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| import torch.nn as nn | |
| __all__ = ['ResNet31'] | |
| def conv3x3(in_channel, out_channel, stride=1): | |
| return nn.Conv2d(in_channel, | |
| out_channel, | |
| kernel_size=3, | |
| stride=stride, | |
| padding=1, | |
| bias=False) | |
| class BasicBlock(nn.Module): | |
| expansion = 1 | |
| def __init__(self, in_channels, channels, stride=1, downsample=False): | |
| super().__init__() | |
| self.conv1 = conv3x3(in_channels, channels, stride) | |
| self.bn1 = nn.BatchNorm2d(channels) | |
| self.relu = nn.ReLU() | |
| self.conv2 = conv3x3(channels, channels) | |
| self.bn2 = nn.BatchNorm2d(channels) | |
| self.downsample = downsample | |
| if downsample: | |
| self.downsample = nn.Sequential( | |
| nn.Conv2d(in_channels, | |
| channels * self.expansion, | |
| 1, | |
| stride, | |
| bias=False), | |
| nn.BatchNorm2d(channels * self.expansion), | |
| ) | |
| else: | |
| self.downsample = nn.Sequential() | |
| self.stride = stride | |
| def forward(self, x): | |
| residual = x | |
| out = self.conv1(x) | |
| out = self.bn1(out) | |
| out = self.relu(out) | |
| out = self.conv2(out) | |
| out = self.bn2(out) | |
| if self.downsample: | |
| residual = self.downsample(x) | |
| out += residual | |
| out = self.relu(out) | |
| return out | |
| class ResNet31(nn.Module): | |
| """ | |
| Args: | |
| in_channels (int): Number of channels of input image tensor. | |
| layers (list[int]): List of BasicBlock number for each stage. | |
| channels (list[int]): List of out_channels of Conv2d layer. | |
| out_indices (None | Sequence[int]): Indices of output stages. | |
| last_stage_pool (bool): If True, add `MaxPool2d` layer to last stage. | |
| """ | |
| def __init__( | |
| self, | |
| in_channels=3, | |
| layers=[1, 2, 5, 3], | |
| channels=[64, 128, 256, 256, 512, 512, 512], | |
| out_indices=None, | |
| last_stage_pool=False, | |
| ): | |
| super(ResNet31, self).__init__() | |
| assert isinstance(in_channels, int) | |
| assert isinstance(last_stage_pool, bool) | |
| self.out_indices = out_indices | |
| self.last_stage_pool = last_stage_pool | |
| # conv 1 (Conv Conv) | |
| self.conv1_1 = nn.Conv2d(in_channels, | |
| channels[0], | |
| kernel_size=3, | |
| stride=1, | |
| padding=1) | |
| self.bn1_1 = nn.BatchNorm2d(channels[0]) | |
| self.relu1_1 = nn.ReLU(inplace=True) | |
| self.conv1_2 = nn.Conv2d(channels[0], | |
| channels[1], | |
| kernel_size=3, | |
| stride=1, | |
| padding=1) | |
| self.bn1_2 = nn.BatchNorm2d(channels[1]) | |
| self.relu1_2 = nn.ReLU(inplace=True) | |
| # conv 2 (Max-pooling, Residual block, Conv) | |
| self.pool2 = nn.MaxPool2d(kernel_size=2, | |
| stride=2, | |
| padding=0, | |
| ceil_mode=True) | |
| self.block2 = self._make_layer(channels[1], channels[2], layers[0]) | |
| self.conv2 = nn.Conv2d(channels[2], | |
| channels[2], | |
| kernel_size=3, | |
| stride=1, | |
| padding=1) | |
| self.bn2 = nn.BatchNorm2d(channels[2]) | |
| self.relu2 = nn.ReLU(inplace=True) | |
| # conv 3 (Max-pooling, Residual block, Conv) | |
| self.pool3 = nn.MaxPool2d(kernel_size=2, | |
| stride=2, | |
| padding=0, | |
| ceil_mode=True) | |
| self.block3 = self._make_layer(channels[2], channels[3], layers[1]) | |
| self.conv3 = nn.Conv2d(channels[3], | |
| channels[3], | |
| kernel_size=3, | |
| stride=1, | |
| padding=1) | |
| self.bn3 = nn.BatchNorm2d(channels[3]) | |
| self.relu3 = nn.ReLU(inplace=True) | |
| # conv 4 (Max-pooling, Residual block, Conv) | |
| self.pool4 = nn.MaxPool2d(kernel_size=(2, 1), | |
| stride=(2, 1), | |
| padding=0, | |
| ceil_mode=True) | |
| self.block4 = self._make_layer(channels[3], channels[4], layers[2]) | |
| self.conv4 = nn.Conv2d(channels[4], | |
| channels[4], | |
| kernel_size=3, | |
| stride=1, | |
| padding=1) | |
| self.bn4 = nn.BatchNorm2d(channels[4]) | |
| self.relu4 = nn.ReLU(inplace=True) | |
| # conv 5 ((Max-pooling), Residual block, Conv) | |
| self.pool5 = None | |
| if self.last_stage_pool: | |
| self.pool5 = nn.MaxPool2d(kernel_size=2, | |
| stride=2, | |
| padding=0, | |
| ceil_mode=True) | |
| self.block5 = self._make_layer(channels[4], channels[5], layers[3]) | |
| self.conv5 = nn.Conv2d(channels[5], | |
| channels[5], | |
| kernel_size=3, | |
| stride=1, | |
| padding=1) | |
| self.bn5 = nn.BatchNorm2d(channels[5]) | |
| self.relu5 = nn.ReLU(inplace=True) | |
| self.out_channels = channels[-1] | |
| def _make_layer(self, input_channels, output_channels, blocks): | |
| layers = [] | |
| for _ in range(blocks): | |
| downsample = None | |
| if input_channels != output_channels: | |
| downsample = nn.Sequential( | |
| nn.Conv2d( | |
| input_channels, | |
| output_channels, | |
| kernel_size=1, | |
| stride=1, | |
| bias=False, | |
| ), | |
| nn.BatchNorm2d(output_channels), | |
| ) | |
| layers.append( | |
| BasicBlock(input_channels, | |
| output_channels, | |
| downsample=downsample)) | |
| input_channels = output_channels | |
| return nn.Sequential(*layers) | |
| def forward(self, x): | |
| x = self.conv1_1(x) | |
| x = self.bn1_1(x) | |
| x = self.relu1_1(x) | |
| x = self.conv1_2(x) | |
| x = self.bn1_2(x) | |
| x = self.relu1_2(x) | |
| outs = [] | |
| for i in range(4): | |
| layer_index = i + 2 | |
| pool_layer = getattr(self, 'pool{}'.format(layer_index)) | |
| block_layer = getattr(self, 'block{}'.format(layer_index)) | |
| conv_layer = getattr(self, 'conv{}'.format(layer_index)) | |
| bn_layer = getattr(self, 'bn{}'.format(layer_index)) | |
| relu_layer = getattr(self, 'relu{}'.format(layer_index)) | |
| if pool_layer is not None: | |
| x = pool_layer(x) | |
| x = block_layer(x) | |
| x = conv_layer(x) | |
| x = bn_layer(x) | |
| x = relu_layer(x) | |
| outs.append(x) | |
| if self.out_indices is not None: | |
| return tuple([outs[i] for i in self.out_indices]) | |
| return x | |