def get_model(model, hidden_size, k, num_channels, resolution, num_classes):
if model == "vqvae":
if hidden_size == 256:
CKPT_DIR = "models/imagenet/best.pt"
elif hidden_size == 128:
CKPT_DIR = "models/imagenet/hs_{}/best.pt".format(hidden_size)
else:
CKPT_DIR = "models/imagenet/hs_{}/best.pt".format(hidden_size)
#CKPT_DIR = "models/imagenet/hs_32_4/best.pt"#.format(hidden_size)
model = VectorQuantizedVAE(num_channels, hidden_size, k)
elif model == "vae":
CKPT_DIR = f"models/imagenet_hs_128_{hidden_size}_vae.pt"
model = VAE(num_channels, hidden_size, hidden_size)
elif model == "aae":
CKPT_DIR = f"models/aae/imagenet_hs_32_{hidden_size}/best.pt"
model = AAE(32, num_channels, hidden_size)
else:
model = None
imgnetclassifier = ImgnetClassifier(model, hidden_size, resolution)
if hidden_size > 0:
ckpt = torch.load(CKPT_DIR)
model.load_state_dict(ckpt)
return imgnetclassifier
def get_model(model,
hidden_size,
num_channels,
resolution,
enc_type,
dec_type,
num_classes,
k=512):
CKPT_DIR = f"models/{model}_{args.recons_loss}/{args.train_dataset}/depth_{enc_type}_{dec_type}_hs_{args.img_res}_{hidden_size}/best.pt"
if model == "vqvae":
#CKPT_DIR = "models/imagenet/hs_32_4/best.pt"#.format(hidden_size)
model = VectorQuantizedVAE(num_channels, hidden_size, k, enc_type,
dec_type)
elif model == "vae":
model = VAE(num_channels, hidden_size, enc_type, dec_type)
elif model == "acai":
model = ACAI(resolution, num_channels, hidden_size, enc_type, dec_type)
else:
model = None
if model != "supervised":
imgclassifier = ImgClassifier(model, hidden_size, resolution,
num_classes)
else:
imgclassifier = SupervisedImgClassifier(hidden_size, enc_type,
resolution, num_classes)
if hidden_size > 0:
ckpt = torch.load(CKPT_DIR)
model.load_state_dict(ckpt["model"])
return imgclassifier
def get_recons(loader, hidden_size=256):
model = VectorQuantizedVAE(3, hidden_size, 512).to(DEVICE)
if hidden_size < 256:
ckpt = torch.load(
"./models/imagenet/hs_{}/best.pt".format(hidden_size))
else:
ckpt = torch.load("./models/imagenet/best.pt".format(hidden_size))
model.load_state_dict(ckpt)
args = type('', (), {})()
args.device = DEVICE
gen_img, _ = next(iter(loader))
# grid = make_grid(gen_img.cpu(), nrow=8)
# torchvision.utils.save_image(grid, "hs_{}_recons.png".format(hidden_size))
#exit()
reconstruction = generate_samples(gen_img, model, args)
grid = make_grid(reconstruction.cpu(), nrow=8)
return grid
def main(args):
writer = SummaryWriter("./logs/{0}".format(args.output_folder))
save_filename = "./models/{0}".format(args.output_folder)
# Define the train, valid & test datasets
all_dataset = TripletWhaleDataset(args.data_folder, min_instance_count=10)
from torch.utils.data import random_split
k = len(all_dataset)
train_s = int(k * 0.6)
test_s = int(k * 0.2)
valid_s = k - train_s - test_s
train_dataset, test_dataset, valid_dataset = random_split(
all_dataset, (train_s, test_s, valid_s)
)
num_channels = 3
# Define the data loaders
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
)
valid_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=args.batch_size,
shuffle=False,
drop_last=True,
num_workers=args.num_workers,
pin_memory=True,
)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=16, shuffle=True)
# Fixed images for Tensorboard
fixed_images, _, fixed_ids = next(iter(test_loader))[:3]
fixed_grid = make_grid(fixed_images, nrow=8, range=(-1, 1), normalize=True)
writer.add_image("original", fixed_grid, 0)
n_classes = len(all_dataset.index_to_class)
vae = VectorQuantizedVAE(num_channels, args.hidden_size, args.k)
vae.load_state_dict(torch.load(args.model_path))
encoder = vae.encoder.eval().to(args.device)
model = AffineClassifier(all_dataset, encoder, n_classes).to(args.device).train()
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
best_loss = -1.0
for epoch in range(args.num_epochs):
with torch.autograd.set_detect_anomaly(True):
train(train_loader, model, optimizer, args, writer)
loss = test(valid_loader, model, args, writer)
if (epoch == 0) or (loss < best_loss):
best_loss = loss
with open("{0}/best.pt".format(save_filename), "wb") as f:
torch.save(model.state_dict(), f)
with open("{0}/model_{1}.pt".format(save_filename, epoch + 1), "wb") as f:
torch.save(model.state_dict(), f)
def main(args):
writer = SummaryWriter('./logs/{0}'.format(args.output_folder))
save_filename = './models/{0}'.format(args.output_folder)
if args.dataset == 'whales':
# Define the train, valid & test datasets
all_dataset = WhaleDataset(args.data_folder, image_transformation=BASIC_IMAGE_T)
from torch.utils.data import random_split
k = len(all_dataset)
train_s = int(k * .6)
test_s = int(k * .2)
valid_s = k - train_s - test_s
train_dataset, test_dataset, valid_dataset = random_split(all_dataset, (train_s, test_s, valid_s))
num_channels = 3
# Define the data loaders
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=args.batch_size, shuffle=False, drop_last=True,
num_workers=args.num_workers, pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=16, shuffle=True)
# Fixed images for Tensorboard
fixed_images, _, fixed_ids = next(iter(test_loader))
fixed_grid = make_grid(fixed_images, nrow=8, range=(-1, 1), normalize=True)
writer.add_image('original', fixed_grid, 0)
vae = VectorQuantizedVAE(num_channels, args.hidden_size, args.k)
vae.load_state_dict(torch.load(args.model_path))
encoder = vae.encoder.eval().to(args.device)
affine_resample = AffineCropper(all_dataset, args.hidden_size, encoder).to(args.device)
generator = affine_resample.generator.train()
optimizer = torch.optim.Adam(generator.parameters(), lr=args.lr)
# Generate the samples first once
reconstruction = generate_samples(fixed_images, fixed_ids, affine_resample, args)
grid = make_grid(reconstruction.cpu(), nrow=8, range=(-1, 1), normalize=True)
writer.add_image('cropped', grid, 0)
best_loss = -1.
for epoch in range(args.num_epochs):
with torch.autograd.set_detect_anomaly(True):
train(train_loader, encoder, affine_resample, optimizer, args, writer)
loss = test(valid_loader, encoder, affine_resample, args, writer)
reconstruction = generate_samples(fixed_images, fixed_ids, affine_resample, args)
grid = make_grid(reconstruction.cpu(), nrow=8, range=(-1, 1), normalize=True)
writer.add_image('cropped', grid, epoch + 1)
if (epoch == 0) or (loss < best_loss):
best_loss = loss
with open('{0}/best.pt'.format(save_filename), 'wb') as f:
torch.save(generator.state_dict(), f)
with open('{0}/model_{1}.pt'.format(save_filename, epoch + 1), 'wb') as f:
torch.save(generator.state_dict(), f)
def get_model(ae_type, hidden_size, k, num_channels):
CKPT_DIR = "models/imagenet/hs_32_4/best.pt" #.format(hidden_size)
model = VectorQuantizedVAE(num_channels, hidden_size, k)
imgnetclassifier = ImgnetClassifier(model, hidden_size)
ckpt = torch.load(CKPT_DIR)
model.load_state_dict(ckpt)
return imgnetclassifier
def get_model(ae_type, hidden_size, k, num_channels):
if ae_type == "vqvae":
if hidden_size==256:
CKPT_DIR = "models/imagenet/best.pt"
elif hidden_size==128:
CKPT_DIR = "models/imagenet/hs_{}/best.pt".format(hidden_size)
else:
CKPT_DIR = "models/imagenet/hs_{}/best.pt".format(hidden_size)
model = VectorQuantizedVAE(num_channels, hidden_size, k)
imgnetclassifier = ImgnetClassifier(model, hidden_size)
elif ae_type == "vae":
CKPT_DIR = "models/imagenet_vae.pt"
model = VAE(num_channels, hidden_size, 4096)
imgnetclassifier = ImgnetClassifier(model, 4)
ckpt = torch.load(CKPT_DIR)
model.load_state_dict(ckpt)
return imgnetclassifier
def main(args):
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
print("Start Time =", current_time)
if args.input == "MNIST":
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize([0.5], [0.5])])
# Define the train & test dataSets
test_set = datasets.MNIST("MNIST",
train=False,
download=True,
transform=transform)
# Define the data loaders
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=100,
shuffle=True,
pin_memory=True)
real_batch, _ = next(iter(test_loader))
else:
real_batch = torch.normal(mean=0, std=1, size=(100, 1, 28, 28))
model = VectorQuantizedVAE(1, args.hidden_size_vae, args.k).to(args.device)
with open(args.model, 'rb') as f:
state_dict = torch.load(f)
model.load_state_dict(state_dict)
generated = generate_samples(real_batch, model, args)
save_image(make_grid(generated, nrow=10),
'./generatedImages/{0}.png'.format(args.filename))
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
print("End Time =", current_time)
def main(args):
writer = SummaryWriter('./logs/{0}'.format(args.output_folder))
save_filename = './models/{0}'.format(args.output_folder)
if args.dataset in ['mnist', 'fashion-mnist', 'cifar10']:
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
if args.dataset == 'mnist':
# Define the train & test datasets
train_dataset = datasets.MNIST(args.data_folder,
train=True,
download=True,
transform=transform)
test_dataset = datasets.MNIST(args.data_folder,
train=False,
transform=transform)
num_channels = 1
elif args.dataset == 'fashion-mnist':
# Define the train & test datasets
train_dataset = datasets.FashionMNIST(args.data_folder,
train=True,
download=True,
transform=transform)
test_dataset = datasets.FashionMNIST(args.data_folder,
train=False,
transform=transform)
num_channels = 1
elif args.dataset == 'cifar10':
# Define the train & test datasets
train_dataset = datasets.CIFAR10(args.data_folder,
train=True,
download=True,
transform=transform)
test_dataset = datasets.CIFAR10(args.data_folder,
train=False,
transform=transform)
num_channels = 3
valid_dataset = test_dataset
elif args.dataset == 'miniimagenet':
transform = transforms.Compose([
transforms.RandomResizedCrop(32),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# Define the train, valid & test datasets
train_dataset = MiniImagenet(args.data_folder,
train=True,
download=True,
transform=transform)
valid_dataset = MiniImagenet(args.data_folder,
valid=True,
download=True,
transform=transform)
test_dataset = MiniImagenet(args.data_folder,
test=True,
download=True,
transform=transform)
num_channels = 3
# Define the data loaders
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=args.batch_size,
shuffle=False,
drop_last=True,
num_workers=args.num_workers,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=16,
shuffle=True)
# Fixed images for Tensorboard
fixed_images, _ = next(iter(test_loader))
fixed_grid = make_grid(fixed_images, nrow=8, range=(-1, 1), normalize=True)
writer.add_image('original', fixed_grid, 0)
model = VectorQuantizedVAE(num_channels, args.hidden_size,
args.k).to(args.device)
if args.ckp != "":
model.load_state_dict(torch.load(args.ckp))
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
if args.tmodel != '':
net = vgg.VGG('VGG19')
net = net.to(args.device)
net = torch.nn.DataParallel(net)
checkpoint = torch.load(args.tmodel)
net.load_state_dict(checkpoint['net'])
target_model = net
# Generate the samples first once
reconstruction = generate_samples(fixed_images, model, args)
grid = make_grid(reconstruction.cpu(),
nrow=8,
range=(-1, 1),
normalize=True)
writer.add_image('reconstruction', grid, 0)
best_loss = -1.
for epoch in range(args.num_epochs):
print(epoch)
# if epoch<100:
# args.lr = 1e-5
# if epoch>100 and epoch< 400:
# args.lr = 2e-5
train(train_loader, model, target_model, optimizer, args, writer)
loss, _ = test(valid_loader, model, args, writer)
print("test loss:", loss)
reconstruction = generate_samples(fixed_images, model, args)
grid = make_grid(reconstruction.cpu(),
nrow=8,
range=(-1, 1),
normalize=True)
writer.add_image('reconstruction', grid, epoch + 1)
if (epoch == 0) or (loss < best_loss):
best_loss = loss
with open('{0}/best.pt'.format(save_filename), 'wb') as f:
torch.save(model.state_dict(), f)
with open('{0}/model_{1}.pt'.format(save_filename, epoch + 1),
'wb') as f:
torch.save(model.state_dict(), f)
def main(args):
writer = SummaryWriter('./logs/{0}'.format(args.output_folder))
save_filename = './models/{0}/prior.pt'.format(args.output_folder)
if args.dataset in ['mnist', 'fashion-mnist', 'cifar10']:
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
if args.dataset == 'mnist':
# Define the train & test datasets
train_dataset = datasets.MNIST(args.data_folder,
train=True,
download=True,
transform=transform)
test_dataset = datasets.MNIST(args.data_folder,
train=False,
transform=transform)
num_channels = 1
elif args.dataset == 'fashion-mnist':
# Define the train & test datasets
train_dataset = datasets.FashionMNIST(args.data_folder,
train=True,
download=True,
transform=transform)
test_dataset = datasets.FashionMNIST(args.data_folder,
train=False,
transform=transform)
num_channels = 1
elif args.dataset == 'cifar10':
# Define the train & test datasets
train_dataset = datasets.CIFAR10(args.data_folder,
train=True,
download=True,
transform=transform)
test_dataset = datasets.CIFAR10(args.data_folder,
train=False,
transform=transform)
num_channels = 3
valid_dataset = test_dataset
elif args.dataset == 'miniimagenet':
transform = transforms.Compose([
transforms.RandomResizedCrop(32),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# Define the train, valid & test datasets
train_dataset = MiniImagenet(args.data_folder,
train=True,
download=True,
transform=transform)
valid_dataset = MiniImagenet(args.data_folder,
valid=True,
download=True,
transform=transform)
test_dataset = MiniImagenet(args.data_folder,
test=True,
download=True,
transform=transform)
num_channels = 3
# Define the data loaders
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=args.batch_size,
shuffle=False,
drop_last=True,
num_workers=args.num_workers,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=16,
shuffle=True)
# Save the label encoder
# with open('./models/{0}/labels.json'.format(args.output_folder), 'w') as f:
# json.dump(train_dataset._label_encoder, f)
# Fixed images for Tensorboard
fixed_images, _ = next(iter(test_loader))
fixed_grid = make_grid(fixed_images, nrow=8, range=(-1, 1), normalize=True)
writer.add_image('original', fixed_grid, 0)
model = VectorQuantizedVAE(num_channels, args.hidden_size_vae,
args.k).to(args.device)
with open(args.model, 'rb') as f:
state_dict = torch.load(f)
model.load_state_dict(state_dict)
model.eval()
prior = GatedPixelCNN(args.k,
args.hidden_size_prior,
args.num_layers,
n_classes=32).to(args.device)
# args.num_layers, n_classes=len(train_dataset._label_encoder)).to(args.device)
optimizer = torch.optim.Adam(prior.parameters(), lr=args.lr)
best_loss = -1.
for epoch in range(args.num_epochs):
print(epoch)
train(train_loader, model, prior, optimizer, args, writer)
# The validation loss is not properly computed since
# the classes in the train and valid splits of Mini-Imagenet
# do not overlap.
loss = test(valid_loader, model, prior, args, writer)
if (epoch == 0) or (loss < best_loss):
best_loss = loss
with open(save_filename, 'wb') as f:
torch.save(prior.state_dict(), f)
def main(args):
writer = SummaryWriter('./logs/{0}'.format(args.output_folder))
save_filename = './models/{0}/prior.pt'.format(args.output_folder)
if args.dataset in ['mnist', 'fashion-mnist', 'cifar10']:
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])
if args.dataset == 'mnist':
# Define the train & test datasets
train_dataset = datasets.MNIST(args.data_folder,
train=True,
download=True,
transform=transform)
test_dataset = datasets.MNIST(args.data_folder,
train=False,
transform=transform)
num_channels = 1
elif args.dataset == 'fashion-mnist':
# Define the train & test datasets
train_dataset = datasets.FashionMNIST(args.data_folder,
train=True,
download=True,
transform=transform)
test_dataset = datasets.FashionMNIST(args.data_folder,
train=False,
transform=transform)
num_channels = 1
elif args.dataset == 'cifar10':
# Define the train & test datasets
train_dataset = datasets.CIFAR10(args.data_folder,
train=True,
download=True,
transform=transform)
test_dataset = datasets.CIFAR10(args.data_folder,
train=False,
transform=transform)
num_channels = 3
valid_dataset = test_dataset
elif args.dataset == 'miniimagenet':
transform = transforms.Compose([
transforms.RandomResizedCrop(128),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# Define the train, valid & test datasets
train_dataset = MiniImagenet(args.data_folder,
train=True,
download=True,
transform=transform)
valid_dataset = MiniImagenet(args.data_folder,
valid=True,
download=True,
transform=transform)
test_dataset = MiniImagenet(args.data_folder,
test=True,
download=True,
transform=transform)
num_channels = 3
# Define the data loaders
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=args.batch_size,
shuffle=False,
drop_last=True,
num_workers=args.num_workers,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=16,
shuffle=True)
# Save the label encoder
# with open('./models/{0}/labels.json'.format(args.output_folder), 'w') as f:
# json.dump(train_dataset._label_encoder, f)
# Fixed images for Tensorboard
fixed_images, _ = next(iter(test_loader))
fixed_grid = make_grid(fixed_images, nrow=8, range=(-1, 1), normalize=True)
writer.add_image('original', fixed_grid, 0)
model = VectorQuantizedVAE(num_channels, args.hidden_size_vae,
args.k).to(args.device)
with open(args.model, 'rb') as f:
state_dict = torch.load(f)
model.load_state_dict(state_dict)
model.eval()
prior = GatedPixelCNN(args.k,
args.hidden_size_prior,
args.num_layers,
n_classes=10).to(args.device)
state_dict = torch.load('models/mnist-10-prior/prior.pt')
prior.load_state_dict(state_dict)
# label_ = [int(i%10) for i in range(16)]
# label_ = torch.tensor(label_).cuda()
# reconstruction = model.decode(prior.generate(label=label_, batch_size=16,sample_index=10))
# grid = make_grid(reconstruction.cpu(), nrow=8, range=(-1, 1), normalize=True)
# writer.add_image('Partial sampling result', grid, 0)
for epoch in range(args.num_epochs):
label_ = [int(i % 10) for i in range(16)]
label_ = torch.tensor(label_).cuda()
reconstruction = model.decode(
prior.generate(label=label_, batch_size=16, sample_index=20))
grid = make_grid(reconstruction.cpu(),
nrow=8,
range=(-1, 1),
normalize=True)
writer.add_image('Full sampling result', grid, epoch + 1)
def main(args):
if args.dataset == 'miniimagenet':
readable_labels = {
38: 'organ',
42: 'prayer_rug',
31: 'file',
61: 'cliff',
58: 'consomme',
59: 'hotdog',
21: 'aircraft_carrier',
14: 'French_bulldog',
28: 'cocktail_shaker',
63: 'ear',
3: 'green_mamba',
4: 'harvestman',
17: 'Arctic_fox',
32: 'fire_screen',
11: 'komondor',
43: 'reel',
18: 'ladybug',
45: 'snorkel',
24: 'beer_bottle',
36: 'lipstick',
5: 'toucan',
0: 'house_finch',
16: 'miniature_poodle',
50: 'tile_roof',
15: 'Newfoundland',
46: 'solar_dish',
10: 'Gordon_setter',
7: 'dugong',
52: 'unicycle',
20: 'rock_beauty',
48: 'stage',
22: 'ashcan',
34: 'hair_slide',
30: 'dome',
13: 'Tibetan_mastiff',
53: 'upright',
62: 'bolete',
2: 'triceratops',
40: 'pencil_box',
26: 'chime',
47: 'spider_web',
51: 'tobacco_shop',
60: 'orange',
49: 'tank',
8: 'Walker_hound',
23: 'barrel',
6: 'jellyfish',
33: 'frying_pan',
9: 'Saluki',
37: 'oboe',
1: 'robin',
19: 'three-toed_sloth',
39: 'parallel_bars',
55: 'worm_fence',
27: 'clog',
41: 'photocopier',
25: 'carousel',
29: 'dishrag',
57: 'street_sign',
35: 'holster',
12: 'boxer',
56: 'yawl',
54: 'wok',
44: 'slot'
}
elif args.dataset == 'cifar10':
readable_labels = {
0: 'airplane',
1: 'automobile',
2: 'bird',
3: 'cat',
4: 'deer',
5: 'dog',
6: 'frog',
7: 'horse',
8: 'ship',
9: 'truck'
}
writer = SummaryWriter('./VQVAE/logs/{0}'.format(args.output_folder))
save_filename = './VQVAE/models/{0}/prior.pt'.format(args.output_folder)
if args.dataset in ['mnist', 'fashion-mnist', 'cifar10']:
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
if args.dataset == 'mnist':
# Define the train & test datasets
train_dataset = datasets.MNIST(args.data_folder,
train=True,
download=True,
transform=transform)
test_dataset = datasets.MNIST(args.data_folder,
train=False,
transform=transform)
num_channels = 1
elif args.dataset == 'fashion-mnist':
# Define the train & test datasets
train_dataset = datasets.FashionMNIST(args.data_folder,
train=True,
download=True,
transform=transform)
test_dataset = datasets.FashionMNIST(args.data_folder,
train=False,
transform=transform)
num_channels = 1
elif args.dataset == 'cifar10':
# Define the train & test datasets
train_dataset = datasets.CIFAR10(args.data_folder,
train=True,
download=True,
transform=transform)
test_dataset = datasets.CIFAR10(args.data_folder,
train=False,
transform=transform)
num_channels = 3
valid_dataset = test_dataset
elif args.dataset == 'miniimagenet':
transform = transforms.Compose([
transforms.RandomResizedCrop(128),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# Define the train, valid & test datasets
train_dataset = MiniImagenet(args.data_folder,
train=True,
download=True,
transform=transform)
valid_dataset = MiniImagenet(args.data_folder,
valid=True,
download=True,
transform=transform)
test_dataset = MiniImagenet(args.data_folder,
test=True,
download=True,
transform=transform)
num_channels = 3
# Define the data loaders
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=args.batch_size,
shuffle=False,
drop_last=True,
num_workers=args.num_workers,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=1,
shuffle=True)
# Fixed images for Tensorboard
fixed_images, _ = next(iter(test_loader))
fixed_grid = make_grid(fixed_images, nrow=8, range=(-1, 1), normalize=True)
writer.add_image('original', fixed_grid, 0)
model = VectorQuantizedVAE(num_channels, args.hidden_size_vae,
args.k).to(args.device)
with open(args.model, 'rb') as f:
state_dict = torch.load(f)
model.load_state_dict(state_dict)
model.eval()
if args.dataset == 'miniimagenet':
print("number of training classes:", len(train_dataset._label_encoder))
n_classes = len(train_dataset._label_encoder)
shape = (32, 32)
yrange = range(n_classes)
csv_filename = 'miniimagenet_generated.csv'
sample_size = 25
elif args.dataset == 'cifar10':
print("number of training classes:", 10)
n_classes = 10
shape = (8, 8)
yrange = range(n_classes)
csv_filename = 'cifar10_generated.csv'
sample_size = 1000
prior = GatedPixelCNN(args.k,
args.hidden_size_prior,
args.num_layers,
n_classes=n_classes).to(args.device)
with open(args.prior, 'rb') as f:
state_dict = torch.load(f)
prior.load_state_dict(state_dict)
prior.eval()
# maximum number of kept dimensions
max_num_dst = 0
max_num_sparsemax = 0
with torch.no_grad():
f = open(
'./VQVAE/models/{0}/{1}'.format(args.output_folder, csv_filename),
'w')
with f:
writer = csv.writer(f)
writer.writerow(['filename', 'label'])
for y in tqdm(yrange):
label = torch.tensor([y])
label = label.to(args.device)
z = prior.generate(label=label,
shape=shape,
batch_size=sample_size)
x = model.decode(z)
for im in range(x.shape[0]):
save_image(x.cpu()[im],
'./data/{0}/dataset_softmax/{1}_{2}.jpg'.format(
args.output_folder,
str(y).zfill(2),
str(im).zfill(3)),
range=(-1, 1),
normalize=True)
if args.dataset == 'miniimagenet':
y_str = list(train_dataset._label_encoder.keys())[list(
train_dataset._label_encoder.values()).index(y)]
writer.writerow([
'{0}_{1}.jpg'.format(
str(y).zfill(2),
str(im).zfill(3)),
str(y_str)
])
elif args.dataset == 'cifar10':
writer.writerow([
'{0}_{1}.jpg'.format(
str(y).zfill(2),
str(im).zfill(3)),
str(y)
])
z, num_dst = prior.generate_dst(label=label,
shape=shape,
batch_size=sample_size)
x = model.decode(z)
if num_dst > max_num_dst:
max_num_dst = num_dst
for im in range(x.shape[0]):
save_image(x.cpu()[im],
'./data/{0}/dataset_dst/{1}_{2}.jpg'.format(
args.output_folder,
str(y).zfill(2),
str(im).zfill(3)),
range=(-1, 1),
normalize=True)
z, num_sparsemax = prior.generate_sparsemax(
label=label, shape=shape, batch_size=sample_size)
x = model.decode(z)
if num_sparsemax > max_num_sparsemax:
max_num_sparsemax = num_sparsemax
for im in range(x.shape[0]):
save_image(
x.cpu()[im],
'./data/{0}/dataset_sparsemax/{1}_{2}.jpg'.format(
args.output_folder,
str(y).zfill(2),
str(im).zfill(3)),
range=(-1, 1),
normalize=True)
pkl.dump([max_num_dst, max_num_sparsemax],
open(
"max_num_" + str(args.dataset) + "_" + str(sample_size) +
"_correct_dst.pkl", "wb"))
download=True,
transform=preproc_transform,
),
batch_size=BATCH_SIZE,
shuffle=False,
num_workers=NUM_WORKERS,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(eval('datasets.' + DATASET)(
'../data/{}/'.format(DATASET), train=False, transform=preproc_transform),
batch_size=BATCH_SIZE,
shuffle=False,
num_workers=NUM_WORKERS,
pin_memory=True)
autoencoder = VectorQuantizedVAE(INPUT_DIM, VAE_DIM, K).to(DEVICE)
autoencoder.load_state_dict(torch.load('models/{}_vqvae.pt'.format(DATASET)))
autoencoder.eval()
model = GatedPixelCNN(K, DIM, N_LAYERS).to(DEVICE)
criterion = nn.CrossEntropyLoss().to(DEVICE)
opt = torch.optim.Adam(model.parameters(), lr=LR, amsgrad=True)
def train():
train_loss = []
for batch_idx, (x, label) in enumerate(train_loader):
start_time = time.time()
x = x.to(DEVICE)
label = label.to(DEVICE)
# Get the latent codes for image x
def main(args):
writer = SummaryWriter('./logs/{0}'.format(args.output_folder))
save_filename = './models/{0}'.format(args.output_folder)
if args.dataset in ['mnist', 'fashion-mnist', 'cifar10']:
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
if args.dataset == 'mnist':
# Define the train & test datasets
train_dataset = datasets.MNIST(args.data_folder,
train=True,
download=True,
transform=transform)
test_dataset = datasets.MNIST(args.data_folder,
train=False,
transform=transform)
num_channels = 1
elif args.dataset == 'fashion-mnist':
# Define the train & test datasets
train_dataset = datasets.FashionMNIST(args.data_folder,
train=True,
download=True,
transform=transform)
test_dataset = datasets.FashionMNIST(args.data_folder,
train=False,
transform=transform)
num_channels = 1
elif args.dataset == 'cifar10':
# Define the train & test datasets
train_dataset = datasets.CIFAR10(args.data_folder,
train=True,
download=True,
transform=transform)
test_dataset = datasets.CIFAR10(args.data_folder,
train=False,
transform=transform)
num_channels = 3
valid_dataset = test_dataset
elif args.dataset == 'clevr':
transform = transforms.Compose([
# transforms.RandomResizedCrop(128),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
import socket
if "Alien" in socket.gethostname():
dataset_name = "/media/mihir/dataset/clevr_veggies/"
else:
dataset_name = "/projects/katefgroup/datasets/clevr_veggies/"
dataset_name = '/home/mprabhud/dataset/clevr_veggies'
# Define the train, valid & test datasets
train_dataset = Clevr(dataset_name,mod = args.modname\
, train=True, transform=transform,object_level= args.object_level)
valid_dataset = Clevr(dataset_name,mod = args.modname,\
valid=True,transform=transform,object_level= args.object_level)
test_dataset = Clevr(dataset_name,mod = args.modname,\
test=True, transform=transform,object_level= args.object_level)
num_channels = 3
elif args.dataset == 'carla':
if args.use_depth:
transform = transforms.Compose([
# transforms.RandomResizedCrop(128),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5, 0.5),
(0.5, 0.5, 0.5, 0.5))
])
num_channels = 4
else:
transform = transforms.Compose([
# transforms.RandomResizedCrop(128),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
num_channels = 3
import socket
if "Alien" in socket.gethostname():
dataset_name = "/media/mihir/dataset/clevr_veggies/"
else:
dataset_name = '/home/shamitl/datasets/carla'
dataset_name = "/projects/katefgroup/datasets/carla/"
dataset_name = '/home/mprabhud/dataset/carla'
# Define the train, valid & test datasets
train_dataset = Clevr(dataset_name,mod = args.modname\
, train=True, transform=transform,object_level= args.object_level,use_depth=args.use_depth)
valid_dataset = Clevr(dataset_name,mod = args.modname,\
valid=True,transform=transform,object_level= args.object_level,use_depth=args.use_depth)
test_dataset = Clevr(dataset_name,mod = args.modname,\
test=True, transform=transform,object_level= args.object_level,use_depth=args.use_depth)
# elif args.dataset == 'miniimagenet':
# transform = transforms.Compose([
# transforms.RandomResizedCrop(128),
# transforms.ToTensor(),
# transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
# ])
# # Define the train, valid & test datasets
# train_dataset = MiniImagenet(args.data_folder, train=True,
# download=True, transform=transform)
# # valid_dataset = MiniImagenet(args.data_folder, valid=True,
# # download=True, transform=transform)
# # test_dataset = MiniImagenet(args.data_folder, test=True,
# # download=True, transform=transform)
# # num_channels = 3
# Define the data loaders
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=args.batch_size,
shuffle=False,
drop_last=True,
num_workers=args.num_workers,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=16,
shuffle=True)
fixed_images, _ = next(iter(train_loader))
# Fixed images for Tensorboard
fixed_images, _ = next(iter(test_loader))
fixed_grid = make_grid(fixed_images, nrow=8, range=(-1, 1), normalize=True)
writer.add_image('original', fixed_grid, 0)
model = VectorQuantizedVAE(num_channels, args.hidden_size,
args.object_level, args.k).to(args.device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
if args.load_model is not "":
with open(args.load_model, 'rb') as f:
state_dict = torch.load(f)
model.load_state_dict(state_dict)
# Generate the samples first once
reconstruction = generate_samples(fixed_images, model, args)
grid = make_grid(reconstruction.cpu(),
nrow=8,
range=(-1, 1),
normalize=True)
writer.add_image('reconstruction', grid, 0)
# st()
best_loss = -1.
for epoch in range(args.num_epochs):
if not args.test_mode:
train(train_loader, model, optimizer, args, writer, epoch)
# st()
loss, _ = test_old(valid_loader, model, args, writer)
reconstruction = generate_samples(fixed_images, model, args)
grid = make_grid(reconstruction.cpu(),
nrow=8,
range=(-1, 1),
normalize=True)
writer.add_image('reconstruction', grid, epoch + 1)
# st()
with open('{0}/recent.pt'.format(save_filename), 'wb') as f:
torch.save(model.state_dict(), f)
if (epoch == 0) or (loss < best_loss):
best_loss = loss
with open('{0}/best.pt'.format(save_filename), 'wb') as f:
torch.save(model.state_dict(), f)
# else:
# print("nothing")
else:
test(train_loader, model, args, writer)
reconstruction = generate_samples(fixed_images, model, args)
grid = make_grid(reconstruction.cpu(),
nrow=8,
range=(-1, 1),
normalize=True)
writer.add_image('reconstruction', grid, epoch + 1)