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train.py
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train.py
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import time
from options.train_options import TrainOptions
from models.models import create_model
from data.data_loader import *
from util.visualizer import Visualizer
import pdb
from tensorboardX import SummaryWriter
from val import *
def main():
# pdb.set_trace()
opt, val_opt = TrainOptions().parse()
# pdb.set_trace()
data_loader = CreateDataLoader(opt)
dataset = data_loader.load_data()
dataset_size = len(data_loader)
print('# training videos = %d' % dataset_size)
model = create_model(opt)
visualizer = Visualizer(opt)
total_steps = 0 # total # of videos
writer = SummaryWriter(log_dir=os.path.join(opt.tensorboard_dir, opt.name))
for epoch in range(model.start_epoch, opt.nepoch + opt.nepoch_decay + 1):
epoch_start_time = time.time()
epoch_iters = 0 # # of videos in this epoch
for i, data in enumerate(dataset):
# pdb.set_trace()
iter_start_time = time.time()
total_steps += opt.batch_size
epoch_iters += opt.batch_size
model.set_inputs(data)
model.optimize_parameters()
if total_steps % opt.print_freq == 0:
errors = model.get_current_errors()
t = (time.time()-iter_start_time)/opt.batch_size
writer.add_scalar('iter_time', t, total_steps / opt.batch_size)
for key in errors.keys():
writer.add_scalar('loss/%s' % (key), errors[key], total_steps/opt.batch_size)
visualizer.print_current_errors(epoch, epoch_iters, errors, t)
if total_steps % opt.display_freq == 0:
visuals = model.get_current_visuals()
grid = visual_grid(visuals['seq_batch'], visuals['pred'], opt.K, opt.T)
writer.add_image('current_batch', grid, total_steps / opt.batch_size)
if total_steps % opt.save_latest_freq == 0:
print('saving the latest model (epoch %d, total_steps %d)' %
(epoch, total_steps))
model.save('latest', epoch)
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', epoch)
model.save(epoch, epoch)
psnr_plot, ssim_plot, grid = val(val_opt)
# pdb.set_trace()
writer.add_image('psnr', psnr_plot, epoch)
writer.add_image('ssim', ssim_plot, epoch)
writer.add_image('samples', grid, epoch)
print('End of epoch %d / %d \t Time Taken: %d sec' %
(epoch, opt.nepoch + opt.nepoch_decay, time.time() - epoch_start_time))
if __name__ == "__main__":
main()