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