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utils.py
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"""
Copyright (C) 2018 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).
"""
# from torch.utils.serialization import load_lua
import torchfile
# from torch.utils.serialization import load_lua
# from torch.utils.serialization import load_lua
from torch.utils.data import DataLoader
from networks import Vgg16
from torch.autograd import Variable
from torch.optim import lr_scheduler
from torchvision import transforms
from data import ImageFilelist, ImageFolder
import torch
import torch.nn as nn
import os
import math
import torchvision.utils as vutils
import yaml
import numpy as np
import torch.nn.init as init
import time
# Methods
# get_all_data_loaders : primary data loader interface (load trainA, testA, trainB, testB)
# get_data_loader_list : list-based data loader
# get_data_loader_folder : folder-based data loader
# get_config : load yaml file
# eformat :
# write_2images : save output image
# prepare_sub_folder : create checkpoints and images folders for saving outputs
# write_one_row_html : write one row of the html file for output images
# write_html : create the html file.
# write_loss
# slerp
# get_slerp_interp
# get_model_list
# load_vgg16
# load_inception
# vgg_preprocess
# get_scheduler
# weights_init
def get_all_data_loaders(conf):
batch_size = conf['batch_size']
num_workers = conf['num_workers']
if 'new_size' in conf:
new_size_a = new_size_b = conf['new_size']
else:
new_size_a = conf['new_size_a']
new_size_b = conf['new_size_b']
height = conf['crop_image_height']
width = conf['crop_image_width']
if 'data_root' in conf:
train_loader_a = get_data_loader_folder(os.path.join(conf['data_root'], 'trainA'), batch_size, True,
new_size_a, height, width, num_workers, True)
test_loader_a = get_data_loader_folder(os.path.join(conf['data_root'], 'testA'), batch_size, False,
new_size_a, new_size_a, new_size_a, num_workers, True)
train_loader_b = get_data_loader_folder(os.path.join(conf['data_root'], 'trainB'), batch_size, True,
new_size_b, height, width, num_workers, True)
test_loader_b = get_data_loader_folder(os.path.join(conf['data_root'], 'testB'), batch_size, False,
new_size_b, new_size_b, new_size_b, num_workers, True)
else:
train_loader_a = get_data_loader_list(conf['data_folder_train_a'], conf['data_list_train_a'], batch_size, True,
new_size_a, height, width, num_workers, True)
test_loader_a = get_data_loader_list(conf['data_folder_test_a'], conf['data_list_test_a'], batch_size, False,
new_size_a, new_size_a, new_size_a, num_workers, True)
train_loader_b = get_data_loader_list(conf['data_folder_train_b'], conf['data_list_train_b'], batch_size, True,
new_size_b, height, width, num_workers, True)
test_loader_b = get_data_loader_list(conf['data_folder_test_b'], conf['data_list_test_b'], batch_size, False,
new_size_b, new_size_b, new_size_b, num_workers, True)
return train_loader_a, train_loader_b, test_loader_a, test_loader_b
def get_data_loader_list(root, file_list, batch_size, train, new_size=None,
height=256, width=256, num_workers=4, crop=True):
transform_list = [transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))]
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
transform_list = [transforms.RandomHorizontalFlip()] + transform_list if train else transform_list
transform = transforms.Compose(transform_list)
dataset = ImageFilelist(root, file_list, transform=transform)
loader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=train, drop_last=True, num_workers=num_workers)
return loader
def get_data_loader_folder(input_folder, batch_size, train, new_size=None,
height=256, width=256, num_workers=4, crop=True):
transform_list = [transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))]
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
transform_list = [transforms.RandomHorizontalFlip()] + transform_list if train else transform_list
transform = transforms.Compose(transform_list)
dataset = ImageFolder(input_folder, transform=transform)
loader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=train, drop_last=True, num_workers=num_workers)
return loader
def get_config(config):
with open(config, 'r') as stream:
return yaml.full_load(stream) # yaml.load(stream)
def eformat(f, prec):
s = "%.*e"%(prec, f)
mantissa, exp = s.split('e')
# add 1 to digits as 1 is taken by sign +/-
return "%se%d"%(mantissa, int(exp))
def __write_images(image_outputs, display_image_num, file_name):
image_outputs = [images.expand(-1, 3, -1, -1) for images in image_outputs] # expand gray-scale images to 3 channels
image_tensor = torch.cat([images[:display_image_num] for images in image_outputs], 0)
image_grid = vutils.make_grid(image_tensor.data, nrow=display_image_num, padding=0, normalize=True)
vutils.save_image(image_grid, file_name, nrow=1)
def write_2images(image_outputs, display_image_num, image_directory, postfix):
n = len(image_outputs)
__write_images(image_outputs[0:n//2], display_image_num, '%s/gen_a2b_%s.jpg' % (image_directory, postfix))
__write_images(image_outputs[n//2:n], display_image_num, '%s/gen_b2a_%s.jpg' % (image_directory, postfix))
def prepare_sub_folder(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_one_row_html(html_file, iterations, img_filename, all_size):
html_file.write("<h3>iteration [%d] (%s)</h3>" % (iterations,img_filename.split('/')[-1]))
html_file.write("""
<p><a href="%s">
<img src="%s" style="width:%dpx">
</a><br>
<p>
""" % (img_filename, img_filename, all_size))
return
def write_html(filename, iterations, image_save_iterations, image_directory, 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_one_row_html(html_file, iterations, '%s/gen_a2b_train_current.jpg' % (image_directory), all_size)
write_one_row_html(html_file, iterations, '%s/gen_b2a_train_current.jpg' % (image_directory), all_size)
for j in range(iterations, image_save_iterations-1, -1):
if j % image_save_iterations == 0:
write_one_row_html(html_file, j, '%s/gen_a2b_test_%08d.jpg' % (image_directory, j), all_size)
write_one_row_html(html_file, j, '%s/gen_b2a_test_%08d.jpg' % (image_directory, j), all_size)
write_one_row_html(html_file, j, '%s/gen_a2b_train_%08d.jpg' % (image_directory, j), all_size)
write_one_row_html(html_file, j, '%s/gen_b2a_train_%08d.jpg' % (image_directory, 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)]
for m in members:
train_writer.add_scalar(m, getattr(trainer, m), iterations + 1)
def slerp(val, low, high):
"""
original: Animating Rotation with Quaternion Curves, Ken Shoemake
https://arxiv.org/abs/1609.04468
Code: https://github.com/soumith/dcgan.torch/issues/14, Tom White
"""
omega = np.arccos(np.dot(low / np.linalg.norm(low), high / np.linalg.norm(high)))
so = np.sin(omega)
return np.sin((1.0 - val) * omega) / so * low + np.sin(val * omega) / so * high
def get_slerp_interp(nb_latents, nb_interp, z_dim):
"""
modified from: PyTorch inference for "Progressive Growing of GANs" with CelebA snapshot
https://github.com/ptrblck/prog_gans_pytorch_inference
"""
latent_interps = np.empty(shape=(0, z_dim), dtype=np.float32)
for _ in range(nb_latents):
low = np.random.randn(z_dim)
high = np.random.randn(z_dim) # low + np.random.randn(512) * 0.7
interp_vals = np.linspace(0, 1, num=nb_interp)
latent_interp = np.array([slerp(v, low, high) for v in interp_vals],
dtype=np.float32)
latent_interps = np.vstack((latent_interps, latent_interp))
return latent_interps[:, :, np.newaxis, np.newaxis]
# Get model list for resume
def get_model_list(dirname, key):
if os.path.exists(dirname) is False:
return None
gen_models = [os.path.join(dirname, f) for f in os.listdir(dirname) if
os.path.isfile(os.path.join(dirname, f)) and key in f and ".pt" in f]
if gen_models is None:
return None
gen_models.sort()
last_model_name = gen_models[-1]
return last_model_name
def load_vgg16(model_dir):
""" Use the model from https://github.com/abhiskk/fast-neural-style/blob/master/neural_style/utils.py """
if not os.path.exists(model_dir):
os.mkdir(model_dir)
if not os.path.exists(os.path.join(model_dir, 'vgg16.weight')):
if not os.path.exists(os.path.join(model_dir, 'vgg16.t7')):
os.system('wget https://www.dropbox.com/s/76l3rt4kyi3s8x7/vgg16.t7?dl=1 -O ' + os.path.join(model_dir, 'vgg16.t7'))
# vgglua = load_lua(os.path.join(model_dir, 'vgg16.t7'))
vgglua = torchfile.load(os.path.join(model_dir, 'vgg16.t7'))
vgg = Vgg16()
for (src, dst) in zip(vgglua.parameters()[0], vgg.parameters()):
dst.data[:] = src
torch.save(vgg.state_dict(), os.path.join(model_dir, 'vgg16.weight'))
vgg = Vgg16()
vgg.load_state_dict(torch.load(os.path.join(model_dir, 'vgg16.weight')))
return vgg
def load_inception(model_path):
state_dict = torch.load(model_path)
model = inception_v3(pretrained=False, transform_input=True)
model.aux_logits = False
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, state_dict['fc.weight'].size(0))
model.load_state_dict(state_dict)
for param in model.parameters():
param.requires_grad = False
return model
def vgg_preprocess(batch):
tensortype = type(batch.data)
(r, g, b) = torch.chunk(batch, 3, dim = 1)
batch = torch.cat((b, g, r), dim = 1) # convert RGB to BGR
batch = (batch + 1) * 255 * 0.5 # [-1, 1] -> [0, 255]
mean = tensortype(batch.data.size()).cuda()
mean[:, 0, :, :] = 103.939
mean[:, 1, :, :] = 116.779
mean[:, 2, :, :] = 123.680
batch = batch.sub(Variable(mean)) # subtract mean
return batch
def get_scheduler(optimizer, hyperparameters, iterations=-1):
if 'lr_policy' not in hyperparameters or hyperparameters['lr_policy'] == 'constant':
scheduler = None # constant scheduler
elif hyperparameters['lr_policy'] == 'step':
scheduler = lr_scheduler.StepLR(optimizer, step_size=hyperparameters['step_size'],
gamma=hyperparameters['gamma'], last_epoch=iterations)
else:
return NotImplementedError('learning rate policy [%s] is not implemented', hyperparameters['lr_policy'])
return scheduler
def weights_init(init_type='gaussian'):
def init_fun(m):
classname = m.__class__.__name__
if (classname.find('Conv') == 0 or classname.find('Linear') == 0) and hasattr(m, 'weight'):
# print m.__class__.__name__
if init_type == 'gaussian':
init.normal_(m.weight.data, 0.0, 0.02)
elif init_type == 'xavier':
init.xavier_normal_(m.weight.data, gain=math.sqrt(2))
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=math.sqrt(2))
elif init_type == 'default':
pass
else:
assert 0, "Unsupported initialization: {}".format(init_type)
if hasattr(m, 'bias') and m.bias is not None:
init.constant_(m.bias.data, 0.0)
return init_fun
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))
def pytorch03_to_pytorch04(state_dict_base, trainer_name):
def __conversion_core(state_dict_base, trainer_name):
state_dict = state_dict_base.copy()
if trainer_name == 'MUNIT':
for key, value in state_dict_base.items():
if key.endswith(('enc_content.model.0.norm.running_mean',
'enc_content.model.0.norm.running_var',
'enc_content.model.1.norm.running_mean',
'enc_content.model.1.norm.running_var',
'enc_content.model.2.norm.running_mean',
'enc_content.model.2.norm.running_var',
'enc_content.model.3.model.0.model.1.norm.running_mean',
'enc_content.model.3.model.0.model.1.norm.running_var',
'enc_content.model.3.model.0.model.0.norm.running_mean',
'enc_content.model.3.model.0.model.0.norm.running_var',
'enc_content.model.3.model.1.model.1.norm.running_mean',
'enc_content.model.3.model.1.model.1.norm.running_var',
'enc_content.model.3.model.1.model.0.norm.running_mean',
'enc_content.model.3.model.1.model.0.norm.running_var',
'enc_content.model.3.model.2.model.1.norm.running_mean',
'enc_content.model.3.model.2.model.1.norm.running_var',
'enc_content.model.3.model.2.model.0.norm.running_mean',
'enc_content.model.3.model.2.model.0.norm.running_var',
'enc_content.model.3.model.3.model.1.norm.running_mean',
'enc_content.model.3.model.3.model.1.norm.running_var',
'enc_content.model.3.model.3.model.0.norm.running_mean',
'enc_content.model.3.model.3.model.0.norm.running_var',
)):
del state_dict[key]
else:
def __conversion_core(state_dict_base):
state_dict = state_dict_base.copy()
for key, value in state_dict_base.items():
if key.endswith(('enc.model.0.norm.running_mean',
'enc.model.0.norm.running_var',
'enc.model.1.norm.running_mean',
'enc.model.1.norm.running_var',
'enc.model.2.norm.running_mean',
'enc.model.2.norm.running_var',
'enc.model.3.model.0.model.1.norm.running_mean',
'enc.model.3.model.0.model.1.norm.running_var',
'enc.model.3.model.0.model.0.norm.running_mean',
'enc.model.3.model.0.model.0.norm.running_var',
'enc.model.3.model.1.model.1.norm.running_mean',
'enc.model.3.model.1.model.1.norm.running_var',
'enc.model.3.model.1.model.0.norm.running_mean',
'enc.model.3.model.1.model.0.norm.running_var',
'enc.model.3.model.2.model.1.norm.running_mean',
'enc.model.3.model.2.model.1.norm.running_var',
'enc.model.3.model.2.model.0.norm.running_mean',
'enc.model.3.model.2.model.0.norm.running_var',
'enc.model.3.model.3.model.1.norm.running_mean',
'enc.model.3.model.3.model.1.norm.running_var',
'enc.model.3.model.3.model.0.norm.running_mean',
'enc.model.3.model.3.model.0.norm.running_var',
'dec.model.0.model.0.model.1.norm.running_mean',
'dec.model.0.model.0.model.1.norm.running_var',
'dec.model.0.model.0.model.0.norm.running_mean',
'dec.model.0.model.0.model.0.norm.running_var',
'dec.model.0.model.1.model.1.norm.running_mean',
'dec.model.0.model.1.model.1.norm.running_var',
'dec.model.0.model.1.model.0.norm.running_mean',
'dec.model.0.model.1.model.0.norm.running_var',
'dec.model.0.model.2.model.1.norm.running_mean',
'dec.model.0.model.2.model.1.norm.running_var',
'dec.model.0.model.2.model.0.norm.running_mean',
'dec.model.0.model.2.model.0.norm.running_var',
'dec.model.0.model.3.model.1.norm.running_mean',
'dec.model.0.model.3.model.1.norm.running_var',
'dec.model.0.model.3.model.0.norm.running_mean',
'dec.model.0.model.3.model.0.norm.running_var',
)):
del state_dict[key]
return state_dict
state_dict = dict()
state_dict['a'] = __conversion_core(state_dict_base['a'], trainer_name)
state_dict['b'] = __conversion_core(state_dict_base['b'], trainer_name)
return state_dict