torch_ecg.utils.compute_output_shape¶
- torch_ecg.utils.compute_output_shape(layer_type: str, input_shape: Sequence[Optional[int]], num_filters: Optional[int] = None, kernel_size: Union[Sequence[int], int] = 1, stride: Union[Sequence[int], int] = 1, padding: Union[Sequence[int], int] = 0, output_padding: Union[Sequence[int], int] = 0, dilation: Union[Sequence[int], int] = 1, channel_last: bool = False, asymmetric_padding: Optional[Union[Sequence[int], Sequence[Sequence[int]]]] = None) Tuple[Optional[int]][source]¶
Compute the output shape of a (transpose) convolution/maxpool/avgpool layer.
This function is based on the discussion 1.
- Parameters
layer_type (str) – Type (conv, maxpool, avgpool, etc.) of the layer.
input_shape (Sequence[Union[int, None]]) – Shape of an input
Tensor. The first dimension is the batch dimension, which is allowed to be None.num_filters (int, optional) – Number of filters, also the channel dimension.
kernel_size (int or Sequence[int], default 1) – Kernel size (filter size) of the layer, should be compatible with input_shape.
stride (int or Sequence[int], default 1) – Stride (down-sampling length) of the layer, should be compatible with input_shape.
padding (int or Sequence[int], default 0) – Padding length(s) of the layer, should be compatible with input_shape.
output_padding (int or Sequence[int], default 0) – Additional size added to one side of the output shape, used only for transpose convolution.
dilation (int or Sequence[int], default 1) – Dilation of the layer, should be compatible with input_shape.
channel_last (bool, default False) – Whether the channel dimension is the last dimension, or the second dimension (the first is the batch dimension by convention).
asymmetric_padding (Sequence[int] or Sequence[Sequence[int]], optional) – (2-)sequence of int or sequence of (2-)sequence of int asymmetric paddings for all dimensions or for each dimension.
- Returns
output_shape – Shape of the output
Tensor.- Return type
References