depthwise_conv2d¶

dragon.nn.depthwise_conv2d(
inputs,
kernel_shape=3,
strides=1,
pads=0,
dilations=1,
padding='VALID',
data_format='NCHW',
**kwargs
)[source]¶

Apply the 2d depthwise convolution. [Chollet, 2016].

If data_format is 'NCHW', excepts input shape \((N, C_{\text{in}}, H, W)\), weight shape \((C_{\text{out}}, 1, H_{\text{k}}, W_{\text{k}})\), and output shape is \((N, C_{\text{out}}, H_{\text{out}}, W_{\text{out}})\).
If data_format is 'NHWC', excepts input shape \((N, H, W, C_{\text{in}})\), weight shape \((C_{\text{out}}, H_{\text{k}}, W_{\text{k}}, 1)\), and output shape is \((N, H_{\text{out}}, W_{\text{out}}, C_{\text{out}})\).
If padding is 'VALID', pads controls the explicit padding size. Otherwise, size are computed automatically use the given method.

Examples:

x = dragon.ones((1, 2, 2, 2))
w = dragon.ones((2, 1, 1, 1))
y = dragon.nn.depthwise_conv2d([x, w], kernel_shape=1)
assert y.shape == (1, 2, 2, 2)

Parameters:

inputs (Sequence[dragon.Tensor]) – The tensor x, weight and optional bias.
kernel_shape (Union[int, Sequence[int]], optional, default=3) – The shape of convolution window.
strides (Union[int, Sequence[int]], optional, default=1) – The stride of convolution window.
pads (Union[int, Sequence[int]], optional, default=0) – The zero padding size.
dilations (Union[int, Sequence[int]], optional, default=1) – The rate of dilated convolution.
padding (str, optional, default='VALID') – 'VALID', 'SAME', 'SAME_UPPER' or 'SAME_LOWER'.
data_format (str, optional, default='NCHW') – 'NCHW' or 'NHWC'.