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Source code for mmpose.models.backbones.vipnas_resnet

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

import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule, build_conv_layer, build_norm_layer
from mmcv.cnn.bricks import ContextBlock
from mmengine.model import BaseModule, Sequential
from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm

from mmpose.registry import MODELS
from .base_backbone import BaseBackbone


class ViPNAS_Bottleneck(BaseModule):
    """Bottleneck block for ViPNAS_ResNet.

    Args:
        in_channels (int): Input channels of this block.
        out_channels (int): Output channels of this block.
        expansion (int): The ratio of ``out_channels/mid_channels`` where
            ``mid_channels`` is the input/output channels of conv2. Default: 4.
        stride (int): stride of the block. Default: 1
        dilation (int): dilation of convolution. Default: 1
        downsample (nn.Module): downsample operation on identity branch.
            Default: None.
        style (str): ``"pytorch"`` or ``"caffe"``. If set to "pytorch", the
            stride-two layer is the 3x3 conv layer, otherwise the stride-two
            layer is the first 1x1 conv layer. Default: "pytorch".
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed.
        conv_cfg (dict): dictionary to construct and config conv layer.
            Default: None
        norm_cfg (dict): dictionary to construct and config norm layer.
            Default: dict(type='BN')
        kernel_size (int): kernel size of conv2 searched in ViPANS.
        groups (int): group number of conv2 searched in ViPNAS.
        attention (bool): whether to use attention module in the end of
            the block.
        init_cfg (dict or list[dict], optional): Initialization config dict.
            Default: None
    """

    def __init__(self,
                 in_channels,
                 out_channels,
                 expansion=4,
                 stride=1,
                 dilation=1,
                 downsample=None,
                 style='pytorch',
                 with_cp=False,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 kernel_size=3,
                 groups=1,
                 attention=False,
                 init_cfg=None):
        # Protect mutable default arguments
        norm_cfg = copy.deepcopy(norm_cfg)
        super().__init__(init_cfg=init_cfg)
        assert style in ['pytorch', 'caffe']

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.expansion = expansion
        assert out_channels % expansion == 0
        self.mid_channels = out_channels // expansion
        self.stride = stride
        self.dilation = dilation
        self.style = style
        self.with_cp = with_cp
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg

        if self.style == 'pytorch':
            self.conv1_stride = 1
            self.conv2_stride = stride
        else:
            self.conv1_stride = stride
            self.conv2_stride = 1

        self.norm1_name, norm1 = build_norm_layer(
            norm_cfg, self.mid_channels, postfix=1)
        self.norm2_name, norm2 = build_norm_layer(
            norm_cfg, self.mid_channels, postfix=2)
        self.norm3_name, norm3 = build_norm_layer(
            norm_cfg, out_channels, postfix=3)

        self.conv1 = build_conv_layer(
            conv_cfg,
            in_channels,
            self.mid_channels,
            kernel_size=1,
            stride=self.conv1_stride,
            bias=False)
        self.add_module(self.norm1_name, norm1)
        self.conv2 = build_conv_layer(
            conv_cfg,
            self.mid_channels,
            self.mid_channels,
            kernel_size=kernel_size,
            stride=self.conv2_stride,
            padding=kernel_size // 2,
            groups=groups,
            dilation=dilation,
            bias=False)

        self.add_module(self.norm2_name, norm2)
        self.conv3 = build_conv_layer(
            conv_cfg,
            self.mid_channels,
            out_channels,
            kernel_size=1,
            bias=False)
        self.add_module(self.norm3_name, norm3)

        if attention:
            self.attention = ContextBlock(out_channels,
                                          max(1.0 / 16, 16.0 / out_channels))
        else:
            self.attention = None

        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample

    @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)

    @property
    def norm3(self):
        """nn.Module: the normalization layer named "norm3" """
        return getattr(self, self.norm3_name)

    def forward(self, x):
        """Forward function."""

        def _inner_forward(x):
            identity = x

            out = self.conv1(x)
            out = self.norm1(out)
            out = self.relu(out)

            out = self.conv2(out)
            out = self.norm2(out)
            out = self.relu(out)

            out = self.conv3(out)
            out = self.norm3(out)

            if self.attention is not None:
                out = self.attention(out)

            if self.downsample is not None:
                identity = self.downsample(x)

            out += identity

            return out

        if self.with_cp and x.requires_grad:
            out = cp.checkpoint(_inner_forward, x)
        else:
            out = _inner_forward(x)

        out = self.relu(out)

        return out


def get_expansion(block, expansion=None):
    """Get the expansion of a residual block.

    The block expansion will be obtained by the following order:

    1. If ``expansion`` is given, just return it.
    2. If ``block`` has the attribute ``expansion``, then return
       ``block.expansion``.
    3. Return the default value according the the block type:
       4 for ``ViPNAS_Bottleneck``.

    Args:
        block (class): The block class.
        expansion (int | None): The given expansion ratio.

    Returns:
        int: The expansion of the block.
    """
    if isinstance(expansion, int):
        assert expansion > 0
    elif expansion is None:
        if hasattr(block, 'expansion'):
            expansion = block.expansion
        elif issubclass(block, ViPNAS_Bottleneck):
            expansion = 1
        else:
            raise TypeError(f'expansion is not specified for {block.__name__}')
    else:
        raise TypeError('expansion must be an integer or None')

    return expansion


class ViPNAS_ResLayer(Sequential):
    """ViPNAS_ResLayer to build ResNet style backbone.

    Args:
        block (nn.Module): Residual block used to build ViPNAS ResLayer.
        num_blocks (int): Number of blocks.
        in_channels (int): Input channels of this block.
        out_channels (int): Output channels of this block.
        expansion (int, optional): The expansion for BasicBlock/Bottleneck.
            If not specified, it will firstly be obtained via
            ``block.expansion``. If the block has no attribute "expansion",
            the following default values will be used: 1 for BasicBlock and
            4 for Bottleneck. Default: None.
        stride (int): stride of the first block. Default: 1.
        avg_down (bool): Use AvgPool instead of stride conv when
            downsampling in the bottleneck. Default: False
        conv_cfg (dict): dictionary to construct and config conv layer.
            Default: None
        norm_cfg (dict): dictionary to construct and config norm layer.
            Default: dict(type='BN')
        downsample_first (bool): Downsample at the first block or last block.
            False for Hourglass, True for ResNet. Default: True
        kernel_size (int): Kernel Size of the corresponding convolution layer
            searched in the block.
        groups (int): Group number of the corresponding convolution layer
            searched in the block.
        attention (bool): Whether to use attention module in the end of the
            block.
        init_cfg (dict or list[dict], optional): Initialization config dict.
            Default: None
    """

    def __init__(self,
                 block,
                 num_blocks,
                 in_channels,
                 out_channels,
                 expansion=None,
                 stride=1,
                 avg_down=False,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 downsample_first=True,
                 kernel_size=3,
                 groups=1,
                 attention=False,
                 init_cfg=None,
                 **kwargs):
        # Protect mutable default arguments
        norm_cfg = copy.deepcopy(norm_cfg)
        self.block = block
        self.expansion = get_expansion(block, expansion)

        downsample = None
        if stride != 1 or in_channels != out_channels:
            downsample = []
            conv_stride = stride
            if avg_down and stride != 1:
                conv_stride = 1
                downsample.append(
                    nn.AvgPool2d(
                        kernel_size=stride,
                        stride=stride,
                        ceil_mode=True,
                        count_include_pad=False))
            downsample.extend([
                build_conv_layer(
                    conv_cfg,
                    in_channels,
                    out_channels,
                    kernel_size=1,
                    stride=conv_stride,
                    bias=False),
                build_norm_layer(norm_cfg, out_channels)[1]
            ])
            downsample = nn.Sequential(*downsample)

        layers = []
        if downsample_first:
            layers.append(
                block(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    expansion=self.expansion,
                    stride=stride,
                    downsample=downsample,
                    conv_cfg=conv_cfg,
                    norm_cfg=norm_cfg,
                    kernel_size=kernel_size,
                    groups=groups,
                    attention=attention,
                    **kwargs))
            in_channels = out_channels
            for _ in range(1, num_blocks):
                layers.append(
                    block(
                        in_channels=in_channels,
                        out_channels=out_channels,
                        expansion=self.expansion,
                        stride=1,
                        conv_cfg=conv_cfg,
                        norm_cfg=norm_cfg,
                        kernel_size=kernel_size,
                        groups=groups,
                        attention=attention,
                        **kwargs))
        else:  # downsample_first=False is for HourglassModule
            for i in range(0, num_blocks - 1):
                layers.append(
                    block(
                        in_channels=in_channels,
                        out_channels=in_channels,
                        expansion=self.expansion,
                        stride=1,
                        conv_cfg=conv_cfg,
                        norm_cfg=norm_cfg,
                        kernel_size=kernel_size,
                        groups=groups,
                        attention=attention,
                        **kwargs))
            layers.append(
                block(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    expansion=self.expansion,
                    stride=stride,
                    downsample=downsample,
                    conv_cfg=conv_cfg,
                    norm_cfg=norm_cfg,
                    kernel_size=kernel_size,
                    groups=groups,
                    attention=attention,
                    **kwargs))

        super().__init__(*layers, init_cfg=init_cfg)


[docs]@MODELS.register_module() class ViPNAS_ResNet(BaseBackbone): """ViPNAS_ResNet backbone. "ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search" More details can be found in the `paper <https://arxiv.org/abs/2105.10154>`__ . Args: depth (int): Network depth, from {18, 34, 50, 101, 152}. in_channels (int): Number of input image channels. Default: 3. num_stages (int): Stages of the network. Default: 4. strides (Sequence[int]): Strides of the first block of each stage. Default: ``(1, 2, 2, 2)``. dilations (Sequence[int]): Dilation of each stage. Default: ``(1, 1, 1, 1)``. out_indices (Sequence[int]): Output from which stages. If only one stage is specified, a single tensor (feature map) is returned, otherwise multiple stages are specified, a tuple of tensors will be returned. Default: ``(3, )``. style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer. deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv. Default: False. avg_down (bool): Use AvgPool instead of stride conv when downsampling in the bottleneck. Default: False. frozen_stages (int): Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Default: -1. conv_cfg (dict | None): The config dict for conv layers. Default: None. norm_cfg (dict): The config dict for norm layers. 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. Default: False. zero_init_residual (bool): Whether to use zero init for last norm layer in resblocks to let them behave as identity. Default: True. wid (list(int)): Searched width config for each stage. expan (list(int)): Searched expansion ratio config for each stage. dep (list(int)): Searched depth config for each stage. ks (list(int)): Searched kernel size config for each stage. group (list(int)): Searched group number config for each stage. att (list(bool)): Searched attention config for each stage. 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']) ]`` """ arch_settings = { 50: ViPNAS_Bottleneck, } def __init__(self, depth, in_channels=3, num_stages=4, strides=(1, 2, 2, 2), dilations=(1, 1, 1, 1), out_indices=(3, ), style='pytorch', deep_stem=False, avg_down=False, frozen_stages=-1, conv_cfg=None, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=False, with_cp=False, zero_init_residual=True, wid=[48, 80, 160, 304, 608], expan=[None, 1, 1, 1, 1], dep=[None, 4, 6, 7, 3], ks=[7, 3, 5, 5, 5], group=[None, 16, 16, 16, 16], att=[None, True, False, True, True], 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) if depth not in self.arch_settings: raise KeyError(f'invalid depth {depth} for resnet') self.depth = depth self.stem_channels = dep[0] self.num_stages = num_stages assert 1 <= num_stages <= 4 self.strides = strides self.dilations = dilations assert len(strides) == len(dilations) == num_stages self.out_indices = out_indices assert max(out_indices) < num_stages self.style = style self.deep_stem = deep_stem self.avg_down = avg_down self.frozen_stages = frozen_stages self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.with_cp = with_cp self.norm_eval = norm_eval self.zero_init_residual = zero_init_residual self.block = self.arch_settings[depth] self.stage_blocks = dep[1:1 + num_stages] self._make_stem_layer(in_channels, wid[0], ks[0]) self.res_layers = [] _in_channels = wid[0] for i, num_blocks in enumerate(self.stage_blocks): expansion = get_expansion(self.block, expan[i + 1]) _out_channels = wid[i + 1] * expansion stride = strides[i] dilation = dilations[i] res_layer = self.make_res_layer( block=self.block, num_blocks=num_blocks, in_channels=_in_channels, out_channels=_out_channels, expansion=expansion, stride=stride, dilation=dilation, style=self.style, avg_down=self.avg_down, with_cp=with_cp, conv_cfg=conv_cfg, norm_cfg=norm_cfg, kernel_size=ks[i + 1], groups=group[i + 1], attention=att[i + 1]) _in_channels = _out_channels layer_name = f'layer{i + 1}' self.add_module(layer_name, res_layer) self.res_layers.append(layer_name) self._freeze_stages() self.feat_dim = res_layer[-1].out_channels
[docs] def make_res_layer(self, **kwargs): """Make a ViPNAS ResLayer.""" return ViPNAS_ResLayer(**kwargs)
@property def norm1(self): """nn.Module: the normalization layer named "norm1" """ return getattr(self, self.norm1_name) def _make_stem_layer(self, in_channels, stem_channels, kernel_size): """Make stem layer.""" if self.deep_stem: self.stem = nn.Sequential( ConvModule( in_channels, stem_channels // 2, kernel_size=3, stride=2, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, inplace=True), ConvModule( stem_channels // 2, stem_channels // 2, kernel_size=3, stride=1, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, inplace=True), ConvModule( stem_channels // 2, stem_channels, kernel_size=3, stride=1, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, inplace=True)) else: self.conv1 = build_conv_layer( self.conv_cfg, in_channels, stem_channels, kernel_size=kernel_size, stride=2, padding=kernel_size // 2, bias=False) self.norm1_name, norm1 = build_norm_layer( self.norm_cfg, stem_channels, postfix=1) self.add_module(self.norm1_name, norm1) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) def _freeze_stages(self): """Freeze parameters.""" if self.frozen_stages >= 0: if self.deep_stem: self.stem.eval() for param in self.stem.parameters(): param.requires_grad = False else: self.norm1.eval() for m in [self.conv1, self.norm1]: for param in m.parameters(): param.requires_grad = False for i in range(1, self.frozen_stages + 1): m = getattr(self, f'layer{i}') m.eval() for param in m.parameters(): param.requires_grad = False
[docs] def forward(self, x): """Forward function.""" if self.deep_stem: x = self.stem(x) else: x = self.conv1(x) x = self.norm1(x) x = self.relu(x) x = self.maxpool(x) outs = [] for i, layer_name in enumerate(self.res_layers): res_layer = getattr(self, layer_name) x = res_layer(x) if i in self.out_indices: outs.append(x) return tuple(outs)
[docs] 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(): # trick: eval have effect on BatchNorm only if isinstance(m, _BatchNorm): m.eval()
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