| |
| from typing import List, Tuple, Union |
|
|
| import torch.nn as nn |
| from mmcv.cnn import ConvModule |
| from mmengine.model import BaseModule |
| from torch import Tensor |
|
|
| from mmdet.registry import MODELS |
| from mmdet.utils import OptConfigType, OptMultiConfig |
|
|
|
|
| @MODELS.register_module() |
| class ChannelMapper(BaseModule): |
| """Channel Mapper to reduce/increase channels of backbone features. |
| |
| This is used to reduce/increase channels of backbone features. |
| |
| Args: |
| in_channels (List[int]): Number of input channels per scale. |
| out_channels (int): Number of output channels (used at each scale). |
| kernel_size (int, optional): kernel_size for reducing channels (used |
| at each scale). Default: 3. |
| conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for |
| convolution layer. Default: None. |
| norm_cfg (:obj:`ConfigDict` or dict, optional): Config dict for |
| normalization layer. Default: None. |
| act_cfg (:obj:`ConfigDict` or dict, optional): Config dict for |
| activation layer in ConvModule. Default: dict(type='ReLU'). |
| bias (bool | str): If specified as `auto`, it will be decided by the |
| norm_cfg. Bias will be set as True if `norm_cfg` is None, otherwise |
| False. Default: "auto". |
| num_outs (int, optional): Number of output feature maps. There would |
| be extra_convs when num_outs larger than the length of in_channels. |
| init_cfg (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or dict], |
| optional): Initialization config dict. |
| Example: |
| >>> import torch |
| >>> in_channels = [2, 3, 5, 7] |
| >>> scales = [340, 170, 84, 43] |
| >>> inputs = [torch.rand(1, c, s, s) |
| ... for c, s in zip(in_channels, scales)] |
| >>> self = ChannelMapper(in_channels, 11, 3).eval() |
| >>> outputs = self.forward(inputs) |
| >>> for i in range(len(outputs)): |
| ... print(f'outputs[{i}].shape = {outputs[i].shape}') |
| outputs[0].shape = torch.Size([1, 11, 340, 340]) |
| outputs[1].shape = torch.Size([1, 11, 170, 170]) |
| outputs[2].shape = torch.Size([1, 11, 84, 84]) |
| outputs[3].shape = torch.Size([1, 11, 43, 43]) |
| """ |
|
|
| def __init__( |
| self, |
| in_channels: List[int], |
| out_channels: int, |
| kernel_size: int = 3, |
| conv_cfg: OptConfigType = None, |
| norm_cfg: OptConfigType = None, |
| act_cfg: OptConfigType = dict(type='ReLU'), |
| bias: Union[bool, str] = 'auto', |
| num_outs: int = None, |
| init_cfg: OptMultiConfig = dict( |
| type='Xavier', layer='Conv2d', distribution='uniform') |
| ) -> None: |
| super().__init__(init_cfg=init_cfg) |
| assert isinstance(in_channels, list) |
| self.extra_convs = None |
| if num_outs is None: |
| num_outs = len(in_channels) |
| self.convs = nn.ModuleList() |
| for in_channel in in_channels: |
| self.convs.append( |
| ConvModule( |
| in_channel, |
| out_channels, |
| kernel_size, |
| padding=(kernel_size - 1) // 2, |
| conv_cfg=conv_cfg, |
| norm_cfg=norm_cfg, |
| act_cfg=act_cfg, |
| bias=bias)) |
| if num_outs > len(in_channels): |
| self.extra_convs = nn.ModuleList() |
| for i in range(len(in_channels), num_outs): |
| if i == len(in_channels): |
| in_channel = in_channels[-1] |
| else: |
| in_channel = out_channels |
| self.extra_convs.append( |
| ConvModule( |
| in_channel, |
| out_channels, |
| 3, |
| stride=2, |
| padding=1, |
| conv_cfg=conv_cfg, |
| norm_cfg=norm_cfg, |
| act_cfg=act_cfg, |
| bias=bias)) |
|
|
| def forward(self, inputs: Tuple[Tensor]) -> Tuple[Tensor]: |
| """Forward function.""" |
| assert len(inputs) == len(self.convs) |
| outs = [self.convs[i](inputs[i]) for i in range(len(inputs))] |
| if self.extra_convs: |
| for i in range(len(self.extra_convs)): |
| if i == 0: |
| outs.append(self.extra_convs[0](inputs[-1])) |
| else: |
| outs.append(self.extra_convs[i](outs[-1])) |
| return tuple(outs) |
|
|
