Pytorch implementation of Depthwise Convolution

tf.keras.layers.DepthwiseConv2D(
    kernel_size,
    strides=(1, 1),
    padding="valid",
    depth_multiplier=1,
    data_format=None,
    dilation_rate=(1, 1),
    activation=None,
    use_bias=True,
    depthwise_initializer="glorot_uniform",
    bias_initializer="zeros",
    depthwise_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    depthwise_constraint=None,
    bias_constraint=None,
    **kwargs
)

What is the Pytorch’s implementation of the above Keras equivalent of Depthwise Convolution?

Hi Rituraj,

The depthwise convolutions are implemented in pytorch in the Conv modules with the group parameter.
For an input of c channels, and depth multiplier of d, the nn.Conv2d parameters become
in_channels = c
out_channels = d*c
groups = c