Exemplos de PixelCNN em Python

Exemplo n.º 1

Arquivo: pixelcnn.py Projeto: didriknielsen/pixelcnn_flow

def create_model_fn(args):

    model = AutoregressiveSubsetFlow2d(base_shape = (3,32,32,),
                                       transforms = [
                                       LinearSplineAutoregressiveSubsetTransform2d(PixelCNN(3, num_params=args.num_bins,
                                                                                            num_blocks=args.num_blocks,
                                                                                            filters=args.filters,
                                                                                            kernel_size=args.kernel_size,
                                                                                            kernel_size_in=args.kernel_size_in,
                                                                                            output_filters=args.output_filters,
                                                                                            init_transforms=lambda x: 2*x-1), num_bins=args.num_bins),
                                       ])

    return model

Exemplo n.º 2

def create_model_fn(args):

    if args.bin_cond: lambd = lambda x: 2 * quantize(x, 256) - 1
    else: lambd = lambda x: 2 * x - 1

    net = PixelCNN(3,
                   num_params=2 * args.num_bins + 1,
                   num_blocks=args.num_blocks,
                   filters=args.filters,
                   kernel_size=args.kernel_size,
                   kernel_size_in=args.kernel_size_in,
                   output_filters=args.output_filters,
                   init_transforms=lambd)

    model = UniformlyDequantizedFlow(
        base_dist=StandardUniform((
            3,
            32,
            32,
        )),
        transforms=QuadraticSplineAutoregressiveTransform2d(
            net, num_bins=args.num_bins))

    return model

Exemplo n.º 3

Arquivo: exact_pixelcnn_quad.py Projeto: didriknielsen/pixelcnn_flow

###################
## Specify model ##
###################

model = AutoregressiveSubsetFlow2d(
    base_shape=(
        3,
        32,
        32,
    ),
    transforms=[
        QuadraticSplineAutoregressiveSubsetTransform2d(PixelCNN(
            3,
            num_params=2 * args.num_bins + 1,
            num_blocks=args.num_blocks,
            filters=args.filters,
            kernel_size=args.kernel_size,
            kernel_size_in=args.kernel_size_in,
            output_filters=args.output_filters,
            init_transforms=lambda x: 2 * x - 1),
                                                       num_bins=args.num_bins),
    ])

setup.register_model(model)

#######################
## Specify optimizer ##
#######################

optimizer = optim.Adam(model.parameters(), lr=args.lr)
scheduler = optim.lr_scheduler.MultiStepLR(optimizer,

Exemplo n.º 4

Arquivo: imagenet64_exact_deep_pixelcnn_quad.py Projeto: didriknielsen/pixelcnn_flow

setup.register_data(data.train, data.test)

###################
## Specify model ##
###################

transforms = []
for i in range(args.num_flows):

    transforms += [
        QuadraticSplineAutoregressiveSubsetTransform2d(
            PixelCNN(3,
                     num_params=2 * args.num_bins[i] + 1,
                     num_blocks=args.num_blocks[i],
                     filters=args.filters[i],
                     kernel_size=args.kernel_size[i],
                     kernel_size_in=args.kernel_size_in[i],
                     output_filters=args.output_filters[i],
                     init_transforms=lambda x: 2 * x - 1),
            num_bins=args.num_bins[i])
    ]

model = AutoregressiveSubsetFlow2d(base_shape=(
    3,
    64,
    64,
),
                                   transforms=transforms)

setup.register_model(model)

Exemplo n.º 5

Arquivo: imagenet64_exact_deep_pixelcnn.py Projeto: didriknielsen/pixelcnn_flow

setup.register_data(data.train, data.test)

###################
## Specify model ##
###################

transforms = []
for i in range(args.num_flows):

    transforms += [
        LinearSplineAutoregressiveSubsetTransform2d(PixelCNN(
            3,
            num_params=args.num_bins[i],
            num_blocks=args.num_blocks[i],
            filters=args.filters[i],
            kernel_size=args.kernel_size[i],
            kernel_size_in=args.kernel_size_in[i],
            output_filters=args.output_filters[i],
            init_transforms=lambda x: 2 * x - 1),
                                                    num_bins=args.num_bins[i])
    ]

model = AutoregressiveSubsetFlow2d(base_shape=(
    3,
    64,
    64,
),
                                   transforms=transforms)

setup.register_model(model)

Exemplo n.º 6

Arquivo: elbo_pixelcnn_quad.py Projeto: didriknielsen/pixelcnn_flow

data = CategoricalCIFAR10()

setup.register_data(data.train, data.test)

###################
## Specify model ##
###################

if args.bin_cond: lambd = lambda x: 2 * quantize(x, 256) - 1
else: lambd = lambda x: 2 * x - 1

net = PixelCNN(3,
               num_params=2 * args.num_bins + 1,
               num_blocks=args.num_blocks,
               filters=args.filters,
               kernel_size=args.kernel_size,
               kernel_size_in=args.kernel_size_in,
               output_filters=args.output_filters,
               init_transforms=lambd)

model = UniformlyDequantizedFlow(
    base_dist=StandardUniform((
        3,
        32,
        32,
    )),
    transforms=QuadraticSplineAutoregressiveTransform2d(
        net, num_bins=args.num_bins))

setup.register_model(model)