mmpose.models.backbones.seresnet 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import torch.utils.checkpoint as cp

from mmpose.registry import MODELS
from .resnet import Bottleneck, ResLayer, ResNet
from .utils.se_layer import SELayer

class SEBottleneck(Bottleneck):
    """SEBottleneck block for SEResNet.

        in_channels (int): The input channels of the SEBottleneck block.
        out_channels (int): The output channel of the SEBottleneck block.
        se_ratio (int): Squeeze ratio in SELayer. Default: 16

    def __init__(self, in_channels, out_channels, se_ratio=16, **kwargs):
        super().__init__(in_channels, out_channels, **kwargs)
        self.se_layer = SELayer(out_channels, ratio=se_ratio)

    def forward(self, x):

        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)

            out = self.se_layer(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)
            out = _inner_forward(x)

        out = self.relu(out)

        return out

[文档]@MODELS.register_module() class SEResNet(ResNet): """SEResNet backbone. Please refer to the `paper <>`__ for details. Args: depth (int): Network depth, from {50, 101, 152}. 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 SEResNet >>> 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, se_ratio=16, **kwargs): if depth not in self.arch_settings: raise KeyError(f'invalid depth {depth} for SEResNet') self.se_ratio = se_ratio super().__init__(depth, **kwargs)
[文档] def make_res_layer(self, **kwargs): return ResLayer(se_ratio=self.se_ratio, **kwargs)
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