if __name__ == '__main__':
opt = TrainOptions().parse() # get training options
# Seed RNG
seed_rng(opt.seed)
torch.backends.cudnn.benchmark = True
dataset = create_dataset(
opt) # create a dataset given opt.dataset_mode and other options
dataset_size = len(dataset) # get the number of images in the dataset.
print('The number of training images = %d' % dataset_size)
model = create_model(
opt) # create a model given opt.model and other options
model.setup(
opt) # regular setup: load and print networks; create schedulers
if opt.single_writer:
opt.G_init = 'N02'
opt.D_init = 'N02'
model.netG.init_weights()
model.netD.init_weights()
visualizer = Visualizer(
opt) # create a visualizer that display/save images and plots
total_iters = 0 # the total number of training iterations
opt.iter = 0
# seed_rng(opt.seed)
for epoch in range(
opt.epoch_count, opt.niter + opt.niter_decay + 1
): # outer loop for different epochs; we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>
epoch_start_time = time.time() # timer for entire epoch
iter_data_time = time.time() # timer for data loading per iteration
epoch_iter = 0 # the number of training iterations in current epoch, reset to 0 every epoch
