def train_gpu(rank,world_size,opt,dataset):
torch.cuda.set_device(opt.gpu_ids[rank])
signal.signal(signal.SIGINT, signal_handler) #to really kill the process
signal.signal(signal.SIGTERM, signal_handler)
if len(opt.gpu_ids)>1:
setup(rank, world_size,opt.ddp_port)
dataloader = create_dataloader(opt,rank,dataset) # create a dataset given opt.dataset_mode and other options
dataset_size = len(dataset) # get the number of images in the dataset.
model = create_model(opt,rank) # create a model given opt.model and other options
if hasattr(model,'data_dependent_initialize'):
data=next(iter(dataloader))
model.data_dependent_initialize(data)
model.setup(opt) # regular setup: load and print networks; create schedulers
if len(opt.gpu_ids)>1:
model.parallelize(rank)
else:
model.single_gpu()
if rank==0:
visualizer = Visualizer(opt) # create a visualizer that display/save images and plots
total_iters = 0 # the total number of training iterations
if rank == 0:
model.real_A_val,model.real_B_val = dataset.get_validation_set(opt.pool_size)
model.real_A_val,model.real_B_val=model.real_A_val.to(model.device),model.real_B_val.to(model.device)
if rank==0 and opt.display_networks:
data=next(iter(dataloader))
for path in model.save_networks_img(data):
visualizer.display_img(path+'.png')
for epoch in range(opt.epoch_count, opt.n_epochs + opt.n_epochs_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 rank==0:
visualizer.reset() # reset the visualizer: make sure it saves the results to HTML at least once every epoch
for i, data in enumerate(dataloader): # inner loop (minibatch) within one epoch
iter_start_time = time.time() # timer for computation per iteration
t_data_mini_batch = iter_start_time - iter_data_time
model.set_input(data) # unpack data from dataloader and apply preprocessing
model.optimize_parameters() # calculate loss functions, get gradients, update network weights
t_comp = (time.time() - iter_start_time) / opt.batch_size
batch_size=model.get_current_batch_size() * len(opt.gpu_ids)
total_iters += batch_size
epoch_iter += batch_size
if rank == 0:
if total_iters % opt.display_freq < batch_size: # 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,params=model.get_display_param())
if total_iters % opt.print_freq < batch_size : # print training losses and save logging information to the disk
losses = model.get_current_losses()
visualizer.print_current_losses(epoch, epoch_iter, losses, t_comp, t_data_mini_batch)
if opt.display_id > 0:
visualizer.plot_current_losses(epoch, float(epoch_iter) / dataset_size, losses)
if total_iters % opt.save_latest_freq < batch_size : # 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)
if total_iters % opt.fid_every < batch_size and opt.compute_fid:
model.compute_fid(epoch,total_iters)
if opt.display_id > 0:
fids=model.get_current_fids()
visualizer.plot_current_fid(epoch, float(epoch_iter) / dataset_size, fids)
if total_iters % opt.D_accuracy_every < batch_size and opt.compute_D_accuracy:
model.compute_D_accuracy()
if opt.display_id > 0:
accuracies=model.get_current_D_accuracies()
visualizer.plot_current_D_accuracies(epoch, float(epoch_iter) / dataset_size, accuracies)
if total_iters % opt.display_freq < batch_size and opt.APA:
if opt.display_id > 0:
p=model.get_current_APA_prob()
visualizer.plot_current_APA_prob(epoch, float(epoch_iter) / dataset_size, p)
iter_data_time = time.time()
if epoch % opt.save_epoch_freq == 0: # cache our model every <save_epoch_freq> epochs
if rank == 0:
print('saving the model at the end of epoch %d, iters %d' % (epoch, total_iters))
model.save_networks('latest')
model.save_networks(epoch)
if rank == 0:
print('End of epoch %d / %d \t Time Taken: %d sec' % (epoch, opt.n_epochs + opt.n_epochs_decay, time.time() - epoch_start_time))
model.update_learning_rate() # update learning rates at the end of every epoch.
visualizer.plot_current_losses(
epoch,
float(epoch_iter) / dataset_size, losses)
if total_iters % opt.save_latest_freq < model.opt.batch_size: # 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)
if total_iters % opt.fid_every < model.opt.batch_size and opt.compute_fid:
model.compute_fid(epoch, total_iters)
if opt.display_id > 0:
fids = model.get_current_fids()
visualizer.plot_current_fid(
epoch,
float(epoch_iter) / dataset_size, fids)
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.n_epochs + opt.n_epochs_decay,
time.time() - epoch_start_time))
model.update_learning_rate(
) # update learning rates at the end of every epoch.
