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train_new.py
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train_new.py
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import time
from collections import OrderedDict
from options.train_options import TrainOptions
from data.data_loader import CreateDataLoader
from models.models import create_model
import utils.util as util
from utils.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)
model = create_model(opt)
visualizer = Visualizer(opt)
print ('=================')
total_steps = (start_epoch-1) * dataset_size + epoch_iter
optimizer_G, optimizer_D = model.module.optimizer_G, model.module.optimizer_D
display_delta = total_steps % opt.display_freq
print_delta = total_steps % opt.print_freq
save_delta = total_steps % opt.save_latest_freq
##############
with torch.autograd.set_detect_anomaly(False):
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 == display_delta
############## Forward Pass ######################
# losses_list, generated = model(references =Variable(data['reference_frames']),target_lmark= Variable(data['target_lmark']), \
# real_image= Variable(data['target_rgb']),dif_img= Variable(data['dif_img']), \
# mis_img= Variable(data['mis_img']), infer=save_fake)
# # reference_img , reference_lmark, target_lmark , real_image, warping_ref_img, warping_ref_lmark , ani_img
losses, generated = model(reference_img =Variable(data['ref_image']),reference_lmark= Variable(data['ref_label']), target_lmark = Variable(data['tgt_label']) , \
real_image= Variable(data['tgt_image']), warping_ref_img = Variable(data['warping_ref']), warping_ref_lmark = Variable(data['warping_ref_lmark']) , ani_img = Variable(data['ani_image']) , ani_lmark = Variable(data['ani_lmark']), infer=save_fake)
# sum per device losses
# 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)
############### Backward Pass ####################
# update generator weights
optimizer_G.zero_grad()
loss_G.backward()
optimizer_G.step()
# update discriminator weights
optimizer_D.zero_grad()
loss_D.backward()
optimizer_D.step()
############## Display results and errors ##########
### print out errors
# print (loss_dict['D_fake'], loss_dict['D_real'], loss_dict['G_GAN'], loss_dict.get('G_GAN_Feat',0), loss_dict.get('G_VGG',0))
errors = {}
if total_steps % opt.print_freq == print_delta:
errors = {k: v.data.item() if not isinstance(v, int) else v for k, v in loss_dict.items()}
t = (time.time() - iter_start_time) / opt.print_freq
visualizer.print_current_errors(epoch, epoch_iter, errors, t)
visualizer.plot_current_errors(errors, total_steps)
#call(["nvidia-smi", "--format=csv", "--query-gpu=memory.used,memory.free"])
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
tmp = []
for i in range(opt.num_frames):
tmp.extend([( 'reference%d'%i, util.tensor2im(data['ref_image'][0,i]))])
tmp.extend([('target_lmark', util.tensor2im(data['tgt_label'][0, 0])),
('synthesized_image', util.tensor2im(generated[0].data[0])),
('raw', util.tensor2im(generated[1].data[0])),
('att', util.tensor2im(generated[2].data[0])),
('real_image', util.tensor2im(data['tgt_image'][0, 0])),
('ani_image', util.tensor2im(data['ani_image'][0, 0])),
('ani_lmark', util.tensor2im(data['ani_lmark'][0, 0]))])
visuals = OrderedDict(tmp)
visualizer.display_current_results(visuals, epoch, total_steps)
### 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')
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()
### linearly decay learning rate after certain iterations
if epoch > opt.niter:
model.module.update_learning_rate()
# from options.train_options import TrainOptions
# from dataset import LRSLmark2rgbDataset
# opt = TrainOptions().parse()
# dataset = LRSLmark2rgbDataset(opt)
# sample = dataset[0]
# print (sample)