def __init__(
self,
input_shape,
inner_dim,
activation=torch.nn.Sigmoid,
norm=BatchNorm1d,
):
super().__init__()
self.inner_dim = inner_dim
self.norm = norm
self.activation = activation
bz, t, chn = input_shape
self.conv = Sequential(input_shape=input_shape)
self.conv.append(
DepthwiseSeparableConv1d,
out_channels=chn,
kernel_size=1,
stride=1,
)
self.conv.append(self.norm)
self.conv.append(self.activation())
self.avg_pool = AdaptivePool(1)
self.bottleneck = Sequential(
Linear(input_size=input_shape[-1], n_neurons=self.inner_dim),
self.activation(),
Linear(input_size=self.inner_dim, n_neurons=chn),
self.activation(),
)
def __init__(
self,
out_channels,
kernel_size,
num_layers,
inner_dim,
input_shape,
stride=1,
beta=1,
dropout=0.15,
activation=Swish,
se_activation=torch.nn.Sigmoid,
norm=BatchNorm1d,
residual=True,
):
super().__init__()
self.residual = residual
self.Convs = Sequential(input_shape=input_shape)
for i in range(num_layers):
self.Convs.append(
DepthwiseSeparableConv1d,
out_channels,
kernel_size,
stride=stride if i == num_layers - 1 else 1,
)
self.Convs.append(norm)
self.SE = SEmodule(
input_shape=self.Convs.get_output_shape(),
inner_dim=inner_dim,
activation=se_activation,
norm=norm,
)
self.drop = Dropout(dropout)
self.reduced_cov = None
if residual:
self.reduced_cov = Sequential(input_shape=input_shape)
self.reduced_cov.append(
Conv1d,
out_channels,
kernel_size=3,
stride=stride,
)
self.reduced_cov.append(norm)
if isinstance(activation, Swish):
self.activation = activation(beta)
else:
self.activation = activation()
self._reset_params()
def __init__(
self,
num_layers,
out_channels,
input_shape,
kernel_size=3,
stride=1,
dilation=1,
residual=False,
conv_module=Conv2d,
activation=torch.nn.LeakyReLU,
norm=None,
dropout=0.1,
):
super().__init__()
self.convs = Sequential(input_shape=input_shape)
for i in range(num_layers):
self.convs.append(
conv_module,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride if i == num_layers - 1 else 1,
dilation=dilation,
layer_name=f"conv_{i}",
)
if norm is not None:
self.convs.append(norm, layer_name=f"norm_{i}")
self.convs.append(activation(), layer_name=f"act_{i}")
self.convs.append(
torch.nn.Dropout(dropout), layer_name=f"dropout_{i}"
)
self.reduce_conv = None
self.drop = None
if residual:
self.reduce_conv = Sequential(input_shape=input_shape)
self.reduce_conv.append(
conv_module,
out_channels=out_channels,
kernel_size=1,
stride=stride,
layer_name="conv",
)
self.reduce_conv.append(norm, layer_name="norm")
self.drop = torch.nn.Dropout(dropout)
class SEmodule(torch.nn.Module):
""" This class implements the Squeeze-and-excitation module
Arguements
----------
inner_dim: int
inner dimension of bottle-neck network of the SE Module (default 12)
activation: torch class
activation function for SE Module (default torch.nn.Sigmoid)
norm: torch class
normalization to regularize the model (default BatchNorm1d)
Example
-------
>>> inp = torch.randn([8, 120, 40])
>>> net = SEmodule(input_shape=inp.shape, inner_dim=64)
>>> out = net(inp)
>>> out.shape
torch.Size([8, 120, 40])
"""
def __init__(
self,
input_shape,
inner_dim,
activation=torch.nn.Sigmoid,
norm=BatchNorm1d,
):
super().__init__()
self.inner_dim = inner_dim
self.norm = norm
self.activation = activation
bz, t, chn = input_shape
self.conv = Sequential(input_shape=input_shape)
self.conv.append(
DepthwiseSeparableConv1d,
out_channels=chn,
kernel_size=1,
stride=1,
)
self.conv.append(self.norm)
self.conv.append(self.activation())
self.avg_pool = AdaptivePool(1)
self.bottleneck = Sequential(
Linear(input_size=input_shape[-1], n_neurons=self.inner_dim),
self.activation(),
Linear(input_size=self.inner_dim, n_neurons=chn),
self.activation(),
)
def forward(self, x):
bz, t, chn = x.shape
x = self.conv(x)
avg = self.avg_pool(x)
avg = self.bottleneck(avg)
context = avg.repeat(1, t, 1)
return x * context
class ContextNetBlock(torch.nn.Module):
""" This class implements a block in ContextNet
Arguements
----------
out_channels: int
number of output channels of this model (default 640)
kernel_size: int
kernel size of convolution layers (default 3)
strides: int
striding factor for this context block (default 1)
num_layers: int
number of depthwise convolution layers for this context block (default 5)
inner_dim: int
inner dimension of bottle-neck network of the SE Module (default 12)
beta: float
beta to scale the Swish activation (default 1)
dropout: float
dropout (default 0.15)
activation: torch class
activation function for this context block (default Swish)
se_activation: torch class
activation function for SE Module (default torch.nn.Sigmoid)
norm: torch class
normalization to regularize the model (default BatchNorm1d)
residuals: bool
whether apply residual connection at this context block (default None)
Example
-------
>>> inp = torch.randn([8, 120, 40])
>>> block = ContextNetBlock(256, 3, 5, 12, input_shape=inp.shape, stride=2)
>>> out = block(inp)
>>> out.shape
torch.Size([8, 60, 256])
"""
def __init__(
self,
out_channels,
kernel_size,
num_layers,
inner_dim,
input_shape,
stride=1,
beta=1,
dropout=0.15,
activation=Swish,
se_activation=torch.nn.Sigmoid,
norm=BatchNorm1d,
residual=True,
):
super().__init__()
self.residual = residual
self.Convs = Sequential(input_shape=input_shape)
for i in range(num_layers):
self.Convs.append(
DepthwiseSeparableConv1d,
out_channels,
kernel_size,
stride=stride if i == num_layers - 1 else 1,
)
self.Convs.append(norm)
self.SE = SEmodule(
input_shape=self.Convs.get_output_shape(),
inner_dim=inner_dim,
activation=se_activation,
norm=norm,
)
self.drop = Dropout(dropout)
self.reduced_cov = None
if residual:
self.reduced_cov = Sequential(input_shape=input_shape)
self.reduced_cov.append(
Conv1d,
out_channels,
kernel_size=3,
stride=stride,
)
self.reduced_cov.append(norm)
if isinstance(activation, Swish):
self.activation = activation(beta)
else:
self.activation = activation()
self._reset_params()
def forward(self, x):
out = self.Convs(x)
out = self.SE(out)
if self.reduced_cov:
out = out + self.reduced_cov(x)
out = self.activation(out)
return self.drop(out)
def _reset_params(self):
for p in self.parameters():
if p.dim() > 1:
torch.nn.init.kaiming_normal_(p)
class ConvBlock(torch.nn.Module):
"""An implementation of convolution block with 1d or 2d convolutions (depthwise).
Arguments
----------
out_channels : int
Number of output channels of this model (default 640).
kernel_size : int
Kernel size of convolution layers (default 3).
strides : int
Striding factor for this block (default 1).
num_layers : int
Number of depthwise convolution layers for this block.
activation : torch class
Activation function for this block.
norm : torch class
Normalization to regularize the model (default BatchNorm1d).
residuals: bool
Whether apply residual connection at this block (default None).
Example
-------
>>> x = torch.rand((8, 30, 10))
>>> conv = ConvBlock(2, 16, input_shape=x.shape)
>>> out = conv(x)
>>> x.shape
torch.Size([8, 30, 10])
"""
def __init__(
self,
num_layers,
out_channels,
input_shape,
kernel_size=3,
stride=1,
dilation=1,
residual=False,
conv_module=Conv2d,
activation=torch.nn.LeakyReLU,
norm=None,
dropout=0.1,
):
super().__init__()
self.convs = Sequential(input_shape=input_shape)
for i in range(num_layers):
self.convs.append(
conv_module,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride if i == num_layers - 1 else 1,
dilation=dilation,
layer_name=f"conv_{i}",
)
if norm is not None:
self.convs.append(norm, layer_name=f"norm_{i}")
self.convs.append(activation(), layer_name=f"act_{i}")
self.convs.append(
torch.nn.Dropout(dropout), layer_name=f"dropout_{i}"
)
self.reduce_conv = None
self.drop = None
if residual:
self.reduce_conv = Sequential(input_shape=input_shape)
self.reduce_conv.append(
conv_module,
out_channels=out_channels,
kernel_size=1,
stride=stride,
layer_name="conv",
)
self.reduce_conv.append(norm, layer_name="norm")
self.drop = torch.nn.Dropout(dropout)
def forward(self, x):
out = self.convs(x)
if self.reduce_conv:
out = out + self.reduce_conv(x)
out = self.drop(x)
return out