mmDetection源码分析（三）：Faster R-CNN模块解读（一）

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Faster R-CNN模块解读（一）— 检测器的构建

根据之前的介绍，config文件中的 model 中的 type 指定了检测器是一个Faster R-CNN检测器。

我们知道要调用一个检测模型要用到函数model = build_detector(cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)。接下来，我们看看其是如何调用到Faster R-CNN模块的。

mmdetection/configs/faster_rcnn_r50_fpn_1x.py

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model = dict(
    type='FasterRCNN',
    pretrained='torchvision://resnet50',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        style='pytorch'),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_scales=[8],
        anchor_ratios=[0.5, 1.0, 2.0],
        anchor_strides=[4, 8, 16, 32, 64],
        target_means=[.0, .0, .0, .0],
        target_stds=[1.0, 1.0, 1.0, 1.0],
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
    bbox_roi_extractor=dict(
        type='SingleRoIExtractor',
        roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
        out_channels=256,
        featmap_strides=[4, 8, 16, 32]),
    bbox_head=dict(
        type='SharedFCBBoxHead',
        num_fcs=2,
        in_channels=256,
        fc_out_channels=1024,
        roi_feat_size=7,
        num_classes=81,
        target_means=[0., 0., 0., 0.],
        target_stds=[0.1, 0.1, 0.2, 0.2],
        reg_class_agnostic=False,
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)))

mmdetection/mmdet/models/registry.py

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BACKBONES = Registry('backbone')
NECKS = Registry('neck')
ROI_EXTRACTORS = Registry('roi_extractor')
SHARED_HEADS = Registry('shared_head')
HEADS = Registry('head')
LOSSES = Registry('loss')
DETECTORS = Registry('detector')

mmdetection/mmdet/models/builder.py

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def build(cfg, registry, default_args=None):
    if isinstance(cfg, list):
        modules = [
            build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg
        ]
        return nn.Sequential(*modules)
    else:
        return build_from_cfg(cfg, registry, default_args)

def build_detector(cfg, train_cfg=None, test_cfg=None):
    return build(cfg, DETECTORS, dict(train_cfg=train_cfg, test_cfg=test_cfg))

mmdetection/mmdet/utils/registry.py 

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def build_from_cfg(cfg, registry, default_args=None):
    """Build a module from config dict.

    Args:
        cfg (dict): Config dict. It should at least contain the key "type".
        registry (:obj:`Registry`): The registry to search the type from.
        default_args (dict, optional): Default initialization arguments.

    Returns:
        obj: The constructed object.
    """
    assert isinstance(cfg, dict) and 'type' in cfg
    assert isinstance(default_args, dict) or default_args is None
    args = cfg.copy()
    obj_type = args.pop('type')
    if mmcv.is_str(obj_type):
        obj_type = registry.get(obj_type)
        if obj_type is None:
            raise KeyError('{} is not in the {} registry'.format(
                obj_type, registry.name))
    elif not inspect.isclass(obj_type):
        raise TypeError('type must be a str or valid type, but got {}'.format(
            type(obj_type)))
    if default_args is not None:
        for name, value in default_args.items():
            args.setdefault(name, value)
    return obj_type(**args)

通过build_from_cfg返回了cfg.model.type定义的类。registry指明了该类在mmdetection/mmdet/models/detectors中，查看文件faster_rcnn.py(此处有其框架设计的问题，比如注册器的使用，在本系列中暂时不分析)

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@DETECTORS.register_module
class FasterRCNN(TwoStageDetector):

    def __init__(self,
                 backbone,
                 rpn_head,
                 bbox_roi_extractor,
                 bbox_head,
                 train_cfg,
                 test_cfg,
                 neck=None,
                 shared_head=None,
                 pretrained=None):
        super(FasterRCNN, self).__init__(
            backbone=backbone,
            neck=neck,
            shared_head=shared_head,
            rpn_head=rpn_head,
            bbox_roi_extractor=bbox_roi_extractor,
            bbox_head=bbox_head,
            train_cfg=train_cfg,
            test_cfg=test_cfg,
            pretrained=pretrained)

可以看到FasterRCNN继承于TwoStageDetector。

mmdetection/mmdet/models/detectors/two_stage.py

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@DETECTORS.register_module
class TwoStageDetector(BaseDetector, RPNTestMixin, BBoxTestMixin,
                       MaskTestMixin):

    def __init__(self,
                 backbone,
                 neck=None,
                 shared_head=None,
                 rpn_head=None,
                 bbox_roi_extractor=None,
                 bbox_head=None,
                 mask_roi_extractor=None,
                 mask_head=None,
                 train_cfg=None,
                 test_cfg=None,
                 pretrained=None):
        super(TwoStageDetector, self).__init__()
        self.backbone = builder.build_backbone(backbone)

        if neck is not None:
            self.neck = builder.build_neck(neck)

        if shared_head is not None:
            self.shared_head = builder.build_shared_head(shared_head)

        if rpn_head is not None:
            self.rpn_head = builder.build_head(rpn_head)

        if bbox_head is not None:
            self.bbox_roi_extractor = builder.build_roi_extractor(
                bbox_roi_extractor)
            self.bbox_head = builder.build_head(bbox_head)

        if mask_head is not None:
            if mask_roi_extractor is not None:
                self.mask_roi_extractor = builder.build_roi_extractor(
                    mask_roi_extractor)
                self.share_roi_extractor = False
            else:
                self.share_roi_extractor = True
                self.mask_roi_extractor = self.bbox_roi_extractor
            self.mask_head = builder.build_head(mask_head)

        self.train_cfg = train_cfg
        self.test_cfg = test_cfg

        self.init_weights(pretrained=pretrained)
     
     def init_weights(self, pretrained=None):
        super(TwoStageDetector, self).init_weights(pretrained)
        self.backbone.init_weights(pretrained=pretrained)
        if self.with_neck:
            if isinstance(self.neck, nn.Sequential):
                for m in self.neck:
                    m.init_weights()
            else:
                self.neck.init_weights()
        if self.with_shared_head:
            self.shared_head.init_weights(pretrained=pretrained)
        if self.with_rpn:
            self.rpn_head.init_weights()
        if self.with_bbox:
            self.bbox_roi_extractor.init_weights()
            self.bbox_head.init_weights()
        if self.with_mask:
            self.mask_head.init_weights()
            if not self.share_roi_extractor:
                self.mask_roi_extractor.init_weights()

可以看到通过builder将检测器的backbone，neck，shared_head，bbox_head，mask_head等全部创建了。并通过init_weights初始话权重，后续有：

extract_feat()
forward_train()
simple_test && aug_test

下一篇blog将介绍Backbone的构建。