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train_semi_supervised.py
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train_semi_supervised.py
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"""
Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
SPDX-License-Identifier: MIT
General-purpose semi-supervised training script for ScrabbleGAN.
You need to specify the labeled dataset ('--dataname'), unlabeled dataset ('--unlabeled_dataname'),
('--disjoint') if the training is disjoint (the recognizer sees only the labeled data and the discriminator sees only the unlabeled data)
and experiment name prefix ('--name_prefix').
It first creates model, dataset, and visualizer given the option.
It then does standard network training. During the training, it also visualize/save the images, print/save the loss plot, and save models.
The script supports continue/resume training. Use '--continue_train' to resume your previous training.
Example:
python train_semi_supervised.py --dataname IAMcharH32W16rmPunct --unlabeled_dataname CVLtrH32 --disjoint
See options/base_options.py and options/train_options.py for more training options.
"""
import time
from options.train_options import TrainOptions
from data import create_dataset
from models import create_model
from util.visualizer import Visualizer
from util.util import seed_rng
from util.util import prepare_z_y, get_curr_data
import torch
from itertools import cycle
if __name__ == '__main__':
opt = TrainOptions().parse() # get training options
seed_rng(opt.seed)
torch.backends.cudnn.benchmark = True
opt.labeled = True
dataset_labeled = create_dataset(opt) # create a dataset given opt.dataset_mode and other options
opt.labeled = False
opt.dataroot = opt.unlabeled_dataroot
dataset_unlabeled = create_dataset(opt)
dataset_size = max(len(dataset_labeled),len(dataset_unlabeled)) # 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
visualizer = Visualizer(opt) # create a visualizer that display/save images and plots
total_iters = 0 # the total number of training iterations
opt.iter = 0
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
if len(dataset_unlabeled) > len(dataset_labeled):
zip_dataset = zip(cycle(dataset_labeled), dataset_unlabeled)
else:
zip_dataset = zip(dataset_labeled, cycle(dataset_unlabeled))
for i, (data_labeled, data_unlabeled) in enumerate(zip_dataset): # inner loop within one epoch
opt.iter = i
iter_start_time = time.time() # timer for computation per iteration
if total_iters % opt.print_freq == 0:
t_data = iter_start_time - iter_data_time
visualizer.reset()
total_iters += opt.batch_size*opt.num_accumulations
epoch_iter += opt.batch_size*opt.num_accumulations
if opt.num_critic_train == 1:
curr_data = data_labeled
model.set_input(curr_data) # unpack data from dataset and apply preprocessing
model.optimize_G()
if opt.disjoint:
model.optimize_OCR()
else:
model.optimize_D_OCR()
model.optimize_G_step()
model.optimize_D_OCR_step()
curr_data = data_unlabeled
model.set_input(curr_data) # unpack data from dataset and apply preprocessing
model.optimize_G()
model.optimize_D()
model.optimize_G_step()
model.optimize_D_OCR_step()
else:
if (i % opt.num_critic_train) == 0:
curr_data = data_labeled
model.set_input(curr_data) # unpack data from dataset and apply preprocessing
model.optimize_G()
model.optimize_G_step()
curr_data = data_unlabeled
model.set_input(curr_data) # unpack data from dataset and apply preprocessing
model.optimize_G()
model.optimize_G_step()
curr_data = data_labeled
model.set_input(curr_data) # unpack data from dataset and apply preprocessing
if opt.disjoint:
model.optimize_OCR()
else:
model.optimize_D_OCR()
model.optimize_D_OCR_step()
curr_data = data_unlabeled
model.set_input(curr_data) # unpack data from dataset and apply preprocessing
model.optimize_D()
model.optimize_D_OCR_step()
if total_iters % opt.display_freq == 0: # display images on visdom and save images to a HTML file
save_result = total_iters % opt.update_html_freq == 0
model.compute_visuals()
visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
if total_iters % opt.print_freq == 0: # print training losses and save logging information to the disk
losses = model.get_current_losses()
t_comp = (time.time() - iter_start_time) / (opt.batch_size*opt.num_accumulations)
visualizer.print_current_losses(epoch, epoch_iter, losses, t_comp, t_data)
if opt.display_id > 0:
visualizer.plot_current_losses(epoch, float(epoch_iter) / dataset_size, losses)
if total_iters % opt.save_latest_freq == 0: # cache our latest model every <save_latest_freq> iterations
print('saving the latest model (epoch %d, total_iters %d)' % (epoch, total_iters))
save_suffix = 'iter_%d' % total_iters if opt.save_by_iter else 'latest'
model.save_networks(save_suffix)
torch.cuda.empty_cache()
iter_data_time = time.time()
if epoch % opt.save_epoch_freq == 0: # cache our model every <save_epoch_freq> epochs
print('saving the model at the end of epoch %d, iters %d' % (epoch, total_iters))
model.save_networks('latest')
model.save_networks(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() # update learning rates at the end of every epoch.