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| """Split-Attention""" | |
| import torch | |
| from torch import nn | |
| import torch.nn.functional as F | |
| from torch.nn import Conv2d, Module, Linear, BatchNorm2d, ReLU | |
| from torch.nn.modules.utils import _pair | |
| __all__ = ['SplAtConv2d'] | |
| 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 | |