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train_fullts.py
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train_fullts.py
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### Copyright (C) 2017 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 time
from collections import OrderedDict
from options.train_options import TrainOptions
from data.data_loader import CreateDataLoader
from models.models import create_model_fullts
import util.util as util
from util.visualizer import Visualizer
import os
import numpy as np
import torch
from torch.autograd import Variable
opt = TrainOptions().parse()
iter_path = os.path.join(opt.checkpoints_dir, opt.name, 'iter.txt')
if opt.continue_train:
try:
start_epoch, epoch_iter = np.loadtxt(iter_path , delimiter=',', dtype=int)
except:
start_epoch, epoch_iter = 1, 0
print('Resuming from epoch %d at iteration %d' % (start_epoch, epoch_iter))
else:
start_epoch, epoch_iter = 1, 0
if opt.debug:
opt.display_freq = 1
opt.print_freq = 1
opt.niter = 1
opt.niter_decay = 0
opt.max_dataset_size = 10
data_loader = CreateDataLoader(opt)
dataset = data_loader.load_data()
dataset_size = len(data_loader)
print('#training images = %d' % dataset_size)
""" new residual model """
model = create_model_fullts(opt)
visualizer = Visualizer(opt)
total_steps = (start_epoch-1) * dataset_size + epoch_iter
for epoch in range(start_epoch, opt.niter + opt.niter_decay + 1):
epoch_start_time = time.time()
if epoch != start_epoch:
epoch_iter = epoch_iter % dataset_size
for i, data in enumerate(dataset, start=epoch_iter):
iter_start_time = time.time()
total_steps += opt.batchSize
epoch_iter += opt.batchSize
# whether to collect output images
save_fake = total_steps % opt.display_freq == 0
############## Forward Pass ######################
no_nexts = data['next_label'].dim() > 1 #check if has a next label (last training pair does not have a next label)
if no_nexts:
cond_zeros = torch.zeros(data['label'].size()).float()
losses, generated = model(Variable(data['label']), Variable(data['next_label']), Variable(data['image']), \
Variable(data['next_image']), Variable(data['face_coords']), Variable(cond_zeros), infer=True)
# sum per device losses
losses = [ torch.mean(x) if not isinstance(x, int) else x for x in losses ]
loss_dict = dict(zip(model.module.loss_names, losses))
# calculate final loss scalar
loss_D = (loss_dict['D_fake'] + loss_dict['D_real']) * 0.5 + (loss_dict['D_realface'] + loss_dict['D_fakeface']) * 0.5
loss_G = loss_dict['G_GAN'] + loss_dict['G_GAN_Feat'] + loss_dict['G_VGG'] + loss_dict['G_GANface']
############### Backward Pass ####################
# update generator weights
model.module.optimizer_G.zero_grad()
loss_G.backward()
model.module.optimizer_G.step()
# update discriminator weights
model.module.optimizer_D.zero_grad()
loss_D.backward()
model.module.optimizer_D.step()
#call(["nvidia-smi", "--format=csv", "--query-gpu=memory.used,memory.free"])
############## Display results and errors ##########
### print out errors
if total_steps % opt.print_freq == 0:
errors = {}
if torch.__version__[0] == '1':
errors = {k: v.item() if not isinstance(v, int) else v for k, v in loss_dict.items()}
else:
errors = {k: v.data[0] if not isinstance(v, int) else v for k, v in loss_dict.items()}
t = (time.time() - iter_start_time) / opt.batchSize
visualizer.print_current_errors(epoch, epoch_iter, errors, t)
visualizer.plot_current_errors(errors, total_steps)
### display output images
if save_fake:
syn = generated[0].data[0]
inputs = torch.cat((data['label'], data['next_label']), dim=3)
targets = torch.cat((data['image'], data['next_image']), dim=3)
visuals = OrderedDict([('input_label', util.tensor2im(inputs[0], normalize=False)),
('synthesized_image', util.tensor2im(syn)),
('real_image', util.tensor2im(targets[0]))])
if opt.face_generator: #display face generator on tensorboard
miny, maxy, minx, maxx = data['face_coords'][0]
res_face = generated[2].data[0]
syn_face = generated[1].data[0]
preres = generated[3].data[0]
visuals = OrderedDict([('input_label', util.tensor2im(inputs[0], normalize=False)),
('synthesized_image', util.tensor2im(syn)),
('synthesized_face', util.tensor2im(syn_face)),
('residual', util.tensor2im(res_face)),
('real_face', util.tensor2im(data['image'][0][:, miny:maxy, minx:maxx])),
# ('pre_residual', util.tensor2im(preres)),
# ('pre_residual_face', util.tensor2im(preres[:, miny:maxy, minx:maxx])),
('input_face', util.tensor2im(data['label'][0][:, miny:maxy, minx:maxx], normalize=False)),
('real_image', util.tensor2im(targets[0]))])
visualizer.display_current_results(visuals, epoch, total_steps)
### save latest model
if total_steps % opt.save_latest_freq == 0:
print('saving the latest model (epoch %d, total_steps %d)' % (epoch, total_steps))
model.module.save('latest')
np.savetxt(iter_path, (epoch, epoch_iter), delimiter=',', fmt='%d')
# end of epoch
iter_end_time = time.time()
print('End of epoch %d / %d \t Time Taken: %d sec' %
(epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
### save model for this epoch
if epoch % opt.save_epoch_freq == 0:
print('saving the model at the end of epoch %d, iters %d' % (epoch, total_steps))
model.module.save('latest')
model.module.save(epoch)
np.savetxt(iter_path, (epoch+1, 0), delimiter=',', fmt='%d')
### instead of only training the local enhancer, train the entire network after certain iterations
if (opt.niter_fix_global != 0) and (epoch == opt.niter_fix_global):
print('------------- finetuning Local + Global generators jointly -------------')
model.module.update_fixed_params()
### instead of only training the face discriminator, train the entire network after certain iterations
if (opt.niter_fix_main != 0) and (epoch == opt.niter_fix_main):
print('------------- traing all the discriminators now and not just the face -------------')
model.module.update_fixed_params_netD()
### linearly decay learning rate after certain iterations
if epoch > opt.niter:
model.module.update_learning_rate()