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train.py
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import time
from collections import OrderedDict
import torch.nn.functional as functional
from options.train_options import TrainOptions
from data.data_loader import CreateDataLoader
from models.models import create_model
import util.util as util
from util.visualizer import Visualizer
import os
import numpy as np
import torch
from torch.nn.functional import grid_sample
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)
model = create_model(opt)
visualizer = Visualizer(opt)
total_steps = (start_epoch-1) * dataset_size + epoch_iter
lr_descrease_freq = (opt.niter * dataset_size) // opt.niter_decay + 1
print("Frequency of the learning rate decay = %d iterations" % lr_descrease_freq)
display_delta = total_steps % opt.display_freq
print_delta = total_steps % opt.print_freq
save_delta = total_steps % opt.save_latest_freq
lr_decrease_delta = total_steps % lr_descrease_freq
for epoch in range(start_epoch, opt.niter + 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):
data["input"] = data["input"].permute(1, 0, 2, 3, 4)
data["target"] = data["target"].permute(1, 0, 2, 3, 4)
data["source_frame"] = data["source_frame"].permute(1, 0, 2, 3, 4)
data["grid"] = data["grid"].permute(1, 0, 2, 3, 4)
data["grid_source"] = data["grid_source"].permute(1, 0, 2, 3, 4)
iter_start_time = time.time()
epoch_iter += opt.batchSize
total_steps += opt.batchSize
# whether to collect output images
save_fake = total_steps % opt.display_freq == display_delta
lr_decay = total_steps % lr_descrease_freq == lr_decrease_delta
############## Forward Pass ######################
losses, generated, grid_for_source, grid_for_prev = model(data['input'][0],
data['source_frame'][0], data['source_frame'][0],
data['grid_source'][0], data['grid_source'][0],
image = data['target'][0], infer=save_fake)
# 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_G = loss_dict['G_GAN'] + loss_dict.get('G_GAN_Feat',0) + loss_dict.get('G_VGG',0) + loss_dict.get('G_Warp',0)
# 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()
### display output images
if save_fake:
img = (data['source_frame'][0]).cuda()
warped_source = grid_sample(data['source_frame'][0], data['grid_source'][0].permute(0, 2, 3, 1), padding_mode=opt.grid_padding)
if not (opt.no_coarse_warp or opt.no_refining_warp):
grid = grid_for_source.permute(0, 3, 1, 2).detach()
grid = functional.interpolate(grid, (256,256), mode = 'bilinear' )
warp = functional.grid_sample(img, grid.permute(0, 2, 3, 1), padding_mode=opt.grid_padding)
else:
warp = warped_source
visuals = OrderedDict([('synthesized_video', util.tensor2im(generated.data[0])),
('source_video', util.tensor2im(data['source_frame'][0][0])),
('real_video', util.tensor2im(data['target'][0][0])),
('warped_source', util.tensor2im(warped_source.data[0])),
('warped_with_learned_grid', util.tensor2im(warp[0]))])
visualizer.display_current_results(visuals, epoch, total_steps)
############## Display results and errors ##########
### print out errors
if total_steps % opt.print_freq == print_delta:
errors = {k: v.data 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)
for f in range(1, opt.prev_frame_num):
############## Forward Pass ######################
losses, generated, grid_for_source, grid_for_prev = model(data['input'][f],
data['source_frame'][0], generated,
data['grid_source'][f], data['grid'][f],
image = data['target'][f], infer=save_fake)
# 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_G = loss_dict['G_GAN'] + loss_dict.get('G_GAN_Feat',0) + loss_dict.get('G_VGG',0) + loss_dict.get('G_Warp',0)
# 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()
### display output images
if save_fake:
img = (data['source_frame'][0]).cuda()
warped_source = grid_sample(data['source_frame'][0], data['grid_source'][f].permute(0, 2, 3, 1), padding_mode=opt.grid_padding)
if not (opt.no_coarse_warp or opt.no_refining_warp):
grid = grid_for_source.permute(0, 3, 1, 2).detach()
grid = functional.interpolate(grid, (256,256),mode = 'bilinear' )
warp = functional.grid_sample(img, grid.permute(0, 2, 3, 1), padding_mode=opt.grid_padding)
else:
warp = warped_source
visuals = OrderedDict([('synthesized_video_%d'%f, util.tensor2im(generated.data[0])),
('real_video_%d'%f, util.tensor2im(data['target'][f][0])),
('warped_source_%d'%f, util.tensor2im(warped_source.data[0])),
('warped_with_learned_grid_%d'%f, util.tensor2im(warp[0]))])
visualizer.display_current_results(visuals, epoch, total_steps)
############### Backward Pass ####################
### linearly decay learning rate after certain iterations
if lr_decay:
model.module.update_learning_rate()
### save latest model
if total_steps % opt.save_latest_freq == save_delta:
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')
for key, value in (data['paths']).iteritems():
print key + " " + value[0][0]
if epoch_iter >= dataset_size:
break
# 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):
model.module.update_fixed_params()
#call(["nvidia-smi", "--format=csv", "--query-gpu=memory.used,memory.free"])