mmdetection/mmdet/models/roi_heads/bbox_heads/double_bbox_head.py

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple

import torch.nn as nn
from mmcv.cnn import ConvModule
from mmengine.model import BaseModule, ModuleList
from torch import Tensor

from mmdet.models.backbones.resnet import Bottleneck
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, MultiConfig, OptConfigType, OptMultiConfig
from .bbox_head import BBoxHead


class BasicResBlock(BaseModule):
    """Basic residual block.

    This block is a little different from the block in the ResNet backbone.
    The kernel size of conv1 is 1 in this block while 3 in ResNet BasicBlock.

    Args:
        in_channels (int): Channels of the input feature map.
        out_channels (int): Channels of the output feature map.
        conv_cfg (:obj:`ConfigDict` or dict, optional): The config dict
            for convolution layers.
        norm_cfg (:obj:`ConfigDict` or dict): The config dict for
            normalization layers.
        init_cfg (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or \
            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) -> None:
        super().__init__(init_cfg=init_cfg)

        # main path
        self.conv1 = ConvModule(
            in_channels,
            in_channels,
            kernel_size=3,
            padding=1,
            bias=False,
            conv_cfg=conv_cfg,
            norm_cfg=norm_cfg)
        self.conv2 = ConvModule(
            in_channels,
            out_channels,
            kernel_size=1,
            bias=False,
            conv_cfg=conv_cfg,
            norm_cfg=norm_cfg,
            act_cfg=None)

        # identity path
        self.conv_identity = ConvModule(
            in_channels,
            out_channels,
            kernel_size=1,
            conv_cfg=conv_cfg,
            norm_cfg=norm_cfg,
            act_cfg=None)

        self.relu = nn.ReLU(inplace=True)

    def forward(self, x: Tensor) -> Tensor:
        """Forward function."""
        identity = x

        x = self.conv1(x)
        x = self.conv2(x)

        identity = self.conv_identity(identity)
        out = x + identity

        out = self.relu(out)
        return out


@MODELS.register_module()
class DoubleConvFCBBoxHead(BBoxHead):
    r"""Bbox head used in Double-Head R-CNN

    .. code-block:: none

                                          /-> cls
                      /-> shared convs ->
                                          \-> reg
        roi features
                                          /-> cls
                      \-> shared fc    ->
                                          \-> reg
    """  # noqa: W605

    def __init__(self,
                 num_convs: int = 0,
                 num_fcs: int = 0,
                 conv_out_channels: int = 1024,
                 fc_out_channels: int = 1024,
                 conv_cfg: OptConfigType = None,
                 norm_cfg: ConfigType = dict(type='BN'),
                 init_cfg: MultiConfig = dict(
                     type='Normal',
                     override=[
                         dict(type='Normal', name='fc_cls', std=0.01),
                         dict(type='Normal', name='fc_reg', std=0.001),
                         dict(
                             type='Xavier',
                             name='fc_branch',
                             distribution='uniform')
                     ]),
                 **kwargs) -> None:
        kwargs.setdefault('with_avg_pool', True)
        super().__init__(init_cfg=init_cfg, **kwargs)
        assert self.with_avg_pool
        assert num_convs > 0
        assert num_fcs > 0
        self.num_convs = num_convs
        self.num_fcs = num_fcs
        self.conv_out_channels = conv_out_channels
        self.fc_out_channels = fc_out_channels
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg

        # increase the channel of input features
        self.res_block = BasicResBlock(self.in_channels,
                                       self.conv_out_channels)

        # add conv heads
        self.conv_branch = self._add_conv_branch()
        # add fc heads
        self.fc_branch = self._add_fc_branch()

        out_dim_reg = 4 if self.reg_class_agnostic else 4 * self.num_classes
        self.fc_reg = nn.Linear(self.conv_out_channels, out_dim_reg)

        self.fc_cls = nn.Linear(self.fc_out_channels, self.num_classes + 1)
        self.relu = nn.ReLU()

    def _add_conv_branch(self) -> None:
        """Add the fc branch which consists of a sequential of conv layers."""
        branch_convs = ModuleList()
        for i in range(self.num_convs):
            branch_convs.append(
                Bottleneck(
                    inplanes=self.conv_out_channels,
                    planes=self.conv_out_channels // 4,
                    conv_cfg=self.conv_cfg,
                    norm_cfg=self.norm_cfg))
        return branch_convs

    def _add_fc_branch(self) -> None:
        """Add the fc branch which consists of a sequential of fc layers."""
        branch_fcs = ModuleList()
        for i in range(self.num_fcs):
            fc_in_channels = (
                self.in_channels *
                self.roi_feat_area if i == 0 else self.fc_out_channels)
            branch_fcs.append(nn.Linear(fc_in_channels, self.fc_out_channels))
        return branch_fcs

    def forward(self, x_cls: Tensor, x_reg: Tensor) -> Tuple[Tensor]:
        """Forward features from the upstream network.

        Args:
            x_cls (Tensor): Classification features of rois
            x_reg (Tensor): Regression features from the upstream network.

        Returns:
            tuple: A tuple of classification scores and bbox prediction.

                - cls_score (Tensor): Classification score predictions of rois.
                  each roi predicts num_classes + 1 channels.
                - bbox_pred (Tensor): BBox deltas predictions of rois. each roi
                  predicts 4 * num_classes channels.
        """
        # conv head
        x_conv = self.res_block(x_reg)

        for conv in self.conv_branch:
            x_conv = conv(x_conv)

        if self.with_avg_pool:
            x_conv = self.avg_pool(x_conv)

        x_conv = x_conv.view(x_conv.size(0), -1)
        bbox_pred = self.fc_reg(x_conv)

        # fc head
        x_fc = x_cls.view(x_cls.size(0), -1)
        for fc in self.fc_branch:
            x_fc = self.relu(fc(x_fc))

        cls_score = self.fc_cls(x_fc)

        return cls_score, bbox_pred