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train_composition.py
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train_composition.py
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# ========================================================
# Compositional GAN
# Train different components of the paired/unpaired models
# By Samaneh Azadi
# ========================================================
import time
from options.train_options import TrainOptions
from data.data_loader import CreateDataLoader
from models.models import create_model
from util.visualizer import Visualizer
from models.landmark import landmarkLoader
opt = TrainOptions().parse()
opt.phase = 'train'
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('Train the STN models')
# Only for the unpaired case
if opt.dataset_mode=='comp_decomp_unaligned' and opt.niterSTN:
opt.isPretrain = False
visualizer = Visualizer(opt)
total_steps = 0
for epoch in range(opt.epoch_count, opt.niterSTN + 1):
epoch_start_time = time.time()
epoch_iter = 0
for i, data in enumerate(dataset):
iter_start_time = time.time()
visualizer.reset()
total_steps += opt.batchSize
epoch_iter += opt.batchSize
model.set_input(data)
model.optimize_parameters_STN()
if total_steps % opt.display_freq == 0:
save_result = total_steps % opt.update_html_freq == 0
visualizer.display_current_results(model.get_current_visuals_STN(), total_steps, save_result,opt.update_html_freq,n_latest=opt.n_latest)
if total_steps % opt.print_freq == 0:
errors = model.get_current_errors()
t = (time.time() - iter_start_time) / opt.batchSize
visualizer.print_current_errors(epoch, epoch_iter, errors, t)
if opt.display_id > 0:
visualizer.plot_current_errors(epoch, float(epoch_iter)/dataset_size, opt, errors)
if total_steps % opt.save_latest_freq == 0:
print('saving the latest model (epoch %d, total_steps %d)' %
(epoch, total_steps))
model.save('latest')
if epoch % opt.save_epoch_freq == 0:
print('saving the model at the end of epoch %d, iters %d' %
(epoch, total_steps))
model.save(epoch, STN_pretrain=True)
print('Train the inpainting networks only')
# Only for the unpaired case
if opt.dataset_mode=='comp_decomp_unaligned' and opt.niterCompletion:
opt.isPretrain = False
visualizer = Visualizer(opt)
total_steps = 0
for epoch in range(opt.epoch_count, opt.niterCompletion + 1):
epoch_start_time = time.time()
epoch_iter = 0
for i, data in enumerate(dataset):
iter_start_time = time.time()
visualizer.reset()
total_steps += opt.batchSize
epoch_iter += opt.batchSize
model.set_input(data)
model.optimize_parameters_completion(total_steps)
if total_steps % opt.display_freq == 0:
save_result = total_steps % opt.update_html_freq == 0
visualizer.display_current_results(model.get_current_visuals_completion(), total_steps, save_result,opt.update_html_freq,n_latest=opt.n_latest)
if total_steps % opt.print_freq == 0:
errors = model.get_current_errors()
t = (time.time() - iter_start_time) / opt.batchSize
visualizer.print_current_errors(epoch, epoch_iter, errors, t)
if opt.display_id > 0:
visualizer.plot_current_errors(epoch, float(epoch_iter)/dataset_size, opt, errors)
if total_steps % opt.save_latest_freq == 0:
print('saving the latest model (epoch %d, total_steps %d)' %
(epoch, total_steps))
model.save('latest')
if epoch % opt.save_epoch_freq == 0:
print('saving the model at the end of epoch %d, iters %d' %
(epoch, total_steps))
model.save(epoch, compl_pretrain=True)
data_loader = CreateDataLoader(opt)
dataset = data_loader.load_data()
dataset_size = len(data_loader)
opt.isPretrain = False
visualizer = Visualizer(opt)
total_steps = 0
print('start training end to end')
for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
epoch_start_time = time.time()
epoch_iter = 0
for i, data in enumerate(dataset):
iter_start_time = time.time()
visualizer.reset()
total_steps += opt.batchSize
epoch_iter += opt.batchSize
model.set_input(data)
model.set_input_landmark(data)
#print("set_input_landmark",model.set_input_landmark(data))
#print("set_input",model.set_input(data))
if opt.dataset_mode=='comp_decomp_unaligned':
# model.optimize_parameters(total_steps, epoch)
model.optimize_parameters(total_steps)
else:
model.optimize_parameters(total_steps, epoch)
if total_steps % opt.display_freq == 0:
save_result = total_steps % opt.update_html_freq == 0
if opt.dataset_mode=='comp_decomp_unaligned':
visualizer.display_current_results(model.get_current_visuals_A_segment(), total_steps, save_result,opt.update_html_freq,n_latest=opt.n_latest)
else:
visualizer.display_current_results(model.get_current_visuals(), total_steps, save_result,opt.update_html_freq,n_latest=opt.n_latest)
if total_steps % opt.print_freq == 0:
errors = model.get_current_errors()
t = (time.time() - iter_start_time) / opt.batchSize
visualizer.print_current_errors(epoch, epoch_iter, errors, t)
if opt.display_id > 0:
visualizer.plot_current_errors(epoch, float(epoch_iter)/dataset_size, opt, errors)
if total_steps % opt.save_latest_freq == 0:
print('saving the latest model (epoch %d, total_steps %d)' %
(epoch, total_steps))
model.save('latest')
if epoch % opt.save_epoch_freq == 0:
print('saving the model at the end of epoch %d, iters %d' %
(epoch, total_steps))
model.save('latest')
model.save(epoch)
print('End of epoch %d / %d \t Time Taken: %d sec' %
(epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
model.update_learning_rate()