564 lines
23 KiB
Python
564 lines
23 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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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 mmengine.model import BaseModule, ModuleList, Sequential
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from torch.nn.modules.batchnorm import _BatchNorm
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from mmpretrain.registry import MODELS
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from .resnet import BasicBlock, Bottleneck, ResLayer, get_expansion
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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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Args:
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num_branches (int): The number of branches.
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block (``BaseModule``): Convolution block module.
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num_blocks (tuple): The number of blocks in each branch.
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The length must be equal to ``num_branches``.
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num_channels (tuple): The number of base channels in each branch.
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The length must be equal to ``num_branches``.
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multiscale_output (bool): Whether to output multi-level features
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produced by multiple branches. If False, only the first level
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feature will be output. Defaults to True.
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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. Defaults to False.
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conv_cfg (dict, optional): Dictionary to construct and config conv
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layer. Defaults to None.
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norm_cfg (dict): Dictionary to construct and config norm layer.
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Defaults to ``dict(type='BN')``.
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block_init_cfg (dict, optional): The initialization configs of every
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blocks. Defaults to None.
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init_cfg (dict or list[dict], optional): Initialization config dict.
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Defaults to None.
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"""
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def __init__(self,
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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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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, block, 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_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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out_channels = num_channels[i] * get_expansion(block)
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branches.append(
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ResLayer(
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block=block,
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num_blocks=num_blocks[i],
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in_channels=self.in_channels[i],
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out_channels=out_channels,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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with_cp=self.with_cp,
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init_cfg=self.block_init_cfg,
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))
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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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# Upsample the feature maps of smaller scales.
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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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# Keep the feature map with the same scale.
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fuse_layer.append(None)
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else:
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# Downsample the feature maps of larger scales.
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conv_downsamples = []
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for k in range(i - j):
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# Use stacked convolution layers to downsample.
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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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@MODELS.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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<https://arxiv.org/abs/1904.04514>`_.
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Args:
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arch (str): The preset HRNet architecture, includes 'w18', 'w30',
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'w32', 'w40', 'w44', 'w48', 'w64'. It will only be used if
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extra is ``None``. Defaults to 'w32'.
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extra (dict, optional): Detailed configuration for each stage of HRNet.
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There must be 4 stages, the configuration for each stage must have
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5 keys:
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- num_modules (int): The number of HRModule in this stage.
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- num_branches (int): The number of branches in the HRModule.
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- block (str): The type of convolution block. Please choose between
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'BOTTLENECK' and 'BASIC'.
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- num_blocks (tuple): The number of blocks in each branch.
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The length must be equal to num_branches.
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- num_channels (tuple): The number of base channels in each branch.
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The length must be equal to num_branches.
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Defaults to None.
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in_channels (int): Number of input image channels. Defaults to 3.
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conv_cfg (dict, optional): Dictionary to construct and config conv
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layer. Defaults to None.
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norm_cfg (dict): Dictionary to construct and config norm layer.
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Defaults to ``dict(type='BN')``.
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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. Defaults to False.
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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. Defaults to False.
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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. Defaults to False.
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multiscale_output (bool): Whether to output multi-level features
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produced by multiple branches. If False, only the first level
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feature will be output. Defaults to True.
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init_cfg (dict or list[dict], optional): Initialization config dict.
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Defaults to None.
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Example:
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>>> import torch
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>>> from mmpretrain.models import HRNet
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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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arch_zoo = {
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# num_modules, num_branches, block, num_blocks, num_channels
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'w18': [[1, 1, 'BOTTLENECK', (4, ), (64, )],
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[1, 2, 'BASIC', (4, 4), (18, 36)],
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[4, 3, 'BASIC', (4, 4, 4), (18, 36, 72)],
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[3, 4, 'BASIC', (4, 4, 4, 4), (18, 36, 72, 144)]],
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'w30': [[1, 1, 'BOTTLENECK', (4, ), (64, )],
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[1, 2, 'BASIC', (4, 4), (30, 60)],
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[4, 3, 'BASIC', (4, 4, 4), (30, 60, 120)],
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[3, 4, 'BASIC', (4, 4, 4, 4), (30, 60, 120, 240)]],
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'w32': [[1, 1, 'BOTTLENECK', (4, ), (64, )],
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[1, 2, 'BASIC', (4, 4), (32, 64)],
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[4, 3, 'BASIC', (4, 4, 4), (32, 64, 128)],
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[3, 4, 'BASIC', (4, 4, 4, 4), (32, 64, 128, 256)]],
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'w40': [[1, 1, 'BOTTLENECK', (4, ), (64, )],
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[1, 2, 'BASIC', (4, 4), (40, 80)],
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[4, 3, 'BASIC', (4, 4, 4), (40, 80, 160)],
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[3, 4, 'BASIC', (4, 4, 4, 4), (40, 80, 160, 320)]],
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'w44': [[1, 1, 'BOTTLENECK', (4, ), (64, )],
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[1, 2, 'BASIC', (4, 4), (44, 88)],
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[4, 3, 'BASIC', (4, 4, 4), (44, 88, 176)],
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[3, 4, 'BASIC', (4, 4, 4, 4), (44, 88, 176, 352)]],
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'w48': [[1, 1, 'BOTTLENECK', (4, ), (64, )],
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[1, 2, 'BASIC', (4, 4), (48, 96)],
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[4, 3, 'BASIC', (4, 4, 4), (48, 96, 192)],
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[3, 4, 'BASIC', (4, 4, 4, 4), (48, 96, 192, 384)]],
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'w64': [[1, 1, 'BOTTLENECK', (4, ), (64, )],
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[1, 2, 'BASIC', (4, 4), (64, 128)],
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[4, 3, 'BASIC', (4, 4, 4), (64, 128, 256)],
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[3, 4, 'BASIC', (4, 4, 4, 4), (64, 128, 256, 512)]],
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} # yapf:disable
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def __init__(self,
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arch='w32',
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extra=None,
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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=False,
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with_cp=False,
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zero_init_residual=False,
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multiscale_output=True,
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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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super(HRNet, self).__init__(init_cfg)
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extra = self.parse_arch(arch, extra)
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# Assert configurations of 4 stages are in extra
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for i in range(1, 5):
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assert f'stage{i}' in extra, f'Missing stage{i} config in "extra".'
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# Assert whether the length of `num_blocks` and `num_channels` are
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# equal to `num_branches`
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cfg = extra[f'stage{i}']
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assert len(cfg['num_blocks']) == cfg['num_branches'] and \
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len(cfg['num_channels']) == cfg['num_branches']
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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.conv1 = build_conv_layer(
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self.conv_cfg,
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in_channels,
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out_channels=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.norm1_name, norm1 = build_norm_layer(self.norm_cfg, 64, postfix=1)
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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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in_channels=64,
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out_channels=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.norm2_name, norm2 = build_norm_layer(self.norm_cfg, 64, postfix=2)
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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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base_channels = self.stage1_cfg['num_channels']
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block_type = self.stage1_cfg['block']
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num_blocks = self.stage1_cfg['num_blocks']
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block = self.blocks_dict[block_type]
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num_channels = [
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channel * get_expansion(block) for channel in base_channels
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]
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# To align with the original code, use layer1 instead of stage1 here.
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self.layer1 = ResLayer(
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block,
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in_channels=64,
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out_channels=num_channels[0],
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num_blocks=num_blocks[0])
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pre_num_channels = num_channels
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# -------------------- stage 2~4 --------------------
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for i in range(2, 5):
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stage_cfg = self.extra[f'stage{i}']
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base_channels = stage_cfg['num_channels']
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block = self.blocks_dict[stage_cfg['block']]
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multiscale_output_ = multiscale_output if i == 4 else True
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num_channels = [
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channel * get_expansion(block) for channel in base_channels
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]
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# The transition layer from layer1 to stage2
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transition = self._make_transition_layer(pre_num_channels,
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num_channels)
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self.add_module(f'transition{i-1}', transition)
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stage = self._make_stage(
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stage_cfg, num_channels, multiscale_output=multiscale_output_)
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self.add_module(f'stage{i}', stage)
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pre_num_channels = 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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# For existing scale branches,
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# add conv block when the channels are not the same.
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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(nn.Identity())
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else:
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# For new scale branches, add stacked downsample conv blocks.
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# For example, num_branches_pre = 2, for the 4th branch, add
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# stacked two downsample conv blocks.
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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_stage(self, layer_config, in_channels, multiscale_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 = self.blocks_dict[layer_config['block']]
|
|
|
|
hr_modules = []
|
|
block_init_cfg = None
|
|
if self.zero_init_residual:
|
|
if block is BasicBlock:
|
|
block_init_cfg = dict(
|
|
type='Constant', val=0, override=dict(name='norm2'))
|
|
elif block is Bottleneck:
|
|
block_init_cfg = dict(
|
|
type='Constant', val=0, override=dict(name='norm3'))
|
|
|
|
for i in range(num_modules):
|
|
# multi_scale_output is only used for the last module
|
|
if not multiscale_output and i == num_modules - 1:
|
|
reset_multiscale_output = False
|
|
else:
|
|
reset_multiscale_output = True
|
|
|
|
hr_modules.append(
|
|
HRModule(
|
|
num_branches,
|
|
block,
|
|
num_blocks,
|
|
in_channels,
|
|
num_channels,
|
|
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)
|
|
|
|
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 = [x]
|
|
|
|
for i in range(2, 5):
|
|
# Apply transition
|
|
transition = getattr(self, f'transition{i-1}')
|
|
inputs = []
|
|
for j, layer in enumerate(transition):
|
|
if j < len(x_list):
|
|
inputs.append(layer(x_list[j]))
|
|
else:
|
|
inputs.append(layer(x_list[-1]))
|
|
# Forward HRModule
|
|
stage = getattr(self, f'stage{i}')
|
|
x_list = stage(inputs)
|
|
|
|
return tuple(x_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()
|
|
|
|
def parse_arch(self, arch, extra=None):
|
|
if extra is not None:
|
|
return extra
|
|
|
|
assert arch in self.arch_zoo, \
|
|
('Invalid arch, please choose arch from '
|
|
f'{list(self.arch_zoo.keys())}, or specify `extra` '
|
|
'argument directly.')
|
|
|
|
extra = dict()
|
|
for i, stage_setting in enumerate(self.arch_zoo[arch], start=1):
|
|
extra[f'stage{i}'] = dict(
|
|
num_modules=stage_setting[0],
|
|
num_branches=stage_setting[1],
|
|
block=stage_setting[2],
|
|
num_blocks=stage_setting[3],
|
|
num_channels=stage_setting[4],
|
|
)
|
|
|
|
return extra
|