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mmpose.models.backbones.seresnext 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.cnn import build_conv_layer, build_norm_layer

from mmpose.registry import MODELS
from .resnet import ResLayer
from .seresnet import SEBottleneck as _SEBottleneck
from .seresnet import SEResNet


class SEBottleneck(_SEBottleneck):
    """SEBottleneck block for SEResNeXt.

    Args:
        in_channels (int): Input channels of this block.
        out_channels (int): Output channels of this block.
        base_channels (int): Middle channels of the first stage. Default: 64.
        groups (int): Groups of conv2.
        width_per_group (int): Width per group of conv2. 64x4d indicates
            ``groups=64, width_per_group=4`` and 32x8d indicates
            ``groups=32, width_per_group=8``.
        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
        se_ratio (int): Squeeze ratio in SELayer. Default: 16
        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.
        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')
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed.
        init_cfg (dict or list[dict], optional): Initialization config dict.
            Default: None
    """

    def __init__(self,
                 in_channels,
                 out_channels,
                 base_channels=64,
                 groups=32,
                 width_per_group=4,
                 se_ratio=16,
                 **kwargs):
        super().__init__(in_channels, out_channels, se_ratio, **kwargs)
        self.groups = groups
        self.width_per_group = width_per_group

        # We follow the same rational of ResNext to compute mid_channels.
        # For SEResNet bottleneck, middle channels are determined by expansion
        # and out_channels, but for SEResNeXt bottleneck, it is determined by
        # groups and width_per_group and the stage it is located in.
        if groups != 1:
            assert self.mid_channels % base_channels == 0
            self.mid_channels = (
                groups * width_per_group * self.mid_channels // base_channels)

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

        self.conv1 = build_conv_layer(
            self.conv_cfg,
            self.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(
            self.conv_cfg,
            self.mid_channels,
            self.mid_channels,
            kernel_size=3,
            stride=self.conv2_stride,
            padding=self.dilation,
            dilation=self.dilation,
            groups=groups,
            bias=False)

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


[文档]@MODELS.register_module() class SEResNeXt(SEResNet): """SEResNeXt backbone. Please refer to the `paper <https://arxiv.org/abs/1709.01507>`__ for details. Args: depth (int): Network depth, from {50, 101, 152}. groups (int): Groups of conv2 in Bottleneck. Default: 32. width_per_group (int): Width per group of conv2 in Bottleneck. Default: 4. se_ratio (int): Squeeze ratio in SELayer. Default: 16. in_channels (int): Number of input image channels. Default: 3. stem_channels (int): Output channels of the stem layer. Default: 64. 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. init_cfg (dict or list[dict], optional): Initialization config dict. Default: ``[ dict(type='Kaiming', layer=['Conv2d']), dict( type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm']) ]`` Example: >>> from mmpose.models import SEResNeXt >>> import torch >>> self = SEResNet(depth=50, out_indices=(0, 1, 2, 3)) >>> self.eval() >>> inputs = torch.rand(1, 3, 224, 224) >>> level_outputs = self.forward(inputs) >>> for level_out in level_outputs: ... print(tuple(level_out.shape)) (1, 256, 56, 56) (1, 512, 28, 28) (1, 1024, 14, 14) (1, 2048, 7, 7) """ arch_settings = { 50: (SEBottleneck, (3, 4, 6, 3)), 101: (SEBottleneck, (3, 4, 23, 3)), 152: (SEBottleneck, (3, 8, 36, 3)) } def __init__(self, depth, groups=32, width_per_group=4, **kwargs): self.groups = groups self.width_per_group = width_per_group super().__init__(depth, **kwargs)
[文档] def make_res_layer(self, **kwargs): return ResLayer( groups=self.groups, width_per_group=self.width_per_group, base_channels=self.base_channels, **kwargs)
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