Method __init__

slowfast/models/stem_helper.py:132–177

The `__init__` method of any subclass should also contain these arguments. Args: dim_in (int): the channel dimension of the input. Normally 3 is used for rgb input, and 2 or 3 is used for optical flow input. dim_out (int): the output dimensio

(
        self,
        dim_in,
        dim_out,
        kernel,
        stride,
        padding,
        inplace_relu=True,
        eps=1e-5,
        bn_mmt=0.1,
        norm_module=nn.BatchNorm3d,
    )

    """

    def __init__(
        self,
        dim_in,
        dim_out,
        kernel,
        stride,
        padding,
        inplace_relu=True,
        eps=1e-5,
        bn_mmt=0.1,
        norm_module=nn.BatchNorm3d,
    ):
        """
        The `__init__` method of any subclass should also contain these arguments.

        Args:
            dim_in (int): the channel dimension of the input. Normally 3 is used
                for rgb input, and 2 or 3 is used for optical flow input.
            dim_out (int): the output dimension of the convolution in the stem
                layer.
            kernel (list): the kernel size of the convolution in the stem layer.
                temporal kernel size, height kernel size, width kernel size in
                order.
            stride (list): the stride size of the convolution in the stem layer.
                temporal kernel stride, height kernel size, width kernel size in
                order.
            padding (int): the padding size of the convolution in the stem
                layer, temporal padding size, height padding size, width
                padding size in order.
            inplace_relu (bool): calculate the relu on the original input
                without allocating new memory.
            eps (float): epsilon for batch norm.
            bn_mmt (float): momentum for batch norm. Noted that BN momentum in
                PyTorch = 1 - BN momentum in Caffe2.
            norm_module (nn.Module): nn.Module for the normalization layer. The
                default is nn.BatchNorm3d.
        """
        super(ResNetBasicStem, self).__init__()
        self.kernel = kernel
        self.stride = stride
        self.padding = padding
        self.inplace_relu = inplace_relu
        self.eps = eps
        self.bn_mmt = bn_mmt
        # Construct the stem layer.
        self._construct_stem(dim_in, dim_out, norm_module)

    def _construct_stem(self, dim_in, dim_out, norm_module):
        self.conv = nn.Conv3d(