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89 lines
3.1 KiB
89 lines
3.1 KiB
""" Conv2d + BN + Act
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Hacked together by / Copyright 2020 Ross Wightman
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"""
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import functools
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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 ConvNormAct(nn.Module):
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def __init__(
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self, in_channels, out_channels, kernel_size=1, stride=1, padding='', dilation=1, groups=1,
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bias=False, apply_act=True, norm_layer=nn.BatchNorm2d, act_layer=nn.ReLU, drop_layer=None):
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super(ConvNormAct, self).__init__()
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self.conv = create_conv2d(
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in_channels, out_channels, kernel_size, stride=stride,
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padding=padding, dilation=dilation, groups=groups, bias=bias)
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# NOTE for backwards compatibility with models that use separate norm and act layer definitions
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norm_act_layer = get_norm_act_layer(norm_layer, act_layer)
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# NOTE for backwards (weight) compatibility, norm layer name remains `.bn`
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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.in_channels
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@property
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def out_channels(self):
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return self.conv.out_channels
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def forward(self, x):
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x = self.conv(x)
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x = self.bn(x)
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return x
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ConvBnAct = ConvNormAct
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def create_aa(aa_layer, channels, stride=2, enable=True):
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if not aa_layer or not enable:
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return nn.Identity()
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if isinstance(aa_layer, functools.partial):
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if issubclass(aa_layer.func, nn.AvgPool2d):
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return aa_layer()
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else:
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return aa_layer(channels)
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elif issubclass(aa_layer, nn.AvgPool2d):
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return aa_layer(stride)
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else:
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return aa_layer(channels=channels, stride=stride)
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class ConvNormActAa(nn.Module):
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def __init__(
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self, in_channels, out_channels, kernel_size=1, stride=1, padding='', dilation=1, groups=1,
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bias=False, apply_act=True, norm_layer=nn.BatchNorm2d, act_layer=nn.ReLU, aa_layer=None, drop_layer=None):
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super(ConvNormActAa, self).__init__()
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use_aa = aa_layer is not None and stride == 2
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self.conv = create_conv2d(
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in_channels, out_channels, kernel_size, stride=1 if use_aa else stride,
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padding=padding, dilation=dilation, groups=groups, bias=bias)
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# NOTE for backwards compatibility with models that use separate norm and act layer definitions
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norm_act_layer = get_norm_act_layer(norm_layer, act_layer)
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# NOTE for backwards (weight) compatibility, norm layer name remains `.bn`
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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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self.aa = create_aa(aa_layer, out_channels, stride=stride, enable=use_aa)
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@property
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def in_channels(self):
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return self.conv.in_channels
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@property
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def out_channels(self):
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return self.conv.out_channels
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def forward(self, x):
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x = self.conv(x)
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x = self.bn(x)
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x = self.aa(x)
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return x
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