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""" Depthwise Separable Conv Modules |
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Basic DWS convs. Other variations of DWS exist with batch norm or activations between the |
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DW and PW convs such as the Depthwise modules in MobileNetV2 / EfficientNet and Xception. |
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Hacked together by / Copyright 2020 Ross Wightman |
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""" |
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from torch import nn as nn |
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from .create_conv2d import create_conv2d |
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from .create_norm_act import get_norm_act_layer |
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class SeparableConvNormAct(nn.Module): |
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""" Separable Conv w/ trailing Norm and Activation |
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""" |
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def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1, padding='', bias=False, |
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channel_multiplier=1.0, pw_kernel_size=1, norm_layer=nn.BatchNorm2d, act_layer=nn.ReLU, |
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apply_act=True, drop_layer=None): |
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super(SeparableConvNormAct, self).__init__() |
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self.conv_dw = create_conv2d( |
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in_channels, int(in_channels * channel_multiplier), kernel_size, |
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stride=stride, dilation=dilation, padding=padding, depthwise=True) |
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self.conv_pw = create_conv2d( |
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int(in_channels * channel_multiplier), out_channels, pw_kernel_size, padding=padding, bias=bias) |
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norm_act_layer = get_norm_act_layer(norm_layer, act_layer) |
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norm_kwargs = dict(drop_layer=drop_layer) if drop_layer is not None else {} |
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self.bn = norm_act_layer(out_channels, apply_act=apply_act, **norm_kwargs) |
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@property |
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def in_channels(self): |
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return self.conv_dw.in_channels |
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@property |
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def out_channels(self): |
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return self.conv_pw.out_channels |
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def forward(self, x): |
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x = self.conv_dw(x) |
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x = self.conv_pw(x) |
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x = self.bn(x) |
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return x |
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SeparableConvBnAct = SeparableConvNormAct |
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class SeparableConv2d(nn.Module): |
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""" Separable Conv |
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""" |
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def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1, padding='', bias=False, |
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channel_multiplier=1.0, pw_kernel_size=1): |
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super(SeparableConv2d, self).__init__() |
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self.conv_dw = create_conv2d( |
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in_channels, int(in_channels * channel_multiplier), kernel_size, |
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stride=stride, dilation=dilation, padding=padding, depthwise=True) |
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self.conv_pw = create_conv2d( |
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int(in_channels * channel_multiplier), out_channels, pw_kernel_size, padding=padding, bias=bias) |
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@property |
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def in_channels(self): |
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return self.conv_dw.in_channels |
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@property |
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def out_channels(self): |
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return self.conv_pw.out_channels |
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def forward(self, x): |
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x = self.conv_dw(x) |
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x = self.conv_pw(x) |
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return x |
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