mirror of https://github.com/alibaba/EasyCV.git
800 lines
29 KiB
Python
800 lines
29 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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# Adapt from https://github.com/open-mmlab/mmpose/blob/master/mmpose/models/backbones/hrnet.py
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import copy
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import torch.nn as nn
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from mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init,
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normal_init)
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from torch.nn.modules.batchnorm import _BatchNorm
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from easycv.models.registry import BACKBONES
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from ..modelzoo import hrnet as model_urls
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from .resnet import BasicBlock
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def get_expansion(block, expansion=None):
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"""Get the expansion of a residual block.
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The block expansion will be obtained by the following order:
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1. If ``expansion`` is given, just return it.
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2. If ``block`` has the attribute ``expansion``, then return
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``block.expansion``.
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3. Return the default value according the the block type:
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1 for ``BasicBlock`` and 4 for ``Bottleneck``.
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Args:
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block (class): The block class.
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expansion (int | None): The given expansion ratio.
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Returns:
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int: The expansion of the block.
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"""
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if isinstance(expansion, int):
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assert expansion > 0
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elif expansion is None:
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if hasattr(block, 'expansion'):
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expansion = block.expansion
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elif issubclass(block, BasicBlock):
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expansion = 1
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elif issubclass(block, Bottleneck):
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expansion = 4
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else:
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raise TypeError(f'expansion is not specified for {block.__name__}')
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else:
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raise TypeError('expansion must be an integer or None')
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return expansion
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class Bottleneck(nn.Module):
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"""Bottleneck block for ResNet.
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Args:
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in_channels (int): Input channels of this block.
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out_channels (int): Output channels of this block.
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expansion (int): The ratio of ``out_channels/mid_channels`` where
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``mid_channels`` is the input/output channels of conv2. Default: 4.
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stride (int): stride of the block. Default: 1
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dilation (int): dilation of convolution. Default: 1
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downsample (nn.Module): downsample operation on identity branch.
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Default: None.
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style (str): ``"pytorch"`` or ``"caffe"``. If set to "pytorch", the
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stride-two layer is the 3x3 conv layer, otherwise the stride-two
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layer is the first 1x1 conv layer. Default: "pytorch".
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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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conv_cfg (dict): dictionary to construct and config conv layer.
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Default: None
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norm_cfg (dict): dictionary to construct and config norm layer.
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Default: dict(type='BN')
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"""
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def __init__(self,
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in_channels,
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out_channels,
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expansion=4,
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stride=1,
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dilation=1,
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downsample=None,
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style='pytorch',
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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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# Protect mutable default arguments
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norm_cfg = copy.deepcopy(norm_cfg)
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super().__init__()
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assert style in ['pytorch', 'caffe']
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.expansion = expansion
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assert out_channels % expansion == 0
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self.mid_channels = out_channels // expansion
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self.stride = stride
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self.dilation = dilation
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self.style = style
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self.with_cp = with_cp
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self.conv_cfg = conv_cfg
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self.norm_cfg = norm_cfg
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if self.style == 'pytorch':
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self.conv1_stride = 1
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self.conv2_stride = stride
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else:
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self.conv1_stride = stride
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self.conv2_stride = 1
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self.norm1_name, norm1 = build_norm_layer(
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norm_cfg, self.mid_channels, postfix=1)
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self.norm2_name, norm2 = build_norm_layer(
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norm_cfg, self.mid_channels, postfix=2)
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self.norm3_name, norm3 = build_norm_layer(
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norm_cfg, out_channels, postfix=3)
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self.conv1 = build_conv_layer(
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conv_cfg,
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in_channels,
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self.mid_channels,
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kernel_size=1,
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stride=self.conv1_stride,
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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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conv_cfg,
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self.mid_channels,
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self.mid_channels,
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kernel_size=3,
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stride=self.conv2_stride,
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padding=dilation,
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dilation=dilation,
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bias=False)
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self.add_module(self.norm2_name, norm2)
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self.conv3 = build_conv_layer(
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conv_cfg,
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self.mid_channels,
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out_channels,
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kernel_size=1,
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bias=False)
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self.add_module(self.norm3_name, norm3)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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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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@property
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def norm3(self):
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"""nn.Module: the normalization layer named "norm3" """
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return getattr(self, self.norm3_name)
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def forward(self, x):
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"""Forward function."""
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def _inner_forward(x):
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identity = x
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out = self.conv1(x)
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out = self.norm1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.norm2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.norm3(out)
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if self.downsample is not None:
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identity = self.downsample(x)
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out += identity
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return out
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if self.with_cp and x.requires_grad:
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raise NotImplementedError
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else:
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out = _inner_forward(x)
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out = self.relu(out)
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return out
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class HRModule(nn.Module):
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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=False,
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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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upsample_cfg=dict(mode='nearest', align_corners=None)):
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# Protect mutable default arguments
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norm_cfg = copy.deepcopy(norm_cfg)
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super().__init__()
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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.upsample_cfg = upsample_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=True)
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@staticmethod
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def _check_branches(num_branches, num_blocks, in_channels, num_channels):
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"""Check input to avoid ValueError."""
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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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"""Make one branch."""
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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] * get_expansion(block):
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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] * get_expansion(block),
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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(
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self.norm_cfg,
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num_channels[branch_index] * get_expansion(block))[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] * get_expansion(block),
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stride=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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self.in_channels[branch_index] = \
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num_channels[branch_index] * get_expansion(block)
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for _ 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] * get_expansion(block),
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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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return nn.Sequential(*layers)
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def _make_branches(self, num_branches, block, num_blocks, num_channels):
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"""Make branches."""
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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 nn.ModuleList(branches)
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def _make_fuse_layers(self):
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"""Make fuse layer."""
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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),
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mode=self.upsample_cfg['mode'],
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align_corners=self.
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upsample_cfg['align_corners'])))
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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=True)))
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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(nn.Module):
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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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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. Default: 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.
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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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Example:
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>>> from mmpose.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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"""
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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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multi_scale_output=False):
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# Protect mutable default arguments
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norm_cfg = copy.deepcopy(norm_cfg)
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super().__init__()
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extra = self.parse_arch(arch, extra)
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self.extra = extra
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self.conv_cfg = conv_cfg
|
|
self.norm_cfg = norm_cfg
|
|
self.norm_eval = norm_eval
|
|
self.with_cp = with_cp
|
|
self.zero_init_residual = zero_init_residual
|
|
|
|
# stem net
|
|
self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, 64, postfix=1)
|
|
self.norm2_name, norm2 = build_norm_layer(self.norm_cfg, 64, postfix=2)
|
|
|
|
self.conv1 = build_conv_layer(
|
|
self.conv_cfg,
|
|
in_channels,
|
|
64,
|
|
kernel_size=3,
|
|
stride=2,
|
|
padding=1,
|
|
bias=False)
|
|
|
|
self.add_module(self.norm1_name, norm1)
|
|
self.conv2 = build_conv_layer(
|
|
self.conv_cfg,
|
|
64,
|
|
64,
|
|
kernel_size=3,
|
|
stride=2,
|
|
padding=1,
|
|
bias=False)
|
|
|
|
self.add_module(self.norm2_name, norm2)
|
|
self.relu = nn.ReLU(inplace=True)
|
|
|
|
self.upsample_cfg = self.extra.get('upsample', {
|
|
'mode': 'nearest',
|
|
'align_corners': None
|
|
})
|
|
|
|
# stage 1
|
|
self.stage1_cfg = self.extra['stage1']
|
|
num_channels = self.stage1_cfg['num_channels'][0]
|
|
block_type = self.stage1_cfg['block']
|
|
num_blocks = self.stage1_cfg['num_blocks'][0]
|
|
|
|
block = self.blocks_dict[block_type]
|
|
stage1_out_channels = num_channels * get_expansion(block)
|
|
self.layer1 = self._make_layer(block, 64, stage1_out_channels,
|
|
num_blocks)
|
|
|
|
# stage 2
|
|
self.stage2_cfg = self.extra['stage2']
|
|
num_channels = self.stage2_cfg['num_channels']
|
|
block_type = self.stage2_cfg['block']
|
|
|
|
block = self.blocks_dict[block_type]
|
|
num_channels = [
|
|
channel * get_expansion(block) for channel in num_channels
|
|
]
|
|
self.transition1 = self._make_transition_layer([stage1_out_channels],
|
|
num_channels)
|
|
self.stage2, pre_stage_channels = self._make_stage(
|
|
self.stage2_cfg, num_channels)
|
|
|
|
# stage 3
|
|
self.stage3_cfg = self.extra['stage3']
|
|
num_channels = self.stage3_cfg['num_channels']
|
|
block_type = self.stage3_cfg['block']
|
|
|
|
block = self.blocks_dict[block_type]
|
|
num_channels = [
|
|
channel * get_expansion(block) for channel in 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)
|
|
|
|
# stage 4
|
|
self.stage4_cfg = self.extra['stage4']
|
|
num_channels = self.stage4_cfg['num_channels']
|
|
block_type = self.stage4_cfg['block']
|
|
|
|
block = self.blocks_dict[block_type]
|
|
num_channels = [
|
|
channel * get_expansion(block) for channel in 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,
|
|
multiscale_output=self.stage4_cfg.get('multiscale_output',
|
|
multi_scale_output))
|
|
|
|
self.default_pretrained_model_path = model_urls.get(
|
|
self.__class__.__name__ + arch, None)
|
|
|
|
@property
|
|
def norm1(self):
|
|
"""nn.Module: the normalization layer named "norm1" """
|
|
return getattr(self, self.norm1_name)
|
|
|
|
@property
|
|
def norm2(self):
|
|
"""nn.Module: the normalization layer named "norm2" """
|
|
return getattr(self, self.norm2_name)
|
|
|
|
def _make_transition_layer(self, num_channels_pre_layer,
|
|
num_channels_cur_layer):
|
|
"""Make transition 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(
|
|
build_conv_layer(
|
|
self.conv_cfg,
|
|
num_channels_pre_layer[i],
|
|
num_channels_cur_layer[i],
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=1,
|
|
bias=False),
|
|
build_norm_layer(self.norm_cfg,
|
|
num_channels_cur_layer[i])[1],
|
|
nn.ReLU(inplace=True)))
|
|
else:
|
|
transition_layers.append(None)
|
|
else:
|
|
conv_downsamples = []
|
|
for j in range(i + 1 - num_branches_pre):
|
|
in_channels = num_channels_pre_layer[-1]
|
|
out_channels = num_channels_cur_layer[i] \
|
|
if j == i - num_branches_pre else in_channels
|
|
conv_downsamples.append(
|
|
nn.Sequential(
|
|
build_conv_layer(
|
|
self.conv_cfg,
|
|
in_channels,
|
|
out_channels,
|
|
kernel_size=3,
|
|
stride=2,
|
|
padding=1,
|
|
bias=False),
|
|
build_norm_layer(self.norm_cfg, out_channels)[1],
|
|
nn.ReLU(inplace=True)))
|
|
transition_layers.append(nn.Sequential(*conv_downsamples))
|
|
|
|
return nn.ModuleList(transition_layers)
|
|
|
|
def _make_layer(self, block, in_channels, out_channels, blocks, stride=1):
|
|
"""Make layer."""
|
|
downsample = None
|
|
if stride != 1 or in_channels != out_channels:
|
|
downsample = nn.Sequential(
|
|
build_conv_layer(
|
|
self.conv_cfg,
|
|
in_channels,
|
|
out_channels,
|
|
kernel_size=1,
|
|
stride=stride,
|
|
bias=False),
|
|
build_norm_layer(self.norm_cfg, out_channels)[1])
|
|
|
|
layers = []
|
|
layers.append(
|
|
block(
|
|
in_channels,
|
|
out_channels,
|
|
stride=stride,
|
|
downsample=downsample,
|
|
with_cp=self.with_cp,
|
|
norm_cfg=self.norm_cfg,
|
|
conv_cfg=self.conv_cfg))
|
|
for _ in range(1, blocks):
|
|
layers.append(
|
|
block(
|
|
out_channels,
|
|
out_channels,
|
|
with_cp=self.with_cp,
|
|
norm_cfg=self.norm_cfg,
|
|
conv_cfg=self.conv_cfg))
|
|
|
|
return nn.Sequential(*layers)
|
|
|
|
def _make_stage(self, layer_config, in_channels, multiscale_output=True):
|
|
"""Make stage."""
|
|
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 = []
|
|
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,
|
|
upsample_cfg=self.upsample_cfg))
|
|
|
|
in_channels = hr_modules[-1].in_channels
|
|
|
|
return nn.Sequential(*hr_modules), in_channels
|
|
|
|
def init_weights(self):
|
|
for m in self.modules():
|
|
if isinstance(m, nn.Conv2d):
|
|
normal_init(m, std=0.001)
|
|
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
|
|
constant_init(m, 1)
|
|
|
|
if self.zero_init_residual:
|
|
for m in self.modules():
|
|
if isinstance(m, Bottleneck):
|
|
constant_init(m.norm3, 0)
|
|
elif isinstance(m, BasicBlock):
|
|
constant_init(m.norm2, 0)
|
|
|
|
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."""
|
|
super().train(mode)
|
|
if mode and self.norm_eval:
|
|
for m in self.modules():
|
|
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
|