def multiple_modules(regularization: str, name: str):
return getattr(tl, regularization)(
torch.nn.Sequential(tl.Linear(40), tl.ReLU(), tl.Linear(20), tl.ReLU(),
tl.Linear(10)),
weight_decay=1000000,
name=name,
)
def model():
return torchlayers.Sequential(
torchlayers.Conv(64),
torchlayers.ReLU(),
torchlayers.MaxPool(),
torchlayers.Conv(128),
torchlayers.ReLU(),
torchlayers.MaxPool(),
torchlayers.Conv(256),
torchlayers.ReLU(),
torchlayers.Flatten(),
)
def model():
return tl.Sequential(
tl.Conv(64),
tl.ReLU(),
tl.MaxPool(),
tl.BatchNorm(),
tl.Conv(128),
tl.ReLU(),
tl.MaxPool(),
tl.Conv(256),
tl.ReLU(),
tl.Reshape(-1),
)
def model():
return tl.Sequential(
tl.Conv(64),
tl.BatchNorm(),
tl.ReLU(),
tl.Conv(128),
tl.BatchNorm(),
tl.ReLU(),
tl.Conv(256),
tl.GlobalMaxPool(),
tl.Linear(64),
tl.BatchNorm(),
tl.Linear(10),
)
def model():
return torchlayers.Sequential(
torchlayers.Conv(64),
torchlayers.BatchNorm(),
torchlayers.ReLU(),
torchlayers.Conv(128),
torchlayers.BatchNorm(),
torchlayers.ReLU(),
torchlayers.Conv(256),
torchlayers.GlobalMaxPool(),
torchlayers.Linear(64),
torchlayers.BatchNorm(),
torchlayers.Linear(10),
)
def test_text_cnn():
model = torch.nn.Sequential(
torchlayers.Conv(64), # specify ONLY out_channels
torch.nn.ReLU(), # use torch.nn wherever you wish
torchlayers.BatchNorm(), # BatchNormNd inferred from input
torchlayers.Conv(128), # Default kernel_size equal to 3
torchlayers.ReLU(),
torchlayers.Conv(256, kernel_size=11), # "same" padding as default
torchlayers.GlobalMaxPool(), # Known from Keras
torchlayers.Linear(10), # Output for 10 classes
)
torchlayers.build(model, torch.randn(2, 300, 1))
def model():
return torchlayers.Sequential(
torchlayers.Conv(64),
torchlayers.ReLU(),
torchlayers.MaxPool(),
torchlayers.Residual(
torchlayers.Sequential(
torchlayers.Conv(64, groups=16),
torchlayers.ReLU(),
torchlayers.BatchNorm(),
torchlayers.Conv(64, groups=16),
torchlayers.ChannelShuffle(groups=16),
torchlayers.ReLU(),
)
),
torchlayers.SqueezeExcitation(),
torchlayers.Sequential(
torchlayers.Dropout(),
torchlayers.Conv(128),
torchlayers.ReLU(),
torchlayers.InstanceNorm(),
),
torchlayers.Poly(
torchlayers.WayPoly(
torchlayers.Fire(128),
torchlayers.Fire(128),
torchlayers.Fire(128),
torchlayers.Fire(128),
),
order=2,
),
torchlayers.AvgPool(),
torchlayers.StochasticDepth(torchlayers.Fire(128, hidden_channels=64)),
torchlayers.ReLU(),
torchlayers.GlobalAvgPool(),
torchlayers.Linear(64),
)
def classification_model():
return tl.Sequential(
tl.Conv(64),
tl.ReLU(),
tl.MaxPool(),
tl.Residual(
tl.Sequential(
tl.Conv(64, groups=16),
tl.ReLU(),
tl.GroupNorm(num_groups=4),
tl.Conv(64, groups=16),
tl.ChannelShuffle(groups=16),
tl.ReLU(),
)),
tl.SqueezeExcitation(),
tl.Sequential(
tl.Dropout(),
tl.Conv(128),
tl.ReLU(),
tl.InstanceNorm(),
),
tl.Poly(
tl.WayPoly(
tl.Fire(128),
tl.Fire(128),
tl.Fire(128),
tl.Fire(128),
),
order=2,
),
tl.AvgPool(),
tl.StochasticDepth(tl.Fire(128, hidden_channels=64)),
tl.ReLU(),
tl.GlobalAvgPool(),
tl.Linear(64),
)
def __init__(self):
super().__init__()
self.encoder = torchlayers.Sequential(
torchlayers.Conv(64, kernel_size=7),
torchlayers.activations.Swish(
), # Direct access to module .activations
torchlayers.InvertedResidualBottleneck(squeeze_excitation=False),
torchlayers.AvgPool(
), # shape 64 x 128 x 128, kernel_size=2 by default
torchlayers.HardSwish(), # Access simply through torchlayers
torchlayers.SeparableConv(128), # Up number of channels to 128
torchlayers.InvertedResidualBottleneck(
), # Default with squeeze excitation
torch.nn.ReLU(),
torchlayers.AvgPool(
), # shape 128 x 64 x 64, kernel_size=2 by default
torchlayers.DepthwiseConv(256), # DepthwiseConv easier to use
# Pass input thrice through the same weights like in PolyNet
torchlayers.Poly(torchlayers.InvertedResidualBottleneck(),
order=3),
torchlayers.ReLU(), # all torch.nn can be accessed via torchlayers
torchlayers.MaxPool(), # shape 256 x 32 x 32
torchlayers.Fire(out_channels=512), # shape 512 x 32 x 32
torchlayers.SqueezeExcitation(hidden=64),
torchlayers.InvertedResidualBottleneck(),
torchlayers.MaxPool(), # shape 512 x 16 x 16
torchlayers.InvertedResidualBottleneck(squeeze_excitation=False),
torchlayers.Dropout(), # Default 0.5 and Dropout2d for images
# Randomly Switch the last two layers with 0.5 probability
torchlayers.StochasticDepth(
torch.nn.Sequential(
torchlayers.InvertedResidualBottleneck(
squeeze_excitation=False),
torchlayers.InvertedResidualBottleneck(
squeeze_excitation=False),
),
p=0.5,
),
torchlayers.AvgPool(), # shape 512 x 8 x 8
)
# Will make this one easier and repetitive
self.decoder = torchlayers.Sequential(
torchlayers.Poly(torchlayers.InvertedResidualBottleneck(),
order=2),
# Has ICNR initialization by default as well
torchlayers.ConvPixelShuffle(out_channels=512, upscale_factor=2),
# Shape 512 x 16 x 16 after PixelShuffle
torchlayers.Poly(torchlayers.InvertedResidualBottleneck(),
order=3),
torchlayers.ConvPixelShuffle(out_channels=256, upscale_factor=2),
torchlayers.StandardNormalNoise(), # add Gaussian Noise
# Shape 256 x 32 x 32
torchlayers.Poly(torchlayers.InvertedResidualBottleneck(),
order=3),
torchlayers.ConvPixelShuffle(out_channels=128, upscale_factor=2),
# Shape 128 x 64 x 64
torchlayers.Poly(torchlayers.InvertedResidualBottleneck(),
order=4),
torchlayers.ConvPixelShuffle(out_channels=64, upscale_factor=2),
# Shape 64 x 128 x 128
torchlayers.InvertedResidualBottleneck(),
torchlayers.Conv(256),
torchlayers.Swish(),
torchlayers.BatchNorm(),
torchlayers.ConvPixelShuffle(out_channels=32, upscale_factor=2),
# Shape 32 x 256 x 256
torchlayers.Conv(16),
torchlayers.Swish(),
torchlayers.Conv(3),
# Shape 3 x 256 x 256
)