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| from __future__ import absolute_import, division, print_function
|
| import logging
|
| import torch.nn as nn
|
|
|
| from detectron2.layers import ShapeSpec
|
| from detectron2.modeling.backbone import BACKBONE_REGISTRY
|
| from detectron2.modeling.backbone.backbone import Backbone
|
|
|
| BN_MOMENTUM = 0.1
|
| logger = logging.getLogger(__name__)
|
|
|
| __all__ = ["build_pose_hrnet_backbone", "PoseHigherResolutionNet"]
|
|
|
|
|
| def conv3x3(in_planes, out_planes, stride=1):
|
| """3x3 convolution with padding"""
|
| return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
|
|
|
|
|
| class BasicBlock(nn.Module):
|
| expansion = 1
|
|
|
| def __init__(self, inplanes, planes, stride=1, downsample=None):
|
| super(BasicBlock, self).__init__()
|
| self.conv1 = conv3x3(inplanes, planes, stride)
|
| self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
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| self.relu = nn.ReLU(inplace=True)
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| self.conv2 = conv3x3(planes, planes)
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| self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
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| self.downsample = downsample
|
| self.stride = stride
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|
|
| def forward(self, x):
|
| residual = x
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|
|
| out = self.conv1(x)
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| out = self.bn1(out)
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| out = self.relu(out)
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|
|
| out = self.conv2(out)
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| out = self.bn2(out)
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|
|
| if self.downsample is not None:
|
| residual = self.downsample(x)
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|
|
| out += residual
|
| out = self.relu(out)
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|
|
| return out
|
|
|
|
|
| class Bottleneck(nn.Module):
|
| expansion = 4
|
|
|
| def __init__(self, inplanes, planes, stride=1, downsample=None):
|
| super(Bottleneck, self).__init__()
|
| self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
|
| self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
|
| self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
|
| self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
|
| self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
|
| self.bn3 = nn.BatchNorm2d(planes * self.expansion, momentum=BN_MOMENTUM)
|
| 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)
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|
|
| out = self.conv3(out)
|
| out = self.bn3(out)
|
|
|
| if self.downsample is not None:
|
| residual = self.downsample(x)
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|
|
| out += residual
|
| out = self.relu(out)
|
|
|
| return out
|
|
|
|
|
| class HighResolutionModule(nn.Module):
|
| """HighResolutionModule
|
| Building block of the PoseHigherResolutionNet (see lower)
|
| arXiv: https://arxiv.org/abs/1908.10357
|
| Args:
|
| num_branches (int): number of branches of the modyle
|
| blocks (str): type of block of the module
|
| num_blocks (int): number of blocks of the module
|
| num_inchannels (int): number of input channels of the module
|
| num_channels (list): number of channels of each branch
|
| multi_scale_output (bool): only used by the last module of PoseHigherResolutionNet
|
| """
|
|
|
| def __init__(
|
| self,
|
| num_branches,
|
| blocks,
|
| num_blocks,
|
| num_inchannels,
|
| num_channels,
|
| multi_scale_output=True,
|
| ):
|
| super(HighResolutionModule, self).__init__()
|
| self._check_branches(num_branches, blocks, num_blocks, num_inchannels, num_channels)
|
|
|
| self.num_inchannels = num_inchannels
|
| self.num_branches = num_branches
|
|
|
| self.multi_scale_output = multi_scale_output
|
|
|
| self.branches = self._make_branches(num_branches, blocks, num_blocks, num_channels)
|
| self.fuse_layers = self._make_fuse_layers()
|
| self.relu = nn.ReLU(True)
|
|
|
| def _check_branches(self, num_branches, blocks, num_blocks, num_inchannels, num_channels):
|
| if num_branches != len(num_blocks):
|
| error_msg = "NUM_BRANCHES({}) <> NUM_BLOCKS({})".format(num_branches, len(num_blocks))
|
| logger.error(error_msg)
|
| raise ValueError(error_msg)
|
|
|
| if num_branches != len(num_channels):
|
| error_msg = "NUM_BRANCHES({}) <> NUM_CHANNELS({})".format(
|
| num_branches, len(num_channels)
|
| )
|
| logger.error(error_msg)
|
| raise ValueError(error_msg)
|
|
|
| if num_branches != len(num_inchannels):
|
| error_msg = "NUM_BRANCHES({}) <> NUM_INCHANNELS({})".format(
|
| num_branches, len(num_inchannels)
|
| )
|
| logger.error(error_msg)
|
| raise ValueError(error_msg)
|
|
|
| def _make_one_branch(self, branch_index, block, num_blocks, num_channels, stride=1):
|
| downsample = None
|
| if (
|
| stride != 1
|
| or self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion
|
| ):
|
| downsample = nn.Sequential(
|
| nn.Conv2d(
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| self.num_inchannels[branch_index],
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| num_channels[branch_index] * block.expansion,
|
| kernel_size=1,
|
| stride=stride,
|
| bias=False,
|
| ),
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| nn.BatchNorm2d(num_channels[branch_index] * block.expansion, momentum=BN_MOMENTUM),
|
| )
|
|
|
| layers = []
|
| layers.append(
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| block(self.num_inchannels[branch_index], num_channels[branch_index], stride, downsample)
|
| )
|
| self.num_inchannels[branch_index] = num_channels[branch_index] * block.expansion
|
| for _ in range(1, num_blocks[branch_index]):
|
| layers.append(block(self.num_inchannels[branch_index], num_channels[branch_index]))
|
|
|
| return nn.Sequential(*layers)
|
|
|
| def _make_branches(self, num_branches, block, num_blocks, num_channels):
|
| branches = []
|
|
|
| for i in range(num_branches):
|
| branches.append(self._make_one_branch(i, block, num_blocks, num_channels))
|
|
|
| return nn.ModuleList(branches)
|
|
|
| def _make_fuse_layers(self):
|
| if self.num_branches == 1:
|
| return None
|
|
|
| num_branches = self.num_branches
|
| num_inchannels = self.num_inchannels
|
| fuse_layers = []
|
| for i in range(num_branches if self.multi_scale_output else 1):
|
| fuse_layer = []
|
| for j in range(num_branches):
|
| if j > i:
|
| fuse_layer.append(
|
| nn.Sequential(
|
| nn.Conv2d(num_inchannels[j], num_inchannels[i], 1, 1, 0, bias=False),
|
| nn.BatchNorm2d(num_inchannels[i]),
|
| nn.Upsample(scale_factor=2 ** (j - i), mode="nearest"),
|
| )
|
| )
|
| elif j == i:
|
| fuse_layer.append(None)
|
| else:
|
| conv3x3s = []
|
| for k in range(i - j):
|
| if k == i - j - 1:
|
| num_outchannels_conv3x3 = num_inchannels[i]
|
| conv3x3s.append(
|
| nn.Sequential(
|
| nn.Conv2d(
|
| num_inchannels[j],
|
| num_outchannels_conv3x3,
|
| 3,
|
| 2,
|
| 1,
|
| bias=False,
|
| ),
|
| nn.BatchNorm2d(num_outchannels_conv3x3),
|
| )
|
| )
|
| else:
|
| num_outchannels_conv3x3 = num_inchannels[j]
|
| conv3x3s.append(
|
| nn.Sequential(
|
| nn.Conv2d(
|
| num_inchannels[j],
|
| num_outchannels_conv3x3,
|
| 3,
|
| 2,
|
| 1,
|
| bias=False,
|
| ),
|
| nn.BatchNorm2d(num_outchannels_conv3x3),
|
| nn.ReLU(True),
|
| )
|
| )
|
| fuse_layer.append(nn.Sequential(*conv3x3s))
|
| fuse_layers.append(nn.ModuleList(fuse_layer))
|
|
|
| return nn.ModuleList(fuse_layers)
|
|
|
| def get_num_inchannels(self):
|
| return self.num_inchannels
|
|
|
| def forward(self, x):
|
| if self.num_branches == 1:
|
| return [self.branches[0](x[0])]
|
|
|
| for i in range(self.num_branches):
|
| x[i] = self.branches[i](x[i])
|
|
|
| x_fuse = []
|
|
|
| for i in range(len(self.fuse_layers)):
|
| y = x[0] if i == 0 else self.fuse_layers[i][0](x[0])
|
| for j in range(1, self.num_branches):
|
| if i == j:
|
| y = y + x[j]
|
| else:
|
| z = self.fuse_layers[i][j](x[j])[:, :, : y.shape[2], : y.shape[3]]
|
| y = y + z
|
| x_fuse.append(self.relu(y))
|
|
|
| return x_fuse
|
|
|
|
|
| blocks_dict = {"BASIC": BasicBlock, "BOTTLENECK": Bottleneck}
|
|
|
|
|
| class PoseHigherResolutionNet(Backbone):
|
| """PoseHigherResolutionNet
|
| Composed of several HighResolutionModule tied together with ConvNets
|
| Adapted from the GitHub version to fit with HRFPN and the Detectron2 infrastructure
|
| arXiv: https://arxiv.org/abs/1908.10357
|
| """
|
|
|
| def __init__(self, cfg, **kwargs):
|
| self.inplanes = cfg.MODEL.HRNET.STEM_INPLANES
|
| super(PoseHigherResolutionNet, self).__init__()
|
|
|
|
|
| self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
|
| self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
|
| self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=False)
|
| self.bn2 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
|
| self.relu = nn.ReLU(inplace=True)
|
| self.layer1 = self._make_layer(Bottleneck, 64, 4)
|
|
|
| self.stage2_cfg = cfg.MODEL.HRNET.STAGE2
|
| num_channels = self.stage2_cfg.NUM_CHANNELS
|
| block = blocks_dict[self.stage2_cfg.BLOCK]
|
| num_channels = [num_channels[i] * block.expansion for i in range(len(num_channels))]
|
| self.transition1 = self._make_transition_layer([256], num_channels)
|
| self.stage2, pre_stage_channels = self._make_stage(self.stage2_cfg, num_channels)
|
|
|
| self.stage3_cfg = cfg.MODEL.HRNET.STAGE3
|
| num_channels = self.stage3_cfg.NUM_CHANNELS
|
| block = blocks_dict[self.stage3_cfg.BLOCK]
|
| num_channels = [num_channels[i] * block.expansion for i in range(len(num_channels))]
|
| self.transition2 = self._make_transition_layer(pre_stage_channels, num_channels)
|
| self.stage3, pre_stage_channels = self._make_stage(self.stage3_cfg, num_channels)
|
|
|
| self.stage4_cfg = cfg.MODEL.HRNET.STAGE4
|
| num_channels = self.stage4_cfg.NUM_CHANNELS
|
| block = blocks_dict[self.stage4_cfg.BLOCK]
|
| num_channels = [num_channels[i] * block.expansion for i in range(len(num_channels))]
|
| self.transition3 = self._make_transition_layer(pre_stage_channels, num_channels)
|
| self.stage4, pre_stage_channels = self._make_stage(
|
| self.stage4_cfg, num_channels, multi_scale_output=True
|
| )
|
|
|
| self._out_features = []
|
| self._out_feature_channels = {}
|
| self._out_feature_strides = {}
|
|
|
| for i in range(cfg.MODEL.HRNET.STAGE4.NUM_BRANCHES):
|
| self._out_features.append("p%d" % (i + 1))
|
| self._out_feature_channels.update(
|
| {self._out_features[-1]: cfg.MODEL.HRNET.STAGE4.NUM_CHANNELS[i]}
|
| )
|
| self._out_feature_strides.update({self._out_features[-1]: 1})
|
|
|
| def _get_deconv_cfg(self, deconv_kernel):
|
| if deconv_kernel == 4:
|
| padding = 1
|
| output_padding = 0
|
| elif deconv_kernel == 3:
|
| padding = 1
|
| output_padding = 1
|
| elif deconv_kernel == 2:
|
| padding = 0
|
| output_padding = 0
|
|
|
| return deconv_kernel, padding, output_padding
|
|
|
| def _make_transition_layer(self, num_channels_pre_layer, num_channels_cur_layer):
|
| num_branches_cur = len(num_channels_cur_layer)
|
| num_branches_pre = len(num_channels_pre_layer)
|
|
|
| transition_layers = []
|
| for i in range(num_branches_cur):
|
| if i < num_branches_pre:
|
| if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
|
| transition_layers.append(
|
| nn.Sequential(
|
| nn.Conv2d(
|
| num_channels_pre_layer[i],
|
| num_channels_cur_layer[i],
|
| 3,
|
| 1,
|
| 1,
|
| bias=False,
|
| ),
|
| nn.BatchNorm2d(num_channels_cur_layer[i]),
|
| nn.ReLU(inplace=True),
|
| )
|
| )
|
| else:
|
| transition_layers.append(None)
|
| else:
|
| conv3x3s = []
|
| for j in range(i + 1 - num_branches_pre):
|
| inchannels = num_channels_pre_layer[-1]
|
| outchannels = (
|
| num_channels_cur_layer[i] if j == i - num_branches_pre else inchannels
|
| )
|
| conv3x3s.append(
|
| nn.Sequential(
|
| nn.Conv2d(inchannels, outchannels, 3, 2, 1, bias=False),
|
| nn.BatchNorm2d(outchannels),
|
| nn.ReLU(inplace=True),
|
| )
|
| )
|
| transition_layers.append(nn.Sequential(*conv3x3s))
|
|
|
| return nn.ModuleList(transition_layers)
|
|
|
| def _make_layer(self, block, planes, blocks, stride=1):
|
| downsample = None
|
| if stride != 1 or self.inplanes != planes * block.expansion:
|
| downsample = nn.Sequential(
|
| nn.Conv2d(
|
| self.inplanes,
|
| planes * block.expansion,
|
| kernel_size=1,
|
| stride=stride,
|
| bias=False,
|
| ),
|
| nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
|
| )
|
|
|
| layers = []
|
| layers.append(block(self.inplanes, planes, stride, downsample))
|
| self.inplanes = planes * block.expansion
|
| for _ in range(1, blocks):
|
| layers.append(block(self.inplanes, planes))
|
|
|
| return nn.Sequential(*layers)
|
|
|
| def _make_stage(self, layer_config, num_inchannels, multi_scale_output=True):
|
| num_modules = layer_config["NUM_MODULES"]
|
| num_branches = layer_config["NUM_BRANCHES"]
|
| num_blocks = layer_config["NUM_BLOCKS"]
|
| num_channels = layer_config["NUM_CHANNELS"]
|
| block = blocks_dict[layer_config["BLOCK"]]
|
|
|
| modules = []
|
| for i in range(num_modules):
|
|
|
| if not multi_scale_output and i == num_modules - 1:
|
| reset_multi_scale_output = False
|
| else:
|
| reset_multi_scale_output = True
|
|
|
| modules.append(
|
| HighResolutionModule(
|
| num_branches,
|
| block,
|
| num_blocks,
|
| num_inchannels,
|
| num_channels,
|
| reset_multi_scale_output,
|
| )
|
| )
|
| num_inchannels = modules[-1].get_num_inchannels()
|
|
|
| return nn.Sequential(*modules), num_inchannels
|
|
|
| def forward(self, x):
|
| x = self.conv1(x)
|
| x = self.bn1(x)
|
| x = self.relu(x)
|
| x = self.conv2(x)
|
| x = self.bn2(x)
|
| x = self.relu(x)
|
| x = self.layer1(x)
|
|
|
| x_list = []
|
| for i in range(self.stage2_cfg.NUM_BRANCHES):
|
| if self.transition1[i] is not None:
|
| x_list.append(self.transition1[i](x))
|
| else:
|
| x_list.append(x)
|
| y_list = self.stage2(x_list)
|
|
|
| x_list = []
|
| for i in range(self.stage3_cfg.NUM_BRANCHES):
|
| if self.transition2[i] is not None:
|
| x_list.append(self.transition2[i](y_list[-1]))
|
| else:
|
| x_list.append(y_list[i])
|
| y_list = self.stage3(x_list)
|
|
|
| x_list = []
|
| for i in range(self.stage4_cfg.NUM_BRANCHES):
|
| if self.transition3[i] is not None:
|
| x_list.append(self.transition3[i](y_list[-1]))
|
| else:
|
| x_list.append(y_list[i])
|
| y_list = self.stage4(x_list)
|
|
|
| assert len(self._out_features) == len(y_list)
|
| return dict(zip(self._out_features, y_list))
|
|
|
|
|
| @BACKBONE_REGISTRY.register()
|
| def build_pose_hrnet_backbone(cfg, input_shape: ShapeSpec):
|
| model = PoseHigherResolutionNet(cfg)
|
| return model
|
|
|
