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Source code for mmpretrain.models.backbones.hrnet

# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmengine.model import BaseModule, ModuleList, Sequential
from torch.nn.modules.batchnorm import _BatchNorm

from mmpretrain.registry import MODELS
from .resnet import BasicBlock, Bottleneck, ResLayer, 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.

    Args:
        num_branches (int): The number of branches.
        block (``BaseModule``): Convolution block module.
        num_blocks (tuple): The number of blocks in each branch.
            The length must be equal to ``num_branches``.
        num_channels (tuple): The number of base channels in each branch.
            The length must be equal to ``num_branches``.
        multiscale_output (bool): Whether to output multi-level features
            produced by multiple branches. If False, only the first level
            feature will be output. Defaults to True.
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed. Defaults to False.
        conv_cfg (dict, optional): Dictionary to construct and config conv
            layer. Defaults to None.
        norm_cfg (dict): Dictionary to construct and config norm layer.
            Defaults to ``dict(type='BN')``.
        block_init_cfg (dict, optional): The initialization configs of every
            blocks. Defaults to None.
        init_cfg (dict or list[dict], optional): Initialization config dict.
            Defaults to None.
    """

    def __init__(self,
                 num_branches,
                 block,
                 num_blocks,
                 in_channels,
                 num_channels,
                 multiscale_output=True,
                 with_cp=False,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 block_init_cfg=None,
                 init_cfg=None):
        super(HRModule, self).__init__(init_cfg)
        self.block_init_cfg = block_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.with_cp = with_cp
        self.branches = self._make_branches(num_branches, block, num_blocks,
                                            num_channels)
        self.fuse_layers = self._make_fuse_layers()
        self.relu = nn.ReLU(inplace=False)

    def _check_branches(self, num_branches, num_blocks, in_channels,
                        num_channels):
        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_branches(self, num_branches, block, num_blocks, num_channels):
        branches = []

        for i in range(num_branches):
            out_channels = num_channels[i] * get_expansion(block)
            branches.append(
                ResLayer(
                    block=block,
                    num_blocks=num_blocks[i],
                    in_channels=self.in_channels[i],
                    out_channels=out_channels,
                    conv_cfg=self.conv_cfg,
                    norm_cfg=self.norm_cfg,
                    with_cp=self.with_cp,
                    init_cfg=self.block_init_cfg,
                ))

        return ModuleList(branches)

    def _make_fuse_layers(self):
        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:
                    # Upsample the feature maps of smaller scales.
                    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='nearest')))
                elif j == i:
                    # Keep the feature map with the same scale.
                    fuse_layer.append(None)
                else:
                    # Downsample the feature maps of larger scales.
                    conv_downsamples = []
                    for k in range(i - j):
                        # Use stacked convolution layers to downsample.
                        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=False)))
                    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


[docs]@MODELS.register_module() class HRNet(BaseModule): """HRNet backbone. `High-Resolution Representations for Labeling Pixels and Regions <https://arxiv.org/abs/1904.04514>`_. Args: arch (str): The preset HRNet architecture, includes 'w18', 'w30', 'w32', 'w40', 'w44', 'w48', 'w64'. It will only be used if extra is ``None``. Defaults to 'w32'. extra (dict, optional): Detailed configuration for each stage of HRNet. There must be 4 stages, the configuration for each stage must have 5 keys: - num_modules (int): The number of HRModule in this stage. - num_branches (int): The number of branches in the HRModule. - block (str): The type of convolution block. Please choose between 'BOTTLENECK' and 'BASIC'. - num_blocks (tuple): The number of blocks in each branch. The length must be equal to num_branches. - num_channels (tuple): The number of base channels in each branch. The length must be equal to num_branches. Defaults to None. in_channels (int): Number of input image channels. Defaults to 3. conv_cfg (dict, optional): Dictionary to construct and config conv layer. Defaults to None. norm_cfg (dict): Dictionary to construct and config norm layer. Defaults to ``dict(type='BN')``. 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. Defaults to False. with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Defaults to False. zero_init_residual (bool): Whether to use zero init for last norm layer in resblocks to let them behave as identity. Defaults to False. multiscale_output (bool): Whether to output multi-level features produced by multiple branches. If False, only the first level feature will be output. Defaults to True. init_cfg (dict or list[dict], optional): Initialization config dict. Defaults to None. Example: >>> import torch >>> from mmpretrain.models import HRNet >>> 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) (1, 64, 4, 4) (1, 128, 2, 2) (1, 256, 1, 1) """ blocks_dict = {'BASIC': BasicBlock, 'BOTTLENECK': Bottleneck} arch_zoo = { # num_modules, num_branches, block, num_blocks, num_channels 'w18': [[1, 1, 'BOTTLENECK', (4, ), (64, )], [1, 2, 'BASIC', (4, 4), (18, 36)], [4, 3, 'BASIC', (4, 4, 4), (18, 36, 72)], [3, 4, 'BASIC', (4, 4, 4, 4), (18, 36, 72, 144)]], 'w30': [[1, 1, 'BOTTLENECK', (4, ), (64, )], [1, 2, 'BASIC', (4, 4), (30, 60)], [4, 3, 'BASIC', (4, 4, 4), (30, 60, 120)], [3, 4, 'BASIC', (4, 4, 4, 4), (30, 60, 120, 240)]], 'w32': [[1, 1, 'BOTTLENECK', (4, ), (64, )], [1, 2, 'BASIC', (4, 4), (32, 64)], [4, 3, 'BASIC', (4, 4, 4), (32, 64, 128)], [3, 4, 'BASIC', (4, 4, 4, 4), (32, 64, 128, 256)]], 'w40': [[1, 1, 'BOTTLENECK', (4, ), (64, )], [1, 2, 'BASIC', (4, 4), (40, 80)], [4, 3, 'BASIC', (4, 4, 4), (40, 80, 160)], [3, 4, 'BASIC', (4, 4, 4, 4), (40, 80, 160, 320)]], 'w44': [[1, 1, 'BOTTLENECK', (4, ), (64, )], [1, 2, 'BASIC', (4, 4), (44, 88)], [4, 3, 'BASIC', (4, 4, 4), (44, 88, 176)], [3, 4, 'BASIC', (4, 4, 4, 4), (44, 88, 176, 352)]], 'w48': [[1, 1, 'BOTTLENECK', (4, ), (64, )], [1, 2, 'BASIC', (4, 4), (48, 96)], [4, 3, 'BASIC', (4, 4, 4), (48, 96, 192)], [3, 4, 'BASIC', (4, 4, 4, 4), (48, 96, 192, 384)]], 'w64': [[1, 1, 'BOTTLENECK', (4, ), (64, )], [1, 2, 'BASIC', (4, 4), (64, 128)], [4, 3, 'BASIC', (4, 4, 4), (64, 128, 256)], [3, 4, 'BASIC', (4, 4, 4, 4), (64, 128, 256, 512)]], } # yapf:disable def __init__(self, arch='w32', extra=None, in_channels=3, conv_cfg=None, norm_cfg=dict(type='BN'), norm_eval=False, with_cp=False, zero_init_residual=False, multiscale_output=True, init_cfg=[ dict(type='Kaiming', layer='Conv2d'), dict( type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm']) ]): super(HRNet, self).__init__(init_cfg) extra = self.parse_arch(arch, extra) # Assert configurations of 4 stages are in extra for i in range(1, 5): assert f'stage{i}' in extra, f'Missing stage{i} config in "extra".' # Assert whether the length of `num_blocks` and `num_channels` are # equal to `num_branches` cfg = extra[f'stage{i}'] assert len(cfg['num_blocks']) == cfg['num_branches'] and \ len(cfg['num_channels']) == cfg['num_branches'] self.extra = extra 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.conv1 = build_conv_layer( self.conv_cfg, in_channels, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False) self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, 64, postfix=1) self.add_module(self.norm1_name, norm1) self.conv2 = build_conv_layer( self.conv_cfg, in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False) self.norm2_name, norm2 = build_norm_layer(self.norm_cfg, 64, postfix=2) self.add_module(self.norm2_name, norm2) self.relu = nn.ReLU(inplace=True) # -------------------- stage 1 -------------------- self.stage1_cfg = self.extra['stage1'] base_channels = self.stage1_cfg['num_channels'] block_type = self.stage1_cfg['block'] num_blocks = self.stage1_cfg['num_blocks'] block = self.blocks_dict[block_type] num_channels = [ channel * get_expansion(block) for channel in base_channels ] # To align with the original code, use layer1 instead of stage1 here. self.layer1 = ResLayer( block, in_channels=64, out_channels=num_channels[0], num_blocks=num_blocks[0]) pre_num_channels = num_channels # -------------------- stage 2~4 -------------------- for i in range(2, 5): stage_cfg = self.extra[f'stage{i}'] base_channels = stage_cfg['num_channels'] block = self.blocks_dict[stage_cfg['block']] multiscale_output_ = multiscale_output if i == 4 else True num_channels = [ channel * get_expansion(block) for channel in base_channels ] # The transition layer from layer1 to stage2 transition = self._make_transition_layer(pre_num_channels, num_channels) self.add_module(f'transition{i-1}', transition) stage = self._make_stage( stage_cfg, num_channels, multiscale_output=multiscale_output_) self.add_module(f'stage{i}', stage) pre_num_channels = num_channels @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): 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: # For existing scale branches, # add conv block when the channels are not the same. 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(nn.Identity()) else: # For new scale branches, add stacked downsample conv blocks. # For example, num_branches_pre = 2, for the 4th branch, add # stacked two downsample conv blocks. 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_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)
[docs] 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)
[docs] 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
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