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train_bins_dnet_polar.py
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train_bins_dnet_polar.py
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import numpy as np
import torch
import os
import csv
import math
import random
import cv2
from argparse import ArgumentParser
import torch.nn as nn
from torch.optim import Adam, SGD
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision.transforms import Normalize
from torch.nn.functional import interpolate
from dataset import PairFromBinDataset, AllPairsDataset, PairMinMaxBinDataset, PairFromBinDatasetPolar, PairMinMaxBinDatasetPolar
from network import SIFLayerPolar, TVLoss, VGGPerceptualLoss, LPIPSLoss, DenseUNetPolar, L1LossWithSoftLabels, BCELogitsLossWithSoftLabels, CrossEntropyLoss2d
from scipy import io
import shutil
import datetime
import os
import re
from typing import List, Optional, Tuple, Union
from modules.irisRecognition import irisRecognition
from modules.utils import get_cfg
import click
import dnnlib
import PIL.Image
import legacy
import pickle
from torchvision import models, transforms
from skimage import img_as_bool
import lpips
import warnings
warnings.filterwarnings("ignore")
from tqdm import tqdm
from math import pi
from PIL import Image, ImageDraw
from scipy import io
#from modules.layers import ConvOffset2D
import math
import sys
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
#torch.backends.cudnn.deterministic = True
def init_weights(m):
if isinstance(m, nn.Conv2d):
nn.init.kaiming_uniform_(m.weight.data, nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias.data, 0.01)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight.data, 1)
nn.init.constant_(m.bias.data, 0)
elif isinstance(m, nn.Linear):
nn.init.kaiming_uniform_(m.weight.data, nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias.data, 0.01)
def parse_vec2(s: Union[str, Tuple[float, float]]) -> Tuple[float, float]:
'''Parse a floating point 2-vector of syntax 'a,b'.
Example:
'0,1' returns (0,1)
'''
if isinstance(s, tuple): return s
parts = s.split(',')
if len(parts) == 2:
return (float(parts[0]), float(parts[1]))
raise ValueError(f'cannot parse 2-vector {s}')
def make_transform(translate: Tuple[float,float], angle: float):
m = np.eye(3)
s = np.sin(angle/360.0*np.pi*2)
c = np.cos(angle/360.0*np.pi*2)
m[0][0] = c
m[0][1] = s
m[0][2] = translate[0]
m[1][0] = -s
m[1][1] = c
m[1][2] = translate[1]
return m
def img_transform(img_tensor):
img_t = img_tensor[0]
img_t = np.clip(img_t.clone().detach().cpu().numpy() * 255, 0, 255)
img = Image.fromarray(img_t.astype(np.uint8))
return img
def get_concat_v(im1, im2):
dst = Image.new('RGB', (im1.width, im1.height + im2.height))
dst.paste(im1, (0, 0))
dst.paste(im2, (0, im1.height))
return dst
class CrossEntropyLoss2d(nn.Module):
def __init__(self, weight=None):
super().__init__()
self.loss = nn.NLLLoss(weight)
self.softmax = nn.LogSoftmax(dim=1)
def forward(self, outputs, targets):
return self.loss(self.softmax(outputs), targets)
class matchSIFCode(nn.Module):
def __init__(self, filter_size, num_filters, max_shift):
super().__init__()
self.loss = nn.SmoothL1Loss()
self.margin = int(math.ceil(filter_size/2))
self.num_filters = num_filters
self.max_shift = max_shift
def forward(self, outputs, targets, masks):
self.code1 = outputs[:, margin:-margin, :, :]
self.code2 = outputs[:, margin:-margin, :, :]
self.mask = masks[:, margin:-margin, :]
scoreC = torch.zeros((self.num_filters, 2*self.max_shift+1))
for shift in range(-self.max_shift, self.max_shift+1):
andMasks = torch.logical_and(self.mask, torch.roll(self.mask, shift, dims=2))
xorCodes = torch.logical_xor(self.code1, torch.roll(self.code2, shift, dims=2))
xorCodesMasked = torch.logical_and(xorCodes, torch.tile(torch.unsqueeze(andMasks,dim=3),self.num_filters))
scoreC[:,shift] = torch.sum(xorCodesMasked, dims=(1,2)) / torch.sum(andMasks)
return torch.min(scoreC)
def plot_grad_flow(named_parameters, tag):
ave_grads = []
layers = []
for n, p in named_parameters:
if(p.requires_grad) and ("bias" not in n):
layers.append('.')
ave_grads.append(p.grad.abs().mean().item())
#print(ave_grads)
plt.plot(ave_grads, alpha=0.3, color="b")
plt.hlines(0, 0, len(ave_grads)+1, linewidth=1, color="k" )
plt.xticks(range(0,len(ave_grads), 1), layers, rotation="vertical")
plt.xlim(xmin=0, xmax=len(ave_grads))
plt.xlabel("Layers")
plt.ylabel("Average gradient")
plt.title("Gradient flow")
plt.grid(True)
plt.savefig(tag+'_gradient_flow.png')
def resume(args):
checkpoint_dir = './dnet_polar_cp_' + datetime.datetime.now().strftime('%Y%m%d%H%M%S%f') + '/'
softmax = nn.LogSoftmax(dim=1)
# Declare Dataloaders
train_dataset = PairFromBinDatasetPolar(args.train_bins_path, args.parent_dir, input_size=(240,320))
train_dataloader = DataLoader(train_dataset, num_workers=args.num_workers, batch_size=args.batch_size, shuffle=True)
if args.val_bins_path:
val_dataset = PairFromBinDatasetPolar(args.val_bins_path, args.parent_dir, input_size=(240,320))
val_dataloader = DataLoader(val_dataset, num_workers=args.num_workers, batch_size=args.batch_size, shuffle=False)
else:
val_dataloader = None
print('No. of training batches', len(train_dataloader))
if val_dataloader is not None:
print('No. of validation batches', len(val_dataloader))
# Declare Models
if args.cuda and torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
#conv_map_net = models.resnet50(pretrained=False)
#conv_map_net.conv1 = nn.Conv2d(2, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
#conv_map_net.fc = nn.Linear(2048, G.z_dim)
#conv_map_net = conv_map_net.to(device)
#conv_map_net = models.resnet18(pretrained=False)
#conv_map_net.conv1 = nn.Conv2d(2, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
#conv_map_net.fc = DenseLinearNetwork(n_layers=7, input_dim=512, hidden_dim=512, output_dim=G.z_dim)
deform_net = torch.load(args.weight_path, map_location=device).to(device)
print(deform_net)
deform_net = deform_net.to(device)
if args.use_tv_loss:
tvLossModel = TVLoss(p=2)
if args.use_vgg_loss:
vggLossModel = VGGPerceptualLoss(device)
if args.use_lpips_loss:
lpipsLossModel = LPIPSLoss(device)
'''
if args.use_mask_loss:
maskLossModel = UNet(num_classes=2, num_channels=1).to(device)
maskLossModel.load_state_dict(torch.load(args.mask_model_path, map_location=device))
maskLossModel.eval()
'''
if args.use_sif_loss:
filter_mat = io.loadmat(args.sif_filter_path)['ICAtextureFilters']
'''
sifLossModel = SIFLayerMask(polar_height = 64, polar_width = 512, filter_mat = filter_mat, device=device).to(device)
sifLossModel.eval()
'''
sifLossModel = SIFLayerPolar(fiter_mat = filter_mat, device=device).to(device)
sifLossModel.eval()
if args.optim_type == 'adam':
optimizer = Adam(deform_net.parameters(), lr = args.lr, betas=(0.9, 0.99), eps=1e-8, amsgrad=True, weight_decay=1e-5)
elif args.optim_type == 'sgd':
optimizer = SGD(deform_net.parameters(), lr=args.lr, momentum=0.9)
elif args.optim_type == 'cyclic_lr':
optimizer = SGD(deform_net.parameters(), lr=args.lr, momentum=0.9)
scheduler = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.01*args.lr, max_lr=args.lr)
optimizer.zero_grad()
#print(len(optimizer.param_groups))
best_val_bit_diff = 100
print('Starting Training...')
sample_no = 0
for epoch in range(1, args.num_epochs+1):
deform_net.train()
epoch_loss = []
epoch_mse_loss = []
epoch_mask_loss = []
epoch_sif_loss = []
epoch_tv_loss = []
epoch_percept_loss = []
epoch_lpips_loss = []
epoch_sif_diff = []
if epoch != 1:
train_dataloader.dataset.reset()
vb_count = 0
vb_loss = 0
vb_batch = -1
for batch, data in enumerate(train_dataloader):
small_imgs_polar = data['small_img']
small_masks_polar = data['small_mask']
big_imgs_polar = data['big_img']
big_masks_polar = data['big_mask']
small_img_pxyr = data['small_img_pxyr']
small_img_ixyr = data['small_img_ixyr']
big_img_pxyr = data['big_img_pxyr']
big_img_ixyr = data['big_img_ixyr']
inp = Variable(torch.cat([small_imgs_polar, small_masks_polar], dim=1)).to(device)
tar = Variable(big_imgs_polar).to(device)
tar_mask = Variable(data['big_mask']).to(device)
tar_mask_norm = (tar_mask+1)/2
all_out = deform_net(inp, big_img_pxyr[:,2], big_img_ixyr[:,2])
out = nn.Tanh()(all_out[:, 0, :, :])
out_mask = all_out[:, 1:3, :, :]
out_norm = (out+1)/2
tar_norm = (tar+1)/2
mse_loss = nn.L1Loss()(out, tar.requires_grad_(False))
epoch_mse_loss.append(args.alpha * mse_loss.item())
if args.use_tv_loss:
tv_loss = tvLossModel(out_norm)
epoch_tv_loss.append(args.epsilon * tv_loss)
else:
tv_loss = 0.
if args.use_vgg_loss:
percept_loss = vggLossModel(out_norm, tar_norm.requires_grad_(False))
epoch_percept_loss.append(args.sigma * percept_loss)
else:
percept_loss = 0.
if args.use_lpips_loss:
lpips_loss = torch.mean(lpipsLossModel(out, tar.requires_grad_(False)))
epoch_lpips_loss.append(args.sigma * lpips_loss)
else:
lpips_loss = 0.
if args.use_mask_loss:
#mask_loss = nn.CrossEntropyLoss(label_smoothing=0.1)(out_mask, tar_mask_norm.requires_grad_(False))
mask_loss = CrossEntropyLoss2d()(out_mask, tar_mask_norm.requires_grad_(False))
epoch_mask_loss.append(args.beta * mask_loss.item())
else:
mask_loss = 0.
if args.use_sif_loss:
sif_out = sifLossModel(out_norm * 255)
sif_tar = sifLossModel(tar_norm * 255)
tar_mask_polar_rep = torch.cat([tar_mask_norm]*7, dim=1).requires_grad_(False)
minus_one = -1 * torch.ones(sif_tar.shape).to(device)
sif_tar_binary = torch.where(sif_tar > 0, one, zero).requires_grad_(False)
sif_tar_binary_masked = torch.clamp((sif_tar_binary * tar_mask_polar_rep).requires_grad_(False), 0, 1).float()
sif_tar_hinge_masked = torch.where(sif_tar_binary_masked < 0.5, one, minus_one)
sif_out_hinge_masked = (nn.Tanh()(sif_out) * tar_mask_polar_rep).requires_grad_(True)
sif_out_hinge_masked = torch.where(sif_out_hinge_masked == 0, minus_one, sif_out_hinge_masked)
if args.sif_hinge:
sif_loss = nn.HingeEmbeddingLoss()(nn.Flatten()(sif_out_hinge_masked), nn.Flatten()(sif_tar_hinge_masked)) + 1
else:
sif_tar_l1_masked = torch.where(sif_tar_binary_masked > 0.5, one, minus_one)
sif_loss = L1LossWithSoftLabels(device)(sif_out_hinge_masked, sif_tar_l1_masked)
epoch_sif_loss.append(args.gamma * sif_loss.item())
sif_out_binary = torch.where(sif_out_masked.clone().detach().requires_grad_(False) > 0.5, one, zero)
epoch_sif_diff.append(torch.mean(torch.abs(sif_out_binary * tar_mask_polar_rep - sif_tar_binary_masked).flatten()) * 100)
else:
sif_loss = 0.
loss = args.alpha * mse_loss + args.beta * mask_loss + args.gamma * sif_loss + args.epsilon * tv_loss + args.sigma * (percept_loss + lpips_loss)
vb_loss += loss.item()
loss.backward()
vb_count += 1
if vb_count == args.virtual_batch_mult:
optimizer.step()
if args.optim_type == 'cyclic_lr':
scheduler.step()
epoch_loss.append(vb_loss/args.virtual_batch_mult)
vb_loss = 0
vb_count = 0
vb_batch += 1
optimizer.zero_grad()
if vb_batch % args.log_batch == 0 and vb_count == 0:
train_loss_average = sum(epoch_loss) / len(epoch_loss)
mse_loss_average = sum(epoch_mse_loss) / len(epoch_mse_loss)
loss_string = "Train loss: {aver} (epoch: {epoch}, batch: {batch}) Direct loss: {mse}".format(aver = train_loss_average, epoch = epoch, batch = vb_batch, mse = mse_loss_average)
if args.use_tv_loss:
tv_loss_average = sum(epoch_tv_loss) / len(epoch_tv_loss)
loss_string += ", TV loss: {tv}".format(tv = tv_loss_average)
if args.use_vgg_loss:
percept_loss_average = sum(epoch_percept_loss) / len(epoch_percept_loss)
loss_string += ", Perceptual loss: {percept}".format(percept = percept_loss_average)
if args.use_lpips_loss:
lpips_loss_average = sum(epoch_lpips_loss) / len(epoch_lpips_loss)
loss_string += ", LPIPS loss: {lpips}".format(lpips = lpips_loss_average)
if args.use_mask_loss:
mask_loss_average = sum(epoch_mask_loss) / len(epoch_mask_loss)
loss_string += ", Mask loss: {mask}".format(mask = mask_loss_average)
if args.use_sif_loss:
sif_loss_average = sum(epoch_sif_loss) / len(epoch_sif_loss)
loss_string += ", SIF loss: {sif}".format(sif = sif_loss_average)
sif_bit_average = sum(epoch_sif_diff) / len(epoch_sif_diff)
loss_string += ", SIF bit diff: {sif_bit}%".format(sif_bit = sif_bit_average)
print(loss_string)
if args.log_file is not None:
with open(args.log_file, 'a') as f:
f.write(loss_string + '\n')
if val_dataloader is not None:
deform_net.eval()
val_loss_average = 0
val_bit_diff_average = 0
with torch.no_grad():
for i in range(args.val_repeats):
val_dataloader.dataset.reset()
val_epoch_loss = []
val_bit_diff = []
for batch, data in enumerate(val_dataloader):
small_masks_polar = data['small_mask']
big_imgs_polar = data['big_img']
big_masks_polar = data['big_mask']
small_img_pxyr = data['small_img_pxyr']
small_img_ixyr = data['small_img_ixyr']
big_img_pxyr = data['big_img_pxyr']
big_img_ixyr = data['big_img_ixyr']
inp = Variable(torch.cat([small_imgs_polar, small_masks_polar], dim=1)).to(device)
tar = Variable(big_imgs_polar).to(device)
tar_mask = Variable(data['big_mask']).to(device)
tar_mask_norm = (tar_mask+1)/2
all_out = deform_net(inp, big_img_pxyr[:,2], big_img_ixyr[:,2])
out = nn.Tanh()(all_out[:, 0, :, :])
out_mask = all_out[:, 1:3, :, :]
out_norm = (out+1)/2
tar_norm = (tar+1)/2
mse_loss = nn.L1Loss()(out, tar.requires_grad_(False))
if args.use_tv_loss:
tv_loss = tvLossModel(out_norm)
else:
tv_loss = 0.
if args.use_vgg_loss:
percept_loss = vggLossModel(out_norm, tar_norm.requires_grad_(False))
else:
percept_loss = 0.
if args.use_lpips_loss:
lpips_loss = torch.mean(lpipsLossModel(out, tar.requires_grad_(False)))
else:
lpips_loss = 0.
if args.use_mask_loss:
#mask_loss = nn.CrossEntropyLoss(label_smoothing=0.1)(out_mask, tar_mask_norm.requires_grad_(False))
mask_loss = CrossEntropyLoss2d()(out_mask, tar_mask_norm.requires_grad_(False))
else:
mask_loss = 0.
if args.use_sif_loss:
sif_out = sifLossModel(out_norm * 255)
sif_tar = sifLossModel(tar_norm * 255)
tar_mask_polar_rep = torch.cat([tar_mask_norm]*7, dim=1).requires_grad_(False)
minus_one = -1 * torch.ones(sif_tar.shape).to(device)
sif_tar_binary = torch.where(sif_tar > 0, one, zero).requires_grad_(False)
sif_tar_binary_masked = torch.clamp((sif_tar_binary * tar_mask_polar_rep).requires_grad_(False), 0, 1).float()
sif_tar_hinge_masked = torch.where(sif_tar_binary_masked < 0.5, one, minus_one)
sif_out_hinge_masked = (nn.Tanh()(sif_out) * tar_mask_polar_rep).requires_grad_(True)
sif_out_hinge_masked = torch.where(sif_out_hinge_masked == 0, minus_one, sif_out_hinge_masked)
if args.sif_hinge:
sif_loss = nn.HingeEmbeddingLoss()(nn.Flatten()(sif_out_hinge_masked), nn.Flatten()(sif_tar_hinge_masked)) + 1
else:
sif_tar_l1_masked = torch.where(sif_tar_binary_masked > 0.5, one, minus_one)
sif_loss = L1LossWithSoftLabels(device)(sif_out_hinge_masked, sif_tar_l1_masked)
sif_out_binary = torch.where(sif_out_masked.clone().detach().requires_grad_(False) > 0.5, one, zero)
val_bit_diff.append(torch.mean(torch.abs(sif_out_binary * tar_mask_polar_rep - sif_tar_binary_masked).flatten()) * 100)
else:
sif_loss = 0.
loss = args.alpha * mse_loss + args.beta * mask_loss + args.gamma * sif_loss + args.epsilon * tv_loss + args.sigma * (percept_loss + lpips_loss)
val_epoch_loss.append(loss.item())
val_loss_average += (sum(val_epoch_loss) / len(val_epoch_loss))
val_bit_diff_average += (sum(val_bit_diff) / len(val_bit_diff))
val_loss_average /= args.val_repeats
val_bit_diff_average /= args.val_repeats
print("Val loss: {aver}, Val bit diff: {bit_diff} (epoch: {epoch})".format(aver = val_loss_average, bit_diff = val_bit_diff_average, epoch = epoch))
if args.log_file is not None:
with open(args.log_file, 'a') as f:
f.write("Val loss: {aver}, Val bit diff: {bit_diff} (epoch: {epoch})".format(aver = val_loss_average, bit_diff = val_bit_diff_average, epoch = epoch) + '\n')
if val_bit_diff_average < best_val_bit_diff:
best_val_bit_diff = val_bit_diff_average
if not os.path.exists(checkpoint_dir):
os.mkdir(checkpoint_dir)
filename = checkpoint_dir + "{epoch:04}-val_bit_diff-{bit_diff}.pth".format(epoch = epoch, bit_diff=val_bit_diff_average)
torch.save(deform_net, filename)
return True
def train(args):
checkpoint_dir = './dnet_polar_cp_' + datetime.datetime.now().strftime('%Y%m%d%H%M%S%f') + '/'
softmax = nn.LogSoftmax(dim=1)
# Declare Dataloaders
train_dataset = PairFromBinDatasetPolar(args.train_bins_path, args.parent_dir, input_size=(240,320), polar_width=512, polar_height=64)
train_dataloader = DataLoader(train_dataset, num_workers=args.num_workers, batch_size=args.batch_size, shuffle=True)
if args.val_bins_path:
val_dataset = PairFromBinDatasetPolar(args.val_bins_path, args.parent_dir, input_size=(240,320), polar_width=512, polar_height=64)
val_dataloader = DataLoader(val_dataset, num_workers=args.num_workers, batch_size=args.batch_size, shuffle=False)
else:
val_dataloader = None
print('No. of training batches', len(train_dataloader))
if val_dataloader is not None:
print('No. of validation batches', len(val_dataloader))
# Declare Models
if args.cuda and torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
#conv_map_net = models.resnet50(pretrained=False)
#conv_map_net.conv1 = nn.Conv2d(2, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
#conv_map_net.fc = nn.Linear(2048, G.z_dim)
#conv_map_net = conv_map_net.to(device)
#conv_map_net = models.resnet18(pretrained=False)
#conv_map_net.conv1 = nn.Conv2d(2, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
#conv_map_net.fc = DenseLinearNetwork(n_layers=7, input_dim=512, hidden_dim=512, output_dim=G.z_dim)
deform_net = DenseUNetPolar(num_classes=3, num_channels=2, width=4, image_shape=(64, 512))
deform_net.apply(init_weights)
print(deform_net)
deform_net = deform_net.to(device)
if args.use_tv_loss:
tvLossModel = TVLoss(p=2)
if args.use_vgg_loss:
vggLossModel = VGGPerceptualLoss(device)
if args.use_lpips_loss:
lpipsLossModel = LPIPSLoss(device)
'''
if args.use_mask_loss:
maskLossModel = UNet(num_classes=2, num_channels=1).to(device)
maskLossModel.load_state_dict(torch.load(args.mask_model_path, map_location=device))
maskLossModel.eval()
'''
if args.use_sif_loss:
filter_mat = io.loadmat(args.sif_filter_path)['ICAtextureFilters']
'''
sifLossModel = SIFLayerMask(polar_height = 64, polar_width = 512, filter_mat = filter_mat, device=device).to(device)
sifLossModel.eval()
'''
sifLossModel = SIFLayerPolar(filter_mat = filter_mat, device=device).to(device)
sifLossModel.eval()
if args.optim_type == 'adam':
optimizer = Adam(deform_net.parameters(), lr = args.lr, betas=(0.9, 0.99), eps=1e-8, amsgrad=True, weight_decay=1e-5)
elif args.optim_type == 'sgd':
optimizer = SGD(deform_net.parameters(), lr=args.lr, momentum=0.9)
elif args.optim_type == 'cyclic_lr':
optimizer = SGD(deform_net.parameters(), lr=args.lr, momentum=0.9)
scheduler = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.01*args.lr, max_lr=args.lr)
optimizer.zero_grad()
#print(len(optimizer.param_groups))
best_val_bit_diff = 100
print('Starting Training...')
sample_no = 0
for epoch in range(1, args.num_epochs+1):
deform_net.train()
epoch_loss = []
epoch_mse_loss = []
epoch_mask_loss = []
epoch_sif_loss = []
epoch_tv_loss = []
epoch_percept_loss = []
epoch_lpips_loss = []
epoch_sif_diff = []
epoch_sif_diff_nomask = []
if epoch != 1:
train_dataloader.dataset.reset()
vb_count = 0
vb_loss = 0
vb_batch = -1
for batch, data in enumerate(train_dataloader):
small_imgs_polar = data['small_img']
small_masks_polar = data['small_mask']
big_imgs_polar = data['big_img']
big_masks_polar = data['big_mask']
small_img_pxyr = data['small_img_pxyr']
small_img_ixyr = data['small_img_ixyr']
big_img_pxyr = Variable(data['big_img_pxyr']).to(device)
big_img_ixyr = Variable(data['big_img_ixyr']).to(device)
inp = Variable(torch.cat([small_imgs_polar, small_masks_polar], dim=1)).to(device)
tar = Variable(big_imgs_polar).to(device)
tar_mask = Variable(data['big_mask']).to(device)
tar_mask_norm = (tar_mask+1)/2
all_out = deform_net(inp, big_img_pxyr[:,2], big_img_ixyr[:,2])
out = nn.Tanh()(all_out[:, 0, :, :]).reshape(inp.shape[0], 1, inp.shape[2], inp.shape[3])
out_mask = all_out[:, 1:3, :, :]
out_norm = (out+1)/2
tar_norm = (tar+1)/2
mse_loss = nn.L1Loss()(out, tar.requires_grad_(False))
epoch_mse_loss.append(args.alpha * mse_loss.item())
if args.use_tv_loss:
tv_loss = tvLossModel(out_norm)
epoch_tv_loss.append(args.epsilon * tv_loss)
else:
tv_loss = 0.
if args.use_vgg_loss:
percept_loss = vggLossModel(out_norm, tar_norm.requires_grad_(False))
epoch_percept_loss.append(args.sigma * percept_loss)
else:
percept_loss = 0.
if args.use_lpips_loss:
lpips_loss = torch.mean(lpipsLossModel(out, tar.requires_grad_(False)))
epoch_lpips_loss.append(args.sigma * lpips_loss)
else:
lpips_loss = 0.
if args.use_mask_loss:
#mask_loss = nn.CrossEntropyLoss(label_smoothing=0.1)(out_mask, tar_mask_norm.requires_grad_(False))
mask_loss = CrossEntropyLoss2d()(out_mask, tar_mask_norm[:,0].long().requires_grad_(False))
epoch_mask_loss.append(args.beta * mask_loss.item())
else:
mask_loss = 0.
if args.use_sif_loss:
sif_out = sifLossModel(out_norm * 255)
sif_tar = sifLossModel(tar_norm * 255)
tar_mask_polar_rep = torch.cat([tar_mask_norm]*7, dim=1).requires_grad_(False).to(device)
one = torch.ones(sif_tar.shape).to(device)
minus_one = -1 * torch.ones(sif_tar.shape).to(device)
zero = torch.zeros(sif_tar.shape).to(device)
sif_tar_binary = torch.where(sif_tar > 0, one, zero).requires_grad_(False)
sif_tar_binary_masked = torch.clamp((sif_tar_binary * tar_mask_polar_rep).requires_grad_(False), 0, 1).float()
sif_tar_hinge_masked = torch.where(sif_tar_binary_masked < 0.5, one, minus_one)
sif_out_hinge_masked = (nn.Tanh()(sif_out) * tar_mask_polar_rep).requires_grad_(True)
sif_out_hinge_masked = torch.where(sif_out_hinge_masked == 0, minus_one, sif_out_hinge_masked)
if args.sif_hinge:
sif_loss = nn.HingeEmbeddingLoss()(nn.Flatten()(sif_out_hinge_masked), nn.Flatten()(sif_tar_hinge_masked)) + 1
elif args.sif_bce:
#sif_loss = BCELogitsLossWithSoftLabels(device)(nn.Flatten()(sif_out), nn.Flatten()(sif_tar_binary), nn.Flatten()(tar_mask_polar_rep))
sif_loss = nn.BCEWithLogitsLoss(pos_weight=nn.Flatten()(tar_mask_polar_rep))(nn.Flatten()(sif_out), nn.Flatten()(sif_tar_binary))
else:
sif_tar_l1_masked = torch.where(sif_tar_binary_masked > 0.5, one, minus_one)
sif_loss = L1LossWithSoftLabels(device)(sif_out_hinge_masked, sif_tar_l1_masked)
epoch_sif_loss.append(args.gamma * sif_loss.item())
sif_out_binary = torch.where(sif_out.clone().detach().requires_grad_(False) > 0, one, zero)
out_mask_prob = nn.LogSoftmax(dim=1)(out_mask.clone().detach().requires_grad_(False))
out_mask_binary = torch.argmax(out_mask_prob, dim=1)
out_mask_rep = torch.cat([out_mask_binary.reshape(-1, 1, out_mask.shape[2], out_mask.shape[3])]*7, dim=1)
andMasks = out_mask_rep * tar_mask_polar_rep
epoch_sif_diff.append(torch.mean((torch.abs(sif_out_binary - sif_tar_binary) * andMasks).flatten()) * 100)
epoch_sif_diff_nomask.append(torch.mean((torch.abs(sif_out_binary - sif_tar_binary) * tar_mask_polar_rep).flatten()) * 100)
else:
sif_loss = 0.
loss = args.alpha * mse_loss + args.beta * mask_loss + args.gamma * sif_loss + args.epsilon * tv_loss + args.sigma * (percept_loss + lpips_loss)
vb_loss += loss.item()
loss.backward()
vb_count += 1
if vb_count == args.virtual_batch_mult:
optimizer.step()
if args.optim_type == 'cyclic_lr':
scheduler.step()
epoch_loss.append(vb_loss/args.virtual_batch_mult)
vb_loss = 0
vb_count = 0
vb_batch += 1
optimizer.zero_grad()
if vb_batch % args.log_batch == 0 and vb_count == 0:
train_loss_average = sum(epoch_loss) / len(epoch_loss)
mse_loss_average = sum(epoch_mse_loss) / len(epoch_mse_loss)
loss_string = "Train loss: {aver} (epoch: {epoch}, batch: {batch}) Direct loss: {mse}".format(aver = train_loss_average, epoch = epoch, batch = vb_batch, mse = mse_loss_average)
if args.use_tv_loss:
tv_loss_average = sum(epoch_tv_loss) / len(epoch_tv_loss)
loss_string += ", TV loss: {tv}".format(tv = tv_loss_average)
if args.use_vgg_loss:
percept_loss_average = sum(epoch_percept_loss) / len(epoch_percept_loss)
loss_string += ", Perceptual loss: {percept}".format(percept = percept_loss_average)
if args.use_lpips_loss:
lpips_loss_average = sum(epoch_lpips_loss) / len(epoch_lpips_loss)
loss_string += ", LPIPS loss: {lpips}".format(lpips = lpips_loss_average)
if args.use_mask_loss:
mask_loss_average = sum(epoch_mask_loss) / len(epoch_mask_loss)
loss_string += ", Mask loss: {mask}".format(mask = mask_loss_average)
if args.use_sif_loss:
sif_loss_average = sum(epoch_sif_loss) / len(epoch_sif_loss)
loss_string += ", SIF loss: {sif}".format(sif = sif_loss_average)
sif_bit_average = sum(epoch_sif_diff) / len(epoch_sif_diff)
loss_string += ", SIF bit diff: {sif_bit}%".format(sif_bit = sif_bit_average)
sif_bit_nomask_average = sum(epoch_sif_diff_nomask) / len(epoch_sif_diff_nomask)
loss_string += ", SIF bit diff no mask: {sif_bit}%".format(sif_bit = sif_bit_nomask_average)
print(loss_string)
if args.log_file is not None:
with open(args.log_file, 'a') as f:
f.write(loss_string + '\n')
if val_dataloader is not None:
deform_net.eval()
val_loss_average = 0
val_bit_diff_average = 0
val_bit_diff_nomask_average = 0
with torch.no_grad():
for i in range(args.val_repeats):
val_dataloader.dataset.reset()
val_epoch_loss = []
val_bit_diff = []
val_bit_diff_nomask = []
for batch, data in enumerate(val_dataloader):
small_imgs_polar = data['small_img']
small_masks_polar = data['small_mask']
big_imgs_polar = data['big_img']
big_masks_polar = data['big_mask']
small_img_pxyr = data['small_img_pxyr']
small_img_ixyr = data['small_img_ixyr']
big_img_pxyr = Variable(data['big_img_pxyr']).to(device)
big_img_ixyr = Variable(data['big_img_ixyr']).to(device)
inp = Variable(torch.cat([small_imgs_polar, small_masks_polar], dim=1)).to(device)
tar = Variable(big_imgs_polar).to(device)
tar_mask = Variable(data['big_mask']).to(device)
tar_mask_norm = (tar_mask+1)/2
all_out = deform_net(inp, big_img_pxyr[:,2], big_img_ixyr[:,2])
out = nn.Tanh()(all_out[:, 0, :, :]).reshape(inp.shape[0], 1, inp.shape[2], inp.shape[3])
out_mask = all_out[:, 1:3, :, :]
out_norm = (out+1)/2
tar_norm = (tar+1)/2
mse_loss = nn.L1Loss()(out, tar.requires_grad_(False))
if args.use_tv_loss:
tv_loss = tvLossModel(out_norm)
else:
tv_loss = 0.
if args.use_vgg_loss:
percept_loss = vggLossModel(out_norm, tar_norm.requires_grad_(False))
else:
percept_loss = 0.
if args.use_lpips_loss:
lpips_loss = torch.mean(lpipsLossModel(out, tar.requires_grad_(False)))
else:
lpips_loss = 0.
if args.use_mask_loss:
#mask_loss = nn.CrossEntropyLoss(label_smoothing=0.1)(out_mask, tar_mask_norm.requires_grad_(False))
mask_loss = CrossEntropyLoss2d()(out_mask, tar_mask_norm[:,0].long().requires_grad_(False))
else:
mask_loss = 0.
if args.use_sif_loss:
sif_out = sifLossModel(out_norm * 255)
sif_tar = sifLossModel(tar_norm * 255)
tar_mask_polar_rep = torch.cat([tar_mask_norm]*7, dim=1).requires_grad_(False)
one = torch.ones(sif_tar.shape).to(device)
minus_one = -1 * torch.ones(sif_tar.shape).to(device)
zero = torch.zeros(sif_tar.shape).to(device)
sif_tar_binary = torch.where(sif_tar > 0, one, zero).requires_grad_(False)
sif_tar_binary_masked = torch.clamp((sif_tar_binary * tar_mask_polar_rep).requires_grad_(False), 0, 1).float()
sif_tar_hinge_masked = torch.where(sif_tar_binary_masked < 0.5, one, minus_one)
sif_out_hinge_masked = (nn.Tanh()(sif_out) * tar_mask_polar_rep).requires_grad_(True)
sif_out_hinge_masked = torch.where(sif_out_hinge_masked == 0, minus_one, sif_out_hinge_masked)
if args.sif_hinge:
sif_loss = nn.HingeEmbeddingLoss()(nn.Flatten()(sif_out_hinge_masked), nn.Flatten()(sif_tar_hinge_masked)) + 1
elif args.sif_bce:
#sif_loss = BCELogitsLossWithSoftLabels(device)(nn.Flatten()(sif_out), nn.Flatten()(sif_tar_binary), nn.Flatten()(tar_mask_polar_rep))
sif_loss = nn.BCEWithLogitsLoss(pos_weight=nn.Flatten()(tar_mask_polar_rep))(nn.Flatten()(sif_out), nn.Flatten()(sif_tar_binary))
else:
sif_tar_l1_masked = torch.where(sif_tar_binary_masked > 0.5, one, minus_one)
sif_loss = L1LossWithSoftLabels(device)(sif_out_hinge_masked, sif_tar_l1_masked)
sif_out_binary = torch.where(sif_out.clone().detach().requires_grad_(False) > 0, one, zero)
out_mask_prob = nn.LogSoftmax(dim=1)(out_mask.clone().detach().requires_grad_(False))
out_mask_binary = torch.argmax(out_mask_prob, dim=1)
out_mask_rep = torch.cat([out_mask_binary.reshape(-1, 1, out_mask.shape[2], out_mask.shape[3])]*7, dim=1)
andMasks = out_mask_rep * tar_mask_polar_rep
val_bit_diff.append(torch.mean((torch.abs(sif_out_binary - sif_tar_binary) * andMasks).flatten()) * 100)
val_bit_diff_nomask.append(torch.mean((torch.abs(sif_out_binary - sif_tar_binary) * tar_mask_polar_rep).flatten()) * 100)
else:
sif_loss = 0.
loss = args.alpha * mse_loss + args.beta * mask_loss + args.gamma * sif_loss + args.epsilon * tv_loss + args.sigma * (percept_loss + lpips_loss)
val_epoch_loss.append(loss.item())
val_loss_average += (sum(val_epoch_loss) / len(val_epoch_loss))
val_bit_diff_average += (sum(val_bit_diff) / len(val_bit_diff))
val_bit_diff_nomask_average += (sum(val_bit_diff_nomask) / len(val_bit_diff_nomask))
val_loss_average /= args.val_repeats
val_bit_diff_average /= args.val_repeats
val_bit_diff_nomask_average /= args.val_repeats
print("Val loss: {aver}, Val bit diff: {bit_diff}, Val bit diff no mask: {bit_diff_nm}, (epoch: {epoch})".format(aver = val_loss_average, bit_diff = val_bit_diff_average, bit_diff_nm = val_bit_diff_nomask_average, epoch = epoch))
if args.log_file is not None:
with open(args.log_file, 'a') as f:
f.write("Val loss: {aver}, Val bit diff: {bit_diff}, Val bit diff no mask: {bit_diff_nm}, (epoch: {epoch})".format(aver = val_loss_average, bit_diff = val_bit_diff_average, bit_diff_nm = val_bit_diff_nomask_average, epoch = epoch) + '\n')
if val_bit_diff_nomask_average < best_val_bit_diff:
best_val_bit_diff = val_bit_diff_nomask_average
if not os.path.exists(checkpoint_dir):
os.mkdir(checkpoint_dir)
filename = checkpoint_dir + "{epoch:04}-val_bit_diff-{bit_diff}.pth".format(epoch = epoch, bit_diff=val_bit_diff_nomask_average)
torch.save(deform_net, filename)
return True
def evaluate(cfg, args):
test_dataset = PairMinMaxBinDataset(args.test_bins_path, args.parent_dir, max_pairs_inp=4, input_size=(256,256))
test_dataloader = DataLoader(test_dataset, num_workers=args.num_workers, batch_size=args.batch_size, shuffle=False)
if args.cuda and torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
deform_net = torch.load(args.weight_path, map_location=device).to(device)
deform_net.eval()
code_rminp = []
mask_rminp = []
code_rmaxp = []
mask_rmaxp = []
code_pmaxp = []
mask_pmaxp = []
irisRec = irisRecognition(cfg, use_hough=False)
args.val_repeats = 1
with torch.no_grad():
for rep in range(args.val_repeats):
print('Repeat no:', str(rep+1)+'/'+str(args.val_repeats))
if rep != 0:
test_dataloader.dataset.reset()
for batch, data in tqdm(enumerate(test_dataloader), total=len(test_dataloader)):
small_imgs = data['small_img']
small_masks = data['small_mask']
big_imgs = data['big_img']
big_masks = data['big_mask']
inp_mask = Variable(data['big_mask'])
if args.cuda:
inp_mask.cuda()
#Prepare input data
small_img_pxyr = data['small_img_pxyr']
small_img_ixyr = data['small_img_ixyr']
big_img_pxyr = data['big_img_pxyr']
big_img_ixyr = data['big_img_ixyr']
#Prepare mapping input
inp = Variable(torch.cat([small_imgs, big_masks], dim=1))
if args.cuda:
inp = inp.cuda()
#Find mapping to z
out = deform_net(inp)
#Unnormalize everything (resize output to 320x240)
o_img_t = interpolate((out + 1)/2, size=(240, 320), mode='bilinear')
small_img_t = interpolate((small_imgs + 1)/2, size=(240, 320), mode='bilinear')
big_img_t = interpolate((big_imgs + 1)/2, size=(240, 320), mode='bilinear')
small_mask_t = interpolate((small_masks + 1)/2, size=(240, 320), mode='nearest')
big_mask_t = interpolate((big_masks + 1)/2, size=(240, 320), mode='nearest')
#Find and append codes
for b in tqdm(range(small_img_t.shape[0]), total=small_img_t.shape[0]):
s_img = img_transform(small_img_t[b])
b_img = img_transform(big_img_t[b])
o_img = img_transform(o_img_t[b])
s_mask = (small_mask_t[b][0].clone().detach().cpu().numpy()).astype(np.uint8)
b_mask = (big_mask_t[b][0].clone().detach().cpu().numpy()).astype(np.uint8)
s_pxyr = np.around(small_img_pxyr[b].clone().detach().cpu().numpy()).astype(np.intc)
s_ixyr = np.around(small_img_ixyr[b].clone().detach().cpu().numpy()).astype(np.intc)
b_pxyr = np.around(big_img_pxyr[b].clone().detach().cpu().numpy()).astype(np.intc)
b_ixyr = np.around(big_img_ixyr[b].clone().detach().cpu().numpy()).astype(np.intc)
s_img_polar, s_mask_polar = irisRec.cartToPol(s_img, s_mask, s_pxyr, s_ixyr)
b_img_polar, b_mask_polar = irisRec.cartToPol(b_img, b_mask, b_pxyr, b_ixyr)
o_mask = irisRec.segment(o_img)
o_pxyr, o_ixyr = irisRec.circApprox(image=o_img)
o_img_polar, o_mask_polar = irisRec.cartToPol(o_img, o_mask, o_pxyr, o_ixyr)
code_rminp.append(irisRec.extractCode(s_img_polar))
mask_rminp.append(s_mask_polar)
code_rmaxp.append(irisRec.extractCode(b_img_polar))
mask_rmaxp.append(b_mask_polar)
code_pmaxp.append(irisRec.extractCode(o_img_polar))
mask_pmaxp.append(o_mask_polar)
if batch == 0:
all_polar_imgs = get_concat_v(get_concat_v(get_concat_v(Image.fromarray(s_img_polar.astype(np.uint8)), Image.fromarray(s_mask_polar.astype(np.uint8) * 255)), get_concat_v(Image.fromarray(b_img_polar.astype(np.uint8)), Image.fromarray(b_mask_polar.astype(np.uint8) * 255))), get_concat_v(Image.fromarray(o_img_polar.astype(np.uint8)), Image.fromarray(o_mask_polar.astype(np.uint8) * 255)))
#code_shape = code_rminp[0].shape
#code1 = Image.fromarray(np.where(code_rminp[-1].reshape(code_shape[0]*code_shape[2], code_shape[1])>0, 255, 0).astype(np.uint8))
#code2 = Image.fromarray(np.where(code_rmaxp[-1].reshape(code_shape[0]*code_shape[2], code_shape[1])>0, 255, 0).astype(np.uint8))
#code3 = Image.fromarray(np.where(code_pmaxp[-1].reshape(code_shape[0]*code_shape[2], code_shape[1])>0, 255, 0).astype(np.uint8))
#all_codes = get_concat_v(get_concat_v(code1, code2), code3)
all_polar_imgs.save('samples_eval/img_polar_sample_'+str(b)+'.png')
#all_codes.save('samples/code_sample_'+str(b)+'.png')
s_img_c = s_img.convert('RGB')
b_img_c = b_img.convert('RGB')
o_img_c = o_img.convert('RGB')
s_draw = ImageDraw.Draw(s_img_c)
s_draw.ellipse((s_pxyr[0]-s_pxyr[2], s_pxyr[1]-s_pxyr[2], s_pxyr[0]+s_pxyr[2], s_pxyr[1]+s_pxyr[2]), outline ="red")
s_draw.ellipse((s_ixyr[0]-s_ixyr[2], s_ixyr[1]-s_ixyr[2], s_ixyr[0]+s_ixyr[2], s_ixyr[1]+s_ixyr[2]), outline ="blue")
b_draw = ImageDraw.Draw(b_img_c)
b_draw.ellipse((b_pxyr[0]-b_pxyr[2], b_pxyr[1]-b_pxyr[2], b_pxyr[0]+b_pxyr[2], b_pxyr[1]+b_pxyr[2]), outline ="red")
b_draw.ellipse((b_ixyr[0]-b_ixyr[2], b_ixyr[1]-b_ixyr[2], b_ixyr[0]+b_ixyr[2], b_ixyr[1]+b_ixyr[2]), outline ="blue")
o_draw = ImageDraw.Draw(o_img_c)
o_draw.ellipse((o_pxyr[0]-o_pxyr[2], o_pxyr[1]-o_pxyr[2], o_pxyr[0]+o_pxyr[2], o_pxyr[1]+o_pxyr[2]), outline ="red")
o_draw.ellipse((o_ixyr[0]-o_ixyr[2], o_ixyr[1]-b_ixyr[2], o_ixyr[0]+b_ixyr[2], o_ixyr[1]+o_ixyr[2]), outline ="blue")
all_imgs = get_concat_v(get_concat_v(get_concat_v(s_img_c, Image.fromarray(s_mask.astype(np.uint8) * 255)), get_concat_v(b_img_c, Image.fromarray(b_mask.astype(np.uint8) * 255))), get_concat_v(o_img_c, Image.fromarray(o_mask.astype(np.uint8) * 255)))
all_imgs.save('samples_eval/img_sample_'+str(b)+'.png')
inp_tar_scores_genuine = []
out_tar_scores_genuine = []
inp_tar_scores_imposter = []
out_tar_scores_imposter = []
fte = 0
total = 0
for i in tqdm(range(len(code_rminp)), total=len(code_rminp)):
#print("Genuine: ")
score_rminrmax, shift_rminrmax = irisRec.matchCodes(code_rminp[i], code_rmaxp[i], mask_rminp[i], mask_rmaxp[i])
score_pmaxrmax, shift_pmaxrmax = irisRec.matchCodes(code_pmaxp[i], code_rmaxp[i], mask_pmaxp[i], mask_rmaxp[i])
inp_tar_score = float(score_rminrmax)
inp_tar_shift = float(shift_rminrmax)
out_tar_score = float(score_pmaxrmax)
out_tar_shift = float(shift_pmaxrmax)
if out_tar_score < inp_tar_score:
condition = "better"
elif out_tar_score > inp_tar_score:
condition = "worse"
else:
condition = "same"
print(i, "Input <-> Target : {:.3f} (mutual rot: {:.1f} deg), Output <-> Target : {:.3f} (mutual rot: {:.1f} deg)".format(inp_tar_score,360*inp_tar_shift/512 ,out_tar_score,360*out_tar_shift/512), condition)
with open('scores.txt', 'a') as f:
f.write(str(i) + " Input <-> Target : {:.3f} (mutual rot: {:.1f} deg), Output <-> Target : {:.3f} (mutual rot: {:.1f} deg) ".format(inp_tar_score,360*inp_tar_shift/512 ,out_tar_score,360*out_tar_shift/512) + str(condition) + '\n')
if inp_tar_score is not None and out_tar_score is not None and not math.isnan(inp_tar_score) and not math.isnan(out_tar_score):
inp_tar_scores_genuine.append(inp_tar_score)
out_tar_scores_genuine.append(out_tar_score)
total += 1
else:
fte += 1
total += 1
for j in tqdm(range(i+1, len(code_rminp)), total=(len(code_rminp)-i-1)):
score_rminrmax, shift_rminrmax = irisRec.matchCodes(code_rminp[i], code_rmaxp[j], mask_rminp[i], mask_rmaxp[j])
score_pmaxrmax, shift_pmaxrmax = irisRec.matchCodes(code_pmaxp[i], code_rmaxp[j], mask_pmaxp[i], mask_rmaxp[j])
inp_tar_score = float(score_rminrmax)
inp_tar_shift = float(shift_rminrmax)
out_tar_score = float(score_pmaxrmax)
out_tar_shift = float(shift_pmaxrmax)
if out_tar_score < inp_tar_score:
condition = "better"
elif out_tar_score > inp_tar_score: