126 lines
4.1 KiB
Python
126 lines
4.1 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import torch.nn as nn
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import torch.utils.checkpoint as cp
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from mmcv.cnn import ConvModule
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from mmcv.cnn.bricks import DropPath
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from mmcv.runner import BaseModule
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from .se_layer import SELayer
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class InvertedResidual(BaseModule):
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"""Inverted Residual Block.
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Args:
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in_channels (int): The input channels of this module.
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out_channels (int): The output channels of this module.
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mid_channels (int): The input channels of the depthwise convolution.
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kernel_size (int): The kernel size of the depthwise convolution.
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Defaults to 3.
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stride (int): The stride of the depthwise convolution. Defaults to 1.
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se_cfg (dict, optional): Config dict for se layer. Defaults to None,
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which means no se layer.
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conv_cfg (dict): Config dict for convolution layer. Defaults to None,
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which means using conv2d.
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norm_cfg (dict): Config dict for normalization layer.
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Defaults to ``dict(type='BN')``.
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act_cfg (dict): Config dict for activation layer.
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Defaults to ``dict(type='ReLU')``.
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drop_path_rate (float): stochastic depth rate. Defaults to 0.
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with_cp (bool): Use checkpoint or not. Using checkpoint will save some
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memory while slowing down the training speed. Defaults to False.
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init_cfg (dict | list[dict], optional): Initialization config dict.
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"""
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def __init__(self,
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in_channels,
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out_channels,
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mid_channels,
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kernel_size=3,
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stride=1,
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se_cfg=None,
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conv_cfg=None,
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norm_cfg=dict(type='BN'),
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act_cfg=dict(type='ReLU'),
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drop_path_rate=0.,
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with_cp=False,
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init_cfg=None):
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super(InvertedResidual, self).__init__(init_cfg)
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self.with_res_shortcut = (stride == 1 and in_channels == out_channels)
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assert stride in [1, 2]
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self.with_cp = with_cp
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self.drop_path = DropPath(
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drop_path_rate) if drop_path_rate > 0 else nn.Identity()
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self.with_se = se_cfg is not None
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self.with_expand_conv = (mid_channels != in_channels)
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if self.with_se:
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assert isinstance(se_cfg, dict)
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if self.with_expand_conv:
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self.expand_conv = ConvModule(
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in_channels=in_channels,
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out_channels=mid_channels,
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kernel_size=1,
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stride=1,
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padding=0,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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act_cfg=act_cfg)
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self.depthwise_conv = ConvModule(
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in_channels=mid_channels,
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out_channels=mid_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=kernel_size // 2,
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groups=mid_channels,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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act_cfg=act_cfg)
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if self.with_se:
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self.se = SELayer(**se_cfg)
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self.linear_conv = ConvModule(
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in_channels=mid_channels,
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out_channels=out_channels,
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kernel_size=1,
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stride=1,
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padding=0,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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act_cfg=None)
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def forward(self, x):
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"""Forward function.
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Args:
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x (torch.Tensor): The input tensor.
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Returns:
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torch.Tensor: The output tensor.
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"""
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def _inner_forward(x):
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out = x
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if self.with_expand_conv:
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out = self.expand_conv(out)
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out = self.depthwise_conv(out)
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if self.with_se:
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out = self.se(out)
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out = self.linear_conv(out)
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if self.with_res_shortcut:
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return x + self.drop_path(out)
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else:
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return out
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if self.with_cp and x.requires_grad:
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out = cp.checkpoint(_inner_forward, x)
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else:
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out = _inner_forward(x)
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return out
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