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https://github.com/huggingface/pytorch-image-models.git
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ab49d275de
* ConvBnAct layer renamed -> ConvNormAct and ConvNormActAa for anti-aliased * Significant update to EfficientNet and MobileNetV3 arch to support NormAct layers and grouped conv (as alternative to depthwise) * Update RegNet to add Z variant * Add Pre variant of XceptionAligned that works with NormAct layers * EvoNorm matches bits_and_tpu branch for merge
77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
""" 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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