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| from annotator.uniformer.mmcv.cnn import ConvModule | |
| from torch import nn | |
| from torch.utils import checkpoint as cp | |
| from .se_layer import SELayer | |
| class InvertedResidual(nn.Module): | |
| """InvertedResidual block for MobileNetV2. | |
| Args: | |
| in_channels (int): The input channels of the InvertedResidual block. | |
| out_channels (int): The output channels of the InvertedResidual block. | |
| stride (int): Stride of the middle (first) 3x3 convolution. | |
| expand_ratio (int): Adjusts number of channels of the hidden layer | |
| in InvertedResidual by this amount. | |
| dilation (int): Dilation rate of depthwise conv. Default: 1 | |
| conv_cfg (dict): Config dict for convolution layer. | |
| Default: None, which means using conv2d. | |
| norm_cfg (dict): Config dict for normalization layer. | |
| Default: dict(type='BN'). | |
| act_cfg (dict): Config dict for activation layer. | |
| Default: dict(type='ReLU6'). | |
| with_cp (bool): Use checkpoint or not. Using checkpoint will save some | |
| memory while slowing down the training speed. Default: False. | |
| Returns: | |
| Tensor: The output tensor. | |
| """ | |
| def __init__(self, | |
| in_channels, | |
| out_channels, | |
| stride, | |
| expand_ratio, | |
| dilation=1, | |
| conv_cfg=None, | |
| norm_cfg=dict(type='BN'), | |
| act_cfg=dict(type='ReLU6'), | |
| with_cp=False): | |
| super(InvertedResidual, self).__init__() | |
| self.stride = stride | |
| assert stride in [1, 2], f'stride must in [1, 2]. ' \ | |
| f'But received {stride}.' | |
| self.with_cp = with_cp | |
| self.use_res_connect = self.stride == 1 and in_channels == out_channels | |
| hidden_dim = int(round(in_channels * expand_ratio)) | |
| layers = [] | |
| if expand_ratio != 1: | |
| layers.append( | |
| ConvModule( | |
| in_channels=in_channels, | |
| out_channels=hidden_dim, | |
| kernel_size=1, | |
| conv_cfg=conv_cfg, | |
| norm_cfg=norm_cfg, | |
| act_cfg=act_cfg)) | |
| layers.extend([ | |
| ConvModule( | |
| in_channels=hidden_dim, | |
| out_channels=hidden_dim, | |
| kernel_size=3, | |
| stride=stride, | |
| padding=dilation, | |
| dilation=dilation, | |
| groups=hidden_dim, | |
| conv_cfg=conv_cfg, | |
| norm_cfg=norm_cfg, | |
| act_cfg=act_cfg), | |
| ConvModule( | |
| in_channels=hidden_dim, | |
| out_channels=out_channels, | |
| kernel_size=1, | |
| conv_cfg=conv_cfg, | |
| norm_cfg=norm_cfg, | |
| act_cfg=None) | |
| ]) | |
| self.conv = nn.Sequential(*layers) | |
| def forward(self, x): | |
| def _inner_forward(x): | |
| if self.use_res_connect: | |
| return x + self.conv(x) | |
| else: | |
| return self.conv(x) | |
| if self.with_cp and x.requires_grad: | |
| out = cp.checkpoint(_inner_forward, x) | |
| else: | |
| out = _inner_forward(x) | |
| return out | |
| class InvertedResidualV3(nn.Module): | |
| """Inverted Residual Block for MobileNetV3. | |
| Args: | |
| in_channels (int): The input channels of this Module. | |
| out_channels (int): The output channels of this Module. | |
| mid_channels (int): The input channels of the depthwise convolution. | |
| kernel_size (int): The kernel size of the depthwise convolution. | |
| Default: 3. | |
| stride (int): The stride of the depthwise convolution. Default: 1. | |
| se_cfg (dict): Config dict for se layer. Default: None, which means no | |
| se layer. | |
| with_expand_conv (bool): Use expand conv or not. If set False, | |
| mid_channels must be the same with in_channels. Default: True. | |
| conv_cfg (dict): Config dict for convolution layer. Default: None, | |
| which means using conv2d. | |
| norm_cfg (dict): Config dict for normalization layer. | |
| Default: dict(type='BN'). | |
| act_cfg (dict): Config dict for activation layer. | |
| Default: dict(type='ReLU'). | |
| with_cp (bool): Use checkpoint or not. Using checkpoint will save some | |
| memory while slowing down the training speed. Default: False. | |
| Returns: | |
| Tensor: The output tensor. | |
| """ | |
| def __init__(self, | |
| in_channels, | |
| out_channels, | |
| mid_channels, | |
| kernel_size=3, | |
| stride=1, | |
| se_cfg=None, | |
| with_expand_conv=True, | |
| conv_cfg=None, | |
| norm_cfg=dict(type='BN'), | |
| act_cfg=dict(type='ReLU'), | |
| with_cp=False): | |
| super(InvertedResidualV3, self).__init__() | |
| self.with_res_shortcut = (stride == 1 and in_channels == out_channels) | |
| assert stride in [1, 2] | |
| self.with_cp = with_cp | |
| self.with_se = se_cfg is not None | |
| self.with_expand_conv = with_expand_conv | |
| if self.with_se: | |
| assert isinstance(se_cfg, dict) | |
| if not self.with_expand_conv: | |
| assert mid_channels == in_channels | |
| if self.with_expand_conv: | |
| self.expand_conv = ConvModule( | |
| in_channels=in_channels, | |
| out_channels=mid_channels, | |
| kernel_size=1, | |
| stride=1, | |
| padding=0, | |
| conv_cfg=conv_cfg, | |
| norm_cfg=norm_cfg, | |
| act_cfg=act_cfg) | |
| self.depthwise_conv = ConvModule( | |
| in_channels=mid_channels, | |
| out_channels=mid_channels, | |
| kernel_size=kernel_size, | |
| stride=stride, | |
| padding=kernel_size // 2, | |
| groups=mid_channels, | |
| conv_cfg=dict( | |
| type='Conv2dAdaptivePadding') if stride == 2 else conv_cfg, | |
| norm_cfg=norm_cfg, | |
| act_cfg=act_cfg) | |
| if self.with_se: | |
| self.se = SELayer(**se_cfg) | |
| self.linear_conv = ConvModule( | |
| in_channels=mid_channels, | |
| out_channels=out_channels, | |
| kernel_size=1, | |
| stride=1, | |
| padding=0, | |
| conv_cfg=conv_cfg, | |
| norm_cfg=norm_cfg, | |
| act_cfg=None) | |
| def forward(self, x): | |
| def _inner_forward(x): | |
| out = x | |
| if self.with_expand_conv: | |
| out = self.expand_conv(out) | |
| out = self.depthwise_conv(out) | |
| if self.with_se: | |
| out = self.se(out) | |
| out = self.linear_conv(out) | |
| if self.with_res_shortcut: | |
| return x + out | |
| else: | |
| return out | |
| if self.with_cp and x.requires_grad: | |
| out = cp.checkpoint(_inner_forward, x) | |
| else: | |
| out = _inner_forward(x) | |
| return out | |