def save_images_png(run_name, data_loader, num_samples, num_classes, generator,
discriminator, is_generate, truncated_factor, prior,
latent_op, latent_op_step, latent_op_alpha, latent_op_beta,
device):
if is_generate is True:
batch_size = data_loader.batch_size
n_batches = math.ceil(float(num_samples) / float(batch_size))
else:
batch_size = data_loader.batch_size
total_instance = len(data_loader.dataset)
n_batches = math.ceil(float(num_samples) / float(batch_size))
data_iter = iter(data_loader)
data_iter = iter(data_loader)
type = "fake" if is_generate is True else "real"
print("Save {num_samples} {type} images in png format....".format(
num_samples=num_samples, type=type))
directory = join('./samples', run_name, type, "png")
if exists(abspath(directory)):
shutil.rmtree(abspath(directory))
os.makedirs(directory)
for f in range(num_classes):
os.makedirs(join(directory, str(f)))
with torch.no_grad() if latent_op is False else dummy_context_mgr() as mpc:
for i in tqdm(range(0, n_batches), disable=False):
start = i * batch_size
end = start + batch_size
if is_generate:
images, labels = generate_images(batch_size, generator,
discriminator,
truncated_factor, prior,
latent_op, latent_op_step,
latent_op_alpha,
latent_op_beta, device)
else:
try:
images, labels = next(data_iter)
except StopIteration:
break
for idx, img in enumerate(images.detach()):
if batch_size * i + idx < num_samples:
save_image(
(img + 1) / 2,
join(directory, str(labels[idx].item()),
'{idx}.png'.format(idx=batch_size * i + idx)))
else:
pass
print('Save png to ./generated_images/%s' % run_name)
def save_images_npz(run_name, data_loader, num_samples, num_classes, generator,
discriminator, is_generate, truncated_factor, prior,
latent_op, latent_op_step, latent_op_alpha, latent_op_beta,
device):
if is_generate is True:
batch_size = data_loader.batch_size
n_batches = math.ceil(float(num_samples) / float(batch_size))
else:
batch_size = data_loader.batch_size
total_instance = len(data_loader.dataset)
n_batches = math.ceil(float(num_samples) / float(batch_size))
data_iter = iter(data_loader)
data_iter = iter(data_loader)
type = "fake" if is_generate is True else "real"
print("Save {num_samples} {type} images in npz format....".format(
num_samples=num_samples, type=type))
directory = join('./samples', run_name, type, "npz")
if exists(abspath(directory)):
shutil.rmtree(abspath(directory))
os.makedirs(directory)
x = []
y = []
with torch.no_grad() if latent_op is False else dummy_context_mgr() as mpc:
for i in tqdm(range(0, n_batches), disable=False):
start = i * batch_size
end = start + batch_size
if is_generate:
images, labels = generate_images(batch_size, generator,
discriminator,
truncated_factor, prior,
latent_op, latent_op_step,
latent_op_alpha,
latent_op_beta, device)
else:
try:
images, labels = next(data_iter)
except StopIteration:
break
x += [np.uint8(255 * (images.detach().cpu().numpy() + 1) / 2.)]
y += [labels.detach().cpu().numpy()]
x = np.concatenate(x, 0)[:num_samples]
y = np.concatenate(y, 0)[:num_samples]
print('Images shape: %s, Labels shape: %s' % (x.shape, y.shape))
npz_filename = join(directory, "samples.npz")
print('Saving npz to %s' % npz_filename)
np.savez(npz_filename, **{'x': x, 'y': y})
