test_result = discriminator(test_fake)
test_relevance = discriminator.relprop()
#
# test_relevance = vectors_to_images(test_relevance)
test_fake = vectors_to_images(test_fake)
# set ngpu back to opt.ngpu
if (opt.ngpu > 1):
discriminator.setngpu(opt.ngpu)
# discriminator.train()
# Add up relevance of all color channels
# test_relevance = torch.sum(test_relevance, 1, keepdim=True)
logger.log_images(test_fake.detach(), test_relevance.detach(), 1,
epoch, n_batch, len(dataloader))
# show images inline
subprocess.call([
os.path.expanduser('~/.iterm2/imgcat'),
outf + '/mnist/hori_epoch_' + str(epoch) + '_batch_' +
str(n_batch) + '.png'
])
# Display status Logs
logger.display_status(epoch, num_epochs, n_batch, num_batches,
d_error, g_error, d_pred_real, d_pred_fake)
# do checkpointing
torch.save(discriminator.state_dict(), '%s/generator.pth' % checkpointdir)
torch.save(generator.state_dict(), '%s/discriminator.pth' % checkpointdir)
log_epoch = epoch
else:
log_epoch = epoch + opt.cont
img_name = logger.log_images(
test_fake_cat.detach(), test_relevance_cat.detach(), test_fake.size(0),
log_epoch, n_batch, len(dataloader), printdata, noLabel=opt.nolabel
)
# show images inline
if opt.imgcat:
comment = '{:.4f}-{:.4f}'.format(printdata['test_prob'], printdata['real_test_prob'])
subprocess.call([os.path.expanduser('~/.iterm2/imgcat'),
outf + '/' + opt.dataset + '/epoch_' + str(epoch) + '_batch_' + str(n_batch) + '_' + comment + '.png'])
status = logger.display_status(epoch, opt.epochs, n_batch, len(dataloader), d_error_total, g_err,
prediction_real, prediction_fake)
Logger.epoch += 1
# do checkpointing
torch.save(generator.state_dict(), '%s/generator_epoch_{}.pth'.format(str(log_epoch)) % (checkpointdir))
torch.save(discriminator.state_dict(), '%s/discriminator_epoch_{}.pth'.format(str(log_epoch)) % (checkpointdir))
dic_compare = torch.load('%s/discriminator_epoch_{}.pth'.format(str(log_epoch)) % (checkpointdir),
map_location='cuda:0' if torch.cuda.is_available() else 'cpu')
def compare_models(dict1, dict2):
models_differ = 0
for key_item_1, key_item_2 in zip(dict1.items(), dict2.items()):
if torch.equal(key_item_1[1], key_item_2[1]):
logger.log(d_training_loss, g_training_loss, epoch, n_batch,
num_batches)
print(
'[%d/%d][%d/%d] Loss_D: %f Loss_G: %f Loss_D_real: %f Loss_D_fake %f'
% (epoch, num_epochs, n_batch, num_batches, d_training_loss,
g_training_loss, d_loss_real, d_loss_fake))
# Display Progress every few batches
if n_batch % 100 == 0 or n_batch == num_batches:
test_fake = generator(test_noise)
if (opt.ngpu > 1):
discriminator.setngpu(1)
discriminator.eval()
test_result = discriminator(test_fake)
discriminator.train()
test_relevance = discriminator.relprop()
if (opt.ngpu > 1):
discriminator.setngpu(opt.ngpu)
# Add up relevance of all color channels
test_relevance = torch.sum(test_relevance, 1, keepdim=True)
# print('Test fake', test_fake.shape, 'test_rel', test_relevance.shape)
logger.log_images(test_fake.data, test_relevance, num_test_samples,
epoch, n_batch, num_batches)
status = logger.display_status(epoch, num_epochs, n_batch,
num_batches, d_training_loss,
g_training_loss, d_prediction_real,
d_prediction_fake)
