93 lines
3.1 KiB
Python
93 lines
3.1 KiB
Python
from mmcv.cnn import ConvModule
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from torch import nn as nn
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from torch.utils import checkpoint as cp
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class InvertedResidual(nn.Module):
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"""InvertedResidual block for MobileNetV2.
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Args:
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in_channels (int): The input channels of the InvertedResidual block.
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out_channels (int): The output channels of the InvertedResidual block.
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stride (int): Stride of the middle (first) 3x3 convolution.
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expand_ratio (int): Adjusts number of channels of the hidden layer
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in InvertedResidual by this amount.
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dilation (int): Dilation rate of depthwise conv. Default: 1
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conv_cfg (dict): Config dict for convolution layer.
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Default: None, which means using conv2d.
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norm_cfg (dict): Config dict for normalization layer.
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Default: dict(type='BN').
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act_cfg (dict): Config dict for activation layer.
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Default: dict(type='ReLU6').
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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. Default: False.
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Returns:
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Tensor: The output tensor
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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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stride,
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expand_ratio,
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dilation=1,
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conv_cfg=None,
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norm_cfg=dict(type='BN'),
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act_cfg=dict(type='ReLU6'),
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with_cp=False):
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super(InvertedResidual, self).__init__()
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self.stride = stride
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assert stride in [1, 2], f'stride must in [1, 2]. ' \
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f'But received {stride}.'
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self.with_cp = with_cp
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self.use_res_connect = self.stride == 1 and in_channels == out_channels
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hidden_dim = int(round(in_channels * expand_ratio))
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layers = []
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if expand_ratio != 1:
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layers.append(
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ConvModule(
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in_channels=in_channels,
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out_channels=hidden_dim,
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kernel_size=1,
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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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layers.extend([
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ConvModule(
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in_channels=hidden_dim,
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out_channels=hidden_dim,
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kernel_size=3,
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stride=stride,
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padding=dilation,
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dilation=dilation,
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groups=hidden_dim,
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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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ConvModule(
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in_channels=hidden_dim,
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out_channels=out_channels,
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kernel_size=1,
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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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])
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self.conv = nn.Sequential(*layers)
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def forward(self, x):
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def _inner_forward(x):
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if self.use_res_connect:
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return x + self.conv(x)
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else:
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return self.conv(x)
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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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