mirror of https://github.com/hero-y/BHRL
565 lines
21 KiB
Python
565 lines
21 KiB
Python
import warnings
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import torch.nn as nn
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from mmcv.cnn import build_conv_layer, build_norm_layer
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from mmcv.runner import BaseModule, ModuleList, Sequential
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from torch.nn.modules.batchnorm import _BatchNorm
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from ..builder import BACKBONES
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from .resnet import BasicBlock, Bottleneck
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class HRModule(BaseModule):
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"""High-Resolution Module for HRNet.
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In this module, every branch has 4 BasicBlocks/Bottlenecks. Fusion/Exchange
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is in this module.
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"""
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def __init__(self,
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num_branches,
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blocks,
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num_blocks,
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in_channels,
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num_channels,
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multiscale_output=True,
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with_cp=False,
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conv_cfg=None,
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norm_cfg=dict(type='BN'),
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block_init_cfg=None,
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init_cfg=None):
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super(HRModule, self).__init__(init_cfg)
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self.block_init_cfg = block_init_cfg
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self._check_branches(num_branches, num_blocks, in_channels,
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num_channels)
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self.in_channels = in_channels
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self.num_branches = num_branches
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self.multiscale_output = multiscale_output
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self.norm_cfg = norm_cfg
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self.conv_cfg = conv_cfg
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self.with_cp = with_cp
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self.branches = self._make_branches(num_branches, blocks, num_blocks,
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num_channels)
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self.fuse_layers = self._make_fuse_layers()
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self.relu = nn.ReLU(inplace=False)
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def _check_branches(self, num_branches, num_blocks, in_channels,
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num_channels):
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if num_branches != len(num_blocks):
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error_msg = f'NUM_BRANCHES({num_branches}) ' \
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f'!= NUM_BLOCKS({len(num_blocks)})'
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raise ValueError(error_msg)
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if num_branches != len(num_channels):
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error_msg = f'NUM_BRANCHES({num_branches}) ' \
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f'!= NUM_CHANNELS({len(num_channels)})'
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raise ValueError(error_msg)
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if num_branches != len(in_channels):
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error_msg = f'NUM_BRANCHES({num_branches}) ' \
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f'!= NUM_INCHANNELS({len(in_channels)})'
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raise ValueError(error_msg)
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def _make_one_branch(self,
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branch_index,
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block,
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num_blocks,
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num_channels,
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stride=1):
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downsample = None
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if stride != 1 or \
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self.in_channels[branch_index] != \
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num_channels[branch_index] * block.expansion:
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downsample = nn.Sequential(
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build_conv_layer(
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self.conv_cfg,
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self.in_channels[branch_index],
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num_channels[branch_index] * block.expansion,
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kernel_size=1,
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stride=stride,
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bias=False),
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build_norm_layer(self.norm_cfg, num_channels[branch_index] *
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block.expansion)[1])
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layers = []
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layers.append(
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block(
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self.in_channels[branch_index],
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num_channels[branch_index],
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stride,
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downsample=downsample,
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with_cp=self.with_cp,
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norm_cfg=self.norm_cfg,
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conv_cfg=self.conv_cfg,
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init_cfg=self.block_init_cfg))
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self.in_channels[branch_index] = \
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num_channels[branch_index] * block.expansion
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for i in range(1, num_blocks[branch_index]):
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layers.append(
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block(
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self.in_channels[branch_index],
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num_channels[branch_index],
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with_cp=self.with_cp,
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norm_cfg=self.norm_cfg,
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conv_cfg=self.conv_cfg,
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init_cfg=self.block_init_cfg))
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return Sequential(*layers)
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def _make_branches(self, num_branches, block, num_blocks, num_channels):
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branches = []
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for i in range(num_branches):
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branches.append(
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self._make_one_branch(i, block, num_blocks, num_channels))
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return ModuleList(branches)
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def _make_fuse_layers(self):
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if self.num_branches == 1:
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return None
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num_branches = self.num_branches
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in_channels = self.in_channels
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fuse_layers = []
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num_out_branches = num_branches if self.multiscale_output else 1
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for i in range(num_out_branches):
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fuse_layer = []
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for j in range(num_branches):
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if j > i:
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fuse_layer.append(
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nn.Sequential(
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build_conv_layer(
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self.conv_cfg,
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in_channels[j],
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in_channels[i],
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kernel_size=1,
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stride=1,
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padding=0,
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bias=False),
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build_norm_layer(self.norm_cfg, in_channels[i])[1],
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nn.Upsample(
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scale_factor=2**(j - i), mode='nearest')))
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elif j == i:
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fuse_layer.append(None)
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else:
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conv_downsamples = []
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for k in range(i - j):
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if k == i - j - 1:
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conv_downsamples.append(
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nn.Sequential(
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build_conv_layer(
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self.conv_cfg,
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in_channels[j],
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in_channels[i],
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kernel_size=3,
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stride=2,
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padding=1,
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bias=False),
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build_norm_layer(self.norm_cfg,
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in_channels[i])[1]))
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else:
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conv_downsamples.append(
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nn.Sequential(
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build_conv_layer(
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self.conv_cfg,
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in_channels[j],
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in_channels[j],
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kernel_size=3,
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stride=2,
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padding=1,
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bias=False),
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build_norm_layer(self.norm_cfg,
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in_channels[j])[1],
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nn.ReLU(inplace=False)))
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fuse_layer.append(nn.Sequential(*conv_downsamples))
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fuse_layers.append(nn.ModuleList(fuse_layer))
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return nn.ModuleList(fuse_layers)
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def forward(self, x):
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"""Forward function."""
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if self.num_branches == 1:
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return [self.branches[0](x[0])]
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for i in range(self.num_branches):
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x[i] = self.branches[i](x[i])
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x_fuse = []
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for i in range(len(self.fuse_layers)):
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y = 0
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for j in range(self.num_branches):
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if i == j:
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y += x[j]
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else:
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y += self.fuse_layers[i][j](x[j])
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x_fuse.append(self.relu(y))
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return x_fuse
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@BACKBONES.register_module()
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class HRNet(BaseModule):
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"""HRNet backbone.
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High-Resolution Representations for Labeling Pixels and Regions
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arXiv: https://arxiv.org/abs/1904.04514
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Args:
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extra (dict): detailed configuration for each stage of HRNet.
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in_channels (int): Number of input image channels. Default: 3.
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conv_cfg (dict): dictionary to construct and config conv layer.
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norm_cfg (dict): dictionary to construct and config norm layer.
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norm_eval (bool): Whether to set norm layers to eval mode, namely,
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freeze running stats (mean and var). Note: Effect on Batch Norm
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and its variants only.
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with_cp (bool): Use checkpoint or not. Using checkpoint will save some
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memory while slowing down the training speed.
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zero_init_residual (bool): whether to use zero init for last norm layer
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in resblocks to let them behave as identity.
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pretrained (str, optional): model pretrained path. Default: None
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init_cfg (dict or list[dict], optional): Initialization config dict.
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Default: None
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Example:
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>>> from mmdet.models import HRNet
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>>> import torch
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>>> extra = dict(
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>>> stage1=dict(
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>>> num_modules=1,
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>>> num_branches=1,
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>>> block='BOTTLENECK',
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>>> num_blocks=(4, ),
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>>> num_channels=(64, )),
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>>> stage2=dict(
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>>> num_modules=1,
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>>> num_branches=2,
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>>> block='BASIC',
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>>> num_blocks=(4, 4),
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>>> num_channels=(32, 64)),
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>>> stage3=dict(
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>>> num_modules=4,
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>>> num_branches=3,
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>>> block='BASIC',
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>>> num_blocks=(4, 4, 4),
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>>> num_channels=(32, 64, 128)),
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>>> stage4=dict(
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>>> num_modules=3,
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>>> num_branches=4,
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>>> block='BASIC',
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>>> num_blocks=(4, 4, 4, 4),
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>>> num_channels=(32, 64, 128, 256)))
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>>> self = HRNet(extra, in_channels=1)
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>>> self.eval()
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>>> inputs = torch.rand(1, 1, 32, 32)
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>>> level_outputs = self.forward(inputs)
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>>> for level_out in level_outputs:
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... print(tuple(level_out.shape))
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(1, 32, 8, 8)
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(1, 64, 4, 4)
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(1, 128, 2, 2)
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(1, 256, 1, 1)
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"""
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blocks_dict = {'BASIC': BasicBlock, 'BOTTLENECK': Bottleneck}
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def __init__(self,
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extra,
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in_channels=3,
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conv_cfg=None,
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norm_cfg=dict(type='BN'),
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norm_eval=True,
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with_cp=False,
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zero_init_residual=False,
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pretrained=None,
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init_cfg=None):
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super(HRNet, self).__init__(init_cfg)
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self.pretrained = pretrained
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assert not (init_cfg and pretrained), \
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'init_cfg and pretrained cannot be setting at the same time'
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if isinstance(pretrained, str):
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warnings.warn('DeprecationWarning: pretrained is a deprecated, '
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'please use "init_cfg" instead')
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self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
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elif pretrained is None:
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if init_cfg is None:
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self.init_cfg = [
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dict(type='Kaiming', layer='Conv2d'),
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dict(
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type='Constant',
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val=1,
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layer=['_BatchNorm', 'GroupNorm'])
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]
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else:
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raise TypeError('pretrained must be a str or None')
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self.extra = extra
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self.conv_cfg = conv_cfg
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self.norm_cfg = norm_cfg
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self.norm_eval = norm_eval
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self.with_cp = with_cp
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self.zero_init_residual = zero_init_residual
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# stem net
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self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, 64, postfix=1)
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self.norm2_name, norm2 = build_norm_layer(self.norm_cfg, 64, postfix=2)
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self.conv1 = build_conv_layer(
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self.conv_cfg,
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in_channels,
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64,
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kernel_size=3,
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stride=2,
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padding=1,
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bias=False)
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self.add_module(self.norm1_name, norm1)
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self.conv2 = build_conv_layer(
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self.conv_cfg,
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64,
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64,
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kernel_size=3,
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stride=2,
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padding=1,
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bias=False)
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self.add_module(self.norm2_name, norm2)
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self.relu = nn.ReLU(inplace=True)
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# stage 1
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self.stage1_cfg = self.extra['stage1']
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num_channels = self.stage1_cfg['num_channels'][0]
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block_type = self.stage1_cfg['block']
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num_blocks = self.stage1_cfg['num_blocks'][0]
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block = self.blocks_dict[block_type]
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stage1_out_channels = num_channels * block.expansion
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self.layer1 = self._make_layer(block, 64, num_channels, num_blocks)
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# stage 2
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self.stage2_cfg = self.extra['stage2']
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num_channels = self.stage2_cfg['num_channels']
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block_type = self.stage2_cfg['block']
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block = self.blocks_dict[block_type]
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num_channels = [channel * block.expansion for channel in num_channels]
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self.transition1 = self._make_transition_layer([stage1_out_channels],
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num_channels)
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self.stage2, pre_stage_channels = self._make_stage(
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self.stage2_cfg, num_channels)
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# stage 3
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self.stage3_cfg = self.extra['stage3']
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num_channels = self.stage3_cfg['num_channels']
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block_type = self.stage3_cfg['block']
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block = self.blocks_dict[block_type]
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num_channels = [channel * block.expansion for channel in num_channels]
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self.transition2 = self._make_transition_layer(pre_stage_channels,
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num_channels)
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self.stage3, pre_stage_channels = self._make_stage(
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self.stage3_cfg, num_channels)
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# stage 4
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self.stage4_cfg = self.extra['stage4']
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num_channels = self.stage4_cfg['num_channels']
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block_type = self.stage4_cfg['block']
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block = self.blocks_dict[block_type]
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num_channels = [channel * block.expansion for channel in num_channels]
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self.transition3 = self._make_transition_layer(pre_stage_channels,
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num_channels)
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self.stage4, pre_stage_channels = self._make_stage(
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self.stage4_cfg, num_channels)
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@property
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def norm1(self):
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"""nn.Module: the normalization layer named "norm1" """
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return getattr(self, self.norm1_name)
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@property
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def norm2(self):
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"""nn.Module: the normalization layer named "norm2" """
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return getattr(self, self.norm2_name)
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def _make_transition_layer(self, num_channels_pre_layer,
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num_channels_cur_layer):
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num_branches_cur = len(num_channels_cur_layer)
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num_branches_pre = len(num_channels_pre_layer)
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transition_layers = []
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for i in range(num_branches_cur):
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if i < num_branches_pre:
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if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
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transition_layers.append(
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nn.Sequential(
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build_conv_layer(
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self.conv_cfg,
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num_channels_pre_layer[i],
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num_channels_cur_layer[i],
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kernel_size=3,
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stride=1,
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padding=1,
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bias=False),
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build_norm_layer(self.norm_cfg,
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num_channels_cur_layer[i])[1],
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nn.ReLU(inplace=True)))
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else:
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transition_layers.append(None)
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else:
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conv_downsamples = []
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for j in range(i + 1 - num_branches_pre):
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in_channels = num_channels_pre_layer[-1]
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out_channels = num_channels_cur_layer[i] \
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if j == i - num_branches_pre else in_channels
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conv_downsamples.append(
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nn.Sequential(
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build_conv_layer(
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self.conv_cfg,
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in_channels,
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out_channels,
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kernel_size=3,
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stride=2,
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padding=1,
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bias=False),
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build_norm_layer(self.norm_cfg, out_channels)[1],
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nn.ReLU(inplace=True)))
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transition_layers.append(nn.Sequential(*conv_downsamples))
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return nn.ModuleList(transition_layers)
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def _make_layer(self, block, inplanes, planes, blocks, stride=1):
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downsample = None
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if stride != 1 or inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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build_conv_layer(
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self.conv_cfg,
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inplanes,
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planes * block.expansion,
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kernel_size=1,
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stride=stride,
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bias=False),
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build_norm_layer(self.norm_cfg, planes * block.expansion)[1])
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layers = []
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block_init_cfg = None
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if self.pretrained is None and not hasattr(
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self, 'init_cfg') and self.zero_init_residual:
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if block is BasicBlock:
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block_init_cfg = dict(
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type='Constant', val=0, override=dict(name='norm2'))
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elif block is Bottleneck:
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block_init_cfg = dict(
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type='Constant', val=0, override=dict(name='norm3'))
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layers.append(
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block(
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inplanes,
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planes,
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stride,
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downsample=downsample,
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with_cp=self.with_cp,
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norm_cfg=self.norm_cfg,
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conv_cfg=self.conv_cfg,
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init_cfg=block_init_cfg,
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))
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inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(
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block(
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inplanes,
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planes,
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with_cp=self.with_cp,
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norm_cfg=self.norm_cfg,
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conv_cfg=self.conv_cfg,
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init_cfg=block_init_cfg))
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return Sequential(*layers)
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def _make_stage(self, layer_config, in_channels, multiscale_output=True):
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num_modules = layer_config['num_modules']
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num_branches = layer_config['num_branches']
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num_blocks = layer_config['num_blocks']
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num_channels = layer_config['num_channels']
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block = self.blocks_dict[layer_config['block']]
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hr_modules = []
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block_init_cfg = None
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if self.pretrained is None and not hasattr(
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self, 'init_cfg') and self.zero_init_residual:
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if block is BasicBlock:
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block_init_cfg = dict(
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type='Constant', val=0, override=dict(name='norm2'))
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elif block is Bottleneck:
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block_init_cfg = dict(
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type='Constant', val=0, override=dict(name='norm3'))
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for i in range(num_modules):
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# multi_scale_output is only used for the last module
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if not multiscale_output and i == num_modules - 1:
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reset_multiscale_output = False
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else:
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reset_multiscale_output = True
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hr_modules.append(
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HRModule(
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num_branches,
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block,
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num_blocks,
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in_channels,
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num_channels,
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reset_multiscale_output,
|
|
with_cp=self.with_cp,
|
|
norm_cfg=self.norm_cfg,
|
|
conv_cfg=self.conv_cfg,
|
|
block_init_cfg=block_init_cfg))
|
|
|
|
return Sequential(*hr_modules), in_channels
|
|
|
|
def forward(self, x):
|
|
"""Forward function."""
|
|
x = self.conv1(x)
|
|
x = self.norm1(x)
|
|
x = self.relu(x)
|
|
x = self.conv2(x)
|
|
x = self.norm2(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)
|
|
|
|
return y_list
|
|
|
|
def train(self, mode=True):
|
|
"""Convert the model into training mode will keeping the normalization
|
|
layer freezed."""
|
|
super(HRNet, self).train(mode)
|
|
if mode and self.norm_eval:
|
|
for m in self.modules():
|
|
# trick: eval have effect on BatchNorm only
|
|
if isinstance(m, _BatchNorm):
|
|
m.eval()
|