| |
| import torch |
| import torch.nn as nn |
| from torch.nn import functional as F |
| from collections import OrderedDict |
|
|
| class IntermediateLayerGetter(nn.ModuleDict): |
| """ |
| Module wrapper that returns intermediate layers from a model |
| |
| It has a strong assumption that the modules have been registered |
| into the model in the same order as they are used. |
| This means that one should **not** reuse the same nn.Module |
| twice in the forward if you want this to work. |
| |
| Additionally, it is only able to query submodules that are directly |
| assigned to the model. So if `model` is passed, `model.feature1` can |
| be returned, but not `model.feature1.layer2`. |
| |
| Arguments: |
| model (nn.Module): model on which we will extract the features |
| return_layers (Dict[name, new_name]): a dict containing the names |
| of the modules for which the activations will be returned as |
| the key of the dict, and the value of the dict is the name |
| of the returned activation (which the user can specify). |
| |
| Examples:: |
| |
| >>> m = torchvision.models.resnet18(pretrained=True) |
| >>> # extract layer1 and layer3, giving as names `feat1` and feat2` |
| >>> new_m = torchvision.models._utils.IntermediateLayerGetter(m, |
| >>> {'layer1': 'feat1', 'layer3': 'feat2'}) |
| >>> out = new_m(torch.rand(1, 3, 224, 224)) |
| >>> print([(k, v.shape) for k, v in out.items()]) |
| >>> [('feat1', torch.Size([1, 64, 56, 56])), |
| >>> ('feat2', torch.Size([1, 256, 14, 14]))] |
| """ |
| def __init__(self, model, return_layers, hrnet_flag=False): |
| if not set(return_layers).issubset([name for name, _ in model.named_children()]): |
| raise ValueError("return_layers are not present in model") |
|
|
| self.hrnet_flag = hrnet_flag |
|
|
| orig_return_layers = return_layers |
| return_layers = {k: v for k, v in return_layers.items()} |
| layers = OrderedDict() |
| for name, module in model.named_children(): |
| layers[name] = module |
| if name in return_layers: |
| del return_layers[name] |
| if not return_layers: |
| break |
|
|
| super(IntermediateLayerGetter, self).__init__(layers) |
| self.return_layers = orig_return_layers |
|
|
| def forward(self, x): |
| out = OrderedDict() |
| for name, module in self.named_children(): |
| if self.hrnet_flag and name.startswith('transition'): |
| if name == 'transition1': |
| x = [trans(x) for trans in module] |
| else: |
| x.append(module(x[-1])) |
| else: |
| x = module(x) |
|
|
| if name in self.return_layers: |
| out_name = self.return_layers[name] |
| if name == 'stage4' and self.hrnet_flag: |
| output_h, output_w = x[0].size(2), x[0].size(3) |
| x1 = F.interpolate(x[1], size=(output_h, output_w), mode='bilinear', align_corners=False) |
| x2 = F.interpolate(x[2], size=(output_h, output_w), mode='bilinear', align_corners=False) |
| x3 = F.interpolate(x[3], size=(output_h, output_w), mode='bilinear', align_corners=False) |
| x = torch.cat([x[0], x1, x2, x3], dim=1) |
| out[out_name] = x |
| else: |
| out[out_name] = x |
| return out |
|
|
| class _SimpleSegmentationModel(nn.Module): |
| def __init__(self, backbone, classifier): |
| super(_SimpleSegmentationModel, self).__init__() |
| self.backbone = backbone |
| self.classifier = classifier |
| |
| |
| |
| |
| def forward(self, x): |
| |
| |
| |
| |
| |
| input_shape = x.shape[-2:] |
| features = self.backbone(x) |
| x = self.classifier(features) |
| x = F.interpolate(x, size=input_shape, mode='bilinear', align_corners=False) |
| |
| |
| |
| |
| |
| |
| return x |
|
|
| class DeepLabV3(_SimpleSegmentationModel): |
| """ |
| Implements DeepLabV3 model from |
| `"Rethinking Atrous Convolution for Semantic Image Segmentation" |
| <https://arxiv.org/abs/1706.05587>`_. |
| |
| Arguments: |
| backbone (nn.Module): the network used to compute the features for the model. |
| The backbone should return an OrderedDict[Tensor], with the key being |
| "out" for the last feature map used, and "aux" if an auxiliary classifier |
| is used. |
| classifier (nn.Module): module that takes the "out" element returned from |
| the backbone and returns a dense prediction. |
| aux_classifier (nn.Module, optional): auxiliary classifier used during training |
| """ |
| pass |
|
|
| class DeepLabHeadV3Plus(nn.Module): |
| def __init__(self, in_channels, low_level_channels, num_classes, aspp_dilate=[12, 24, 36]): |
| super(DeepLabHeadV3Plus, self).__init__() |
| self.project = nn.Sequential( |
| nn.Conv2d(low_level_channels, 48, 1, bias=False), |
| nn.BatchNorm2d(48), |
| nn.ReLU(inplace=True), |
| ) |
|
|
| self.aspp = ASPP(in_channels, aspp_dilate) |
|
|
| self.classifier = nn.Sequential( |
| nn.Conv2d(304, 256, 3, padding=1, bias=False), |
| nn.BatchNorm2d(256), |
| nn.ReLU(inplace=True), |
| nn.Conv2d(256, num_classes, 1) |
| ) |
| self._init_weight() |
|
|
| def forward(self, feature): |
| low_level_feature = self.project( feature['low_level'] ) |
| output_feature = self.aspp(feature['out']) |
| output_feature = F.interpolate(output_feature, size=low_level_feature.shape[2:], mode='bilinear', align_corners=False) |
| return self.classifier( torch.cat( [ low_level_feature, output_feature ], dim=1 ) ) |
| |
| def _init_weight(self): |
| for m in self.modules(): |
| if isinstance(m, nn.Conv2d): |
| nn.init.kaiming_normal_(m.weight) |
| elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): |
| nn.init.constant_(m.weight, 1) |
| nn.init.constant_(m.bias, 0) |
|
|
| class DeepLabHead(nn.Module): |
| def __init__(self, in_channels, num_classes, aspp_dilate=[12, 24, 36]): |
| super(DeepLabHead, self).__init__() |
|
|
| self.classifier = nn.Sequential( |
| ASPP(in_channels, aspp_dilate), |
| nn.Conv2d(256, 256, 3, padding=1, bias=False), |
| nn.BatchNorm2d(256), |
| nn.ReLU(inplace=True), |
| nn.Conv2d(256, num_classes, 1) |
| ) |
| self._init_weight() |
|
|
| def forward(self, feature): |
| return self.classifier( feature['out'] ) |
|
|
| def _init_weight(self): |
| for m in self.modules(): |
| if isinstance(m, nn.Conv2d): |
| nn.init.kaiming_normal_(m.weight) |
| elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): |
| nn.init.constant_(m.weight, 1) |
| nn.init.constant_(m.bias, 0) |
|
|
| class AtrousSeparableConvolution(nn.Module): |
| """ Atrous Separable Convolution |
| """ |
| def __init__(self, in_channels, out_channels, kernel_size, |
| stride=1, padding=0, dilation=1, bias=True): |
| super(AtrousSeparableConvolution, self).__init__() |
| self.body = nn.Sequential( |
| |
| nn.Conv2d( in_channels, in_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias, groups=in_channels ), |
| |
| nn.Conv2d( in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=bias), |
| ) |
| |
| self._init_weight() |
|
|
| def forward(self, x): |
| return self.body(x) |
|
|
| def _init_weight(self): |
| for m in self.modules(): |
| if isinstance(m, nn.Conv2d): |
| nn.init.kaiming_normal_(m.weight) |
| elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): |
| nn.init.constant_(m.weight, 1) |
| nn.init.constant_(m.bias, 0) |
|
|
| class ASPPConv(nn.Sequential): |
| def __init__(self, in_channels, out_channels, dilation): |
| modules = [ |
| nn.Conv2d(in_channels, out_channels, 3, padding=dilation, dilation=dilation, bias=False), |
| nn.BatchNorm2d(out_channels), |
| nn.ReLU(inplace=True) |
| ] |
| super(ASPPConv, self).__init__(*modules) |
|
|
| class ASPPPooling(nn.Sequential): |
| def __init__(self, in_channels, out_channels): |
| super(ASPPPooling, self).__init__( |
| nn.AdaptiveAvgPool2d(1), |
| nn.Conv2d(in_channels, out_channels, 1, bias=False), |
| nn.BatchNorm2d(out_channels), |
| nn.ReLU(inplace=True)) |
|
|
| def forward(self, x): |
| size = x.shape[-2:] |
| x = super(ASPPPooling, self).forward(x) |
| return F.interpolate(x, size=size, mode='bilinear', align_corners=False) |
|
|
| class ASPP(nn.Module): |
| def __init__(self, in_channels, atrous_rates): |
| super(ASPP, self).__init__() |
| out_channels = 256 |
| modules = [] |
| modules.append(nn.Sequential( |
| nn.Conv2d(in_channels, out_channels, 1, bias=False), |
| nn.BatchNorm2d(out_channels), |
| nn.ReLU(inplace=True))) |
|
|
| rate1, rate2, rate3 = tuple(atrous_rates) |
| modules.append(ASPPConv(in_channels, out_channels, rate1)) |
| modules.append(ASPPConv(in_channels, out_channels, rate2)) |
| modules.append(ASPPConv(in_channels, out_channels, rate3)) |
| modules.append(ASPPPooling(in_channels, out_channels)) |
|
|
| self.convs = nn.ModuleList(modules) |
|
|
| self.project = nn.Sequential( |
| nn.Conv2d(5 * out_channels, out_channels, 1, bias=False), |
| nn.BatchNorm2d(out_channels), |
| nn.ReLU(inplace=True), |
| nn.Dropout(0.1),) |
|
|
| def forward(self, x): |
| res = [] |
| for conv in self.convs: |
| res.append(conv(x)) |
| res = torch.cat(res, dim=1) |
| return self.project(res) |
|
|
|
|
|
|
| def convert_to_separable_conv(module): |
| new_module = module |
| if isinstance(module, nn.Conv2d) and module.kernel_size[0]>1: |
| new_module = AtrousSeparableConvolution(module.in_channels, |
| module.out_channels, |
| module.kernel_size, |
| module.stride, |
| module.padding, |
| module.dilation, |
| module.bias) |
| for name, child in module.named_children(): |
| new_module.add_module(name, convert_to_separable_conv(child)) |
| return new_module |
|
|
|
|
| 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 |
|
|
| 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") |
| |
| 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 |
|
|
| 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.)) * groups |
| |
| 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, n_frames, block, layers, num_classes=1000, zero_init_residual=False, |
| groups=1, width_per_group=64, replace_stride_with_dilation=None, |
| norm_layer=None): |
| super(ResNet, self).__init__() |
| if norm_layer is None: |
| norm_layer = nn.BatchNorm2d |
| self._norm_layer = norm_layer |
|
|
| self.inplanes = 64 |
| self.dilation = 1 |
| if replace_stride_with_dilation is None: |
| |
| |
| 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(n_frames, 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, 64, layers[0]) |
| self.layer2 = self._make_layer(block, 128, layers[1], stride=2, |
| dilate=replace_stride_with_dilation[0]) |
| self.layer3 = self._make_layer(block, 256, layers[2], stride=2, |
| dilate=replace_stride_with_dilation[1]) |
| self.layer4 = self._make_layer(block, 512, layers[3], stride=2, |
| dilate=replace_stride_with_dilation[2]) |
| self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) |
| self.fc = nn.Linear(512 * block.expansion, num_classes) |
|
|
| 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) |
|
|
| |
| |
| |
| 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(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) |
|
|
| x = self.avgpool(x) |
| x = torch.flatten(x, 1) |
| x = self.fc(x) |
|
|
| return x |
|
|
|
|
| def _resnet(arch, n_frames, block, layers, pretrained, progress, **kwargs): |
| model = ResNet(n_frames, block, layers, **kwargs) |
| return model |
|
|
| def resnet101(n_frames, pretrained=False, progress=True, **kwargs): |
| r"""ResNet-101 model from |
| `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ |
| |
| Args: |
| pretrained (bool): If True, returns a model pre-trained on ImageNet |
| progress (bool): If True, displays a progress bar of the download to stderr |
| """ |
| return _resnet('resnet101', n_frames, Bottleneck, [3, 4, 23, 3], pretrained, progress, |
| **kwargs) |
|
|
|
|
| def _segm_resnet(name, backbone_name, num_classes, n_frames, output_stride, pretrained_backbone): |
|
|
| if output_stride==8: |
| replace_stride_with_dilation=[False, True, True] |
| aspp_dilate = [12, 24, 36] |
| else: |
| replace_stride_with_dilation=[False, False, True] |
| aspp_dilate = [6, 12, 18] |
|
|
| backbone = resnet101(n_frames, pretrained=pretrained_backbone, |
| replace_stride_with_dilation=replace_stride_with_dilation) |
| |
| inplanes = 2048 |
| low_level_planes = 256 |
|
|
| return_layers = {'layer4': 'out', 'layer1': 'low_level'} |
| classifier = DeepLabHeadV3Plus(inplanes, low_level_planes, num_classes, aspp_dilate) |
| backbone = IntermediateLayerGetter(backbone, return_layers=return_layers) |
|
|
| model = DeepLabV3(backbone, classifier) |
| return model |
|
|
| def DeepLabV3Plus_Res101(n_classes=21, n_frames=5, output_stride=8, pretrained_backbone=False): |
| """Constructs a DeepLabV3+ model with a ResNet-101 backbone. |
| |
| Args: |
| num_classes (int): number of classes. |
| output_stride (int): output stride for deeplab. |
| pretrained_backbone (bool): If True, use the pretrained backbone. |
| """ |
| return _segm_resnet('deeplabv3plus', 'resnet101', n_classes, n_frames, output_stride=output_stride, pretrained_backbone=pretrained_backbone) |
