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utils.py
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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).
"""
import os
import math
import time
import codecs
import yaml
import numpy as np
try:
from yaml import CLoader as Loader
except ImportError:
from yaml import Loader
import torch
import torch.nn as nn
import torch.nn.init as init
from torch.autograd import Variable
from torch.optim import lr_scheduler
from torchvision import transforms
import torchvision.utils as vutils
try:
from torch.utils.serialization import load_lua
except ImportError: # will be 3.x series
from torchfile import load as load_lua
from networks.networks import Vgg16
# 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 moving_average(model, model_copy, beta=0.999):
for param, param_copy in zip(model.parameters(), model_copy.parameters()):
param_copy.data = torch.lerp(param.data, param_copy.data, beta)
def get_config(config):
with codecs.open(config, 'r', encoding='utf-8') as stream:
return yaml.load(stream, Loader=Loader)
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 write_2images_single(image_outputs, display_image_num, image_directory, postfix):
n = len(image_outputs)
__write_images(image_outputs, display_image_num, '%s/gen_a2b_%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'))
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, device):
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()).to(device)
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'] == 'const':
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)
elif hyperparameters['lr_policy'] == 'cosa':
scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=hyperparameters['step_size'],
eta_min=hyperparameters['eta_min'], 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