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 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_save():
inputs = torch.randn(16, 32)
temp = pathlib.Path(tempfile.gettempdir())
layer = tl.build(tl.Linear(64), inputs)
output = layer(inputs)
torch.save(layer, temp / "linear_model.pt")
new_layer = torch.load(temp / "linear_model.pt")
new_output = new_layer(inputs)
assert torch.allclose(output, new_output)
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 test_last_dimension_linear():
module = tl.Linear(64)
module = tl.build(module, torch.randn(1, 3, 32, 32))
assert module(torch.randn(2, 6, 24, 32)).shape == (2, 6, 24, 64)
def single_module(regularization: str, name: str):
return getattr(tl, regularization)(tl.Linear(10),
weight_decay=1000000,
name=name)
