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utils.py
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utils.py
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import datetime
import os
import random
import sys
import time
import numpy as np
import torch
import torch.nn as nn
from PIL import Image
from torch.autograd import Variable
from visdom import Visdom
def tensor2image(tensor):
image = 127.5*(tensor[0].cpu().float().numpy() + 1.0)
if image.shape[0] == 1:
image = np.tile(image, (3,1,1))
return image.astype(np.uint8)
class Logger():
def __init__(self, n_epochs, batches_epoch, start_epoch=1, env='test', port=8097):
self.viz = Visdom(env=f'{env}', port=port)
self.n_epochs = n_epochs
self.batches_epoch = batches_epoch
self.set_epoch = 1
self.epoch = start_epoch + 1
self.batch = 1
self.prev_time = time.time()
self.mean_period = 0
self.losses = {}
self.loss_windows = {}
self.image_windows = {}
self.acc_windows = {}
self.acc = {}
def log(self, losses=None, images=None):
self.mean_period += (time.time() - self.prev_time)
self.prev_time = time.time()
sys.stdout.write('\rEpoch %03d/%03d [%04d/%04d] -- ' % (self.epoch, self.n_epochs, self.batch, self.batches_epoch))
for i, loss_name in enumerate(losses.keys()):
if loss_name not in self.losses:
self.losses[loss_name] = losses[loss_name].item()
else:
self.losses[loss_name] += losses[loss_name].item()
if (i+1) == len(losses.keys()):
sys.stdout.write('%s: %.4f -- ' % (loss_name, self.losses[loss_name]/self.batch))
else:
sys.stdout.write('%s: %.4f | ' % (loss_name, self.losses[loss_name]/self.batch))
batches_done = self.batches_epoch*(self.set_epoch - 1) + self.batch
batches_left = self.batches_epoch*(self.n_epochs - self.epoch) + self.batches_epoch - self.batch
sys.stdout.write('ETA: %s' % (datetime.timedelta(seconds=batches_left*self.mean_period/batches_done)))
sys.stdout.flush()
# Draw images
for image_name, tensor in images.items():
if image_name not in self.image_windows:
self.image_windows[image_name] = self.viz.image(tensor2image(tensor.data), opts={'title':image_name})
else:
self.viz.image(tensor2image(tensor.data), win=self.image_windows[image_name], opts={'title':image_name})
# End of epoch
if (self.batch % self.batches_epoch) == 0:
# Plot losses
for loss_name, loss in self.losses.items():
if loss_name not in self.loss_windows:
self.loss_windows[loss_name] = self.viz.line(X=np.array([self.epoch]), Y=np.array([loss/self.batch]),
opts={'xlabel': 'epochs', 'ylabel': loss_name, 'title': loss_name})
else:
self.viz.line(X=np.array([self.epoch]), Y=np.array([loss/self.batch]), win=self.loss_windows[loss_name], update='append')
# Reset losses for next epoch
self.losses[loss_name] = 0.0
self.epoch += 1
self.set_epoch += 1
self.batch = 1
sys.stdout.write('\n')
else:
self.batch += 1
class ReplayBuffer():
def __init__(self, max_size=50):
assert (max_size > 0), 'Empty buffer or trying to create a black hole. Be careful.'
self.max_size = max_size
self.data = []
def push_and_pop(self, data):
to_return = []
for element in data.data:
element = torch.unsqueeze(element, 0)
if len(self.data) < self.max_size:
self.data.append(element)
to_return.append(element)
else:
if random.uniform(0,1) > 0.5:
i = random.randint(0, self.max_size-1)
to_return.append(self.data[i].clone())
self.data[i] = element
else:
to_return.append(element)
return Variable(torch.cat(to_return))
class LambdaLR():
def __init__(self, n_epochs, offset, decay_start_epoch):
assert ((n_epochs - decay_start_epoch) > 0), "Decay must start before the training session ends!"
self.n_epochs = n_epochs
self.offset = offset
self.decay_start_epoch = decay_start_epoch
def step(self, epoch):
return 1.0 - max(0, epoch + self.offset - self.decay_start_epoch)/(self.n_epochs - self.decay_start_epoch)
def weights_init_normal(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm2d') != -1:
torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
torch.nn.init.constant(m.bias.data, 0.0)
def show(img):
img = (img + 1) * 127.5
img = Image.fromarray(img)
img.show()
class CentnetLoss(nn.Module):
def __init__(self, threshold_A=35, threshold_B=180, data_size=None):
super(CentnetLoss, self).__init__()
self.threshold_A = threshold_A
self.threshold_B = threshold_B
self.data_size = data_size
self.total_iters = 0
self.L1_function = nn.L1Loss()
def forward(self, real_A, real_B, fake_A, fake_B):
real_A_mean = torch.mean(real_A, dim=1, keepdim=True)
real_B_mean = torch.mean(real_B, dim=1, keepdim=True)
fake_A_mean = torch.mean(fake_A, dim=1, keepdim=True)
fake_B_mean = torch.mean(fake_B, dim=1, keepdim=True)
real_A_normal = (real_A_mean - (self.threshold_A/127.5-1))*100
real_B_normal = (real_B_mean - (self.threshold_B/127.5-1))*100
fake_A_normal = (fake_A_mean - (self.threshold_A/127.5-1))*100
fake_B_normal = (fake_B_mean - (self.threshold_B/127.5-1))*100
real_A_sigmoid = torch.sigmoid(real_A_normal)#.detach().numpy().astype(np.uint8)
real_B_sigmoid = (1 - torch.sigmoid(real_B_normal))#.detach().numpy().astype(np.uint8)
fake_A_sigmoid = torch.sigmoid(fake_A_normal)
fake_B_sigmoid = 1 - torch.sigmoid(fake_B_normal)
content_loss_A = self.L1_function( real_A_sigmoid , fake_B_sigmoid )
content_loss_B = self.L1_function( fake_A_sigmoid , real_B_sigmoid )
content_loss_rate = 50*np.exp(-(self.total_iters/self.data_size))
content_loss = (content_loss_A + content_loss_B) * content_loss_rate
self.total_iters += 1
return content_loss
def save_checkpoint(model, model_name, model_root, optimizer=None, scheduler=None, epoch=None):
if optimizer:
if model_name=='netG_A2B':
state = {
'model': model.state_dict(),
'epoch': epoch,
'optimizer': optimizer.state_dict(),
'schedule': scheduler.state_dict(),
}
else:
state = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'schedule': scheduler.state_dict(),
}
else:
state = {
'model': model.state_dict(),
}
torch.save(state, os.path.join(model_root, '%s.pth'%model_name))
def load_checkpoint(model, model_name, model_root, optimizer=None, scheduler=None, ):
checkpoint = torch.load(os.path.join(model_root, '%s.pth'%model_name), map_location='cuda')
if 'optimizer' in checkpoint.keys():
if 'epoch' in checkpoint.keys():
model.load_state_dict({k.replace('module.', ''): v for k, v in checkpoint['model'].items()},
strict=True)
optimizer.load_state_dict(checkpoint['optimizer'])
epoch = checkpoint['epoch']
scheduler.load_state_dict(checkpoint['schedule'])
return model, optimizer, scheduler, epoch
else:
model.load_state_dict({k.replace('module.', ''): v for k, v in checkpoint['model'].items()},
strict=True)
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['schedule'])
return model, optimizer, scheduler
else:
model.load_state_dict({k.replace('module.', ''): v for k, v in checkpoint['model'].items()},
strict=True)
return model
def print_options(opt):
"""Print and save options
It will print both current options and default values(if different).
It will save options into a text file / [checkpoints_dir] / opt.txt
"""
message = ''
message += '----------------- Options ---------------\n'
for k, v in sorted(vars(opt).items()):
comment = ''
message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
message += '----------------- End -------------------'
print(message)
# save to the disk
file_name = os.path.join(opt.outf, 'opt.txt')
with open(file_name, 'wt') as opt_file:
opt_file.write(message)
opt_file.write('\n')