Units
- class ezflow.modules.units.Conv2x(in_channels, out_channels, deconv=False, concat=True, norm='batch', activation='relu')[source]
Double convolutional layer with the option to perform deconvolution and concatenation
- Parameters
in_channels (int) – Number of input channels
out_channels (int) – Number of output channels
deconv (bool) – Whether to perform deconvolution instead of convolution
concat (bool) – Whether to concatenate the input and the output of the first convolution layer
norm (str) – Type of normalization to use. Can be “batch”, “instance”, “group”, or “none”
activation (str) – Type of activation to use. Can be “relu” or “leakyrelu”
- forward(x, rem)[source]
Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- class ezflow.modules.units.ConvNormRelu(in_channels, out_channels, deconv=False, norm='batch', activation='relu', **kwargs)[source]
Block for a convolutional layer with normalization and activation
- Parameters
in_channels (int) – Number of input channels
out_channels (int) – Number of output channels
deconv (bool, optional) – If True, use a transposed convolution
norm (str, optional) – Normalization method
activation (str, optional) – Activation function
- forward(x)[source]
Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- ezflow.modules.units.conv(in_channels, out_channels, kernel_size=3, stride=1, padding=1, dilation=1, norm=None)[source]
2D convolution layer with optional normalization followed by an inplace LeakyReLU activation of 0.1 negative slope.
- Parameters
in_channels (int) – Number of input channels
out_channels (int) – Number of output channels
kernel_size (int, default: 3) – Size of the convolutional kernel
stride (int, default: 1) – Stride of the convolutional kernel
padding (int, default: 1) – Padding of the convolutional kernel
dilation (int, default: 1) – Dilation of the convolutional kernel
norm (str, default: None) – Type of normalization to use. Can be None, ‘batch’, ‘layer’, ‘group’
- ezflow.modules.units.deconv(in_channels, out_channels, kernel_size=4, stride=2, padding=1)[source]
2D transpose convolution layer followed by an activation function
- Parameters
in_channels (int) – Number of input channels
out_channels (int) – Number of output channels
kernel_size (int, optional) – Size of the convolutional kernel
stride (int, optional) – Stride of the convolutional kernel
padding (int, optional) – Padding of the convolutional kernel
dilation (int, optional) – Dilation of the convolutional kernel