# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from .conv_module import ConvModule
[文档]class DepthwiseSeparableConvModule(nn.Module):
"""Depthwise separable convolution module.
See https://arxiv.org/pdf/1704.04861.pdf for details.
This module can replace a ConvModule with the conv block replaced by two
conv block: depthwise conv block and pointwise conv block. The depthwise
conv block contains depthwise-conv/norm/activation layers. The pointwise
conv block contains pointwise-conv/norm/activation layers. It should be
noted that there will be norm/activation layer in the depthwise conv block
if `norm_cfg` and `act_cfg` are specified.
Args:
in_channels (int): Number of channels in the input feature map.
Same as that in ``nn._ConvNd``.
out_channels (int): Number of channels produced by the convolution.
Same as that in ``nn._ConvNd``.
kernel_size (int | tuple[int]): Size of the convolving kernel.
Same as that in ``nn._ConvNd``.
stride (int | tuple[int]): Stride of the convolution.
Same as that in ``nn._ConvNd``. Default: 1.
padding (int | tuple[int]): Zero-padding added to both sides of
the input. Same as that in ``nn._ConvNd``. Default: 0.
dilation (int | tuple[int]): Spacing between kernel elements.
Same as that in ``nn._ConvNd``. Default: 1.
norm_cfg (dict): Default norm config for both depthwise ConvModule and
pointwise ConvModule. Default: None.
act_cfg (dict): Default activation config for both depthwise ConvModule
and pointwise ConvModule. Default: dict(type='ReLU').
dw_norm_cfg (dict): Norm config of depthwise ConvModule. If it is
'default', it will be the same as `norm_cfg`. Default: 'default'.
dw_act_cfg (dict): Activation config of depthwise ConvModule. If it is
'default', it will be the same as `act_cfg`. Default: 'default'.
pw_norm_cfg (dict): Norm config of pointwise ConvModule. If it is
'default', it will be the same as `norm_cfg`. Default: 'default'.
pw_act_cfg (dict): Activation config of pointwise ConvModule. If it is
'default', it will be the same as `act_cfg`. Default: 'default'.
kwargs (optional): Other shared arguments for depthwise and pointwise
ConvModule. See ConvModule for ref.
"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
norm_cfg=None,
act_cfg=dict(type='ReLU'),
dw_norm_cfg='default',
dw_act_cfg='default',
pw_norm_cfg='default',
pw_act_cfg='default',
**kwargs):
super(DepthwiseSeparableConvModule, self).__init__()
assert 'groups' not in kwargs, 'groups should not be specified'
# if norm/activation config of depthwise/pointwise ConvModule is not
# specified, use default config.
dw_norm_cfg = dw_norm_cfg if dw_norm_cfg != 'default' else norm_cfg
dw_act_cfg = dw_act_cfg if dw_act_cfg != 'default' else act_cfg
pw_norm_cfg = pw_norm_cfg if pw_norm_cfg != 'default' else norm_cfg
pw_act_cfg = pw_act_cfg if pw_act_cfg != 'default' else act_cfg
# depthwise convolution
self.depthwise_conv = ConvModule(
in_channels,
in_channels,
kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=in_channels,
norm_cfg=dw_norm_cfg,
act_cfg=dw_act_cfg,
**kwargs)
self.pointwise_conv = ConvModule(
in_channels,
out_channels,
1,
norm_cfg=pw_norm_cfg,
act_cfg=pw_act_cfg,
**kwargs)
[文档] def forward(self, x):
x = self.depthwise_conv(x)
x = self.pointwise_conv(x)
return x