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import torch.nn as nn |
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from mmcv.cnn import ConvModule |
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from ..builder import BACKBONES |
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from ..utils import ResLayer |
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from .resnet import BasicBlock |
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class HourglassModule(nn.Module): |
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"""Hourglass Module for HourglassNet backbone. |
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Generate module recursively and use BasicBlock as the base unit. |
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Args: |
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depth (int): Depth of current HourglassModule. |
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stage_channels (list[int]): Feature channels of sub-modules in current |
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and follow-up HourglassModule. |
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stage_blocks (list[int]): Number of sub-modules stacked in current and |
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follow-up HourglassModule. |
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norm_cfg (dict): Dictionary to construct and config norm layer. |
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""" |
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def __init__(self, |
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depth, |
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stage_channels, |
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stage_blocks, |
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norm_cfg=dict(type='BN', requires_grad=True)): |
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super(HourglassModule, self).__init__() |
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self.depth = depth |
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cur_block = stage_blocks[0] |
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next_block = stage_blocks[1] |
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cur_channel = stage_channels[0] |
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next_channel = stage_channels[1] |
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self.up1 = ResLayer( |
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BasicBlock, cur_channel, cur_channel, cur_block, norm_cfg=norm_cfg) |
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self.low1 = ResLayer( |
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BasicBlock, |
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cur_channel, |
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next_channel, |
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cur_block, |
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stride=2, |
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norm_cfg=norm_cfg) |
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if self.depth > 1: |
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self.low2 = HourglassModule(depth - 1, stage_channels[1:], |
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stage_blocks[1:]) |
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else: |
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self.low2 = ResLayer( |
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BasicBlock, |
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next_channel, |
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next_channel, |
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next_block, |
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norm_cfg=norm_cfg) |
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self.low3 = ResLayer( |
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BasicBlock, |
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next_channel, |
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cur_channel, |
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cur_block, |
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norm_cfg=norm_cfg, |
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downsample_first=False) |
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self.up2 = nn.Upsample(scale_factor=2) |
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def forward(self, x): |
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"""Forward function.""" |
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up1 = self.up1(x) |
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low1 = self.low1(x) |
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low2 = self.low2(low1) |
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low3 = self.low3(low2) |
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up2 = self.up2(low3) |
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return up1 + up2 |
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@BACKBONES.register_module() |
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class HourglassNet(nn.Module): |
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"""HourglassNet backbone. |
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Stacked Hourglass Networks for Human Pose Estimation. |
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More details can be found in the `paper |
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<https://arxiv.org/abs/1603.06937>`_ . |
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Args: |
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downsample_times (int): Downsample times in a HourglassModule. |
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num_stacks (int): Number of HourglassModule modules stacked, |
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1 for Hourglass-52, 2 for Hourglass-104. |
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stage_channels (list[int]): Feature channel of each sub-module in a |
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HourglassModule. |
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stage_blocks (list[int]): Number of sub-modules stacked in a |
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HourglassModule. |
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feat_channel (int): Feature channel of conv after a HourglassModule. |
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norm_cfg (dict): Dictionary to construct and config norm layer. |
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Example: |
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>>> from mmdet.models import HourglassNet |
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>>> import torch |
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>>> self = HourglassNet() |
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>>> self.eval() |
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>>> inputs = torch.rand(1, 3, 511, 511) |
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>>> level_outputs = self.forward(inputs) |
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>>> for level_output in level_outputs: |
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... print(tuple(level_output.shape)) |
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(1, 256, 128, 128) |
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(1, 256, 128, 128) |
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""" |
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def __init__(self, |
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downsample_times=5, |
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num_stacks=2, |
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stage_channels=(256, 256, 384, 384, 384, 512), |
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stage_blocks=(2, 2, 2, 2, 2, 4), |
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feat_channel=256, |
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norm_cfg=dict(type='BN', requires_grad=True)): |
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super(HourglassNet, self).__init__() |
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self.num_stacks = num_stacks |
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assert self.num_stacks >= 1 |
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assert len(stage_channels) == len(stage_blocks) |
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assert len(stage_channels) > downsample_times |
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cur_channel = stage_channels[0] |
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self.stem = nn.Sequential( |
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ConvModule(3, 128, 7, padding=3, stride=2, norm_cfg=norm_cfg), |
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ResLayer(BasicBlock, 128, 256, 1, stride=2, norm_cfg=norm_cfg)) |
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self.hourglass_modules = nn.ModuleList([ |
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HourglassModule(downsample_times, stage_channels, stage_blocks) |
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for _ in range(num_stacks) |
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]) |
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self.inters = ResLayer( |
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BasicBlock, |
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cur_channel, |
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cur_channel, |
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num_stacks - 1, |
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norm_cfg=norm_cfg) |
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self.conv1x1s = nn.ModuleList([ |
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ConvModule( |
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cur_channel, cur_channel, 1, norm_cfg=norm_cfg, act_cfg=None) |
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for _ in range(num_stacks - 1) |
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]) |
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self.out_convs = nn.ModuleList([ |
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ConvModule( |
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cur_channel, feat_channel, 3, padding=1, norm_cfg=norm_cfg) |
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for _ in range(num_stacks) |
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]) |
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self.remap_convs = nn.ModuleList([ |
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ConvModule( |
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feat_channel, cur_channel, 1, norm_cfg=norm_cfg, act_cfg=None) |
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for _ in range(num_stacks - 1) |
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]) |
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self.relu = nn.ReLU(inplace=True) |
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def init_weights(self, pretrained=None): |
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"""Init module weights. |
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We do nothing in this function because all modules we used |
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(ConvModule, BasicBlock and etc.) have default initialization, and |
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currently we don't provide pretrained model of HourglassNet. |
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Detector's __init__() will call backbone's init_weights() with |
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pretrained as input, so we keep this function. |
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""" |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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m.reset_parameters() |
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def forward(self, x): |
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"""Forward function.""" |
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inter_feat = self.stem(x) |
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out_feats = [] |
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for ind in range(self.num_stacks): |
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single_hourglass = self.hourglass_modules[ind] |
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out_conv = self.out_convs[ind] |
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hourglass_feat = single_hourglass(inter_feat) |
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out_feat = out_conv(hourglass_feat) |
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out_feats.append(out_feat) |
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if ind < self.num_stacks - 1: |
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inter_feat = self.conv1x1s[ind]( |
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inter_feat) + self.remap_convs[ind]( |
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out_feat) |
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inter_feat = self.inters[ind](self.relu(inter_feat)) |
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return out_feats |
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