Convolutional Decoder
- class ezflow.decoder.conv_decoder.ConvDecoder(config=[128, 128, 96, 64, 32], concat_channels=None, to_flow=True, block=None)[source]
Applies a 2D Convolutional decoder to the input feature map. Used in PWCNet (https://arxiv.org/abs/1709.02371)
- Parameters
config (List[int], default : [128, 128, 96, 64, 32]) – List containing all output channels of the decoder
concat_channels (int, optional) – Additional input channels to be concatenated for convolution layers
to_flow (bool, default : True) – If True, convoloves decoder output to optical flow of shape N x 2 x H x W
block (object, default : None) – the conv block to be used to build the decoder layers.
- class ezflow.decoder.conv_decoder.FlowNetConvDecoder(in_channels=1024, config=[512, 256, 128, 64])[source]
Applies a 2D Convolutional decoder to regress the optical flow from the intermediate outputs convolutions of the encoder. Used in FlowNetSimple (https://arxiv.org/abs/1504.06852)
- Parameters
in_channels (int, default: 1024) – Number of input channels of the decoder. This value should be equal to the final output channels of the encoder
config (List[int], default : [512, 256, 128, 64]) – List containing all output channels of the decoder
- ezflow.decoder.conv_decoder.conv(in_channels, out_channels, kernel_size=3, stride=1, padding=1, dilation=1)[source]
Block for a 2D Convolutional layer with Leaky ReLU activation
- Parameters
in_channels (int) – Number of input channels
out_channels (int) – Number of output channels
kernel_size (int, default : 3) – Size of the kernel
stride (int, default : 1) – Stride of the convolution
dilation (int, default : 1) – Spacing between kernel elements
- Returns
block containing nn.Conv2d layer and leaky relu
- Return type
torch.nn.Sequential
- ezflow.decoder.conv_decoder.deconv(in_channels, out_channels)[source]
Block for a 2D Transpose Convolutional layer with Leaky ReLU activation
- Parameters
in_channels (int) – Number of input channels
out_channels (int) – Number of output channels
- Returns
block containing nn.ConvTranspose2d layer and leaky relu
- Return type
torch.nn.Sequential