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import torch | |
from torch import nn | |
import torch.nn.functional as F | |
from torch.nn import Conv2d, Module, ReLU | |
from torch.nn.modules.utils import _pair | |
__all__ = ['SplAtConv2d', 'DropBlock2D'] | |
class DropBlock2D(object): | |
def __init__(self, *args, **kwargs): | |
raise NotImplementedError | |
class SplAtConv2d(Module): | |
"""Split-Attention Conv2d | |
""" | |
def __init__(self, | |
in_channels, | |
channels, | |
kernel_size, | |
stride=(1, 1), | |
padding=(0, 0), | |
dilation=(1, 1), | |
groups=1, | |
bias=True, | |
radix=2, | |
reduction_factor=4, | |
rectify=False, | |
rectify_avg=False, | |
norm_layer=None, | |
dropblock_prob=0.0, | |
**kwargs): | |
super(SplAtConv2d, self).__init__() | |
padding = _pair(padding) | |
self.rectify = rectify and (padding[0] > 0 or padding[1] > 0) | |
self.rectify_avg = rectify_avg | |
inter_channels = max(in_channels * radix // reduction_factor, 32) | |
self.radix = radix | |
self.cardinality = groups | |
self.channels = channels | |
self.dropblock_prob = dropblock_prob | |
if self.rectify: | |
from rfconv import RFConv2d | |
self.conv = RFConv2d(in_channels, | |
channels * radix, | |
kernel_size, | |
stride, | |
padding, | |
dilation, | |
groups=groups * radix, | |
bias=bias, | |
average_mode=rectify_avg, | |
**kwargs) | |
else: | |
self.conv = Conv2d(in_channels, | |
channels * radix, | |
kernel_size, | |
stride, | |
padding, | |
dilation, | |
groups=groups * radix, | |
bias=bias, | |
**kwargs) | |
self.use_bn = norm_layer is not None | |
if self.use_bn: | |
self.bn0 = norm_layer(channels * radix) | |
self.relu = ReLU(inplace=True) | |
self.fc1 = Conv2d(channels, inter_channels, 1, groups=self.cardinality) | |
if self.use_bn: | |
self.bn1 = norm_layer(inter_channels) | |
self.fc2 = Conv2d(inter_channels, | |
channels * radix, | |
1, | |
groups=self.cardinality) | |
if dropblock_prob > 0.0: | |
self.dropblock = DropBlock2D(dropblock_prob, 3) | |
self.rsoftmax = rSoftMax(radix, groups) | |
def forward(self, x): | |
x = self.conv(x) | |
if self.use_bn: | |
x = self.bn0(x) | |
if self.dropblock_prob > 0.0: | |
x = self.dropblock(x) | |
x = self.relu(x) | |
batch, rchannel = x.shape[:2] | |
if self.radix > 1: | |
if torch.__version__ < '1.5': | |
splited = torch.split(x, int(rchannel // self.radix), dim=1) | |
else: | |
splited = torch.split(x, rchannel // self.radix, dim=1) | |
gap = sum(splited) | |
else: | |
gap = x | |
gap = F.adaptive_avg_pool2d(gap, 1) | |
gap = self.fc1(gap) | |
if self.use_bn: | |
gap = self.bn1(gap) | |
gap = self.relu(gap) | |
atten = self.fc2(gap) | |
atten = self.rsoftmax(atten).view(batch, -1, 1, 1) | |
if self.radix > 1: | |
if torch.__version__ < '1.5': | |
attens = torch.split(atten, int(rchannel // self.radix), dim=1) | |
else: | |
attens = torch.split(atten, rchannel // self.radix, dim=1) | |
out = sum([att * split for (att, split) in zip(attens, splited)]) | |
else: | |
out = atten * x | |
return out.contiguous() | |
class rSoftMax(nn.Module): | |
def __init__(self, radix, cardinality): | |
super().__init__() | |
self.radix = radix | |
self.cardinality = cardinality | |
def forward(self, x): | |
batch = x.size(0) | |
if self.radix > 1: | |
x = x.view(batch, self.cardinality, self.radix, -1).transpose(1, 2) | |
x = F.softmax(x, dim=1) | |
x = x.reshape(batch, -1) | |
else: | |
x = torch.sigmoid(x) | |
return x | |