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pytorch-image-models/timm/models/layers/conv_bn_act.py

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3.1 KiB

""" Conv2d + BN + Act
Hacked together by / Copyright 2020 Ross Wightman
"""
import functools
from torch import nn as nn
from .create_conv2d import create_conv2d
from .create_norm_act import get_norm_act_layer
class ConvNormAct(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, drop_layer=None):
super(ConvNormAct, self).__init__()
self.conv = create_conv2d(
in_channels, out_channels, kernel_size, stride=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 = get_norm_act_layer(norm_layer, act_layer)
# NOTE for backwards (weight) compatibility, norm layer name remains `.bn`
norm_kwargs = dict(drop_layer=drop_layer) if drop_layer is not None else {}
self.bn = norm_act_layer(out_channels, apply_act=apply_act, **norm_kwargs)
@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)
return x
ConvBnAct = ConvNormAct
def create_aa(aa_layer, channels, stride=2, enable=True):
if not aa_layer or not enable:
return nn.Identity()
if isinstance(aa_layer, functools.partial):
if issubclass(aa_layer.func, nn.AvgPool2d):
return aa_layer()
else:
return aa_layer(channels)
elif issubclass(aa_layer, nn.AvgPool2d):
return aa_layer(stride)
else:
return aa_layer(channels=channels, stride=stride)
class ConvNormActAa(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_layer=None):
super(ConvNormActAa, self).__init__()
use_aa = aa_layer is not None and stride == 2
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 = get_norm_act_layer(norm_layer, act_layer)
# NOTE for backwards (weight) compatibility, norm layer name remains `.bn`
norm_kwargs = dict(drop_layer=drop_layer) if drop_layer is not None else {}
self.bn = norm_act_layer(out_channels, apply_act=apply_act, **norm_kwargs)
self.aa = create_aa(aa_layer, out_channels, stride=stride, enable=use_aa)
@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)
x = self.aa(x)
return x