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68 lines
2.3 KiB
68 lines
2.3 KiB
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 convert_norm_act_type
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class SeparableConvBnAct(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, norm_kwargs=None,
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act_layer=nn.ReLU, apply_act=True, drop_block=None):
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super(SeparableConvBnAct, self).__init__()
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norm_kwargs = norm_kwargs or {}
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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, norm_act_args = convert_norm_act_type(norm_layer, act_layer, norm_kwargs)
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self.bn = norm_act_layer(out_channels, apply_act=apply_act, drop_block=drop_block, **norm_act_args)
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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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if self.bn is not None:
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x = self.bn(x)
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return x
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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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