import torch import torch.nn as nn from .Attention import AxialAttention def conv1x1(in_planes, out_planes, stride=1): """1x1 convolution""" return nn.Conv2d( in_planes, out_planes, kernel_size=1, stride=stride, bias=False ) class AxialBlock(nn.Module): expansion = 2 def __init__( self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None, kernel_size=56, ): super(AxialBlock, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d width = int(planes * (base_width / 64.0)) # Both self.conv2 and self.downsample layers downsample the input when stride != 1 self.conv_down = conv1x1(inplanes, width) self.bn1 = norm_layer(width) self.hight_block = AxialAttention( width, width, groups=groups, kernel_size=kernel_size ) self.width_block = AxialAttention( width, width, groups=groups, kernel_size=kernel_size, stride=stride, width=True, ) self.conv_up = conv1x1(width, planes * self.expansion) self.bn2 = norm_layer(planes * self.expansion) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): identity = x out = self.conv_down(x) out = self.bn1(out) out = self.relu(out) out = self.hight_block(out) out = self.width_block(out) out = self.relu(out) out = self.conv_up(out) out = self.bn2(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu(out) return out class AxialAttentionNet(nn.Module): def __init__( self, layers, num_classes=1000, zero_init_residual=True, groups=8, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None, s=0.5, ): super(AxialAttentionNet, self).__init__() block = AxialBlock if norm_layer is None: norm_layer = nn.BatchNorm2d self._norm_layer = norm_layer self.inplanes = int(64 * s) self.dilation = 1 if replace_stride_with_dilation is None: # each element in the tuple indicates if we should replace # the 2x2 stride with a dilated convolution instead replace_stride_with_dilation = [False, False, False] if len(replace_stride_with_dilation) != 3: raise ValueError( "replace_stride_with_dilation should be None " "or a 3-element tuple, got {}".format( replace_stride_with_dilation ) ) self.groups = groups self.base_width = width_per_group self.conv1 = nn.Conv2d( 3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False ) self.bn1 = norm_layer(self.inplanes) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer( block, int(128 * s), layers[0], kernel_size=56 ) self.layer2 = self._make_layer( block, int(256 * s), layers[1], stride=2, kernel_size=56, dilate=replace_stride_with_dilation[0], ) self.layer3 = self._make_layer( block, int(512 * s), layers[2], stride=2, kernel_size=28, dilate=replace_stride_with_dilation[1], ) self.layer4 = self._make_layer( block, int(1024 * s), layers[3], stride=2, kernel_size=14, dilate=replace_stride_with_dilation[2], ) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(int(1024 * block.expansion * s), num_classes) for m in self.modules(): if isinstance(m, (nn.Conv2d, nn.Conv1d)): if isinstance(m, nn.Conv1d): pass else: nn.init.kaiming_normal_( m.weight, mode='fan_out', nonlinearity='relu' ) elif isinstance(m, (nn.BatchNorm2d, nn.BatchNorm1d, nn.GroupNorm)): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) # Zero-initialize the last BN in each residual branch, # so that the residual branch starts with zeros, and each residual block behaves like an identity. # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 if zero_init_residual: for m in self.modules(): if isinstance(m, AxialBlock): nn.init.constant_(m.bn2.weight, 0) def _make_layer( self, block, planes, blocks, kernel_size=56, stride=1, dilate=False ): norm_layer = self._norm_layer downsample = None previous_dilation = self.dilation if dilate: self.dilation *= stride stride = 1 if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), norm_layer(planes * block.expansion), ) layers = [] layers.append( block( self.inplanes, planes, stride, downsample, groups=self.groups, base_width=self.base_width, dilation=previous_dilation, norm_layer=norm_layer, kernel_size=kernel_size, ) ) self.inplanes = planes * block.expansion if stride != 1: kernel_size = kernel_size // 2 for _ in range(1, blocks): layers.append( block( self.inplanes, planes, groups=self.groups, base_width=self.base_width, dilation=self.dilation, norm_layer=norm_layer, kernel_size=kernel_size, ) ) return nn.Sequential(*layers) def _forward_impl(self, x, include_conv5=False, include_top=False): # See note [TorchScript super()] x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) if include_conv5: x = self.layer4(x) if include_top: x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x def forward(self, x, include_conv5=False, include_top=False): return self._forward_impl(x, include_conv5, include_top) if __name__ == "__main__": model = AxialAttentionNet([1, 2, 4, 1], s=0.5) # model = AxialAttentionNet([3, 4, 6, 3], s=0.25) # print(model) x = torch.randn([8, 3, 224, 224]) print(model(x).shape) print(model(x, include_conv5=True).shape) print(model(x, include_conv5=True, include_top=True).shape)