def save_inception_score(gan, i_iter):
print("Evaluating...")
num_images_to_eval = 50000
eval_images = []
num_batches = num_images_to_eval // 100 + 1
print("Calculating Inception Score. Sampling {} images...".format(num_images_to_eval))
np.random.seed(0)
gan.netG.eval()
for _ in range(num_batches):
index = np.arange(100) * 10 + np.tile(np.arange(10), 10)
images = gan.gen_fake_data(100, opt.nz).data.cpu().numpy()
images = images.reshape((-1, 3, 32, 32))
images = np.rollaxis(images, 1, 4)
images = images[index]
eval_images.append(images)
gan.netG.train()
np.random.seed()
eval_images = np.vstack(eval_images)
eval_images = eval_images[:num_images_to_eval]
eval_images = np.clip((eval_images + 1.0) * 127.5, 0.0, 255.0).astype(np.uint8)
# Calc Inception score
eval_images = list(eval_images)
inception_score_mean, inception_score_std = get_inception_score(eval_images)
print("Inception Score: Mean = {} \tStd = {}.".format(inception_score_mean, inception_score_std))
log.add('inception_score', dict(mean=inception_score_mean, std=inception_score_std), i_iter)
def save_samples(gan, i_iter):
if 'noise' not in save_samples.__dict__:
save_samples.noise = Variable(gan.gen_latent_noise(64, opt.nz))
if not os.path.exists(opt.path + 'tmp/'):
os.makedirs(opt.path + 'tmp/')
fake = gan.gen_fake_data(64, opt.nz, noise=save_samples.noise)
# fake = next(data_iter)
fake_01 = (fake[0].data.cpu() + 1.0) * 0.5
save_image(fake_01, opt.path + 'tmp/' + '{:0>5}.jpeg'.format(i_iter))
def save_samples(gan, i_iter):
gan.netG.eval()
if 'noise' not in save_samples.__dict__:
save_samples.noise = Variable(gan.gen_latent_noise(64, opt.nz))
if not os.path.exists(opt.path + 'tmp/'):
os.makedirs(opt.path + 'tmp/')
fake = gan.gen_fake_data(64, opt.nz, noise=save_samples.noise)
# fake = next(data_iter)
# print(fake.min(), fake.max())
fake = fake.view(-1, 3, 32, 32)
fake_01 = (fake.data.cpu() + 1.0) * 0.5
# print(fake_01.min(), fake_01.max())
save_image(fake_01, opt.path + 'tmp/' + '{:0>5}.jpeg'.format(i_iter))
# alkjfd
gan.netG.train()
def save_samples(gan, i_iter):
gan.netG.eval()
if 'noise' not in save_samples.__dict__:
save_samples.noise = Variable(gan.gen_latent_noise(64, opt.nz))
if not os.path.exists(opt.path + 'tmp/'):
os.makedirs(opt.path + 'tmp/')
fake = gan.gen_fake_data(64, opt.nz, noise=save_samples.noise)
# fake = next(data_iter)
fake_list = []
for i in range(len(fake)):
for j in range(41):
img = torch.stack([fake[i, 0, :, :], fake[i, j + 1, :, :]], dim=0)
fake_list.append(img)
fake = torch.FloatTensor(len(fake_list), 3, 48, 80)
fake_tensor = torch.stack(fake_list, dim=0)
# print(type(fake_tensor))
# print(type(fake))
fake[:, :2, :, :] = fake_tensor.data
fake[:, 2, :, :] = -1
# fake = next(data_iter)
# print(fake.min(), fake.max())
# fake = fake.view(-1, 3, 32, 32)
fake_01 = (fake.cpu() + 1.0) * 0.5
# print(fake_01.min(), fake_01.max())
save_image(fake_01,
opt.path + 'tmp/' + '{:0>5}.png'.format(i_iter),
nrow=41)
gan.netG.train()