VGGConvolutionGAPBlock

class maze.perception.blocks.joint_blocks.vgg_conv_gap.VGGConvolutionGAPBlock(*args: Any, **kwargs: Any)

A block containing multiple subsequent vgg style convolution stacks followed by global average pooling.

For details on the convolution part see VGGConvolutionBlock. For details on gap see GlobalAveragePoolingBlock.

Parameters:

• in_keys – One key identifying the input tensors.

• out_keys – One key identifying the output tensors.

• in_shapes – List of input shapes.

• hidden_channels – List containing the number of hidden channels for hidden layers.

• non_lin – The non-linearity to apply after each layer.

• use_batch_norm_conv – Whether to apply batch normalization after convolution.

forward(block_input: Dict[str, torch.Tensor]) → Dict[str, torch.Tensor]

(overrides PerceptionBlock)

implementation of ShapeNormalizationBlock interface