eval.py

import io as sysio
import time

import numba
import numpy as np
from numba import cuda

from second.core.non_max_suppression.nms_gpu import rotate_iou_gpu_eval


def get_mAP(prec):
    sums = 0
    for i in range(0, len(prec), 4):
        sums += prec[i]
    return sums / 11 * 100


@numba.jit
def get_thresholds(scores: np.ndarray, num_gt, num_sample_pts=41):
    scores.sort()
    scores = scores[::-1]
    current_recall = 0
    thresholds = []
    for i, score in enumerate(scores):
        l_recall = (i + 1) / num_gt
        if i < (len(scores) - 1):
            r_recall = (i + 2) / num_gt
        else:
            r_recall = l_recall
        if (((r_recall - current_recall) < (current_recall - l_recall))
                and (i < (len(scores) - 1))):
            continue
        # recall = l_recall
        thresholds.append(score)
        current_recall += 1 / (num_sample_pts - 1.0)
    return thresholds


def clean_data(gt_anno, dt_anno, current_class, difficulty):
    CLASS_NAMES = ['car', 'pedestrian', 'cyclist', 'van', 'person_sitting', 'car', 'tractor', 'trailer']
    MIN_HEIGHT = [40, 25, 25]
    MAX_OCCLUSION = [0, 1, 2]
    MAX_TRUNCATION = [0.15, 0.3, 0.5]
    dc_bboxes, ignored_gt, ignored_dt = [], [], []
    current_cls_name = CLASS_NAMES[current_class].lower()
    num_gt = len(gt_anno["name"])
    num_dt = len(dt_anno["name"])
    num_valid_gt = 0
    for i in range(num_gt):
        bbox = gt_anno["bbox"][i]
        gt_name = gt_anno["name"][i].lower()
        height = bbox[3] - bbox[1]
        valid_class = -1
        if (gt_name == current_cls_name):
            valid_class = 1
        elif (current_cls_name == "Pedestrian".lower()
              and "Person_sitting".lower() == gt_name):
            valid_class = 0
        elif (current_cls_name == "Car".lower() and "Van".lower() == gt_name):
            valid_class = 0
        else:
            valid_class = -1
        ignore = False
        if ((gt_anno["occluded"][i] > MAX_OCCLUSION[difficulty])
                or (gt_anno["truncated"][i] > MAX_TRUNCATION[difficulty])
                or (height <= MIN_HEIGHT[difficulty])):
            # if gt_anno["difficulty"][i] > difficulty or gt_anno["difficulty"][i] == -1:
            ignore = True
        if valid_class == 1 and not ignore:
            ignored_gt.append(0)
            num_valid_gt += 1
        elif (valid_class == 0 or (ignore and (valid_class == 1))):
            ignored_gt.append(1)
        else:
            ignored_gt.append(-1)
    # for i in range(num_gt):
        if gt_anno["name"][i] == "DontCare":
            dc_bboxes.append(gt_anno["bbox"][i])
    for i in range(num_dt):
        if (dt_anno["name"][i].lower() == current_cls_name):
            valid_class = 1
        else:
            valid_class = -1
        height = abs(dt_anno["bbox"][i, 3] - dt_anno["bbox"][i, 1])
        if height < MIN_HEIGHT[difficulty]:
            ignored_dt.append(1)
        elif valid_class == 1:
            ignored_dt.append(0)
        else:
            ignored_dt.append(-1)

    return num_valid_gt, ignored_gt, ignored_dt, dc_bboxes


@numba.jit(nopython=True)
def image_box_overlap(boxes, query_boxes, criterion=-1):
    N = boxes.shape[0]
    K = query_boxes.shape[0]
    overlaps = np.zeros((N, K), dtype=boxes.dtype)
    for k in range(K):
        qbox_area = ((query_boxes[k, 2] - query_boxes[k, 0]) *
                     (query_boxes[k, 3] - query_boxes[k, 1]))
        for n in range(N):
            iw = (min(boxes[n, 2], query_boxes[k, 2]) -
                  max(boxes[n, 0], query_boxes[k, 0]))
            if iw > 0:
                ih = (min(boxes[n, 3], query_boxes[k, 3]) -
                      max(boxes[n, 1], query_boxes[k, 1]))
                if ih > 0:
                    if criterion == -1:
                        ua = (
                            (boxes[n, 2] - boxes[n, 0]) *
                            (boxes[n, 3] - boxes[n, 1]) + qbox_area - iw * ih)
                    elif criterion == 0:
                        ua = ((boxes[n, 2] - boxes[n, 0]) *
                              (boxes[n, 3] - boxes[n, 1]))
                    elif criterion == 1:
                        ua = qbox_area
                    else:
                        ua = 1.0
                    overlaps[n, k] = iw * ih / ua
    return overlaps


# pang added to build the tensor for the second stage of training
@numba.jit(nopython=True,parallel=True)
def build_stage2_training(boxes, query_boxes, criterion, scores_3d, scores_2d, dis_to_lidar_3d,overlaps,tensor_index):
    N = boxes.shape[0] #70400
    K = query_boxes.shape[0] #30
    max_num = 900000
    ind=0
    ind_max = ind
    for k in range(K):
        qbox_area = ((query_boxes[k, 2] - query_boxes[k, 0]) *
                     (query_boxes[k, 3] - query_boxes[k, 1]))
        for n in range(N):
            iw = (min(boxes[n, 2], query_boxes[k, 2]) -
                  max(boxes[n, 0], query_boxes[k, 0]))
            if iw > 0:
                ih = (min(boxes[n, 3], query_boxes[k, 3]) -
                      max(boxes[n, 1], query_boxes[k, 1]))
                if ih > 0:
                    if criterion == -1:
                        ua = (
                            (boxes[n, 2] - boxes[n, 0]) *
                            (boxes[n, 3] - boxes[n, 1]) + qbox_area - iw * ih)
                    elif criterion == 0:
                        ua = ((boxes[n, 2] - boxes[n, 0]) *
                              (boxes[n, 3] - boxes[n, 1]))
                    elif criterion == 1:
                        ua = qbox_area
                    else:
                        ua = 1.0
                    overlaps[ind,0] = iw * ih / ua
                    overlaps[ind,1] = scores_3d[n,0]
                    overlaps[ind,2] = scores_2d[k,0]
                    overlaps[ind,3] = dis_to_lidar_3d[n,0]
                    tensor_index[ind,0] = k
                    tensor_index[ind,1] = n
                    ind = ind+1

                elif k==K-1:
                    overlaps[ind,0] = -10
                    overlaps[ind,1] = scores_3d[n,0]
                    overlaps[ind,2] = -10
                    overlaps[ind,3] = dis_to_lidar_3d[n,0]
                    tensor_index[ind,0] = k
                    tensor_index[ind,1] = n
                    ind = ind+1
            elif k==K-1:
                overlaps[ind,0] = -10
                overlaps[ind,1] = scores_3d[n,0]
                overlaps[ind,2] = -10
                overlaps[ind,3] = dis_to_lidar_3d[n,0]
                tensor_index[ind,0] = k
                tensor_index[ind,1] = n
                ind = ind+1
    if ind > ind_max:
        ind_max = ind
    return overlaps, tensor_index, ind


# pang added to build the tensor for the second stage of training, added july 13 2019
@numba.jit(nopython=True, parallel=True)
def build_stage2_training_2(boxes, query_boxes, box_2d_corner):
    N = boxes.shape[0]  #3D_projected
    K = query_boxes.shape[0]   #2D
    overlaps_2 = np.zeros((N, K, 24), dtype=boxes.dtype)
    for k in range(K):
        qbox_area = ((query_boxes[k, 2] - query_boxes[k, 0]) *
                     (query_boxes[k, 5] - query_boxes[k, 1]))
        for n in range(N):
            iw = (min(boxes[n, 2], query_boxes[k, 2]) -
                  max(boxes[n, 0], query_boxes[k, 0]))
            if iw > 0:
                ih = (min(boxes[n, 3], query_boxes[k, 5]) -
                      max(boxes[n, 1], query_boxes[k, 1]))
                if ih > 0:
                    overlaps_2[n, k, 0:8] = query_boxes[k,:]
                    overlaps_2[n, k, 8:] = box_2d_corner[n,:]
                    '''
                    overlaps_2[n, k, 0] = query_boxes[k,0]
                    overlaps_2[n, k, 1] = query_boxes[k,1]
                    overlaps_2[n, k, 2] = query_boxes[k,2]
                    overlaps_2[n, k, 3] = query_boxes[k,3]
                    overlaps_2[n, k, 4] = box_2d_corner[n,0]
                    overlaps_2[n, k, 5] = box_2d_corner[n,1]
                    overlaps_2[n, k, 6] = box_2d_corner[n,2]
                    overlaps_2[n, k, 7] = box_2d_corner[n,3]
                    overlaps_2[n, k, 8] = box_2d_corner[n,4]
                    overlaps_2[n, k, 9] = box_2d_corner[n,5]
                    overlaps_2[n, k, 10] = box_2d_corner[n,6]
                    overlaps_2[n, k, 11] = box_2d_corner[n,7]
                    '''
            '''
            elif scores_3d[n,0] >= 0.1:
                overlaps[n, k, 0] = 0
                overlaps[n, k, 1] = scores_3d[n,0]
                overlaps[n, k, 2] = 0
                overlaps[n, k, 3] = dis_to_lidar_3d[n,0]'''
    return overlaps_2



def bev_box_overlap(boxes, qboxes, criterion=-1):
    riou = rotate_iou_gpu_eval(boxes, qboxes, criterion)
    return riou


@numba.jit(nopython=True, parallel=True)
def d3_box_overlap_kernel(boxes, qboxes, rinc, criterion=-1):
    # ONLY support overlap in CAMERA, not lider.
    N, K = boxes.shape[0], qboxes.shape[0]
    for i in range(N):
        for j in range(K):
            if rinc[i, j] > 0:
                iw = (min(boxes[i, 1], qboxes[j, 1]) - max(
                    boxes[i, 1] - boxes[i, 4], qboxes[j, 1] - qboxes[j, 4]))

                if iw > 0:
                    area1 = boxes[i, 3] * boxes[i, 4] * boxes[i, 5]
                    area2 = qboxes[j, 3] * qboxes[j, 4] * qboxes[j, 5]
                    inc = iw * rinc[i, j]
                    if criterion == -1:
                        ua = (area1 + area2 - inc)
                    elif criterion == 0:
                        ua = area1
                    elif criterion == 1:
                        ua = area2
                    else:
                        ua = 1.0
                    rinc[i, j] = inc / ua
                else:
                    rinc[i, j] = 0.0


def d3_box_overlap(boxes, qboxes, criterion=-1):
    rinc = rotate_iou_gpu_eval(boxes[:, [0, 2, 3, 5, 6]],
                               qboxes[:, [0, 2, 3, 5, 6]], 2)
    d3_box_overlap_kernel(boxes, qboxes, rinc, criterion)
    return rinc


@numba.jit(nopython=True)
def compute_statistics_jit(overlaps,
                           gt_datas,
                           dt_datas,
                           ignored_gt,
                           ignored_det,
                           dc_bboxes,
                           metric,
                           min_overlap,
                           thresh=0,
                           compute_fp=False,
                           compute_aos=False):

    det_size = dt_datas.shape[0]
    gt_size = gt_datas.shape[0]
    dt_scores = dt_datas[:, -1]
    dt_alphas = dt_datas[:, 4]
    gt_alphas = gt_datas[:, 4]
    dt_bboxes = dt_datas[:, :4]
    # gt_bboxes = gt_datas[:, :4]

    assigned_detection = [False] * det_size
    ignored_threshold = [False] * det_size
    if compute_fp:
        for i in range(det_size):
            if (dt_scores[i] < thresh):
                ignored_threshold[i] = True
    NO_DETECTION = -10000000
    tp, fp, fn, similarity = 0, 0, 0, 0
    # thresholds = [0.0]
    # delta = [0.0]
    thresholds = np.zeros((gt_size, ))
    thresh_idx = 0
    delta = np.zeros((gt_size, ))
    delta_idx = 0
    for i in range(gt_size):
        if ignored_gt[i] == -1:
            continue
        det_idx = -1
        valid_detection = NO_DETECTION
        max_overlap = 0
        assigned_ignored_det = False

        for j in range(det_size):
            if (ignored_det[j] == -1):
                continue
            if (assigned_detection[j]):
                continue
            if (ignored_threshold[j]):
                continue
            overlap = overlaps[j, i]
            dt_score = dt_scores[j]
            if (not compute_fp and (overlap > min_overlap)
                    and dt_score > valid_detection):
                det_idx = j
                valid_detection = dt_score
            elif (compute_fp and (overlap > min_overlap)
                  and (overlap > max_overlap or assigned_ignored_det)
                  and ignored_det[j] == 0):
                max_overlap = overlap
                det_idx = j
                valid_detection = 1
                assigned_ignored_det = False
            elif (compute_fp and (overlap > min_overlap)
                  and (valid_detection == NO_DETECTION)
                  and ignored_det[j] == 1):
                det_idx = j
                valid_detection = 1
                assigned_ignored_det = True

        if (valid_detection == NO_DETECTION) and ignored_gt[i] == 0:
            fn += 1
        elif ((valid_detection != NO_DETECTION)
              and (ignored_gt[i] == 1 or ignored_det[det_idx] == 1)):
            assigned_detection[det_idx] = True
        elif valid_detection != NO_DETECTION:
            # only a tp add a threshold.
            tp += 1
            # thresholds.append(dt_scores[det_idx])
            thresholds[thresh_idx] = dt_scores[det_idx]
            thresh_idx += 1
            if compute_aos:
                # delta.append(gt_alphas[i] - dt_alphas[det_idx])
                delta[delta_idx] = gt_alphas[i] - dt_alphas[det_idx]
                delta_idx += 1

            assigned_detection[det_idx] = True
    if compute_fp:
        for i in range(det_size):
            if (not (assigned_detection[i] or ignored_det[i] == -1
                     or ignored_det[i] == 1 or ignored_threshold[i])):
                fp += 1
        nstuff = 0
        if metric == 0:
            overlaps_dt_dc = image_box_overlap(dt_bboxes, dc_bboxes, 0)
            for i in range(dc_bboxes.shape[0]):
                for j in range(det_size):
                    if (assigned_detection[j]):
                        continue
                    if (ignored_det[j] == -1 or ignored_det[j] == 1):
                        continue
                    if (ignored_threshold[j]):
                        continue
                    if overlaps_dt_dc[j, i] > min_overlap:
                        assigned_detection[j] = True
                        nstuff += 1
        fp -= nstuff
        if compute_aos:
            tmp = np.zeros((fp + delta_idx, ))
            # tmp = [0] * fp
            for i in range(delta_idx):
                tmp[i + fp] = (1.0 + np.cos(delta[i])) / 2.0
                # tmp.append((1.0 + np.cos(delta[i])) / 2.0)
            # assert len(tmp) == fp + tp
            # assert len(delta) == tp
            if tp > 0 or fp > 0:
                similarity = np.sum(tmp)
            else:
                similarity = -1
    return tp, fp, fn, similarity, thresholds[:thresh_idx]


def get_split_parts(num, num_part):
    same_part = num // num_part
    remain_num = num % num_part
    if remain_num == 0:
        return [same_part] * num_part
    else:
        return [same_part] * num_part + [remain_num]


@numba.jit(nopython=True)
def fused_compute_statistics(overlaps,
                             pr,
                             gt_nums,
                             dt_nums,
                             dc_nums,
                             gt_datas,
                             dt_datas,
                             dontcares,
                             ignored_gts,
                             ignored_dets,
                             metric,
                             min_overlap,
                             thresholds,
                             compute_aos=False):
    gt_num = 0
    dt_num = 0
    dc_num = 0
    for i in range(gt_nums.shape[0]):
        for t, thresh in enumerate(thresholds):
            overlap = overlaps[dt_num:dt_num + dt_nums[i], gt_num:
                               gt_num + gt_nums[i]]

            gt_data = gt_datas[gt_num:gt_num + gt_nums[i]]
            dt_data = dt_datas[dt_num:dt_num + dt_nums[i]]
            ignored_gt = ignored_gts[gt_num:gt_num + gt_nums[i]]
            ignored_det = ignored_dets[dt_num:dt_num + dt_nums[i]]
            dontcare = dontcares[dc_num:dc_num + dc_nums[i]]
            tp, fp, fn, similarity, _ = compute_statistics_jit(
                overlap,
                gt_data,
                dt_data,
                ignored_gt,
                ignored_det,
                dontcare,
                metric,
                min_overlap=min_overlap,
                thresh=thresh,
                compute_fp=True,
                compute_aos=compute_aos)
            pr[t, 0] += tp
            pr[t, 1] += fp
            pr[t, 2] += fn
            if similarity != -1:
                pr[t, 3] += similarity
        gt_num += gt_nums[i]
        dt_num += dt_nums[i]
        dc_num += dc_nums[i]


def calculate_iou_partly(gt_annos, dt_annos, metric, num_parts=50):
    """fast iou algorithm. this function can be used independently to
    do result analysis. Must be used in CAMERA coordinate system.
    Args:
        gt_annos: dict, must from get_label_annos() in kitti_common.py
        dt_annos: dict, must from get_label_annos() in kitti_common.py
        metric: eval type. 0: bbox, 1: bev, 2: 3d
        num_parts: int. a parameter for fast calculate algorithm
    """
    assert len(gt_annos) == len(dt_annos)
    total_dt_num = np.stack([len(a["name"]) for a in dt_annos], 0)
    total_gt_num = np.stack([len(a["name"]) for a in gt_annos], 0)
    num_examples = len(gt_annos)
    split_parts = get_split_parts(num_examples, num_parts)
    parted_overlaps = []
    example_idx = 0

    for num_part in split_parts:
        gt_annos_part = gt_annos[example_idx:example_idx + num_part]
        dt_annos_part = dt_annos[example_idx:example_idx + num_part]
        if metric == 0:
            gt_boxes = np.concatenate([a["bbox"] for a in gt_annos_part], 0)
            dt_boxes = np.concatenate([a["bbox"] for a in dt_annos_part], 0)
            overlap_part = image_box_overlap(gt_boxes, dt_boxes)
        elif metric == 1:
            loc = np.concatenate(
                [a["location"][:, [0, 2]] for a in gt_annos_part], 0)
            dims = np.concatenate(
                [a["dimensions"][:, [0, 2]] for a in gt_annos_part], 0)
            rots = np.concatenate([a["rotation_y"] for a in gt_annos_part], 0)
            gt_boxes = np.concatenate(
                [loc, dims, rots[..., np.newaxis]], axis=1)
            loc = np.concatenate(
                [a["location"][:, [0, 2]] for a in dt_annos_part], 0)
            dims = np.concatenate(
                [a["dimensions"][:, [0, 2]] for a in dt_annos_part], 0)
            rots = np.concatenate([a["rotation_y"] for a in dt_annos_part], 0)
            dt_boxes = np.concatenate(
                [loc, dims, rots[..., np.newaxis]], axis=1)
            overlap_part = bev_box_overlap(gt_boxes, dt_boxes).astype(
                np.float64)
        elif metric == 2:
            loc = np.concatenate([a["location"] for a in gt_annos_part], 0)
            dims = np.concatenate([a["dimensions"] for a in gt_annos_part], 0)
            rots = np.concatenate([a["rotation_y"] for a in gt_annos_part], 0)
            gt_boxes = np.concatenate(
                [loc, dims, rots[..., np.newaxis]], axis=1)
            loc = np.concatenate([a["location"] for a in dt_annos_part], 0)
            dims = np.concatenate([a["dimensions"] for a in dt_annos_part], 0)
            rots = np.concatenate([a["rotation_y"] for a in dt_annos_part], 0)
            dt_boxes = np.concatenate(
                [loc, dims, rots[..., np.newaxis]], axis=1)
            overlap_part = d3_box_overlap(gt_boxes, dt_boxes).astype(
                np.float64)
        else:
            raise ValueError("unknown metric")
        parted_overlaps.append(overlap_part)
        example_idx += num_part
    overlaps = []
    example_idx = 0
    for j, num_part in enumerate(split_parts):
        gt_annos_part = gt_annos[example_idx:example_idx + num_part]
        dt_annos_part = dt_annos[example_idx:example_idx + num_part]
        gt_num_idx, dt_num_idx = 0, 0
        for i in range(num_part):
            gt_box_num = total_gt_num[example_idx + i]
            dt_box_num = total_dt_num[example_idx + i]
            overlaps.append(
                parted_overlaps[j][gt_num_idx:gt_num_idx + gt_box_num,
                                   dt_num_idx:dt_num_idx + dt_box_num])
            gt_num_idx += gt_box_num
            dt_num_idx += dt_box_num
        example_idx += num_part

    return overlaps, parted_overlaps, total_gt_num, total_dt_num


def _prepare_data(gt_annos, dt_annos, current_class, difficulty):
    gt_datas_list = []
    dt_datas_list = []
    total_dc_num = []
    ignored_gts, ignored_dets, dontcares = [], [], []
    total_num_valid_gt = 0
    for i in range(len(gt_annos)):
        rets = clean_data(gt_annos[i], dt_annos[i], current_class, difficulty)
        num_valid_gt, ignored_gt, ignored_det, dc_bboxes = rets
        ignored_gts.append(np.array(ignored_gt, dtype=np.int64))
        ignored_dets.append(np.array(ignored_det, dtype=np.int64))
        if len(dc_bboxes) == 0:
            dc_bboxes = np.zeros((0, 4)).astype(np.float64)
        else:
            dc_bboxes = np.stack(dc_bboxes, 0).astype(np.float64)
        total_dc_num.append(dc_bboxes.shape[0])
        dontcares.append(dc_bboxes)
        total_num_valid_gt += num_valid_gt
        gt_datas = np.concatenate(
            [gt_annos[i]["bbox"], gt_annos[i]["alpha"][..., np.newaxis]], 1)
        dt_datas = np.concatenate([
            dt_annos[i]["bbox"], dt_annos[i]["alpha"][..., np.newaxis],
            dt_annos[i]["score"][..., np.newaxis]
        ], 1)
        gt_datas_list.append(gt_datas)
        dt_datas_list.append(dt_datas)
    total_dc_num = np.stack(total_dc_num, axis=0)
    return (gt_datas_list, dt_datas_list, ignored_gts, ignored_dets, dontcares,
            total_dc_num, total_num_valid_gt)


def eval_class(gt_annos,
               dt_annos,
               current_class,
               difficulty,
               metric,
               min_overlap,
               compute_aos=False,
               num_parts=50):
    """Kitti eval. Only support 2d/bev/3d/aos eval for now.
    Args:
        gt_annos: dict, must from get_label_annos() in kitti_common.py
        dt_annos: dict, must from get_label_annos() in kitti_common.py
        current_class: int, 0: car, 1: pedestrian, 2: cyclist
        difficulty: int. eval difficulty, 0: easy, 1: normal, 2: hard
        metric: eval type. 0: bbox, 1: bev, 2: 3d
        min_overlap: float, min overlap. official:
            [[0.7, 0.5, 0.5], [0.7, 0.5, 0.5], [0.7, 0.5, 0.5]]
            format: [metric, class]. choose one from matrix above.
        num_parts: int. a parameter for fast calculate algorithm

    Returns:
        dict of recall, precision and aos
    """
    assert len(gt_annos) == len(dt_annos)
    num_examples = len(gt_annos)
    split_parts = get_split_parts(num_examples, num_parts)
    thresholdss = []
    rets = calculate_iou_partly(dt_annos, gt_annos, metric, num_parts)
    overlaps, parted_overlaps, total_dt_num, total_gt_num = rets
    rets = _prepare_data(gt_annos, dt_annos, current_class, difficulty)
    (gt_datas_list, dt_datas_list, ignored_gts, ignored_dets, dontcares,
     total_dc_num, total_num_valid_gt) = rets

    for i in range(len(gt_annos)):
        rets = compute_statistics_jit(
            overlaps[i],
            gt_datas_list[i],
            dt_datas_list[i],
            ignored_gts[i],
            ignored_dets[i],
            dontcares[i],
            metric,
            min_overlap=min_overlap,
            thresh=0.0,
            compute_fp=False)
        tp, fp, fn, similarity, thresholds = rets
        thresholdss += thresholds.tolist()
    thresholdss = np.array(thresholdss)
    thresholds = get_thresholds(thresholdss, total_num_valid_gt)
    thresholds = np.array(thresholds)
    pr = np.zeros([len(thresholds), 4])
    idx = 0
    for j, num_part in enumerate(split_parts):
        gt_datas_part = np.concatenate(gt_datas_list[idx:idx + num_part], 0)
        dt_datas_part = np.concatenate(dt_datas_list[idx:idx + num_part], 0)
        dc_datas_part = np.concatenate(dontcares[idx:idx + num_part], 0)
        ignored_dets_part = np.concatenate(ignored_dets[idx:idx + num_part], 0)
        ignored_gts_part = np.concatenate(ignored_gts[idx:idx + num_part], 0)
        fused_compute_statistics(
            parted_overlaps[j],
            pr,
            total_gt_num[idx:idx + num_part],
            total_dt_num[idx:idx + num_part],
            total_dc_num[idx:idx + num_part],
            gt_datas_part,
            dt_datas_part,
            dc_datas_part,
            ignored_gts_part,
            ignored_dets_part,
            metric,
            min_overlap=min_overlap,
            thresholds=thresholds,
            compute_aos=compute_aos)
        idx += num_part
    N_SAMPLE_PTS = 41
    precision = np.zeros([N_SAMPLE_PTS])
    recall = np.zeros([N_SAMPLE_PTS])
    aos = np.zeros([N_SAMPLE_PTS])
    for i in range(len(thresholds)):
        recall[i] = pr[i, 0] / (pr[i, 0] + pr[i, 2])
        precision[i] = pr[i, 0] / (pr[i, 0] + pr[i, 1])
        if compute_aos:
            aos[i] = pr[i, 3] / (pr[i, 0] + pr[i, 1])
    for i in range(len(thresholds)):
        precision[i] = np.max(precision[i:])
        recall[i] = np.max(recall[i:])
        if compute_aos:
            aos[i] = np.max(aos[i:])
    ret_dict = {
        "recall": recall,
        "precision": precision,
        "orientation": aos,
    }
    return ret_dict


def eval_class_v3(gt_annos,
                  dt_annos,
                  current_classes,
                  difficultys,
                  metric,
                  min_overlaps,
                  compute_aos=False,
                  num_parts=50):
    """Kitti eval. support 2d/bev/3d/aos eval. support 0.5:0.05:0.95 coco AP.
    Args:
        gt_annos: dict, must from get_label_annos() in kitti_common.py
        dt_annos: dict, must from get_label_annos() in kitti_common.py
        current_class: int, 0: car, 1: pedestrian, 2: cyclist
        difficulty: int. eval difficulty, 0: easy, 1: normal, 2: hard
        metric: eval type. 0: bbox, 1: bev, 2: 3d
        min_overlap: float, min overlap. official:
            [[0.7, 0.5, 0.5], [0.7, 0.5, 0.5], [0.7, 0.5, 0.5]]
            format: [metric, class]. choose one from matrix above.
        num_parts: int. a parameter for fast calculate algorithm

    Returns:
        dict of recall, precision and aos
    """
    assert len(gt_annos) == len(dt_annos)
    num_examples = len(gt_annos)
    split_parts = get_split_parts(num_examples, num_parts)

    rets = calculate_iou_partly(dt_annos, gt_annos, metric, num_parts)
    overlaps, parted_overlaps, total_dt_num, total_gt_num = rets
    N_SAMPLE_PTS = 41
    num_minoverlap = len(min_overlaps)
    num_class = len(current_classes)
    num_difficulty = len(difficultys)
    precision = np.zeros(
        [num_class, num_difficulty, num_minoverlap, N_SAMPLE_PTS])
    recall = np.zeros(
        [num_class, num_difficulty, num_minoverlap, N_SAMPLE_PTS])
    aos = np.zeros([num_class, num_difficulty, num_minoverlap, N_SAMPLE_PTS])
    for m, current_class in enumerate(current_classes):
        for l, difficulty in enumerate(difficultys):
            rets = _prepare_data(gt_annos, dt_annos, current_class, difficulty)
            (gt_datas_list, dt_datas_list, ignored_gts, ignored_dets,
             dontcares, total_dc_num, total_num_valid_gt) = rets
            for k, min_overlap in enumerate(min_overlaps[:, metric, m]):
                thresholdss = []
                for i in range(len(gt_annos)):
                    rets = compute_statistics_jit(
                        overlaps[i],
                        gt_datas_list[i],
                        dt_datas_list[i],
                        ignored_gts[i],
                        ignored_dets[i],
                        dontcares[i],
                        metric,
                        min_overlap=min_overlap,
                        thresh=0.0,
                        compute_fp=False)
                    tp, fp, fn, similarity, thresholds = rets
                    thresholdss += thresholds.tolist()
                thresholdss = np.array(thresholdss)
                thresholds = get_thresholds(thresholdss, total_num_valid_gt)
                thresholds = np.array(thresholds)
                pr = np.zeros([len(thresholds), 4])
                idx = 0
                for j, num_part in enumerate(split_parts):
                    gt_datas_part = np.concatenate(
                        gt_datas_list[idx:idx + num_part], 0)
                    dt_datas_part = np.concatenate(
                        dt_datas_list[idx:idx + num_part], 0)
                    dc_datas_part = np.concatenate(
                        dontcares[idx:idx + num_part], 0)
                    ignored_dets_part = np.concatenate(
                        ignored_dets[idx:idx + num_part], 0)
                    ignored_gts_part = np.concatenate(
                        ignored_gts[idx:idx + num_part], 0)
                    fused_compute_statistics(
                        parted_overlaps[j],
                        pr,
                        total_gt_num[idx:idx + num_part],
                        total_dt_num[idx:idx + num_part],
                        total_dc_num[idx:idx + num_part],
                        gt_datas_part,
                        dt_datas_part,
                        dc_datas_part,
                        ignored_gts_part,
                        ignored_dets_part,
                        metric,
                        min_overlap=min_overlap,
                        thresholds=thresholds,
                        compute_aos=compute_aos)
                    idx += num_part
                for i in range(len(thresholds)):
                    recall[m, l, k, i] = pr[i, 0] / (pr[i, 0] + pr[i, 2])
                    precision[m, l, k, i] = pr[i, 0] / (pr[i, 0] + pr[i, 1])
                    if compute_aos:
                        aos[m, l, k, i] = pr[i, 3] / (pr[i, 0] + pr[i, 1])
                for i in range(len(thresholds)):
                    precision[m, l, k, i] = np.max(
                        precision[m, l, k, i:], axis=-1)
                    recall[m, l, k, i] = np.max(recall[m, l, k, i:], axis=-1)
                    if compute_aos:
                        aos[m, l, k, i] = np.max(aos[m, l, k, i:], axis=-1)
    ret_dict = {
        "recall": recall,
        "precision": precision,
        "orientation": aos,
    }
    return ret_dict


def do_eval(gt_annos, dt_annos, current_class, min_overlaps,
            compute_aos=False):

    mAP_bbox = []
    mAP_aos = []
    for i in range(3):  # i=difficulty
        ret = eval_class(gt_annos, dt_annos, current_class, i, 0,
                         min_overlaps[0], compute_aos)
        mAP_bbox.append(get_mAP(ret["precision"]))
        if compute_aos:
            mAP_aos.append(get_mAP(ret["orientation"]))
    mAP_bev = []
    for i in range(3):
        ret = eval_class(gt_annos, dt_annos, current_class, i, 1,
                         min_overlaps[1])
        mAP_bev.append(get_mAP(ret["precision"]))
    mAP_3d = []
    for i in range(3):
        ret = eval_class(gt_annos, dt_annos, current_class, i, 2,
                         min_overlaps[2])
        mAP_3d.append(get_mAP(ret["precision"]))
    return mAP_bbox, mAP_bev, mAP_3d, mAP_aos

'''
# The old 11 recall points evaluation
def get_mAP_v2(prec):
    sums = 0
    for i in range(0, prec.shape[-1], 4):
        sums = sums + prec[..., i]
    return sums / 11 * 100'''

### The new 40 recall points evaluation
def get_mAP_v2(prec):
    sums = 0
    for i in range(1, prec.shape[-1], 1):
        sums = sums + prec[..., i]
    return sums / 40 * 100

def do_eval_v2(gt_annos,
               dt_annos,
               current_classes,
               min_overlaps,
               compute_aos=False,
               difficultys = [0, 1, 2]):
    # min_overlaps: [num_minoverlap, metric, num_class]
    ret = eval_class_v3(gt_annos, dt_annos, current_classes, difficultys, 0,
                        min_overlaps, compute_aos)
    # ret: [num_class, num_diff, num_minoverlap, num_sample_points]
    mAP_bbox = get_mAP_v2(ret["precision"])
    #print('precision',ret["precision"])
    #print('recall',ret["recall"])
    mAP_aos = None
    if compute_aos:
        mAP_aos = get_mAP_v2(ret["orientation"])
    ret = eval_class_v3(gt_annos, dt_annos, current_classes, difficultys, 1,
                        min_overlaps)
    mAP_bev = get_mAP_v2(ret["precision"])
    ret = eval_class_v3(gt_annos, dt_annos, current_classes, difficultys, 2,
                        min_overlaps)
    mAP_3d = get_mAP_v2(ret["precision"])
    return mAP_bbox, mAP_bev, mAP_3d, mAP_aos


def do_coco_style_eval(gt_annos, dt_annos, current_classes, overlap_ranges,
                       compute_aos):
    # overlap_ranges: [range, metric, num_class]
    min_overlaps = np.zeros([10, *overlap_ranges.shape[1:]])
    for i in range(overlap_ranges.shape[1]):
        for j in range(overlap_ranges.shape[2]):
            min_overlaps[:, i, j] = np.linspace(*overlap_ranges[:, i, j])
    mAP_bbox, mAP_bev, mAP_3d, mAP_aos = do_eval_v2(
        gt_annos, dt_annos, current_classes, min_overlaps, compute_aos)
    # ret: [num_class, num_diff, num_minoverlap]
    mAP_bbox = mAP_bbox.mean(-1)
    mAP_bev = mAP_bev.mean(-1)
    mAP_3d = mAP_3d.mean(-1)
    if mAP_aos is not None:
        mAP_aos = mAP_aos.mean(-1)
    return mAP_bbox, mAP_bev, mAP_3d, mAP_aos


def print_str(value, *arg, sstream=None):
    if sstream is None:
        sstream = sysio.StringIO()
    sstream.truncate(0)
    sstream.seek(0)
    print(value, *arg, file=sstream)
    return sstream.getvalue()


def get_official_eval_result_v1(gt_annos, dt_annos, current_class):
    mAP_0_7 = np.array([[0.7, 0.5, 0.5, 0.7, 0.5], [0.7, 0.5, 0.5, 0.7, 0.5],
                        [0.7, 0.5, 0.5, 0.7, 0.5]])
    mAP_0_5 = np.array([[0.7, 0.5, 0.5, 0.7,
                         0.5], [0.5, 0.25, 0.25, 0.5, 0.25],
                        [0.5, 0.25, 0.25, 0.5, 0.25]])
    mAP_list = [mAP_0_7, mAP_0_5]
    class_to_name = {
        0: 'Car',
        1: 'Pedestrian',
        2: 'Cyclist',
        3: 'Van',
        4: 'Person_sitting',
    }
    name_to_class = {v: n for n, v in class_to_name.items()}
    if isinstance(current_class, str):
        current_class = name_to_class[current_class]
    result = ''
    # check whether alpha is valid
    compute_aos = False
    for anno in dt_annos:
        if anno['alpha'].shape[0] != 0:
            if anno['alpha'][0] != -10:
                compute_aos = True
            break
    for mAP in mAP_list:
        # mAP threshold matrix: [num_minoverlap, metric, class]
        mAPbbox, mAPbev, mAP3d, mAPaos = do_eval(
            gt_annos, dt_annos, current_class, mAP[:, current_class],
            compute_aos)
        # mAP: [num_class, num_diff, num_minoverlap]
        result += print_str(
            (f"{class_to_name[current_class]} "
             "AP@{:.2f}, {:.2f}, {:.2f}:".format(*mAP[:, current_class])))
        result += print_str((f"bbox AP:{mAPbbox[0]:.2f}, "
                             f"{mAPbbox[1]:.2f}, "
                             f"{mAPbbox[2]:.2f}"))
        result += print_str((f"bev  AP:{mAPbev[0]:.2f}, "
                             f"{mAPbev[1]:.2f}, "
                             f"{mAPbev[2]:.2f}"))
        result += print_str((f"3d   AP:{mAP3d[0]:.2f}, "
                             f"{mAP3d[1]:.2f}, "
                             f"{mAP3d[2]:.2f}"))
        if compute_aos:
            result += print_str((f"aos  AP:{mAPaos[0]:.2f}, "
                                 f"{mAPaos[1]:.2f}, "
                                 f"{mAPaos[2]:.2f}"))

    return result


def get_official_eval_result(gt_annos, dt_annos, current_classes, difficultys=[0, 1, 2]):
    overlap_0_7 = np.array([[0.7, 0.5, 0.5, 0.7,
                             0.5, 0.7, 0.7, 0.7], [0.7, 0.5, 0.5, 0.7, 0.5, 0.7, 0.7, 0.7],
                            [0.7, 0.5, 0.5, 0.7, 0.5, 0.7, 0.7, 0.7]])
    overlap_0_5 = np.array([[0.7, 0.5, 0.5, 0.7,
                             0.5, 0.5, 0.5, 0.5], [0.5, 0.25, 0.25, 0.5, 0.25, 0.5, 0.5, 0.5],
                            [0.5, 0.25, 0.25, 0.5, 0.25, 0.5, 0.5, 0.5]])
    min_overlaps = np.stack([overlap_0_7, overlap_0_5], axis=0)  # [2, 3, 5]
    class_to_name = {
        0: 'Car',
        1: 'Pedestrian',
        2: 'Cyclist',
        3: 'Van',
        4: 'Person_sitting',
        5: 'car',
        6: 'tractor',
        7: 'trailer',
    }
    name_to_class = {v: n for n, v in class_to_name.items()}
    if not isinstance(current_classes, (list, tuple)):
        current_classes = [current_classes]
    current_classes_int = []
    for curcls in current_classes:
        if isinstance(curcls, str):
            current_classes_int.append(name_to_class[curcls])
        else:
            current_classes_int.append(curcls)
    current_classes = current_classes_int
    min_overlaps = min_overlaps[:, :, current_classes]
    result = ''
    # check whether alpha is valid
    compute_aos = False
    for anno in dt_annos:
        if anno['alpha'].shape[0] != 0:
            if anno['alpha'][0] != -10:
                compute_aos = True
            break
    mAPbbox, mAPbev, mAP3d, mAPaos = do_eval_v2(
        gt_annos, dt_annos, current_classes, min_overlaps, compute_aos, difficultys)
    for j, curcls in enumerate(current_classes):
        # mAP threshold array: [num_minoverlap, metric, class]
        # mAP result: [num_class, num_diff, num_minoverlap]
        for i in range(min_overlaps.shape[0]):
            result += print_str(
                (f"{class_to_name[curcls]} "
                 "AP@{:.2f}, {:.2f}, {:.2f}:".format(*min_overlaps[i, :, j])))
            result += print_str((f"bbox AP:{mAPbbox[j, 0, i]:.2f}, "
                                 f"{mAPbbox[j, 1, i]:.2f}, "
                                 f"{mAPbbox[j, 2, i]:.2f}"))
            result += print_str((f"bev  AP:{mAPbev[j, 0, i]:.2f}, "
                                 f"{mAPbev[j, 1, i]:.2f}, "
                                 f"{mAPbev[j, 2, i]:.2f}"))
            result += print_str((f"3d   AP:{mAP3d[j, 0, i]:.2f}, "
                                 f"{mAP3d[j, 1, i]:.2f}, "
                                 f"{mAP3d[j, 2, i]:.2f}"))
            if compute_aos:
                result += print_str((f"aos  AP:{mAPaos[j, 0, i]:.2f}, "
                                     f"{mAPaos[j, 1, i]:.2f}, "
                                     f"{mAPaos[j, 2, i]:.2f}"))

    return result

def get_coco_eval_result(gt_annos, dt_annos, current_classes):
    class_to_name = {
        0: 'Car',
        1: 'Pedestrian',
        2: 'Cyclist',
        3: 'Van',
        4: 'Person_sitting',
        5: 'car',
        6: 'tractor',
        7: 'trailer',
    }
    class_to_range = {
        0: [0.5, 1.0, 0.05],
        1: [0.25, 0.75, 0.05],
        2: [0.25, 0.75, 0.05],
        3: [0.5, 1.0, 0.05],
        4: [0.25, 0.75, 0.05],
        5: [0.5, 1.0, 0.05],
        6: [0.5, 1.0, 0.05],
        7: [0.5, 1.0, 0.05],
    }
    class_to_range = {
        0: [0.5, 0.95, 10],
        1: [0.25, 0.7, 10],
        2: [0.25, 0.7, 10],
        3: [0.5, 0.95, 10],
        4: [0.25, 0.7, 10],
        5: [0.5, 0.95, 10],
        6: [0.5, 0.95, 10],
        7: [0.5, 0.95, 10],

    }

    name_to_class = {v: n for n, v in class_to_name.items()}
    if not isinstance(current_classes, (list, tuple)):
        current_classes = [current_classes]
    current_classes_int = []
    for curcls in current_classes:
        if isinstance(curcls, str):
            current_classes_int.append(name_to_class[curcls])
        else:
            current_classes_int.append(curcls)
    current_classes = current_classes_int
    overlap_ranges = np.zeros([3, 3, len(current_classes)])
    for i, curcls in enumerate(current_classes):
        overlap_ranges[:, :, i] = np.array(class_to_range[curcls])[:, np.newaxis]
    result = ''
    # check whether alpha is valid
    compute_aos = False
    for anno in dt_annos:
        if anno['alpha'].shape[0] != 0:
            if anno['alpha'][0] != -10:
                compute_aos = True
            break
    mAPbbox, mAPbev, mAP3d, mAPaos = do_coco_style_eval(
        gt_annos, dt_annos, current_classes, overlap_ranges, compute_aos)
    for j, curcls in enumerate(current_classes):
        # mAP threshold array: [num_minoverlap, metric, class]
        # mAP result: [num_class, num_diff, num_minoverlap]
        o_range = np.array(class_to_range[curcls])[[0, 2, 1]]
        o_range[1] = (o_range[2] - o_range[0]) / (o_range[1] - 1)
        result += print_str(
            (f"{class_to_name[curcls]} "
                "coco AP@{:.2f}:{:.2f}:{:.2f}:".format(*o_range)))
        result += print_str((f"bbox AP:{mAPbbox[j, 0]:.2f}, "
                                f"{mAPbbox[j, 1]:.2f}, "
                                f"{mAPbbox[j, 2]:.2f}"))
        result += print_str((f"bev  AP:{mAPbev[j, 0]:.2f}, "
                                f"{mAPbev[j, 1]:.2f}, "
                                f"{mAPbev[j, 2]:.2f}"))
        result += print_str((f"3d   AP:{mAP3d[j, 0]:.2f}, "
                                f"{mAP3d[j, 1]:.2f}, "
                                f"{mAP3d[j, 2]:.2f}"))
        if compute_aos:
            result += print_str((f"aos  AP:{mAPaos[j, 0]:.2f}, "
                                    f"{mAPaos[j, 1]:.2f}, "
                                    f"{mAPaos[j, 2]:.2f}"))
    return result