dir_save=dir_save)
+            cmd += f' {test_param}'
+            cmd += f' {DDP_PARAM}'
+            res = subprocess.run(cmd,shell=True)
+            assert res.returncode == 0
+            
+            test_param = '--gpu-ids 0 --net-gs unet_512_attention'
+            cmd = CMD_BASIC.format(model=model_info["model"], dataroot=dirs_input[i], 
+                                   modalities_no=model_info["modalities_no"][i], 
+                                   seg_gen=model_info["seg_gen"][i], dir_save=dir_save)
+            cmd += f' {test_param}'
+            cmd += f' {DDP_PARAM}'
+            res = subprocess.run(cmd,shell=True)
+            assert res.returncode == 0
+            torch.cuda.nvtx.range_pop()
+        torch.cuda.nvtx.range_pop()
+    else:
+        pytest.skip(f'Detected {available_gpus} (< 1) available GPUs. Skip.')
+
+
+def test_cli_trainlaunch_single_gpu_withval(tmp_path, model_info):
+    if available_gpus > 0:
+        torch.cuda.nvtx.range_push("test_cli_trainlaunch_single_gpu_withval")
+        dirs_input = model_info['dir_input_train']
+        for i in range(len(dirs_input)):
+            torch.cuda.nvtx.range_push(f"test_cli_trainlaunch_single_gpu {dirs_input[i]}")
+            dir_save = tmp_path
+            
+            fns_input = [f for f in os.listdir(dirs_input[i] + '/train') if os.path.isfile(os.path.join(dirs_input[i] + '/train', f)) and f.endswith('png')]
+            num_input = len(fns_input)
+            assert num_input > 0
+    
+            test_param = '--gpu-ids 0 --with-val'
+            cmd = CMD_BASIC.format(model=model_info["model"], dataroot=dirs_input[i], 
+                                   modalities_no=model_info["modalities_no"][i], 
+                                   seg_gen=model_info["seg_gen"][i], dir_save=dir_save)
+            cmd += f' {test_param}'
+            cmd += f' {DDP_PARAM}'
+            res = subprocess.run(cmd,shell=True)
+            assert res.returncode == 0
+            torch.cuda.nvtx.range_pop()
+        torch.cuda.nvtx.range_pop()
+    else:
+        pytest.skip(f'Detected {available_gpus} (< 1) available GPUs. Skip.')
+
+#----------------------
+#---- gpu: multi ------
+#----------------------
+def test_cli_trainlaunch_multi_gpu_dp(tmp_path, model_info):
+    if available_gpus > 1:
+        torch.cuda.nvtx.range_push("test_cli_trainlaunch_multi_gpu_dp")
+        dirs_input = model_info['dir_input_train']
+        for i in range(len(dirs_input)):
+            torch.cuda.nvtx.range_push(f"test_cli_trainlaunch_multi_gpu_dp {dirs_input[i]}")
+            dir_save = tmp_path
+            
+            fns_input = [f for f in os.listdir(dirs_input[i] + '/train') if os.path.isfile(os.path.join(dirs_input[i] + '/train', f)) and f.endswith('png')]
+            num_input = len(fns_input)
+            assert num_input > 0
+            
+            test_param = '--gpu-ids 0 --gpu-ids 1'
+            cmd = CMD_BASIC.format(model=model_info["model"], dataroot=dirs_input[i], 
+                                   modalities_no=model_info["modalities_no"][i], 
+                                   seg_gen=model_info["seg_gen"][i], dir_save=dir_save)
+            cmd += f' {test_param}'
+            cmd += f' {DDP_PARAM}'
+            res = subprocess.run(cmd,shell=True)
+            assert res.returncode == 0
+            torch.cuda.nvtx.range_pop()
+        torch.cuda.nvtx.range_pop()
+    else:
+        pytest.skip(f'Detected {available_gpus} (< 2) available GPUs. Skip.')
+

From 136540efc13b4271fd5a9c570f9e62ff71724f36 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 21 Aug 2024 17:10:18 +0000
Subject: [PATCH 38/41] cleaned up sdg model

---
 deepliif/models/SDG_model.py | 19 ++-----------------
 1 file changed, 2 insertions(+), 17 deletions(-)

diff --git a/deepliif/models/SDG_model.py b/deepliif/models/SDG_model.py
index 2906d30..0db367f 100644
--- a/deepliif/models/SDG_model.py
+++ b/deepliif/models/SDG_model.py
@@ -16,10 +16,6 @@ def __init__(self, opt):
         BaseModel.__init__(self, opt)
 
         self.mod_gen_no = self.opt.modalities_no
-        opt.resize_conv = 'resizeconv' in opt.checkpoints_dir or 'resizeconv' in opt.name
-        print('opt.resize_conv', opt.resize_conv)
-        opt.tv_loss = 'tv' in opt.checkpoints_dir or 'tv' in opt.name
-        print('opt.tv_loss', opt.tv_loss)
             
 
         # weights of the modalities in generating segmentation mask
@@ -30,7 +26,7 @@ def __init__(self, opt):
 
         # self.seg_weights = opt.seg_weights
         # assert len(self.seg_weights) == self.seg_gen_no, 'The number of the segmentation weights (seg_weights) is not equal to the number of target images (modalities_no)!'
-        # print(self.seg_weights)
+
         # loss weights in calculating the final loss
         self.loss_G_weights = [1 / self.mod_gen_no] * self.mod_gen_no
         self.loss_GS_weights = [1 / self.mod_gen_no] * self.mod_gen_no
@@ -50,7 +46,6 @@ def __init__(self, opt):
         # specify the training losses you want to print out. The training/test scripts will call <BaseModel.get_current_losses>
         for i in range(1, self.mod_gen_no + 1):
             self.loss_names.extend(['G_GAN_' + str(i), 'G_L1_' + str(i), 'G_VGG_'+ str(i),
-                                    #'G_TV_'+ str(i),
                                     'D_real_' + str(i), 'D_fake_' + str(i)])
             self.visual_names.extend(['fake_B_' + str(i), 'real_B_' + str(i)])
 
@@ -75,7 +70,7 @@ def __init__(self, opt):
         self.netGS = [None for _ in range(self.mod_gen_no)]
         for i in range(self.mod_gen_no):
             self.netG[i] = networks.define_G(self.opt.input_nc * self.opt.input_no, self.opt.output_nc, self.opt.ngf, self.opt.net_g[i], self.opt.norm,
-                                             not self.opt.no_dropout, self.opt.init_type, self.opt.init_gain, self.gpu_ids, self.opt.padding, resize_conv=opt.resize_conv)
+                                             not self.opt.no_dropout, self.opt.init_type, self.opt.init_gain, self.gpu_ids, self.opt.padding)
 
         if self.is_train:  # define a discriminator; conditional GANs need to take both input and output images; Therefore, #channels for D is input_nc + output_nc
             self.netD = [None for _ in range(self.mod_gen_no)]
@@ -88,11 +83,8 @@ def __init__(self, opt):
             # define loss functions
             self.criterionGAN_mod = networks.GANLoss(self.opt.gan_mode).to(self.device)
             self.criterionGAN_seg = networks.GANLoss(self.opt.gan_mode_s).to(self.device)
-
             self.criterionSmoothL1 = torch.nn.SmoothL1Loss()
-
             self.criterionVGG = networks.VGGLoss().to(self.device)
-            self.criterionTV = networks.TotalVariationLoss().to(self.device)
 
             # initialize optimizers; schedulers will be automatically created by function <BaseModel.setup>.
             params = []
@@ -161,7 +153,6 @@ def backward_D(self):
             self.loss_D_real.append(self.criterionGAN_mod(pred_real[i], True))
 
         # combine losses and calculate gradients
-        # self.loss_D = (self.loss_D_fake[0] + self.loss_D_real[0]) * 0.5 * self.loss_D_weights[0]
         self.loss_D = torch.tensor(0., device=self.device)
         for i in range(0, self.mod_gen_no):
             self.loss_D += (self.loss_D_fake[i] + self.loss_D_real[i]) * 0.5 * self.loss_D_weights[i]
@@ -188,16 +179,10 @@ def backward_G(self):
         for i in range(self.mod_gen_no):
             self.loss_G_VGG.append(self.criterionVGG(self.fake_B[i], self.real_B[i]) * self.opt.lambda_feat)
         
-        # self.loss_G_TV = []
-        # for i in range(self.mod_gen_no):
-        #     self.loss_G_TV.append(self.criterionTV(self.fake_B[i]) * 0.02)
 
-        # self.loss_G = (self.loss_G_GAN[0] + self.loss_G_L1[0]) * self.loss_G_weights[0]
         self.loss_G = torch.tensor(0., device=self.device)
         for i in range(0, self.mod_gen_no):
-            #self.loss_G += (self.loss_G_GAN[i] + self.loss_G_L1[i]) * self.loss_G_weights[i]
             self.loss_G += (self.loss_G_GAN[i] + self.loss_G_L1[i] + self.loss_G_VGG[i]) * self.loss_G_weights[i]
-            #self.loss_G += (self.loss_G_GAN[i] + self.loss_G_L1[i] + self.loss_G_VGG[i] + self.loss_G_TV[i]) * self.loss_G_weights[i]
         self.loss_G.backward()
 
     def optimize_parameters(self):

From 95698ae1305944cdf748fe06ef8e61062ec080b8 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Wed, 21 Aug 2024 18:04:34 +0000
Subject: [PATCH 39/41] conditional execution of metrics.json loading

---
 cli.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/cli.py b/cli.py
index 8e796dd..bb3c58f 100644
--- a/cli.py
+++ b/cli.py
@@ -292,9 +292,10 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
     if with_val:
         dataset_val = create_dataset(opt,phase='val')
         data_val = [batch for batch in dataset_val]
-        metrics_val = json.load(open(os.path.join(dataset_val.dataset.dir_AB,'metrics.json')))
         click.echo('The number of validation images = %d' % len(dataset_val))
-        click.echo('The number of validation images = %d' % len(data_val))
+        
+        if model in ['DeepLIIF']: 
+            metrics_val = json.load(open(os.path.join(dataset_val.dataset.dir_AB,'metrics.json')))
 
     # create a model given model and other options
     model = create_model(opt)

From 60d600e53ea3f138493a6c5e1ac6518c86d5d58b Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Thu, 22 Aug 2024 20:36:29 +0000
Subject: [PATCH 40/41] added changeds in the previous commit to train.py as
 well

---
 deepliif/scripts/train.py | 5 +++--
 train.py                  | 5 +++--
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/deepliif/scripts/train.py b/deepliif/scripts/train.py
index ec2bcce..4824f38 100644
--- a/deepliif/scripts/train.py
+++ b/deepliif/scripts/train.py
@@ -278,9 +278,10 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
     if with_val:
         dataset_val = create_dataset(opt,phase='val')
         data_val = [batch for batch in dataset_val]
-        metrics_val = json.load(open(os.path.join(dataset_val.dataset.dir_AB,'metrics.json')))
         click.echo('The number of validation images = %d' % len(dataset_val))
-        click.echo('The number of validation images = %d' % len(data_val))
+        
+        if model in ['DeepLIIF']: 
+            metrics_val = json.load(open(os.path.join(dataset_val.dataset.dir_AB,'metrics.json')))
 
     # create a model given model and other options
     model = create_model(opt)
diff --git a/train.py b/train.py
index cc2baf9..a7704c1 100644
--- a/train.py
+++ b/train.py
@@ -282,9 +282,10 @@ def train(dataroot, name, gpu_ids, checkpoints_dir, input_nc, output_nc, ngf, nd
     if with_val:
         dataset_val = create_dataset(opt,phase='val')
         data_val = [batch for batch in dataset_val]
-        metrics_val = json.load(open(os.path.join(dataset_val.dataset.dir_AB,'metrics.json')))
         click.echo('The number of validation images = %d' % len(dataset_val))
-        click.echo('The number of validation images = %d' % len(data_val))
+        
+        if model in ['DeepLIIF']: 
+            metrics_val = json.load(open(os.path.join(dataset_val.dataset.dir_AB,'metrics.json')))
 
     # create a model given model and other options
     model = create_model(opt)

From 0eba7b3259bfc566665c553e946bcb98c2b10a74 Mon Sep 17 00:00:00 2001
From: Wendy Wang <wanting.wang@columbia.edu>
Date: Tue, 3 Sep 2024 20:20:08 +0000
Subject: [PATCH 41/41] fixed match_suffix function for deepliifext/sdg

---
 tests/test_cli_inference.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/tests/test_cli_inference.py b/tests/test_cli_inference.py
index d2cb4be..7b2f2ad 100644
--- a/tests/test_cli_inference.py
+++ b/tests/test_cli_inference.py
@@ -5,7 +5,7 @@
 from util import *
 
 available_gpus = torch.cuda.device_count()
-TOLERANCE = 0.0001
+TOLERANCE = 0.0003
 TOLERANCE_SEG = 0.05
 
 subdir_testpy = 'test_latest/images'
@@ -36,7 +36,8 @@ def match_suffix(l_suffix_cli, model='DeepLIIF'):
             try:
                 img_type = suffix_cli[:3]
                 mod_idx = suffix_cli[3:]
-                res.append(d_cli2testpy[img_type] + mod_idx)
+                res_testpy.append(d_cli2testpy[img_type] + mod_idx)
+                res_cli.append(suffix_cli)
             except:
                 pass
 
@@ -403,6 +404,7 @@ def test_cli_inference_consistency(tmp_path, model_dir, model_info):
         
         remove_contents_in_folder(tmp_path)
         torch.cuda.nvtx.range_pop()
+        
     torch.cuda.nvtx.range_pop()
 
 
@@ -537,6 +539,7 @@ def test_cli_inference_eager_testpy_consistency(tmp_path, model_dir, model_info)
         # filenames from cli inference
         fns = [f for f in os.listdir(dirs_output[0]) if os.path.isfile(os.path.join(dirs_output[0], f)) and f.endswith('png')]
         fns = [x.replace('_Overlaid','').replace('_Refined','') for x in fns] # remove _Overlaid / _Refined
+        
         l_suffix_cli = list(set([fn[:-4].split('_')[-1] for fn in fns]))
         l_suffix_cli, l_suffix_testpy = match_suffix(l_suffix_cli, model=model_info['model'])
         print('suffix:',l_suffix_cli)       
@@ -556,4 +559,4 @@ def test_cli_inference_eager_testpy_consistency(tmp_path, model_dir, model_info)
         
         remove_contents_in_folder(tmp_path)
         torch.cuda.nvtx.range_pop()
-    torch.cuda.nvtx.range_pop()
\ No newline at end of file
+    torch.cuda.nvtx.range_pop()