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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| # | |
| import torch | |
| import torch.nn as nn | |
| def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): | |
| """3x3 convolution with padding""" | |
| return nn.Conv2d( | |
| in_planes, | |
| out_planes, | |
| kernel_size=3, | |
| stride=stride, | |
| padding=dilation, | |
| groups=groups, | |
| bias=False, | |
| dilation=dilation, | |
| ) | |
| 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 BasicBlock(nn.Module): | |
| expansion = 1 | |
| __constants__ = ["downsample"] | |
| def __init__( | |
| self, | |
| inplanes, | |
| planes, | |
| stride=1, | |
| downsample=None, | |
| groups=1, | |
| base_width=64, | |
| dilation=1, | |
| norm_layer=None, | |
| ): | |
| super(BasicBlock, self).__init__() | |
| if norm_layer is None: | |
| norm_layer = nn.BatchNorm2d | |
| if groups != 1 or base_width != 64: | |
| raise ValueError("BasicBlock only supports groups=1 and base_width=64") | |
| if dilation > 1: | |
| raise NotImplementedError("Dilation > 1 not supported in BasicBlock") | |
| # Both self.conv1 and self.downsample layers downsample the input when stride != 1 | |
| self.conv1 = conv3x3(inplanes, planes, stride) | |
| self.bn1 = norm_layer(planes) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.conv2 = conv3x3(planes, planes) | |
| self.bn2 = norm_layer(planes) | |
| self.downsample = downsample | |
| self.stride = stride | |
| def forward(self, x): | |
| identity = 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: | |
| identity = self.downsample(x) | |
| out += identity | |
| out = self.relu(out) | |
| return out | |
| class Bottleneck(nn.Module): | |
| expansion = 4 | |
| __constants__ = ["downsample"] | |
| def __init__( | |
| self, | |
| inplanes, | |
| planes, | |
| stride=1, | |
| downsample=None, | |
| groups=1, | |
| base_width=64, | |
| dilation=1, | |
| norm_layer=None, | |
| ): | |
| super(Bottleneck, self).__init__() | |
| if norm_layer is None: | |
| norm_layer = nn.BatchNorm2d | |
| width = int(planes * (base_width / 64.0)) * groups | |
| # Both self.conv2 and self.downsample layers downsample the input when stride != 1 | |
| self.conv1 = conv1x1(inplanes, width) | |
| self.bn1 = norm_layer(width) | |
| self.conv2 = conv3x3(width, width, stride, groups, dilation) | |
| self.bn2 = norm_layer(width) | |
| self.conv3 = conv1x1(width, planes * self.expansion) | |
| self.bn3 = 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.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: | |
| identity = self.downsample(x) | |
| out += identity | |
| out = self.relu(out) | |
| return out | |
| class ResNet(nn.Module): | |
| def __init__( | |
| self, | |
| block, | |
| layers, | |
| zero_init_residual=False, | |
| groups=1, | |
| widen=1, | |
| width_per_group=64, | |
| replace_stride_with_dilation=None, | |
| norm_layer=None, | |
| normalize=False, | |
| output_dim=0, | |
| hidden_mlp=0, | |
| nmb_prototypes=0, | |
| eval_mode=False, | |
| ): | |
| super(ResNet, self).__init__() | |
| if norm_layer is None: | |
| norm_layer = nn.BatchNorm2d | |
| self._norm_layer = norm_layer | |
| self.eval_mode = eval_mode | |
| self.padding = nn.ConstantPad2d(1, 0.0) | |
| self.inplanes = width_per_group * widen | |
| 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 | |
| # change padding 3 -> 2 compared to original torchvision code because added a padding layer | |
| num_out_filters = width_per_group * widen | |
| self.conv1 = nn.Conv2d( | |
| 3, num_out_filters, kernel_size=3, stride=1, padding=1, bias=False | |
| ) | |
| self.bn1 = norm_layer(num_out_filters) | |
| self.relu = nn.ReLU(inplace=True) | |
| # self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | |
| self.layer1 = self._make_layer(block, num_out_filters, layers[0]) | |
| num_out_filters *= 2 | |
| self.layer2 = self._make_layer( | |
| block, num_out_filters, layers[1], stride=2, dilate=replace_stride_with_dilation[0] | |
| ) | |
| num_out_filters *= 2 | |
| self.layer3 = self._make_layer( | |
| block, num_out_filters, layers[2], stride=2, dilate=replace_stride_with_dilation[1] | |
| ) | |
| num_out_filters *= 2 | |
| self.layer4 = self._make_layer( | |
| block, num_out_filters, layers[3], stride=2, dilate=replace_stride_with_dilation[2] | |
| ) | |
| self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) | |
| # normalize output features | |
| self.l2norm = normalize | |
| # projection head | |
| if output_dim == 0: | |
| self.projection_head = None | |
| elif hidden_mlp == 0: | |
| self.projection_head = nn.Linear(num_out_filters * block.expansion, output_dim) | |
| else: | |
| self.projection_head = nn.Sequential( | |
| nn.Linear(num_out_filters * block.expansion, hidden_mlp), | |
| nn.BatchNorm1d(hidden_mlp), | |
| nn.ReLU(inplace=True), | |
| nn.Linear(hidden_mlp, output_dim), | |
| ) | |
| # prototype layer | |
| self.prototypes = None | |
| if isinstance(nmb_prototypes, list): | |
| self.prototypes = MultiPrototypes(output_dim, nmb_prototypes) | |
| elif nmb_prototypes > 0: | |
| self.prototypes = nn.Linear(output_dim, nmb_prototypes, bias=False) | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") | |
| elif isinstance(m, (nn.BatchNorm2d, 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, Bottleneck): | |
| nn.init.constant_(m.bn3.weight, 0) | |
| elif isinstance(m, BasicBlock): | |
| nn.init.constant_(m.bn2.weight, 0) | |
| def _make_layer(self, block, planes, blocks, 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, | |
| self.groups, | |
| self.base_width, | |
| previous_dilation, | |
| norm_layer, | |
| ) | |
| ) | |
| self.inplanes = planes * block.expansion | |
| 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, | |
| ) | |
| ) | |
| return nn.Sequential(*layers) | |
| def forward_backbone(self, x): | |
| x = self.padding(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.eval_mode: | |
| return x | |
| x = self.avgpool(x) | |
| x = torch.flatten(x, 1) | |
| return x | |
| def forward_head(self, x): | |
| if self.projection_head is not None: | |
| x = self.projection_head(x) | |
| if self.l2norm: | |
| x = nn.functional.normalize(x, dim=1, p=2) | |
| if self.prototypes is not None: | |
| return x, self.prototypes(x) | |
| return x | |
| def forward(self, inputs): | |
| if not isinstance(inputs, list): | |
| inputs = [inputs] | |
| idx_crops = torch.cumsum(torch.unique_consecutive( | |
| torch.tensor([inp.shape[-1] for inp in inputs]), | |
| return_counts=True, | |
| )[1], 0) | |
| start_idx = 0 | |
| for end_idx in idx_crops: | |
| _out = self.forward_backbone(torch.cat(inputs[start_idx: end_idx])) # .cuda(non_blocking=True) | |
| if start_idx == 0: | |
| output = _out | |
| else: | |
| output = torch.cat((output, _out)) | |
| start_idx = end_idx | |
| return self.forward_head(output) | |
| class MultiPrototypes(nn.Module): | |
| def __init__(self, output_dim, nmb_prototypes): | |
| super(MultiPrototypes, self).__init__() | |
| self.nmb_heads = len(nmb_prototypes) | |
| for i, k in enumerate(nmb_prototypes): | |
| self.add_module("prototypes" + str(i), nn.Linear(output_dim, k, bias=False)) | |
| def forward(self, x): | |
| out = [] | |
| for i in range(self.nmb_heads): | |
| out.append(getattr(self, "prototypes" + str(i))(x)) | |
| return out | |
| def resnet18(**kwargs): | |
| return ResNet(Bottleneck, [2, 2, 2, 2], **kwargs) | |
| def resnet50(**kwargs): | |
| return ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) | |
| def resnet50w2(**kwargs): | |
| return ResNet(Bottleneck, [3, 4, 6, 3], widen=2, **kwargs) | |
| def resnet50w4(**kwargs): | |
| return ResNet(Bottleneck, [3, 4, 6, 3], widen=4, **kwargs) | |
| def resnet50w5(**kwargs): | |
| return ResNet(Bottleneck, [3, 4, 6, 3], widen=5, **kwargs) | |