Faster RCNN proposal_layer.py

定义了一个 ProposalLayer 类, 在rpn_test.pt中会用到。

class ProposalLayer(caffe.Layer):
    """
    Outputs object detection proposals by applying estimated bounding-box
    transformations to a set of regular boxes (called "anchors").
    """

def setup(self, bottom, top):

def setup(self, bottom, top):
        # parse the layer parameter string, which must be valid YAML
        layer_params = yaml.load(self.param_str_)

        self._feat_stride = layer_params['feat_stride']
        anchor_scales = layer_params.get('scales', (8, 16, 32))

        # 生成9个anchors
        self._anchors = generate_anchors(scales=np.array(anchor_scales))
        self._num_anchors = self._anchors.shape[0]

        if DEBUG:
            print 'feat_stride: {}'.format(self._feat_stride)
            print 'anchors:'
            print self._anchors

        # rois blob: holds R regions of interest, each is a 5-tuple
        # (n, x1, y1, x2, y2) specifying an image batch index n and a
        # rectangle (x1, y1, x2, y2)
        top[0].reshape(1, 5)

        # scores blob: holds scores for R regions of interest
        if len(top) > 1:
            top[1].reshape(1, 1, 1, 1)

def forward(self, bottom, top): 输出Top[0],是 R个 regions of interest, each is a 5-tuple (n, x1, y1, x2, y2) , 其中n 代表batch index; x1, y1, x2, y2表示矩形的4个点的坐标。
输出Top[1]为每个proposal的得分,即是一个物体的可能性。

def forward(self, bottom, top):
        # Algorithm:
        #
        # for each (H, W) location i
        #   generate A anchor boxes centered on cell i
        #   apply predicted bbox deltas at cell i to each of the A anchors
        # clip predicted boxes to image
        # remove predicted boxes with either height or width < threshold
        # sort all (proposal, score) pairs by score from highest to lowest
        # take top pre_nms_topN proposals before NMS
        # apply NMS with threshold 0.7 to remaining proposals
        # take after_nms_topN proposals after NMS
        # return the top proposals (-> RoIs top, scores top)

        assert bottom[0].data.shape[0] == 1, \
            'Only single item batches are supported'

        cfg_key = str(self.phase) # either 'TRAIN' or 'TEST'
        pre_nms_topN  = cfg[cfg_key].RPN_PRE_NMS_TOP_N
        post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
        nms_thresh    = cfg[cfg_key].RPN_NMS_THRESH
        min_size      = cfg[cfg_key].RPN_MIN_SIZE

        # the first set of _num_anchors channels are bg probs
        # the second set are the fg probs, which we want
        # 前9个通道为背景类;后9个通道为非背景类
        scores = bottom[0].data[:, self._num_anchors:, :, :]

        # bbox_deltas 为 rpn_box_pred,是对targets的预测,后面会据此来对proposals的位置进行预测 
        bbox_deltas = bottom[1].data
        im_info = bottom[2].data[0, :]

        if DEBUG:
            print 'im_size: ({}, {})'.format(im_info[0], im_info[1])
            print 'scale: {}'.format(im_info[2])

        # 1. Generate proposals from bbox deltas and shifted anchors
        height, width = scores.shape[-2:]

        if DEBUG:
            print 'score map size: {}'.format(scores.shape)

        # Enumerate all shifts
        shift_x = np.arange(0, width) * self._feat_stride
        shift_y = np.arange(0, height) * self._feat_stride
        shift_x, shift_y = np.meshgrid(shift_x, shift_y)
        shifts = np.vstack((shift_x.ravel(), shift_y.ravel(),
                            shift_x.ravel(), shift_y.ravel())).transpose()

        # Enumerate all shifted anchors:
        #
        # add A anchors (1, A, 4) to
        # cell K shifts (K, 1, 4) to get
        # shift anchors (K, A, 4)
        # reshape to (K*A, 4) shifted anchors
        A = self._num_anchors
        K = shifts.shape[0]
        anchors = self._anchors.reshape((1, A, 4)) + \
                  shifts.reshape((1, K, 4)).transpose((1, 0, 2))
        anchors = anchors.reshape((K * A, 4))

        # Transpose and reshape predicted bbox transformations to get them
        # into the same order as the anchors:
        #
        # bbox deltas will be (1, 4 * A, H, W) format
        # transpose to (1, H, W, 4 * A)
        # reshape to (1 * H * W * A, 4) where rows are ordered by (h, w, a)
        # in slowest to fastest order
        bbox_deltas = bbox_deltas.transpose((0, 2, 3, 1)).reshape((-1, 4))

        # Same story for the scores:
        #
        # scores are (1, A, H, W) format
        # transpose to (1, H, W, A)
        # reshape to (1 * H * W * A, 1) where rows are ordered by (h, w, a)
        scores = scores.transpose((0, 2, 3, 1)).reshape((-1, 1))

        # Convert anchors into proposals via bbox transformations
        # 利用 bbox_deltas 对anchors进行修正,得到proposals的预测位置
        proposals = bbox_transform_inv(anchors, bbox_deltas)

        # 2. clip predicted boxes to image 裁剪
        proposals = clip_boxes(proposals, im_info[:2])

        # 3. remove predicted boxes with either height or width < threshold
        # (NOTE: convert min_size to input image scale stored in im_info[2])
        keep = _filter_boxes(proposals, min_size * im_info[2])
        proposals = proposals[keep, :]
        scores = scores[keep]

        # 4. sort all (proposal, score) pairs by score from highest to lowest
        # 5. take top pre_nms_topN (e.g. 6000) 选出Top_N,后面再进行 NMS
        order = scores.ravel().argsort()[::-1]
        if pre_nms_topN > 0:
            order = order[:pre_nms_topN]
        proposals = proposals[order, :]
        scores = scores[order]

        # 6. apply nms (e.g. threshold = 0.7)
        # 7. take after_nms_topN (e.g. 300)
        # 8. return the top proposals (-> RoIs top)
        keep = nms(np.hstack((proposals, scores)), nms_thresh)
        if post_nms_topN > 0:
            keep = keep[:post_nms_topN]
        proposals = proposals[keep, :]
        scores = scores[keep]

        # Output rois blob
        # Our RPN implementation only supports a single input image, so all
        # batch inds are 0
        batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
        blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))
        top[0].reshape(*(blob.shape))
        top[0].data[...] = blob

        # [Optional] output scores blob
        if len(top) > 1:
            top[1].reshape(*(scores.shape))
            top[1].data[...] = scores
<think>好的,我现在需要帮助用户解决运行Faster R-CNN训练脚本时遇到的问题。用户提到了错误或者对用法不太清楚,所以我要详细分析可能的情况。 首先,用户提供的命令是python tools/train.py configs/faster_rcnn/faster_rcnn_r50_fpn_1x_didi.py,这可能涉及到配置文件的路径是否正确。我需要检查用户是否在正确的项目目录下运行命令,比如MMDetection框架通常需要从根目录执行。如果用户在其他目录下运行,可能会找不到配置文件或依赖文件。 然后,配置文件faster_rcnn_r50_fpn_1x_didi.py是否存在?用户可能没有正确设置配置文件,或者文件路径有误。比如,如果用户的项目结构中没有configs目录,或者该目录下没有相应的配置文件,就会出错。需要建议用户确认文件路径,并检查_base_中的引用是否正确,比如是否有../_base_/models/faster_rcnn_r50_fpn.py这样的引用,这些基础文件是否存在。 接下来,用户可能缺少依赖库。MMDetection需要PyTorch、CUDA等环境。如果用户没有正确安装这些依赖,或者版本不匹配,会导致运行错误。建议用户检查requirements.txt,确保所有依赖已安装,并确认CUDA版本与PyTorch兼容。 数据集的配置也是常见的问题。用户提到的didi_detection.py可能是指自定义的数据集,需要检查数据集路径是否正确,标注文件是否存在,数据格式是否符合要求。例如,COCO格式的数据集需要特定的目录结构和json文件。 GPU资源问题也可能导致错误。如果用户没有可用的GPU,或者显存不足,训练脚本可能会崩溃。建议用户使用--gpu-id参数指定GPU,或者调整batch size以减少显存占用。 日志和错误信息是关键。用户需要查看终端输出的错误信息,或者日志文件,比如work_dirs中的日志,来定位具体问题。常见的错误包括导入错误、文件未找到、CUDA内存不足等。 另外,用户可能需要了解如何继续训练、多GPU训练或者指定工作目录。比如,使用--resume-from参数继续训练,或者--work-dir指定输出目录。多GPU训练可以通过工具如分布式训练脚本启动。 最后,建议用户参考提供的引用中的示例命令,比如引用[1]和引用[2],里面提到了类似的训练命令,可能帮助用户正确执行。同时,可以提示用户进行FPS测试,如引用[3]中的方法,来验证模型性能。 总结可能的问题点:路径错误、依赖缺失、数据集配置、GPU问题、参数使用。需要逐步引导用户检查这些方面,并提供相应的解决方法和示例命令。</think>根据引用[1][2]提供的训练命令及配置信息,运行Faster R-CNN训练脚本的正确步骤如下: ### 一、基础执行命令 ```bash # 单GPU训练 python tools/train.py configs/faster_rcnn/faster_rcnn_r50_fpn_1x_didi.py --work-dir work_dirs/faster_rcnn # 多GPU训练(例如8卡) bash tools/dist_train.sh configs/faster_rcnn/faster_rcnn_r50_fpn_1x_didi.py 8 --work-dir work_dirs/faster_rcnn ``` 参数说明: - `--work-dir`指定模型和日志保存路径[^2] - 配置文件`faster_rcnn_r50_fpn_1x_didi.py`需包含正确的_base_配置链[^2] ### 二、常见问题排查 1. **路径错误** - 确认在项目根目录执行命令 - 检查配置文件中的`_base_`路径是否正确: ```python # faster_rcnn_r50_fpn_1x_didi.py _base_ = [ '../_base_/models/faster_rcnn_r50_fpn.py', # 模型结构 '../_base_/datasets/didi_detection.py', # 数据集配置 '../_base_/schedules/schedule_1x.py', # 训练策略 '../_base_/default_runtime.py' # 运行时配置 ] ``` 2. **依赖缺失** ```bash # 安装必要依赖 pip install -r requirements.txt # 确认CUDA与PyTorch版本匹配 nvidia-smi # 查看CUDA版本 ``` 3. **数据集配置** - 检查`didi_detection.py`中的路径: ```python data = dict( train=dict( img_prefix='data/didi/train/', ann_file='data/didi/annotations/train.json'), val=dict( img_prefix='data/didi/val/', ann_file='data/didi/annotations/val.json')) ``` 4. **GPU资源问题** ```bash # 指定单卡训练 python tools/train.py configs/faster_rcnn/faster_rcnn_r50_fpn_1x_didi.py --gpu-id 0 # 减少batch_size防止OOM sed -i 's/samples_per_gpu=2/samples_per_gpu=1/g' configs/faster_rcnn/faster_rcnn_r50_fpn_1x_didi.py ``` ### 三、进阶用法 1. **恢复训练** ```bash python tools/train.py configs/faster_rcnn/faster_rcnn_r50_fpn_1x_didi.py \ --resume-from work_dirs/faster_rcnn/latest.pth ``` 2. **性能验证** ```bash # 训练完成后测试FPS(需修改路径) python tools/analysis_tools/benchmark.py \ configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py \ work_dirs/faster_rcnn/epoch_12.pth \ --fuse-conv-bn ``` 引用[3]展示了基准测试的标准方法
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