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utils.py
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utils.py
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"""
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license
(https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import os
import yaml
import time
import torch
from torch.utils.data import DataLoader
from torchvision import transforms
import torchvision.utils as vutils
from data import ImageLabelFilelist
import pdb
def update_average(model_tgt, model_src, beta=0.999):
with torch.no_grad():
param_dict_src = dict(model_src.named_parameters())
for p_name, p_tgt in model_tgt.named_parameters():
p_src = param_dict_src[p_name]
assert(p_src is not p_tgt)
p_tgt.copy_(beta*p_tgt + (1. - beta)*p_src)
def loader_from_list(
root,
file_list,
batch_size,
new_size=None,
height=128,
width=128,
crop=True,
num_workers=4,
shuffle=True,
center_crop=False,
return_paths=False,
drop_last=True):
transform_list = [transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
if center_crop:
transform_list = [transforms.CenterCrop((height, width))] + \
transform_list if crop else transform_list
else:
transform_list = [transforms.RandomCrop((height, width))] + \
transform_list if crop else transform_list
transform_list = [transforms.Resize(new_size)] + transform_list \
if new_size is not None else transform_list
if not center_crop:
transform_list = [transforms.RandomHorizontalFlip()] + transform_list
transform = transforms.Compose(transform_list)
dataset = ImageLabelFilelist(root,
file_list,
transform,
return_paths=return_paths)
loader = DataLoader(dataset,
batch_size,
shuffle=shuffle,
drop_last=drop_last,
num_workers=num_workers)
return loader
def get_evaluation_loaders(conf, shuffle_content=False):
batch_size = conf['batch_size']
num_workers = conf['num_workers']
new_size = conf['new_size']
width = conf['crop_image_width']
height = conf['crop_image_height']
content_loader = loader_from_list(
root=conf['data_folder_train'],
file_list=conf['data_list_train'],
batch_size=batch_size,
new_size=new_size,
height=height,
width=width,
crop=True,
num_workers=num_workers,
shuffle=shuffle_content,
center_crop=True,
return_paths=True,
drop_last=False)
class_loader = loader_from_list(
root=conf['data_folder_test'],
file_list=conf['data_list_test'],
batch_size=batch_size * conf['k_shot'],
new_size=new_size,
height=height,
width=width,
crop=True,
num_workers=1,
shuffle=False,
center_crop=True,
return_paths=True,
drop_last=False)
return content_loader, class_loader
def get_train_loaders(conf):
batch_size = conf['batch_size']
num_workers = conf['num_workers']
new_size = conf['new_size']
width = conf['crop_image_width']
height = conf['crop_image_height']
train_content_loader = loader_from_list(
root=conf['data_folder_train'],
file_list=conf['data_list_train'],
batch_size=batch_size,
new_size=new_size,
height=height,
width=width,
crop=True,
num_workers=num_workers)
train_class_loader = loader_from_list(
root=conf['data_folder_train'],
file_list=conf['data_list_train'],
batch_size=batch_size,
new_size=new_size,
height=height,
width=width,
crop=True,
num_workers=num_workers)
test_content_loader = loader_from_list(
root=conf['data_folder_test'],
file_list=conf['data_list_test'],
batch_size=batch_size,
new_size=new_size,
height=height,
width=width,
crop=True,
num_workers=1)
test_class_loader = loader_from_list(
root=conf['data_folder_test'],
file_list=conf['data_list_test'],
batch_size=batch_size,
new_size=new_size,
height=height,
width=width,
crop=True,
num_workers=1)
return (train_content_loader, train_class_loader, test_content_loader,
test_class_loader)
def get_config(config):
with open(config, 'r') as stream:
return yaml.load(stream, Loader=yaml.FullLoader)
def make_result_folders(output_directory):
image_directory = os.path.join(output_directory, 'images')
if not os.path.exists(image_directory):
print("Creating directory: {}".format(image_directory))
os.makedirs(image_directory)
checkpoint_directory = os.path.join(output_directory, 'checkpoints')
if not os.path.exists(checkpoint_directory):
print("Creating directory: {}".format(checkpoint_directory))
os.makedirs(checkpoint_directory)
return checkpoint_directory, image_directory
def __write_images(im_outs, dis_img_n, file_name):
im_outs = [images.expand(-1, 3, -1, -1) for images in im_outs]
image_tensor = torch.cat([images[:dis_img_n] for images in im_outs], 0)
image_grid = vutils.make_grid(image_tensor.data,
nrow=dis_img_n, padding=0, normalize=True)
vutils.save_image(image_grid, file_name, nrow=1)
def write_1images(image_outputs, image_directory, postfix):
display_image_num = image_outputs[0].size(0)
__write_images(image_outputs, display_image_num,
'%s/gen_%s.jpg' % (image_directory, postfix))
def _write_row(html_file, it, fn, all_size):
html_file.write("<h3>iteration [%d] (%s)</h3>" % (it, fn.split('/')[-1]))
html_file.write("""
<p><a href="%s">
<img src="%s" style="width:%dpx">
</a><br>
<p>
""" % (fn, fn, all_size))
return
def write_html(filename, it, img_save_it, img_dir, all_size=1536):
html_file = open(filename, "w")
html_file.write('''
<!DOCTYPE html>
<html>
<head>
<title>Experiment name = %s</title>
<meta http-equiv="refresh" content="30">
</head>
<body>
''' % os.path.basename(filename))
html_file.write("<h3>current</h3>")
_write_row(html_file, it, '%s/gen_train_current.jpg' % img_dir, all_size)
for j in range(it, img_save_it - 1, -1):
_write_row(html_file, j, '%s/gen_train_%08d.jpg' % (img_dir, j),
all_size)
html_file.write("</body></html>")
html_file.close()
def write_loss(iterations, trainer, train_writer):
members = [attr for attr in dir(trainer)
if ((not callable(getattr(trainer, attr))
and not attr.startswith("__"))
and ('loss' in attr
or 'grad' in attr
or 'nwd' in attr
or 'accuracy' in attr))]
for m in members:
train_writer.add_scalar(m, getattr(trainer, m), iterations + 1)
class Timer:
def __init__(self, msg):
self.msg = msg
self.start_time = None
def __enter__(self):
self.start_time = time.time()
def __exit__(self, exc_type, exc_value, exc_tb):
print(self.msg % (time.time() - self.start_time))