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mmpose.models.backbones.hrnet 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import copy

import torch.nn as nn
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmengine.model import BaseModule, constant_init
from torch.nn.modules.batchnorm import _BatchNorm

from mmpose.registry import MODELS
from .base_backbone import BaseBackbone
from .resnet import BasicBlock, Bottleneck, get_expansion


class HRModule(BaseModule):
    """High-Resolution Module for HRNet.

    In this module, every branch has 4 BasicBlocks/Bottlenecks. Fusion/Exchange
    is in this module.
    """

    def __init__(self,
                 num_branches,
                 blocks,
                 num_blocks,
                 in_channels,
                 num_channels,
                 multiscale_output=False,
                 with_cp=False,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 upsample_cfg=dict(mode='nearest', align_corners=None),
                 init_cfg=None):

        # Protect mutable default arguments
        norm_cfg = copy.deepcopy(norm_cfg)
        super().__init__(init_cfg=init_cfg)
        self._check_branches(num_branches, num_blocks, in_channels,
                             num_channels)

        self.in_channels = in_channels
        self.num_branches = num_branches

        self.multiscale_output = multiscale_output
        self.norm_cfg = norm_cfg
        self.conv_cfg = conv_cfg
        self.upsample_cfg = upsample_cfg
        self.with_cp = with_cp
        self.branches = self._make_branches(num_branches, blocks, num_blocks,
                                            num_channels)
        self.fuse_layers = self._make_fuse_layers()
        self.relu = nn.ReLU(inplace=True)

    @staticmethod
    def _check_branches(num_branches, num_blocks, in_channels, num_channels):
        """Check input to avoid ValueError."""
        if num_branches != len(num_blocks):
            error_msg = f'NUM_BRANCHES({num_branches}) ' \
                f'!= NUM_BLOCKS({len(num_blocks)})'
            raise ValueError(error_msg)

        if num_branches != len(num_channels):
            error_msg = f'NUM_BRANCHES({num_branches}) ' \
                f'!= NUM_CHANNELS({len(num_channels)})'
            raise ValueError(error_msg)

        if num_branches != len(in_channels):
            error_msg = f'NUM_BRANCHES({num_branches}) ' \
                f'!= NUM_INCHANNELS({len(in_channels)})'
            raise ValueError(error_msg)

    def _make_one_branch(self,
                         branch_index,
                         block,
                         num_blocks,
                         num_channels,
                         stride=1):
        """Make one branch."""
        downsample = None
        if stride != 1 or \
                self.in_channels[branch_index] != \
                num_channels[branch_index] * get_expansion(block):
            downsample = nn.Sequential(
                build_conv_layer(
                    self.conv_cfg,
                    self.in_channels[branch_index],
                    num_channels[branch_index] * get_expansion(block),
                    kernel_size=1,
                    stride=stride,
                    bias=False),
                build_norm_layer(
                    self.norm_cfg,
                    num_channels[branch_index] * get_expansion(block))[1])

        layers = []
        layers.append(
            block(
                self.in_channels[branch_index],
                num_channels[branch_index] * get_expansion(block),
                stride=stride,
                downsample=downsample,
                with_cp=self.with_cp,
                norm_cfg=self.norm_cfg,
                conv_cfg=self.conv_cfg))
        self.in_channels[branch_index] = \
            num_channels[branch_index] * get_expansion(block)
        for _ in range(1, num_blocks[branch_index]):
            layers.append(
                block(
                    self.in_channels[branch_index],
                    num_channels[branch_index] * get_expansion(block),
                    with_cp=self.with_cp,
                    norm_cfg=self.norm_cfg,
                    conv_cfg=self.conv_cfg))

        return nn.Sequential(*layers)

    def _make_branches(self, num_branches, block, num_blocks, num_channels):
        """Make branches."""
        branches = []

        for i in range(num_branches):
            branches.append(
                self._make_one_branch(i, block, num_blocks, num_channels))

        return nn.ModuleList(branches)

    def _make_fuse_layers(self):
        """Make fuse layer."""
        if self.num_branches == 1:
            return None

        num_branches = self.num_branches
        in_channels = self.in_channels
        fuse_layers = []
        num_out_branches = num_branches if self.multiscale_output else 1

        for i in range(num_out_branches):
            fuse_layer = []
            for j in range(num_branches):
                if j > i:
                    fuse_layer.append(
                        nn.Sequential(
                            build_conv_layer(
                                self.conv_cfg,
                                in_channels[j],
                                in_channels[i],
                                kernel_size=1,
                                stride=1,
                                padding=0,
                                bias=False),
                            build_norm_layer(self.norm_cfg, in_channels[i])[1],
                            nn.Upsample(
                                scale_factor=2**(j - i),
                                mode=self.upsample_cfg['mode'],
                                align_corners=self.
                                upsample_cfg['align_corners'])))
                elif j == i:
                    fuse_layer.append(None)
                else:
                    conv_downsamples = []
                    for k in range(i - j):
                        if k == i - j - 1:
                            conv_downsamples.append(
                                nn.Sequential(
                                    build_conv_layer(
                                        self.conv_cfg,
                                        in_channels[j],
                                        in_channels[i],
                                        kernel_size=3,
                                        stride=2,
                                        padding=1,
                                        bias=False),
                                    build_norm_layer(self.norm_cfg,
                                                     in_channels[i])[1]))
                        else:
                            conv_downsamples.append(
                                nn.Sequential(
                                    build_conv_layer(
                                        self.conv_cfg,
                                        in_channels[j],
                                        in_channels[j],
                                        kernel_size=3,
                                        stride=2,
                                        padding=1,
                                        bias=False),
                                    build_norm_layer(self.norm_cfg,
                                                     in_channels[j])[1],
                                    nn.ReLU(inplace=True)))
                    fuse_layer.append(nn.Sequential(*conv_downsamples))
            fuse_layers.append(nn.ModuleList(fuse_layer))

        return nn.ModuleList(fuse_layers)

    def forward(self, x):
        """Forward function."""
        if self.num_branches == 1:
            return [self.branches[0](x[0])]

        for i in range(self.num_branches):
            x[i] = self.branches[i](x[i])

        x_fuse = []
        for i in range(len(self.fuse_layers)):
            y = 0
            for j in range(self.num_branches):
                if i == j:
                    y += x[j]
                else:
                    y += self.fuse_layers[i][j](x[j])
            x_fuse.append(self.relu(y))
        return x_fuse


[文档]@MODELS.register_module() class HRNet(BaseBackbone): """HRNet backbone. `High-Resolution Representations for Labeling Pixels and Regions <https://arxiv.org/abs/1904.04514>`__ Args: extra (dict): detailed configuration for each stage of HRNet. in_channels (int): Number of input image channels. Default: 3. conv_cfg (dict): dictionary to construct and config conv layer. norm_cfg (dict): dictionary to construct and config norm layer. norm_eval (bool): Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. zero_init_residual (bool): whether to use zero init for last norm layer in resblocks to let them behave as identity. frozen_stages (int): Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Default: -1. init_cfg (dict or list[dict], optional): Initialization config dict. Default: ``[ dict(type='Normal', std=0.001, layer=['Conv2d']), dict( type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm']) ]`` Example: >>> from mmpose.models import HRNet >>> import torch >>> extra = dict( >>> stage1=dict( >>> num_modules=1, >>> num_branches=1, >>> block='BOTTLENECK', >>> num_blocks=(4, ), >>> num_channels=(64, )), >>> stage2=dict( >>> num_modules=1, >>> num_branches=2, >>> block='BASIC', >>> num_blocks=(4, 4), >>> num_channels=(32, 64)), >>> stage3=dict( >>> num_modules=4, >>> num_branches=3, >>> block='BASIC', >>> num_blocks=(4, 4, 4), >>> num_channels=(32, 64, 128)), >>> stage4=dict( >>> num_modules=3, >>> num_branches=4, >>> block='BASIC', >>> num_blocks=(4, 4, 4, 4), >>> num_channels=(32, 64, 128, 256))) >>> self = HRNet(extra, in_channels=1) >>> self.eval() >>> inputs = torch.rand(1, 1, 32, 32) >>> level_outputs = self.forward(inputs) >>> for level_out in level_outputs: ... print(tuple(level_out.shape)) (1, 32, 8, 8) """ blocks_dict = {'BASIC': BasicBlock, 'BOTTLENECK': Bottleneck} def __init__( self, extra, in_channels=3, conv_cfg=None, norm_cfg=dict(type='BN'), norm_eval=False, with_cp=False, zero_init_residual=False, frozen_stages=-1, init_cfg=[ dict(type='Normal', std=0.001, layer=['Conv2d']), dict(type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm']) ], ): # Protect mutable default arguments norm_cfg = copy.deepcopy(norm_cfg) super().__init__(init_cfg=init_cfg) self.extra = extra self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.init_cfg = init_cfg self.norm_eval = norm_eval self.with_cp = with_cp self.zero_init_residual = zero_init_residual self.frozen_stages = frozen_stages # 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', False)) self._freeze_stages() @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 _freeze_stages(self): """Freeze parameters.""" if self.frozen_stages >= 0: self.norm1.eval() self.norm2.eval() for m in [self.conv1, self.norm1, self.conv2, self.norm2]: for param in m.parameters(): param.requires_grad = False for i in range(1, self.frozen_stages + 1): if i == 1: m = getattr(self, 'layer1') else: m = getattr(self, f'stage{i}') m.eval() for param in m.parameters(): param.requires_grad = False if i < 4: m = getattr(self, f'transition{i}') m.eval() for param in m.parameters(): param.requires_grad = False
[文档] def init_weights(self): """Initialize the weights in backbone.""" super(HRNet, self).init_weights() if (isinstance(self.init_cfg, dict) and self.init_cfg['type'] == 'Pretrained'): # Suppress zero_init_residual if use pretrained model. return 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)
[文档] 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 tuple(y_list)
[文档] def train(self, mode=True): """Convert the model into training mode.""" super().train(mode) self._freeze_stages() if mode and self.norm_eval: for m in self.modules(): if isinstance(m, _BatchNorm): m.eval()
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