def main():
os.makedirs("results/created", exist_ok=True)
root = os.path.join("data")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
transform = tv.transforms.Compose([
tv.transforms.RandomAffine(0,
translate=(5 / 96, 5 / 96),
fillcolor=(255, 255, 255)),
tv.transforms.ColorJitter(hue=0.5),
tv.transforms.RandomHorizontalFlip(p=0.5),
tv.transforms.ToTensor(),
tv.transforms.Normalize((
0.5,
0.5,
0.5,
), (
0.5,
0.5,
0.5,
))
])
dataset = ImageFolder(root=root, transform=transform)
dataloader = DataLoader(dataset,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=8,
drop_last=True)
#X = iter(dataloader)
#test_ims1, _ = next(X)
#test_ims2, _ = next(X)
#test_ims = torch.cat((test_ims1, test_ims2), 0)
#test_ims_show = tv.utils.make_grid(test_ims[:36], normalize=True, nrow=6,)
#test_ims_show = test_ims_show.numpy().transpose((1,2,0))
#test_ims_show = np.array(test_ims_show*255, dtype=np.uint8)
#image = Image.fromarray(test_ims_show)
#image.save("results/reconstructed/test_images.png")
noise_fn = lambda x: torch.randn((x, LATENT_DIM), device=device)
gan = AEGAN(
LATENT_DIM,
noise_fn,
dataloader,
device=device,
batch_size=BATCH_SIZE,
)
gan.generator.load_state_dict(
torch.load(os.path.join("results", "checkpoints", "gen.01349.pt")))
gan.encoder.load_state_dict(
torch.load(os.path.join("results", "checkpoints", "enc.01349.pt")))
#gan.discriminator_image.load_state_dict(torch.load(os.path.join("results","checkpoints","dis_i.00299.pt")))
#gan.discriminator_latent.load_state_dict(torch.load(os.path.join("results","checkpoints","dis_l.00299.pt")))
start = time.time()
for i in range(FAKEDEX):
noise_fn = lambda x: torch.randn((x, LATENT_DIM), device=device)
test_noise = noise_fn(36)
save_images(gan, test_noise,
os.path.join("results", "created", f"cre.{i:04d}.png"))
def main():
os.makedirs("results/generated", exist_ok=True)
os.makedirs("results/reconstructed", exist_ok=True)
os.makedirs("results/checkpoints", exist_ok=True)
root = os.path.join("data")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
transform = tv.transforms.Compose([
tv.transforms.RandomAffine(0, translate=(5/96, 5/96), fillcolor=(255,255,255)),
tv.transforms.ColorJitter(hue=0.5),
tv.transforms.RandomHorizontalFlip(p=0.5),
tv.transforms.ToTensor(),
tv.transforms.Normalize((0.5, 0.5, 0.5,), (0.5, 0.5, 0.5,))
])
dataset = ImageFolder(
root=root,
transform=transform
)
dataloader = DataLoader(dataset,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=8,
drop_last=True
)
noise_fn = lambda x: torch.randn((x, LATENT_DIM), device=device)
gan = AEGAN(
LATENT_DIM,
noise_fn,
dataloader,
device=device,
batch_size=BATCH_SIZE,
)
# Uncomment this line to load an existing model:
# gan.generator.load_state_dict(torch.load('trained_generator_weights.pt'))
# Uncomment this line to train a new model:
# train(gan)
images = gan.generate_samples()
ims = tv.utils.make_grid(images, normalize=True)
plt.imshow(ims.numpy().transpose((1,2,0)))
plt.show()
def load_gan():
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
noise_fn = lambda x: torch.randn((x, LATENT_DIM), device=device)
gan = AEGAN(LATENT_DIM,
noise_fn,
None,
device=device,
batch_size=BATCH_SIZE,
checkpoints_dir=CHECKPOINTS_DIR)
load_checkpoint(gan)
return gan, noise_fn
def main():
os.makedirs("results/generated", exist_ok=True)
os.makedirs("results/reconstructed", exist_ok=True)
os.makedirs("results/checkpoints", exist_ok=True)
root = os.path.join("data")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
transform = tv.transforms.Compose([
tv.transforms.RandomAffine(0,
translate=(5 / 96, 5 / 96),
fillcolor=(255, 255, 255)),
tv.transforms.ColorJitter(hue=0.5),
tv.transforms.RandomHorizontalFlip(p=0.5),
tv.transforms.ToTensor(),
tv.transforms.Normalize((
0.5,
0.5,
0.5,
), (
0.5,
0.5,
0.5,
))
])
dataset = ImageFolder(root=root, transform=transform)
dataloader = DataLoader(dataset,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=8,
drop_last=True)
X = iter(dataloader)
test_ims1, _ = next(X)
test_ims2, _ = next(X)
test_ims = torch.cat((test_ims1, test_ims2), 0)
test_ims_show = tv.utils.make_grid(
test_ims[:36],
normalize=True,
nrow=6,
)
test_ims_show = test_ims_show.numpy().transpose((1, 2, 0))
test_ims_show = np.array(test_ims_show * 255, dtype=np.uint8)
image = Image.fromarray(test_ims_show)
image.save("results/reconstructed/test_images.png")
noise_fn = lambda x: torch.randn((x, LATENT_DIM), device=device)
test_noise = noise_fn(36)
gan = AEGAN(
LATENT_DIM,
noise_fn,
dataloader,
device=device,
batch_size=BATCH_SIZE,
)
gan.generator.load_state_dict(
torch.load(os.path.join("results", "checkpoints", "gen.01049.pt")))
gan.encoder.load_state_dict(
torch.load(os.path.join("results", "checkpoints", "enc.01049.pt")))
gan.discriminator_image.load_state_dict(
torch.load(os.path.join("results", "checkpoints", "dis_i.01049.pt")))
gan.discriminator_latent.load_state_dict(
torch.load(os.path.join("results", "checkpoints", "dis_l.01049.pt")))
start = time.time()
for i in range(EPOCHS):
while True:
try:
with open("pause.json") as f:
pause = json.load(f)
if pause['pause'] == 0:
break
print(f"Pausing for {pause['pause']} seconds")
time.sleep(pause["pause"])
except (KeyError, json.decoder.JSONDecodeError, FileNotFoundError):
break
elapsed = int(time.time() - start)
elapsed = f"{elapsed // 3600:02d}:{(elapsed % 3600) // 60:02d}:{elapsed % 60:02d}"
print(f"Epoch {i+1}; Elapsed time = {elapsed}s")
gan.train_epoch(max_steps=100)
if (i + 1) % 50 == 0:
torch.save(
gan.generator.state_dict(),
os.path.join("results", "checkpoints", f"gen.{i:05d}.pt"))
torch.save(
gan.discriminator_image.state_dict(),
os.path.join("results", "checkpoints", f"dis_i.{i:05d}.pt"))
torch.save(
gan.discriminator_latent.state_dict(),
os.path.join("results", "checkpoints", f"dis_l.{i:05d}.pt"))
torch.save(
gan.encoder.state_dict(),
os.path.join("results", "checkpoints", f"enc.{i:05d}.pt"))
save_images(gan, test_noise,
os.path.join("results", "generated", f"gen.{i:04d}.png"))
with torch.no_grad():
reconstructed = gan.generator(gan.encoder(test_ims.cuda())).cpu()
reconstructed = tv.utils.make_grid(
reconstructed[:36],
normalize=True,
nrow=6,
)
reconstructed = reconstructed.numpy().transpose((1, 2, 0))
reconstructed = np.array(reconstructed * 255, dtype=np.uint8)
reconstructed = Image.fromarray(reconstructed)
reconstructed.save(
os.path.join("results", "reconstructed", f"gen.{i:04d}.png"))
images = gan.generate_samples()
ims = tv.utils.make_grid(images, normalize=True)
plt.imshow(ims.numpy().transpose((1, 2, 0)))
plt.show()
def main():
os.makedirs("results/generated", exist_ok=True)
os.makedirs("results/reconstructed", exist_ok=True)
os.makedirs("results/checkpoints", exist_ok=True)
root = os.path.join("data")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
transform = tv.transforms.Compose([
tv.transforms.RandomAffine(0,
translate=(5 / 96, 5 / 96),
fill=(255, 255, 255)),
tv.transforms.ColorJitter(hue=0.5),
tv.transforms.RandomHorizontalFlip(p=0.5),
tv.transforms.ToTensor(),
tv.transforms.Normalize((
0.5,
0.5,
0.5,
), (
0.5,
0.5,
0.5,
))
])
dataset = ImageFolder(root=root, transform=Transform2Times(transform))
dataloader = DataLoader(dataset,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=2,
drop_last=True)
X = iter(dataloader)
[test_ims, _], _ = next(X)
while len(test_ims) < 36:
[test_ims2, _], _ = next(X)
test_ims = torch.cat((test_ims, test_ims2), 0)
test_ims_show = tv.utils.make_grid(
test_ims[:36],
normalize=True,
nrow=6,
)
test_ims_show = test_ims_show.numpy().transpose((1, 2, 0))
test_ims_show = np.array(test_ims_show * 255, dtype=np.uint8)
image = Image.fromarray(test_ims_show)
image.save("results/reconstructed/test_images.png")
noise_fn = lambda x: torch.randn((x, LATENT_DIM), device=device)
test_noise = noise_fn(36)
gan = AEGAN(LATENT_DIM,
noise_fn,
dataloader,
device=device,
batch_size=BATCH_SIZE,
checkpoints_dir=CHECKPOINTS_DIR)
cache = ImageCache(BATCH_SIZE * 4 * 1024)
last_epoch = load_checkpoint(gan)
start = time.time()
for i in range(last_epoch + 1, EPOCHS):
elapsed = int(time.time() - start)
elapsed = f"{elapsed // 3600:02d}:{(elapsed % 3600) // 60:02d}:{elapsed % 60:02d}"
print(f"Epoch {i}; Elapsed time = {elapsed}s")
gan.train_epoch(cache)
if i > 0 and i % CHECKPOINTS_PERIOD == 0:
torch.save(gan.state_dict(),
os.path.join(CHECKPOINTS_DIR, f"gen.{i:05d}.pt"))
if i % SAVE_IMAGES_PERIOD == 0:
save_images(
gan, test_noise,
os.path.join("results", "generated", f"gen.{i:05d}.png"))
with torch.no_grad():
reconstructed = gan.generator(gan.encoder(
test_ims.cuda())).cpu()
reconstructed = tv.utils.make_grid(
reconstructed[:36],
normalize=True,
nrow=6,
)
reconstructed = reconstructed.numpy().transpose((1, 2, 0))
reconstructed = np.array(reconstructed * 255, dtype=np.uint8)
reconstructed = Image.fromarray(reconstructed)
reconstructed.save(
os.path.join("results", "reconstructed", f"gen.{i:05d}.png"))
images = gan.generate_samples()
ims = tv.utils.make_grid(images, normalize=True)
plt.imshow(ims.numpy().transpose((1, 2, 0)))
plt.show()