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""" Conv2d + BN + Act
Hacked together by / Copyright 2020 Ross Wightman
"""
from torch import nn as nn
from .create_conv2d import create_conv2d
from .create_norm_act import convert_norm_act
class ConvBnAct(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding='', dilation=1, groups=1,
bias=False, apply_act=True, norm_layer=nn.BatchNorm2d, act_layer=nn.ReLU, aa_layer=None,
drop_block=None):
super(ConvBnAct, self).__init__()
use_aa = aa_layer is not None
self.conv = create_conv2d(
in_channels, out_channels, kernel_size, stride=1 if use_aa else stride,
padding=padding, dilation=dilation, groups=groups, bias=bias)
# NOTE for backwards compatibility with models that use separate norm and act layer definitions
norm_act_layer = convert_norm_act(norm_layer, act_layer)
self.bn = norm_act_layer(out_channels, apply_act=apply_act, drop_block=drop_block)
self.aa = aa_layer(channels=out_channels) if stride == 2 and use_aa else None
@property
def in_channels(self):
return self.conv.in_channels
@property
def out_channels(self):
return self.conv.out_channels
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
if self.aa is not None:
x = self.aa(x)
return x
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