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October 28, 2020 13:48
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modified resnet
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| import torch.nn as nn | |
| import math | |
| import torch.utils.model_zoo as model_zoo | |
| import numpy as np | |
| __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', | |
| 'resnet152'] | |
| model_urls = { | |
| 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', | |
| 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', | |
| 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', | |
| 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', | |
| 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', | |
| } | |
| def conv3x3(in_planes, out_planes, stride=1, dilation=1): | |
| "3x3 convolution with padding" | |
| kernel_size = np.asarray((3, 3)) | |
| # Compute the size of the upsampled filter with | |
| # a specified dilation rate. | |
| upsampled_kernel_size = (kernel_size - 1) * (dilation - 1) + kernel_size | |
| # Determine the padding that is necessary for full padding, | |
| # meaning the output spatial size is equal to input spatial size | |
| full_padding = (upsampled_kernel_size - 1) // 2 | |
| # Conv2d doesn't accept numpy arrays as arguments | |
| full_padding, kernel_size = tuple(full_padding), tuple(kernel_size) | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, | |
| padding=full_padding, dilation=dilation, bias=False) | |
| class BasicBlock(nn.Module): | |
| expansion = 1 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1): | |
| super(BasicBlock, self).__init__() | |
| self.conv1 = conv3x3(inplanes, planes, stride, dilation=dilation) | |
| self.bn1 = nn.BatchNorm2d(planes) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.conv2 = conv3x3(planes, planes, dilation=dilation) | |
| self.bn2 = nn.BatchNorm2d(planes) | |
| self.downsample = downsample | |
| 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 is not None: | |
| residual = self.downsample(x) | |
| out += residual | |
| out = self.relu(out) | |
| return out | |
| class Bottleneck(nn.Module): | |
| expansion = 4 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1): | |
| super(Bottleneck, self).__init__() | |
| self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) | |
| self.bn1 = nn.BatchNorm2d(planes) | |
| self.conv2 = conv3x3(planes, planes, stride=stride, dilation=dilation) | |
| #self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, | |
| # padding=1, bias=False) | |
| self.bn2 = nn.BatchNorm2d(planes) | |
| self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) | |
| self.bn3 = nn.BatchNorm2d(planes * 4) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.downsample = downsample | |
| 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) | |
| out = self.relu(out) | |
| out = self.conv3(out) | |
| out = self.bn3(out) | |
| if self.downsample is not None: | |
| residual = self.downsample(x) | |
| out += residual | |
| out = self.relu(out) | |
| return out | |
| class ResNet(nn.Module): | |
| def __init__(self, | |
| block, | |
| layers, | |
| num_classes=1000, | |
| fully_conv=False, | |
| remove_avg_pool_layer=False, | |
| output_stride=32, | |
| additional_blocks=0, | |
| multi_grid=(1,1,1) ): | |
| # Add additional variables to track | |
| # output stride. Necessary to achieve | |
| # specified output stride. | |
| self.output_stride = output_stride | |
| self.current_stride = 4 | |
| self.current_dilation = 1 | |
| self.remove_avg_pool_layer = remove_avg_pool_layer | |
| self.inplanes = 64 | |
| self.fully_conv = fully_conv | |
| super(ResNet, self).__init__() | |
| self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, | |
| bias=False) | |
| self.bn1 = nn.BatchNorm2d(64) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | |
| self.layer1 = self._make_layer(block, 64, layers[0]) | |
| self.layer2 = self._make_layer(block, 128, layers[1], stride=2) | |
| self.layer3 = self._make_layer(block, 256, layers[2], stride=2) | |
| self.layer4 = self._make_layer(block, 512, layers[3], stride=2, multi_grid=multi_grid) | |
| self.additional_blocks = additional_blocks | |
| if additional_blocks == 1: | |
| self.layer5 = self._make_layer(block, 512, layers[3], stride=2, multi_grid=multi_grid) | |
| if additional_blocks == 2: | |
| self.layer5 = self._make_layer(block, 512, layers[3], stride=2, multi_grid=multi_grid) | |
| self.layer6 = self._make_layer(block, 512, layers[3], stride=2, multi_grid=multi_grid) | |
| if additional_blocks == 3: | |
| self.layer5 = self._make_layer(block, 512, layers[3], stride=2, multi_grid=multi_grid) | |
| self.layer6 = self._make_layer(block, 512, layers[3], stride=2, multi_grid=multi_grid) | |
| self.layer7 = self._make_layer(block, 512, layers[3], stride=2, multi_grid=multi_grid) | |
| self.avgpool = nn.AvgPool2d(7) | |
| self.fc = nn.Linear(512 * block.expansion, num_classes) | |
| if self.fully_conv: | |
| self.avgpool = nn.AvgPool2d(7, padding=3, stride=1) | |
| # In the latest unstable torch 4.0 the tensor.copy_ | |
| # method was changed and doesn't work as it used to be | |
| #self.fc = nn.Conv2d(512 * block.expansion, num_classes, 1) | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels | |
| m.weight.data.normal_(0, math.sqrt(2. / n)) | |
| elif isinstance(m, nn.BatchNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| def _make_layer(self, | |
| block, | |
| planes, | |
| blocks, | |
| stride=1, | |
| multi_grid=None): | |
| downsample = None | |
| if stride != 1 or self.inplanes != planes * block.expansion: | |
| # Check if we already achieved desired output stride. | |
| if self.current_stride == self.output_stride: | |
| # If so, replace subsampling with a dilation to preserve | |
| # current spatial resolution. | |
| self.current_dilation = self.current_dilation * stride | |
| stride = 1 | |
| else: | |
| # If not, perform subsampling and update current | |
| # new output stride. | |
| self.current_stride = self.current_stride * stride | |
| # We don't dilate 1x1 convolution. | |
| downsample = nn.Sequential( | |
| nn.Conv2d(self.inplanes, planes * block.expansion, | |
| kernel_size=1, stride=stride, bias=False), | |
| nn.BatchNorm2d(planes * block.expansion), | |
| ) | |
| layers = [] | |
| dilation = multi_grid[0] * self.current_dilation if multi_grid else self.current_dilation | |
| layers.append(block(self.inplanes, planes, stride, downsample, dilation=dilation)) | |
| self.inplanes = planes * block.expansion | |
| for i in range(1, blocks): | |
| dilation = multi_grid[i] * self.current_dilation if multi_grid else self.current_dilation | |
| layers.append(block(self.inplanes, planes, dilation=dilation)) | |
| return nn.Sequential(*layers) | |
| def forward(self, x): | |
| 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) | |
| x = self.layer4(x) | |
| if self.additional_blocks == 1: | |
| x = self.layer5(x) | |
| if self.additional_blocks == 2: | |
| x = self.layer5(x) | |
| x = self.layer6(x) | |
| if self.additional_blocks == 3: | |
| x = self.layer5(x) | |
| x = self.layer6(x) | |
| x = self.layer7(x) | |
| if not self.remove_avg_pool_layer: | |
| x = self.avgpool(x) | |
| if not self.fully_conv: | |
| x = x.view(x.size(0), -1) | |
| x = self.fc(x) | |
| return x | |
| def resnet18(pretrained=False, **kwargs): | |
| """Constructs a ResNet-18 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on ImageNet | |
| """ | |
| model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) | |
| if pretrained: | |
| model.load_state_dict(model_zoo.load_url(model_urls['resnet18'])) | |
| return model | |
| def resnet34(pretrained=False, **kwargs): | |
| """Constructs a ResNet-34 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on ImageNet | |
| """ | |
| model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) | |
| if pretrained: | |
| model.load_state_dict(model_zoo.load_url(model_urls['resnet34'])) | |
| return model | |
| def resnet50(pretrained=False, **kwargs): | |
| """Constructs a ResNet-50 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on ImageNet | |
| """ | |
| model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) | |
| if pretrained: | |
| model.load_state_dict(model_zoo.load_url(model_urls['resnet50'])) | |
| return model | |
| def resnet101(pretrained=False, **kwargs): | |
| """Constructs a ResNet-101 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on ImageNet | |
| """ | |
| model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) | |
| if pretrained: | |
| model.load_state_dict(model_zoo.load_url(model_urls['resnet101'])) | |
| return model | |
| def resnet152(pretrained=False, **kwargs): | |
| """Constructs a ResNet-152 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on ImageNet | |
| """ | |
| model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) | |
| if pretrained: | |
| model.load_state_dict(model_zoo.load_url(model_urls['resnet152'])) | |
| return model |
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