| |
| from typing import List, Tuple |
|
|
| import torch |
| import torch.nn.functional as F |
| from mmcv.cnn import ConvModule |
| from mmengine.model import BaseModule |
|
|
| from mmdet.registry import MODELS |
| from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig |
|
|
|
|
| class SSHContextModule(BaseModule): |
| """This is an implementation of `SSH context module` described in `SSH: |
| Single Stage Headless Face Detector. |
| |
| <https://arxiv.org/pdf/1708.03979.pdf>`_. |
| |
| Args: |
| in_channels (int): Number of input channels used at each scale. |
| out_channels (int): Number of output channels used at each scale. |
| conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for |
| convolution layer. Defaults to None. |
| norm_cfg (:obj:`ConfigDict` or dict): Config dict for normalization |
| layer. Defaults to dict(type='BN'). |
| init_cfg (:obj:`ConfigDict` or list[:obj:`ConfigDict`] or dict or |
| list[dict], optional): Initialization config dict. |
| Defaults to None. |
| """ |
|
|
| def __init__(self, |
| in_channels: int, |
| out_channels: int, |
| conv_cfg: OptConfigType = None, |
| norm_cfg: ConfigType = dict(type='BN'), |
| init_cfg: OptMultiConfig = None): |
| super().__init__(init_cfg=init_cfg) |
| assert out_channels % 4 == 0 |
|
|
| self.in_channels = in_channels |
| self.out_channels = out_channels |
|
|
| self.conv5x5_1 = ConvModule( |
| self.in_channels, |
| self.out_channels // 4, |
| 3, |
| stride=1, |
| padding=1, |
| conv_cfg=conv_cfg, |
| norm_cfg=norm_cfg, |
| ) |
|
|
| self.conv5x5_2 = ConvModule( |
| self.out_channels // 4, |
| self.out_channels // 4, |
| 3, |
| stride=1, |
| padding=1, |
| conv_cfg=conv_cfg, |
| norm_cfg=norm_cfg, |
| act_cfg=None) |
|
|
| self.conv7x7_2 = ConvModule( |
| self.out_channels // 4, |
| self.out_channels // 4, |
| 3, |
| stride=1, |
| padding=1, |
| conv_cfg=conv_cfg, |
| norm_cfg=norm_cfg, |
| ) |
|
|
| self.conv7x7_3 = ConvModule( |
| self.out_channels // 4, |
| self.out_channels // 4, |
| 3, |
| stride=1, |
| padding=1, |
| conv_cfg=conv_cfg, |
| norm_cfg=norm_cfg, |
| act_cfg=None, |
| ) |
|
|
| def forward(self, x: torch.Tensor) -> tuple: |
| conv5x5_1 = self.conv5x5_1(x) |
| conv5x5 = self.conv5x5_2(conv5x5_1) |
| conv7x7_2 = self.conv7x7_2(conv5x5_1) |
| conv7x7 = self.conv7x7_3(conv7x7_2) |
|
|
| return (conv5x5, conv7x7) |
|
|
|
|
| class SSHDetModule(BaseModule): |
| """This is an implementation of `SSH detection module` described in `SSH: |
| Single Stage Headless Face Detector. |
| |
| <https://arxiv.org/pdf/1708.03979.pdf>`_. |
| |
| Args: |
| in_channels (int): Number of input channels used at each scale. |
| out_channels (int): Number of output channels used at each scale. |
| conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for |
| convolution layer. Defaults to None. |
| norm_cfg (:obj:`ConfigDict` or dict): Config dict for normalization |
| layer. Defaults to dict(type='BN'). |
| init_cfg (:obj:`ConfigDict` or list[:obj:`ConfigDict`] or dict or |
| list[dict], optional): Initialization config dict. |
| Defaults to None. |
| """ |
|
|
| def __init__(self, |
| in_channels: int, |
| out_channels: int, |
| conv_cfg: OptConfigType = None, |
| norm_cfg: ConfigType = dict(type='BN'), |
| init_cfg: OptMultiConfig = None): |
| super().__init__(init_cfg=init_cfg) |
| assert out_channels % 4 == 0 |
|
|
| self.in_channels = in_channels |
| self.out_channels = out_channels |
|
|
| self.conv3x3 = ConvModule( |
| self.in_channels, |
| self.out_channels // 2, |
| 3, |
| stride=1, |
| padding=1, |
| conv_cfg=conv_cfg, |
| norm_cfg=norm_cfg, |
| act_cfg=None) |
|
|
| self.context_module = SSHContextModule( |
| in_channels=self.in_channels, |
| out_channels=self.out_channels, |
| conv_cfg=conv_cfg, |
| norm_cfg=norm_cfg) |
|
|
| def forward(self, x: torch.Tensor) -> torch.Tensor: |
| conv3x3 = self.conv3x3(x) |
| conv5x5, conv7x7 = self.context_module(x) |
| out = torch.cat([conv3x3, conv5x5, conv7x7], dim=1) |
| out = F.relu(out) |
|
|
| return out |
|
|
|
|
| @MODELS.register_module() |
| class SSH(BaseModule): |
| """`SSH Neck` used in `SSH: Single Stage Headless Face Detector. |
| |
| <https://arxiv.org/pdf/1708.03979.pdf>`_. |
| |
| Args: |
| num_scales (int): The number of scales / stages. |
| in_channels (list[int]): The number of input channels per scale. |
| out_channels (list[int]): The number of output channels per scale. |
| conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for |
| convolution layer. Defaults to None. |
| norm_cfg (:obj:`ConfigDict` or dict): Config dict for normalization |
| layer. Defaults to dict(type='BN'). |
| init_cfg (:obj:`ConfigDict` or list[:obj:`ConfigDict`] or dict or |
| list[dict], optional): Initialization config dict. |
| |
| Example: |
| >>> import torch |
| >>> in_channels = [8, 16, 32, 64] |
| >>> out_channels = [16, 32, 64, 128] |
| >>> scales = [340, 170, 84, 43] |
| >>> inputs = [torch.rand(1, c, s, s) |
| ... for c, s in zip(in_channels, scales)] |
| >>> self = SSH(num_scales=4, in_channels=in_channels, |
| ... out_channels=out_channels) |
| >>> outputs = self.forward(inputs) |
| >>> for i in range(len(outputs)): |
| ... print(f'outputs[{i}].shape = {outputs[i].shape}') |
| outputs[0].shape = torch.Size([1, 16, 340, 340]) |
| outputs[1].shape = torch.Size([1, 32, 170, 170]) |
| outputs[2].shape = torch.Size([1, 64, 84, 84]) |
| outputs[3].shape = torch.Size([1, 128, 43, 43]) |
| """ |
|
|
| def __init__(self, |
| num_scales: int, |
| in_channels: List[int], |
| out_channels: List[int], |
| conv_cfg: OptConfigType = None, |
| norm_cfg: ConfigType = dict(type='BN'), |
| init_cfg: OptMultiConfig = dict( |
| type='Xavier', layer='Conv2d', distribution='uniform')): |
| super().__init__(init_cfg=init_cfg) |
| assert (num_scales == len(in_channels) == len(out_channels)) |
| self.num_scales = num_scales |
| self.in_channels = in_channels |
| self.out_channels = out_channels |
|
|
| for idx in range(self.num_scales): |
| in_c, out_c = self.in_channels[idx], self.out_channels[idx] |
| self.add_module( |
| f'ssh_module{idx}', |
| SSHDetModule( |
| in_channels=in_c, |
| out_channels=out_c, |
| conv_cfg=conv_cfg, |
| norm_cfg=norm_cfg)) |
|
|
| def forward(self, inputs: Tuple[torch.Tensor]) -> tuple: |
| assert len(inputs) == self.num_scales |
|
|
| outs = [] |
| for idx, x in enumerate(inputs): |
| ssh_module = getattr(self, f'ssh_module{idx}') |
| out = ssh_module(x) |
| outs.append(out) |
|
|
| return tuple(outs) |
|
|
