_get_bboxes

[ZYCi]

When I run the code on the server, I always get an error at the end of 1 epoch. Is _get_bboxes not defined in prs_head.py, I didn't find it when I looked at the code.

[mousecpn]

PRSHead is inherited AnchorHead class，the _get_bboxes function is in the AnchorHead class

[ZYCi]

I didn't find the _get_bboxes function in the AnchorHead class, but there are get_bboxes function and _get_bboxes_single function in the AnchorHead parent class， BaseDenseHead class. Please check the code of the AnchorHead class again, maybe our mmdetection is different? Thank you very very much.

[ZYCi]

mmdetection v2.17.0 had _get_bboxes function, after v2.17.0 there are no _get_bboxes function in the AnchorHead class

[mousecpn]

import torch
import torch.nn as nn
from mmcv.runner import force_fp32

from mmdet.core import (anchor_inside_flags, build_anchor_generator,
build_assigner, build_bbox_coder, build_sampler,
images_to_levels, multi_apply, multiclass_nms, unmap)
from ..builder import HEADS, build_loss
from .base_dense_head import BaseDenseHead
from .dense_test_mixins import BBoxTestMixin

def _get_bboxes(self,
mlvl_cls_scores,
mlvl_bbox_preds,
mlvl_anchors,
img_shapes,
scale_factors,
cfg,
rescale=False,
with_nms=True):
"""Transform outputs for a batch item into bbox predictions.

    Args:
        mlvl_cls_scores (list[Tensor]): Each element in the list is
            the scores of bboxes of single level in the feature pyramid,
            has shape (N, num_anchors * num_classes, H, W).
        mlvl_bbox_preds (list[Tensor]):  Each element in the list is the
            bboxes predictions of single level in the feature pyramid,
            has shape (N, num_anchors * 4, H, W).
        mlvl_anchors (list[Tensor]): Each element in the list is
            the anchors of single level in feature pyramid, has shape
            (num_anchors, 4).
        img_shapes (list[tuple[int]]): Each tuple in the list represent
            the shape(height, width, 3) of single image in the batch.
        scale_factors (list[ndarray]): Scale factor of the batch
            image arange as list[(w_scale, h_scale, w_scale, h_scale)].
        cfg (mmcv.Config): Test / postprocessing configuration,
            if None, test_cfg would be used.
        rescale (bool): If True, return boxes in original image space.
            Default: False.
        with_nms (bool): If True, do nms before return boxes.
            Default: True.

    Returns:
        list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
            The first item is an (n, 5) tensor, where 5 represent
            (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1.
            The shape of the second tensor in the tuple is (n,), and
            each element represents the class label of the corresponding
            box.
    """
    cfg = self.test_cfg if cfg is None else cfg
    assert len(mlvl_cls_scores) == len(mlvl_bbox_preds) == len(
        mlvl_anchors)
    batch_size = mlvl_cls_scores[0].shape[0]
    # convert to tensor to keep tracing
    nms_pre_tensor = torch.tensor(
        cfg.get('nms_pre', -1),
        device=mlvl_cls_scores[0].device,
        dtype=torch.long)

    mlvl_bboxes = []
    mlvl_scores = []
    for cls_score, bbox_pred, anchors in zip(mlvl_cls_scores,
                                             mlvl_bbox_preds,
                                             mlvl_anchors):
        assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
        cls_score = cls_score.permute(0, 2, 3,
                                      1).reshape(batch_size, -1,
                                                 self.cls_out_channels)
        if self.use_sigmoid_cls:
            scores = cls_score.sigmoid()
        else:
            scores = cls_score.softmax(-1)
        bbox_pred = bbox_pred.permute(0, 2, 3,
                                      1).reshape(batch_size, -1, 4)
        anchors = anchors.expand_as(bbox_pred)
        # Always keep topk op for dynamic input in onnx
        from mmdet.core.export import get_k_for_topk
        nms_pre = get_k_for_topk(nms_pre_tensor, bbox_pred.shape[1])
        if nms_pre > 0:
            # Get maximum scores for foreground classes.
            if self.use_sigmoid_cls:
                max_scores, _ = scores.max(-1)
            else:
                # remind that we set FG labels to [0, num_class-1]
                # since mmdet v2.0
                # BG cat_id: num_class
                max_scores, _ = scores[..., :-1].max(-1)

            _, topk_inds = max_scores.topk(nms_pre)
            batch_inds = torch.arange(batch_size).view(
                -1, 1).expand_as(topk_inds)
            anchors = anchors[batch_inds, topk_inds, :]
            bbox_pred = bbox_pred[batch_inds, topk_inds, :]
            scores = scores[batch_inds, topk_inds, :]

        bboxes = self.bbox_coder.decode(
            anchors, bbox_pred, max_shape=img_shapes)
        mlvl_bboxes.append(bboxes)
        mlvl_scores.append(scores)

    batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1)
    if rescale:
        batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor(
            scale_factors).unsqueeze(1)
    batch_mlvl_scores = torch.cat(mlvl_scores, dim=1)

    # Replace multiclass_nms with ONNX::NonMaxSuppression in deployment
    if torch.onnx.is_in_onnx_export() and with_nms:
        from mmdet.core.export import add_dummy_nms_for_onnx
        # ignore background class
        if not self.use_sigmoid_cls:
            num_classes = batch_mlvl_scores.shape[2] - 1
            batch_mlvl_scores = batch_mlvl_scores[..., :num_classes]
        max_output_boxes_per_class = cfg.nms.get(
            'max_output_boxes_per_class', 200)
        iou_threshold = cfg.nms.get('iou_threshold', 0.5)
        score_threshold = cfg.score_thr
        nms_pre = cfg.get('deploy_nms_pre', -1)
        return add_dummy_nms_for_onnx(batch_mlvl_bboxes, batch_mlvl_scores,
                                      max_output_boxes_per_class,
                                      iou_threshold, score_threshold,
                                      nms_pre, cfg.max_per_img)
    if self.use_sigmoid_cls:
        # Add a dummy background class to the backend when using sigmoid
        # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
        # BG cat_id: num_class
        padding = batch_mlvl_scores.new_zeros(batch_size,
                                              batch_mlvl_scores.shape[1],
                                              1)
        batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1)

    if with_nms:
        det_results = []
        for (mlvl_bboxes, mlvl_scores) in zip(batch_mlvl_bboxes,
                                              batch_mlvl_scores):
            det_bbox, det_label = multiclass_nms(mlvl_bboxes, mlvl_scores,
                                                 cfg.score_thr, cfg.nms,
                                                 cfg.max_per_img)
            det_results.append(tuple([det_bbox, det_label]))
    else:
        det_results = [
            tuple(mlvl_bs)
            for mlvl_bs in zip(batch_mlvl_bboxes, batch_mlvl_scores)
        ]
    return det_results

mmdetection v2.17.0 had _get_bboxes function, after v2.17.0 there are no _get_bboxes function in the AnchorHead class

[mousecpn]

mmdetection v2.17.0 had _get_bboxes function, after v2.17.0 there are no _get_bboxes function in the AnchorHead class

please add these on the bottom of the prs_head.py

[ZYCi]

Thanks so much.I had changed my mmdetection form v2.24.1 to v 2.17.0,and your code runned successfully.

[cjplol]

Can your model be tested? I encountered this error when testing：

File "/home/cjp/newmmdet/mmdet/models/dense_heads/prs_head.py", line 263, in forward_post
x = x.unsqueeze(1).expand((shape[0],self.num_anchors[i],shape[1],shape[2],shape[3]))
IndexError: list index out of range

Do you know how to solve this problem？

[mousecpn]

It can be tested. But I can't get a clue what happens to you. May be the version of the mmdet is not right.

[cjplol]

Thank you. I reconfigured the environment and test successfully.