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mmdet.models.backbones.resnet 源代码

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

import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import build_conv_layer, build_norm_layer, build_plugin_layer
from mmcv.runner import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm

from ..builder import BACKBONES
from ..utils import ResLayer


class BasicBlock(BaseModule):
    expansion = 1

    def __init__(self,
                 inplanes,
                 planes,
                 stride=1,
                 dilation=1,
                 downsample=None,
                 style='pytorch',
                 with_cp=False,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 dcn=None,
                 plugins=None,
                 init_cfg=None):
        super(BasicBlock, self).__init__(init_cfg)
        assert dcn is None, 'Not implemented yet.'
        assert plugins is None, 'Not implemented yet.'

        self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
        self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)

        self.conv1 = build_conv_layer(
            conv_cfg,
            inplanes,
            planes,
            3,
            stride=stride,
            padding=dilation,
            dilation=dilation,
            bias=False)
        self.add_module(self.norm1_name, norm1)
        self.conv2 = build_conv_layer(
            conv_cfg, planes, planes, 3, padding=1, bias=False)
        self.add_module(self.norm2_name, norm2)

        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride
        self.dilation = dilation
        self.with_cp = with_cp

    @property
    def norm1(self):
        """nn.Module: normalization layer after the first convolution layer"""
        return getattr(self, self.norm1_name)

    @property
    def norm2(self):
        """nn.Module: normalization layer after the second convolution layer"""
        return getattr(self, self.norm2_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)

            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


class Bottleneck(BaseModule):
    expansion = 4

    def __init__(self,
                 inplanes,
                 planes,
                 stride=1,
                 dilation=1,
                 downsample=None,
                 style='pytorch',
                 with_cp=False,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 dcn=None,
                 plugins=None,
                 init_cfg=None):
        """Bottleneck block for ResNet.

        If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
        it is "caffe", the stride-two layer is the first 1x1 conv layer.
        """
        super(Bottleneck, self).__init__(init_cfg)
        assert style in ['pytorch', 'caffe']
        assert dcn is None or isinstance(dcn, dict)
        assert plugins is None or isinstance(plugins, list)
        if plugins is not None:
            allowed_position = ['after_conv1', 'after_conv2', 'after_conv3']
            assert all(p['position'] in allowed_position for p in plugins)

        self.inplanes = inplanes
        self.planes = planes
        self.stride = stride
        self.dilation = dilation
        self.style = style
        self.with_cp = with_cp
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg
        self.dcn = dcn
        self.with_dcn = dcn is not None
        self.plugins = plugins
        self.with_plugins = plugins is not None

        if self.with_plugins:
            # collect plugins for conv1/conv2/conv3
            self.after_conv1_plugins = [
                plugin['cfg'] for plugin in plugins
                if plugin['position'] == 'after_conv1'
            ]
            self.after_conv2_plugins = [
                plugin['cfg'] for plugin in plugins
                if plugin['position'] == 'after_conv2'
            ]
            self.after_conv3_plugins = [
                plugin['cfg'] for plugin in plugins
                if plugin['position'] == 'after_conv3'
            ]

        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, planes, postfix=1)
        self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
        self.norm3_name, norm3 = build_norm_layer(
            norm_cfg, planes * self.expansion, postfix=3)

        self.conv1 = build_conv_layer(
            conv_cfg,
            inplanes,
            planes,
            kernel_size=1,
            stride=self.conv1_stride,
            bias=False)
        self.add_module(self.norm1_name, norm1)
        fallback_on_stride = False
        if self.with_dcn:
            fallback_on_stride = dcn.pop('fallback_on_stride', False)
        if not self.with_dcn or fallback_on_stride:
            self.conv2 = build_conv_layer(
                conv_cfg,
                planes,
                planes,
                kernel_size=3,
                stride=self.conv2_stride,
                padding=dilation,
                dilation=dilation,
                bias=False)
        else:
            assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
            self.conv2 = build_conv_layer(
                dcn,
                planes,
                planes,
                kernel_size=3,
                stride=self.conv2_stride,
                padding=dilation,
                dilation=dilation,
                bias=False)

        self.add_module(self.norm2_name, norm2)
        self.conv3 = build_conv_layer(
            conv_cfg,
            planes,
            planes * self.expansion,
            kernel_size=1,
            bias=False)
        self.add_module(self.norm3_name, norm3)

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

        if self.with_plugins:
            self.after_conv1_plugin_names = self.make_block_plugins(
                planes, self.after_conv1_plugins)
            self.after_conv2_plugin_names = self.make_block_plugins(
                planes, self.after_conv2_plugins)
            self.after_conv3_plugin_names = self.make_block_plugins(
                planes * self.expansion, self.after_conv3_plugins)

    def make_block_plugins(self, in_channels, plugins):
        """make plugins for block.

        Args:
            in_channels (int): Input channels of plugin.
            plugins (list[dict]): List of plugins cfg to build.

        Returns:
            list[str]: List of the names of plugin.
        """
        assert isinstance(plugins, list)
        plugin_names = []
        for plugin in plugins:
            plugin = plugin.copy()
            name, layer = build_plugin_layer(
                plugin,
                in_channels=in_channels,
                postfix=plugin.pop('postfix', ''))
            assert not hasattr(self, name), f'duplicate plugin {name}'
            self.add_module(name, layer)
            plugin_names.append(name)
        return plugin_names

    def forward_plugin(self, x, plugin_names):
        out = x
        for name in plugin_names:
            out = getattr(self, name)(x)
        return out

    @property
    def norm1(self):
        """nn.Module: normalization layer after the first convolution layer"""
        return getattr(self, self.norm1_name)

    @property
    def norm2(self):
        """nn.Module: normalization layer after the second convolution layer"""
        return getattr(self, self.norm2_name)

    @property
    def norm3(self):
        """nn.Module: normalization layer after the third convolution layer"""
        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)

            if self.with_plugins:
                out = self.forward_plugin(out, self.after_conv1_plugin_names)

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

            if self.with_plugins:
                out = self.forward_plugin(out, self.after_conv2_plugin_names)

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

            if self.with_plugins:
                out = self.forward_plugin(out, self.after_conv3_plugin_names)

            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


[文档]@BACKBONES.register_module() class ResNet(BaseModule): """ResNet backbone. Args: depth (int): Depth of resnet, from {18, 34, 50, 101, 152}. stem_channels (int | None): Number of stem channels. If not specified, it will be the same as `base_channels`. Default: None. base_channels (int): Number of base channels of res layer. Default: 64. in_channels (int): Number of input image channels. Default: 3. num_stages (int): Resnet stages. Default: 4. strides (Sequence[int]): Strides of the first block of each stage. dilations (Sequence[int]): Dilation of each stage. out_indices (Sequence[int]): Output from which stages. 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 avg_down (bool): Use AvgPool instead of stride conv when downsampling in the bottleneck. frozen_stages (int): Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. 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. plugins (list[dict]): List of plugins for stages, each dict contains: - cfg (dict, required): Cfg dict to build plugin. - position (str, required): Position inside block to insert plugin, options are 'after_conv1', 'after_conv2', 'after_conv3'. - stages (tuple[bool], optional): Stages to apply plugin, length should be same as 'num_stages'. 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. pretrained (str, optional): model pretrained path. Default: None init_cfg (dict or list[dict], optional): Initialization config dict. Default: None Example: >>> from mmdet.models import ResNet >>> import torch >>> self = ResNet(depth=18) >>> self.eval() >>> inputs = torch.rand(1, 3, 32, 32) >>> level_outputs = self.forward(inputs) >>> for level_out in level_outputs: ... print(tuple(level_out.shape)) (1, 64, 8, 8) (1, 128, 4, 4) (1, 256, 2, 2) (1, 512, 1, 1) """ arch_settings = { 18: (BasicBlock, (2, 2, 2, 2)), 34: (BasicBlock, (3, 4, 6, 3)), 50: (Bottleneck, (3, 4, 6, 3)), 101: (Bottleneck, (3, 4, 23, 3)), 152: (Bottleneck, (3, 8, 36, 3)) } def __init__(self, depth, in_channels=3, stem_channels=None, base_channels=64, num_stages=4, strides=(1, 2, 2, 2), dilations=(1, 1, 1, 1), out_indices=(0, 1, 2, 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=True, dcn=None, stage_with_dcn=(False, False, False, False), plugins=None, with_cp=False, zero_init_residual=True, pretrained=None, init_cfg=None): super(ResNet, self).__init__(init_cfg) self.zero_init_residual = zero_init_residual if depth not in self.arch_settings: raise KeyError(f'invalid depth {depth} for resnet') block_init_cfg = None assert not (init_cfg and pretrained), \ 'init_cfg and pretrained cannot be specified at the same time' if isinstance(pretrained, str): warnings.warn('DeprecationWarning: pretrained is deprecated, ' 'please use "init_cfg" instead') self.init_cfg = dict(type='Pretrained', checkpoint=pretrained) elif pretrained is None: if init_cfg is None: self.init_cfg = [ dict(type='Kaiming', layer='Conv2d'), dict( type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm']) ] block = self.arch_settings[depth][0] 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')) else: raise TypeError('pretrained must be a str or None') self.depth = depth if stem_channels is None: stem_channels = base_channels self.stem_channels = stem_channels self.base_channels = base_channels self.num_stages = num_stages assert num_stages >= 1 and 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.dcn = dcn self.stage_with_dcn = stage_with_dcn if dcn is not None: assert len(stage_with_dcn) == num_stages self.plugins = plugins self.block, stage_blocks = self.arch_settings[depth] self.stage_blocks = stage_blocks[:num_stages] self.inplanes = stem_channels self._make_stem_layer(in_channels, stem_channels) self.res_layers = [] for i, num_blocks in enumerate(self.stage_blocks): stride = strides[i] dilation = dilations[i] dcn = self.dcn if self.stage_with_dcn[i] else None if plugins is not None: stage_plugins = self.make_stage_plugins(plugins, i) else: stage_plugins = None planes = base_channels * 2**i res_layer = self.make_res_layer( block=self.block, inplanes=self.inplanes, planes=planes, num_blocks=num_blocks, stride=stride, dilation=dilation, style=self.style, avg_down=self.avg_down, with_cp=with_cp, conv_cfg=conv_cfg, norm_cfg=norm_cfg, dcn=dcn, plugins=stage_plugins, init_cfg=block_init_cfg) self.inplanes = planes * self.block.expansion 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 = self.block.expansion * base_channels * 2**( len(self.stage_blocks) - 1)
[文档] def make_stage_plugins(self, plugins, stage_idx): """Make plugins for ResNet ``stage_idx`` th stage. Currently we support to insert ``context_block``, ``empirical_attention_block``, ``nonlocal_block`` into the backbone like ResNet/ResNeXt. They could be inserted after conv1/conv2/conv3 of Bottleneck. An example of plugins format could be: Examples: >>> plugins=[ ... dict(cfg=dict(type='xxx', arg1='xxx'), ... stages=(False, True, True, True), ... position='after_conv2'), ... dict(cfg=dict(type='yyy'), ... stages=(True, True, True, True), ... position='after_conv3'), ... dict(cfg=dict(type='zzz', postfix='1'), ... stages=(True, True, True, True), ... position='after_conv3'), ... dict(cfg=dict(type='zzz', postfix='2'), ... stages=(True, True, True, True), ... position='after_conv3') ... ] >>> self = ResNet(depth=18) >>> stage_plugins = self.make_stage_plugins(plugins, 0) >>> assert len(stage_plugins) == 3 Suppose ``stage_idx=0``, the structure of blocks in the stage would be: .. code-block:: none conv1-> conv2->conv3->yyy->zzz1->zzz2 Suppose 'stage_idx=1', the structure of blocks in the stage would be: .. code-block:: none conv1-> conv2->xxx->conv3->yyy->zzz1->zzz2 If stages is missing, the plugin would be applied to all stages. Args: plugins (list[dict]): List of plugins cfg to build. The postfix is required if multiple same type plugins are inserted. stage_idx (int): Index of stage to build Returns: list[dict]: Plugins for current stage """ stage_plugins = [] for plugin in plugins: plugin = plugin.copy() stages = plugin.pop('stages', None) assert stages is None or len(stages) == self.num_stages # whether to insert plugin into current stage if stages is None or stages[stage_idx]: stage_plugins.append(plugin) return stage_plugins
[文档] def make_res_layer(self, **kwargs): """Pack all blocks in a stage into a ``ResLayer``.""" return 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): if self.deep_stem: self.stem = nn.Sequential( build_conv_layer( self.conv_cfg, in_channels, stem_channels // 2, kernel_size=3, stride=2, padding=1, bias=False), build_norm_layer(self.norm_cfg, stem_channels // 2)[1], nn.ReLU(inplace=True), build_conv_layer( self.conv_cfg, stem_channels // 2, stem_channels // 2, kernel_size=3, stride=1, padding=1, bias=False), build_norm_layer(self.norm_cfg, stem_channels // 2)[1], nn.ReLU(inplace=True), build_conv_layer( self.conv_cfg, stem_channels // 2, stem_channels, kernel_size=3, stride=1, padding=1, bias=False), build_norm_layer(self.norm_cfg, stem_channels)[1], nn.ReLU(inplace=True)) else: self.conv1 = build_conv_layer( self.conv_cfg, in_channels, stem_channels, kernel_size=7, stride=2, padding=3, 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): 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
[文档] 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)
[文档] def train(self, mode=True): """Convert the model into training mode while keep normalization layer freezed.""" super(ResNet, self).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()
[文档]@BACKBONES.register_module() class ResNetV1d(ResNet): r"""ResNetV1d variant described in `Bag of Tricks <https://arxiv.org/pdf/1812.01187.pdf>`_. Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in the input stem with three 3x3 convs. And in the downsampling block, a 2x2 avg_pool with stride 2 is added before conv, whose stride is changed to 1. """ def __init__(self, **kwargs): super(ResNetV1d, self).__init__( deep_stem=True, avg_down=True, **kwargs)
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