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| # Copyright (c) OpenMMLab. All rights reserved. | |
| from typing import List, Tuple | |
| 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 | |
| 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'). | |
| 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'), | |
| 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)) | |
| 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)) | |
| 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) | |