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Commit 955b4419 authored by VVsssssk's avatar VVsssssk Committed by ChaimZhu
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[Refactor]Add kitti metric

parent 7e87d837
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Showing with 736 additions and 25 deletions
mmdet3d/core/evaluation/kitti_utils/eval.py
View file @ 955b4419
...@@ -282,12 +282,13 @@ def compute_statistics_jit(overlaps, ...@@ -282,12 +282,13 @@ def compute_statistics_jit(overlaps,
def get_split_parts(num, num_part): def get_split_parts(num, num_part):
if num % num_part == 0:
same_part = num // num_part same_part = num // num_part
remain_num = num % num_part
if remain_num == 0:
return [same_part] * num_part return [same_part] * num_part
else: else:
return [same_part] * num_part + [remain_num] same_part = num // (num_part - 1)
remain_num = num % (num_part - 1)
return [same_part] * (num_part - 1) + [remain_num]
@numba.jit(nopython=True) @numba.jit(nopython=True)
...@@ -340,57 +341,57 @@ def fused_compute_statistics(overlaps, ...@@ -340,57 +341,57 @@ def fused_compute_statistics(overlaps,
dc_num += dc_nums[i] dc_num += dc_nums[i]
def calculate_iou_partly(gt_annos, dt_annos, metric, num_parts=50): def calculate_iou_partly(dt_annos, gt_annos, metric, num_parts=50):
"""Fast iou algorithm. this function can be used independently to do result """Fast iou algorithm. this function can be used independently to do result
analysis. Must be used in CAMERA coordinate system. analysis. Must be used in CAMERA coordinate system.
Args: 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. dt_annos (dict): Must from get_label_annos() in kitti_common.py.
gt_annos (dict): Must from get_label_annos() in kitti_common.py.
metric (int): Eval type. 0: bbox, 1: bev, 2: 3d. metric (int): Eval type. 0: bbox, 1: bev, 2: 3d.
num_parts (int): A parameter for fast calculate algorithm. num_parts (int): A parameter for fast calculate algorithm.
""" """
assert len(gt_annos) == len(dt_annos) assert len(dt_annos) == len(gt_annos)
total_dt_num = np.stack([len(a['name']) for a in dt_annos], 0) total_dt_num = np.stack([len(a['name']) for a in gt_annos], 0)
total_gt_num = np.stack([len(a['name']) for a in gt_annos], 0) total_gt_num = np.stack([len(a['name']) for a in dt_annos], 0)
num_examples = len(gt_annos) num_examples = len(dt_annos)
split_parts = get_split_parts(num_examples, num_parts) split_parts = get_split_parts(num_examples, num_parts)
parted_overlaps = [] parted_overlaps = []
example_idx = 0 example_idx = 0
for num_part in split_parts: 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] dt_annos_part = dt_annos[example_idx:example_idx + num_part]
gt_annos_part = gt_annos[example_idx:example_idx + num_part]
if metric == 0: if metric == 0:
gt_boxes = np.concatenate([a['bbox'] for a in gt_annos_part], 0) gt_boxes = np.concatenate([a['bbox'] for a in dt_annos_part], 0)
dt_boxes = np.concatenate([a['bbox'] for a in dt_annos_part], 0) dt_boxes = np.concatenate([a['bbox'] for a in gt_annos_part], 0)
overlap_part = image_box_overlap(gt_boxes, dt_boxes) overlap_part = image_box_overlap(gt_boxes, dt_boxes)
elif metric == 1: elif metric == 1:
loc = np.concatenate( loc = np.concatenate(
[a['location'][:, [0, 2]] for a in gt_annos_part], 0) [a['location'][:, [0, 2]] for a in dt_annos_part], 0)
dims = np.concatenate( dims = np.concatenate(
[a['dimensions'][:, [0, 2]] for a in gt_annos_part], 0) [a['dimensions'][:, [0, 2]] for a in dt_annos_part], 0)
rots = np.concatenate([a['rotation_y'] for a in gt_annos_part], 0) rots = np.concatenate([a['rotation_y'] for a in dt_annos_part], 0)
gt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]], gt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]],
axis=1) axis=1)
loc = np.concatenate( loc = np.concatenate(
[a['location'][:, [0, 2]] for a in dt_annos_part], 0) [a['location'][:, [0, 2]] for a in gt_annos_part], 0)
dims = np.concatenate( dims = np.concatenate(
[a['dimensions'][:, [0, 2]] for a in dt_annos_part], 0) [a['dimensions'][:, [0, 2]] for a in gt_annos_part], 0)
rots = np.concatenate([a['rotation_y'] for a in dt_annos_part], 0) rots = np.concatenate([a['rotation_y'] for a in gt_annos_part], 0)
dt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]], dt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]],
axis=1) axis=1)
overlap_part = bev_box_overlap(gt_boxes, overlap_part = bev_box_overlap(gt_boxes,
dt_boxes).astype(np.float64) dt_boxes).astype(np.float64)
elif metric == 2: 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) loc = np.concatenate([a['location'] for a in dt_annos_part], 0)
dims = np.concatenate([a['dimensions'] 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) rots = np.concatenate([a['rotation_y'] for a in dt_annos_part], 0)
gt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]],
axis=1)
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)
dt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]], dt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]],
axis=1) axis=1)
overlap_part = d3_box_overlap(gt_boxes, overlap_part = d3_box_overlap(gt_boxes,
...@@ -402,8 +403,8 @@ def calculate_iou_partly(gt_annos, dt_annos, metric, num_parts=50): ...@@ -402,8 +403,8 @@ def calculate_iou_partly(gt_annos, dt_annos, metric, num_parts=50):
overlaps = [] overlaps = []
example_idx = 0 example_idx = 0
for j, num_part in enumerate(split_parts): 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] dt_annos_part = dt_annos[example_idx:example_idx + num_part]
gt_annos_part = gt_annos[example_idx:example_idx + num_part]
gt_num_idx, dt_num_idx = 0, 0 gt_num_idx, dt_num_idx = 0, 0
for i in range(num_part): for i in range(num_part):
gt_box_num = total_gt_num[example_idx + i] gt_box_num = total_gt_num[example_idx + i]
...@@ -480,6 +481,7 @@ def eval_class(gt_annos, ...@@ -480,6 +481,7 @@ def eval_class(gt_annos,
rets = calculate_iou_partly(dt_annos, gt_annos, metric, num_parts) rets = calculate_iou_partly(dt_annos, gt_annos, metric, num_parts)
overlaps, parted_overlaps, total_dt_num, total_gt_num = rets overlaps, parted_overlaps, total_dt_num, total_gt_num = rets
N_SAMPLE_PTS = 41 N_SAMPLE_PTS = 41
num_minoverlap = len(min_overlaps) num_minoverlap = len(min_overlaps)
num_class = len(current_classes) num_class = len(current_classes)
......
mmdet3d/metrics/__init__.py 0 → 100644
View file @ 955b4419
# Copyright (c) OpenMMLab. All rights reserved.
from .kitti_metric import KittiMetric # noqa: F401,F403
__all_ = ['KittiMetric']
mmdet3d/metrics/kitti_metric.py 0 → 100644
View file @ 955b4419
# Copyright (c) OpenMMLab. All rights reserved.
import tempfile
from os import path as osp
from typing import Dict, List, Optional, Sequence, Union
import mmcv
import numpy as np
import torch
from mmcv.utils import print_log
from mmengine.evaluator import BaseMetric
from mmengine.logging import MMLogger
from mmdet3d.core.bbox import Box3DMode, points_cam2img
from mmdet3d.core.evaluation import kitti_eval
from mmdet3d.registry import METRICS
@METRICS.register_module()
class KittiMetric(BaseMetric):
"""Kitti evaluation metric.
Args:
ann_file (str): Annotation file path.
metric (str | list[str]): Metrics to be evaluated.
Default to 'bbox'.
pcd_limit_range (list): The range of point cloud used to
filter invalid predicted boxes.
Default to [0, -40, -3, 70.4, 40, 0.0].
prefix (str, optional): The prefix that will be added in the metric
names to disambiguate homonymous metrics of different evaluators.
If prefix is not provided in the argument, self.default_prefix
will be used instead. Defaults to None.
pklfile_prefix (str, optional): The prefix of pkl files, including
the file path and the prefix of filename, e.g., "a/b/prefix".
If not specified, a temp file will be created. Default: None.
submission_prefix (str, optional): The prefix of submission data.
If not specified, the submission data will not be generated.
Default: None.
collect_device (str): Device name used for collecting results
from different ranks during distributed training. Must be 'cpu' or
'gpu'. Defaults to 'cpu'.
"""
def __init__(self,
ann_file: str,
metric: Union[str, List[str]] = 'bbox',
pcd_limit_range: List[float] = [0, -40, -3, 70.4, 40, 0.0],
prefix: Optional[str] = None,
pklfile_prefix: str = None,
submission_prefix: str = None,
collect_device: str = 'cpu'):
self.default_prefix = 'Kitti metric'
super(KittiMetric, self).__init__(
collect_device=collect_device, prefix=prefix)
self.pcd_limit_range = pcd_limit_range
self.ann_file = ann_file
self.pklfile_prefix = pklfile_prefix
self.submission_prefix = submission_prefix
allowed_metrics = ['bbox', 'img_bbox', 'mAP']
self.metrics = metric if isinstance(metric, list) else [metric]
for metric in self.metrics:
if metric not in allowed_metrics:
raise KeyError("metric should be one of 'bbox', 'img_bbox', "
'but got {metric}.')
def convert_annos_to_kitti_annos(self, data_annos: list,
classes: list) -> list:
"""Convert loading annotations to Kitti annotations.
Args:
data_annos (list[dict]): Annotations loaded from ann_file.
classes (list[str]): Classes used in the dataset.
Returns:
List[dict]: List of Kitti annotations.
"""
assert 'instances' in data_annos[0]
for i, annos in enumerate(data_annos):
if len(annos['instances']) == 0:
kitti_annos = {
'name': np.array([]),
'truncated': np.array([]),
'occluded': np.array([]),
'alpha': np.array([]),
'bbox': np.zeros([0, 4]),
'dimensions': np.zeros([0, 3]),
'location': np.zeros([0, 3]),
'rotation_y': np.array([]),
'score': np.array([]),
}
else:
kitti_annos = {
'name': [],
'truncated': [],
'occluded': [],
'alpha': [],
'bbox': [],
'location': [],
'dimensions': [],
'rotation_y': [],
'score': []
}
for instance in annos['instances']:
kitti_annos['name'].append(classes[instance['bbox_label']])
kitti_annos['truncated'].append(instance['truncated'])
kitti_annos['occluded'].append(instance['occluded'])
kitti_annos['alpha'].append(instance['alpha'])
kitti_annos['bbox'].append(instance['bbox'])
kitti_annos['location'].append(instance['bbox_3d'][:3])
kitti_annos['dimensions'].append(instance['bbox_3d'][3:6])
kitti_annos['rotation_y'].append(instance['bbox_3d'][6])
kitti_annos['score'].append(instance['score'])
for name in kitti_annos:
kitti_annos[name] = np.array(kitti_annos[name])
data_annos[i]['kitti_annos'] = kitti_annos
return data_annos
def load_annotations(self, ann_file: str) -> list:
"""Load annotations from ann_file.
Args:
ann_file (str): Path of the annotation file.
Returns:
list[dict]: List of annotations.
"""
# loading data from a file-like object needs file format
return mmcv.load(ann_file, file_format='pkl')
def process(self, data_batch: Sequence[dict],
predictions: Sequence[dict]) -> None:
"""Process one batch of data samples and predictions.
The processed results should be stored in ``self.results``,
which will be used to compute the metrics when all batches
have been processed.
Args:
data_batch (Sequence[dict]): A batch of data
from the dataloader.
predictions (Sequence[dict]): A batch of outputs from
the model.
"""
assert len(data_batch) == len(predictions)
for data, pred in zip(data_batch, predictions):
result = dict()
for pred_result in pred:
for attr_name in pred[pred_result]:
pred[pred_result][attr_name] = pred[pred_result][
attr_name].to(self.collect_device)
result[pred_result] = pred[pred_result]
sample_idx = data['data_sample']['sample_idx']
result['sample_idx'] = sample_idx
self.results.append(result)
def compute_metrics(self, results: list) -> Dict[str, float]:
"""Compute the metrics from processed results.
Args:
results (list): The processed results of each batch.
Returns:
Dict[str, float]: The computed metrics. The keys are the names of
the metrics, and the values are corresponding results.
"""
logger: MMLogger = MMLogger.get_current_instance()
self.classes = self.dataset_meta['CLASSES']
# load annotations
pkl_annos = self.load_annotations(self.ann_file)['data_list']
self.data_infos = self.convert_annos_to_kitti_annos(
pkl_annos, self.classes)
result_dict, tmp_dir = self.format_results(
results,
pklfile_prefix=self.pklfile_prefix,
submission_prefix=self.submission_prefix,
classes=self.classes)
gt_annos = [
self.data_infos[result['sample_idx']]['kitti_annos']
for result in results
]
metric_dict = {}
for metric in self.metrics:
ap_dict = self.kitti_evaluate(
result_dict,
gt_annos,
metric=metric,
logger=logger,
classes=self.classes)
for result in ap_dict:
metric_dict[result] = ap_dict[result]
if tmp_dir is not None:
tmp_dir.cleanup()
return metric_dict
def kitti_evaluate(self,
result_dict: List[dict],
gt_annos: List[dict],
metric: str = None,
classes: List[str] = None,
logger: MMLogger = None) -> dict:
"""Evaluation in KITTI protocol.
Args:
results_dict (dict): Formatted results of the dataset.
gt_annos (list[dict]): Contain gt information of each sample.
metric (str, optional): Metrics to be evaluated.
Default: None.
logger (MMLogger, optional): Logger used for printing
related information during evaluation. Default: None.
classes (list[String], optional): A list of class name. Defaults
to None.
Returns:
dict[str, float]: Results of each evaluation metric.
"""
ap_dict = dict()
for name in result_dict:
if name == 'pred_instances' or metric == 'img_bbox':
eval_types = ['bbox']
else:
eval_types = ['bbox', 'bev', '3d']
ap_result_str, ap_dict_ = kitti_eval(
gt_annos, result_dict[name], classes, eval_types=eval_types)
for ap_type, ap in ap_dict_.items():
ap_dict[f'{name}/{ap_type}'] = float('{:.4f}'.format(ap))
print_log(f'Results of {name}:\n' + ap_result_str, logger=logger)
return ap_dict
def format_results(self,
results: List[dict],
pklfile_prefix: str = None,
submission_prefix: str = None,
classes: List[str] = None):
"""Format the results to pkl file.
Args:
results (list[dict]): Testing results of the
dataset.
pklfile_prefix (str, optional): The prefix of pkl files. It
includes the file path and the prefix of filename, e.g.,
"a/b/prefix". If not specified, a temp file will be created.
Default: None.
submission_prefix (str, optional): The prefix of submitted files.
It includes the file path and the prefix of filename, e.g.,
"a/b/prefix". If not specified, a temp file will be created.
Default: None.
classes (list[String], optional): A list of class name. Defaults
to None.
Returns:
tuple: (result_dict, tmp_dir), result_dict is a dict containing
the formatted result, tmp_dir is the temporal directory created
for saving json files when jsonfile_prefix is not specified.
"""
if pklfile_prefix is None:
tmp_dir = tempfile.TemporaryDirectory()
pklfile_prefix = osp.join(tmp_dir.name, 'results')
else:
tmp_dir = None
result_dict = dict()
sample_id_list = [result['sample_idx'] for result in results]
for name in results[0]:
if submission_prefix is not None:
submission_prefix_ = osp.join(submission_prefix, name)
else:
submission_prefix_ = None
if pklfile_prefix is not None:
pklfile_prefix_ = osp.join(pklfile_prefix, name) + '.pkl'
else:
pklfile_prefix_ = None
if 'pred_instances' in name and '3d' in name and name[0] != '_':
net_outputs = [result[name] for result in results]
result_list_ = self.bbox2result_kitti(net_outputs,
sample_id_list, classes,
pklfile_prefix_,
submission_prefix_)
result_dict[name] = result_list_
elif name == 'pred_instances' and name[0] != '_':
net_outputs = [info[name] for info in results]
result_list_ = self.bbox2result_kitti2d(
net_outputs, sample_id_list, classes, pklfile_prefix_,
submission_prefix_)
result_dict[name] = result_list_
return result_dict, tmp_dir
def bbox2result_kitti(self,
net_outputs: list,
sample_id_list: list,
class_names: list,
pklfile_prefix: str = None,
submission_prefix: str = None):
"""Convert 3D detection results to kitti format for evaluation and test
submission.
Args:
net_outputs (list[dict]): List of array storing the
inferenced bounding boxes and scores.
sample_id_list (list[int]): List of input sample id.
class_names (list[String]): A list of class names.
pklfile_prefix (str, optional): The prefix of pkl file.
Defaults to None.
submission_prefix (str, optional): The prefix of submission file.
Defaults to None.
Returns:
list[dict]: A list of dictionaries with the kitti format.
"""
assert len(net_outputs) == len(self.data_infos), \
'invalid list length of network outputs'
if submission_prefix is not None:
mmcv.mkdir_or_exist(submission_prefix)
det_annos = []
print('\nConverting prediction to KITTI format')
for idx, pred_dicts in enumerate(
mmcv.track_iter_progress(net_outputs)):
annos = []
sample_idx = sample_id_list[idx]
info = self.data_infos[sample_idx]
# Here default used 'CAM2' to compute metric. If you want to
# use another camera, please modify it.
image_shape = (info['images']['CAM2']['height'],
info['images']['CAM2']['width'])
box_dict = self.convert_valid_bboxes(pred_dicts, info)
anno = {
'name': [],
'truncated': [],
'occluded': [],
'alpha': [],
'bbox': [],
'dimensions': [],
'location': [],
'rotation_y': [],
'score': []
}
if len(box_dict['bbox']) > 0:
box_2d_preds = box_dict['bbox']
box_preds = box_dict['box3d_camera']
scores = box_dict['scores']
box_preds_lidar = box_dict['box3d_lidar']
label_preds = box_dict['label_preds']
for box, box_lidar, bbox, score, label in zip(
box_preds, box_preds_lidar, box_2d_preds, scores,
label_preds):
bbox[2:] = np.minimum(bbox[2:], image_shape[::-1])
bbox[:2] = np.maximum(bbox[:2], [0, 0])
anno['name'].append(class_names[int(label)])
anno['truncated'].append(0.0)
anno['occluded'].append(0)
anno['alpha'].append(
-np.arctan2(-box_lidar[1], box_lidar[0]) + box[6])
anno['bbox'].append(bbox)
anno['dimensions'].append(box[3:6])
anno['location'].append(box[:3])
anno['rotation_y'].append(box[6])
anno['score'].append(score)
anno = {k: np.stack(v) for k, v in anno.items()}
annos.append(anno)
else:
anno = {
'name': np.array([]),
'truncated': np.array([]),
'occluded': np.array([]),
'alpha': np.array([]),
'bbox': np.zeros([0, 4]),
'dimensions': np.zeros([0, 3]),
'location': np.zeros([0, 3]),
'rotation_y': np.array([]),
'score': np.array([]),
}
annos.append(anno)
if submission_prefix is not None:
curr_file = f'{submission_prefix}/{sample_idx:06d}.txt'
with open(curr_file, 'w') as f:
bbox = anno['bbox']
loc = anno['location']
dims = anno['dimensions'] # lhw -> hwl
for idx in range(len(bbox)):
print(
'{} -1 -1 {:.4f} {:.4f} {:.4f} {:.4f} '
'{:.4f} {:.4f} {:.4f} '
'{:.4f} {:.4f} {:.4f} {:.4f} {:.4f} {:.4f}'.format(
anno['name'][idx], anno['alpha'][idx],
bbox[idx][0], bbox[idx][1], bbox[idx][2],
bbox[idx][3], dims[idx][1], dims[idx][2],
dims[idx][0], loc[idx][0], loc[idx][1],
loc[idx][2], anno['rotation_y'][idx],
anno['score'][idx]),
file=f)
annos[-1]['sample_id'] = np.array(
[sample_idx] * len(annos[-1]['score']), dtype=np.int64)
det_annos += annos
if pklfile_prefix is not None:
if not pklfile_prefix.endswith(('.pkl', '.pickle')):
out = f'{pklfile_prefix}.pkl'
else:
out = pklfile_prefix
mmcv.dump(det_annos, out)
print(f'Result is saved to {out}.')
return det_annos
def bbox2result_kitti2d(self,
net_outputs: list,
sample_id_list,
class_names: list,
pklfile_prefix: str = None,
submission_prefix: str = None):
"""Convert 2D detection results to kitti format for evaluation and test
submission.
Args:
net_outputs (list[dict]): List of array storing the
inferenced bounding boxes and scores.
sample_id_list (list[int]): List of input sample id.
class_names (list[String]): A list of class names.
pklfile_prefix (str, optional): The prefix of pkl file.
Defaults to None.
submission_prefix (str, optional): The prefix of submission file.
Defaults to None.
Returns:
list[dict]: A list of dictionaries have the kitti format
"""
assert len(net_outputs) == len(self.data_infos), \
'invalid list length of network outputs'
det_annos = []
print('\nConverting prediction to KITTI format')
for i, bboxes_per_sample in enumerate(
mmcv.track_iter_progress(net_outputs)):
annos = []
anno = dict(
name=[],
truncated=[],
occluded=[],
alpha=[],
bbox=[],
dimensions=[],
location=[],
rotation_y=[],
score=[])
sample_idx = sample_id_list[i]
num_example = 0
bbox = bboxes_per_sample['bboxes']
for i in range(bbox.shape[0]):
anno['name'].append(class_names[int(
bboxes_per_sample['labels'][i])])
anno['truncated'].append(0.0)
anno['occluded'].append(0)
anno['alpha'].append(0.0)
anno['bbox'].append(bbox[i, :4])
# set dimensions (height, width, length) to zero
anno['dimensions'].append(
np.zeros(shape=[3], dtype=np.float32))
# set the 3D translation to (-1000, -1000, -1000)
anno['location'].append(
np.ones(shape=[3], dtype=np.float32) * (-1000.0))
anno['rotation_y'].append(0.0)
anno['score'].append(bboxes_per_sample['scores'][i])
num_example += 1
if num_example == 0:
annos.append(
dict(
name=np.array([]),
truncated=np.array([]),
occluded=np.array([]),
alpha=np.array([]),
bbox=np.zeros([0, 4]),
dimensions=np.zeros([0, 3]),
location=np.zeros([0, 3]),
rotation_y=np.array([]),
score=np.array([]),
))
else:
anno = {k: np.stack(v) for k, v in anno.items()}
annos.append(anno)
annos[-1]['sample_id'] = np.array(
[sample_idx] * num_example, dtype=np.int64)
det_annos += annos
if pklfile_prefix is not None:
if not pklfile_prefix.endswith(('.pkl', '.pickle')):
out = f'{pklfile_prefix}.pkl'
else:
out = pklfile_prefix
mmcv.dump(det_annos, out)
print(f'Result is saved to {out}.')
if submission_prefix is not None:
# save file in submission format
mmcv.mkdir_or_exist(submission_prefix)
print(f'Saving KITTI submission to {submission_prefix}')
for i, anno in enumerate(det_annos):
sample_idx = self.data_infos[i]['image']['image_idx']
cur_det_file = f'{submission_prefix}/{sample_idx:06d}.txt'
with open(cur_det_file, 'w') as f:
bbox = anno['bbox']
loc = anno['location']
dims = anno['dimensions'][::-1] # lhw -> hwl
for idx in range(len(bbox)):
print(
'{} -1 -1 {:4f} {:4f} {:4f} {:4f} {:4f} {:4f} '
'{:4f} {:4f} {:4f} {:4f} {:4f} {:4f} {:4f}'.format(
anno['name'][idx],
anno['alpha'][idx],
*bbox[idx], # 4 float
*dims[idx], # 3 float
*loc[idx], # 3 float
anno['rotation_y'][idx],
anno['score'][idx]),
file=f,
)
print(f'Result is saved to {submission_prefix}')
return det_annos
def convert_valid_bboxes(self, box_dict: dict, info: dict):
"""Convert the predicted boxes into valid ones.
Args:
box_dict (dict): Box dictionaries to be converted.
- boxes_3d (:obj:`LiDARInstance3DBoxes`): 3D bounding boxes.
- scores_3d (torch.Tensor): Scores of boxes.
- labels_3d (torch.Tensor): Class labels of boxes.
info (dict): Data info.
Returns:
dict: Valid predicted boxes.
- bbox (np.ndarray): 2D bounding boxes.
- box3d_camera (np.ndarray): 3D bounding boxes in
camera coordinate.
- box3d_lidar (np.ndarray): 3D bounding boxes in
LiDAR coordinate.
- scores (np.ndarray): Scores of boxes.
- label_preds (np.ndarray): Class label predictions.
- sample_idx (int): Sample index.
"""
# TODO: refactor this function
box_preds = box_dict['bboxes_3d']
scores = box_dict['scores_3d']
labels = box_dict['labels_3d']
sample_idx = info['sample_id']
box_preds.limit_yaw(offset=0.5, period=np.pi * 2)
if len(box_preds) == 0:
return dict(
bbox=np.zeros([0, 4]),
box3d_camera=np.zeros([0, 7]),
box3d_lidar=np.zeros([0, 7]),
scores=np.zeros([0]),
label_preds=np.zeros([0, 4]),
sample_idx=sample_idx)
# Here default used 'CAM2' to compute metric. If you want to
# use another camera, please modify it.
lidar2cam = np.array(info['images']['CAM2']['lidar2cam']).astype(
np.float32)
P2 = np.array(info['images']['CAM2']['cam2img']).astype(np.float32)
img_shape = (info['images']['CAM2']['height'],
info['images']['CAM2']['width'])
P2 = box_preds.tensor.new_tensor(P2)
box_preds_camera = box_preds.convert_to(Box3DMode.CAM, lidar2cam)
box_corners = box_preds_camera.corners
box_corners_in_image = points_cam2img(box_corners, P2)
# box_corners_in_image: [N, 8, 2]
minxy = torch.min(box_corners_in_image, dim=1)[0]
maxxy = torch.max(box_corners_in_image, dim=1)[0]
box_2d_preds = torch.cat([minxy, maxxy], dim=1)
# Post-processing
# check box_preds_camera
image_shape = box_preds.tensor.new_tensor(img_shape)
valid_cam_inds = ((box_2d_preds[:, 0] < image_shape[1]) &
(box_2d_preds[:, 1] < image_shape[0]) &
(box_2d_preds[:, 2] > 0) & (box_2d_preds[:, 3] > 0))
# check box_preds
limit_range = box_preds.tensor.new_tensor(self.pcd_limit_range)
valid_pcd_inds = ((box_preds.center > limit_range[:3]) &
(box_preds.center < limit_range[3:]))
valid_inds = valid_cam_inds & valid_pcd_inds.all(-1)
if valid_inds.sum() > 0:
return dict(
bbox=box_2d_preds[valid_inds, :].numpy(),
box3d_camera=box_preds_camera[valid_inds].tensor.numpy(),
box3d_lidar=box_preds[valid_inds].tensor.numpy(),
scores=scores[valid_inds].numpy(),
label_preds=labels[valid_inds].numpy(),
sample_idx=sample_idx)
else:
return dict(
bbox=np.zeros([0, 4]),
box3d_camera=np.zeros([0, 7]),
box3d_lidar=np.zeros([0, 7]),
scores=np.zeros([0]),
label_preds=np.zeros([0, 4]),
sample_idx=sample_idx)
tests/test_metrics/test_kitti_metric.py 0 → 100644
View file @ 955b4419
import numpy as np
import pytest
import torch
from mmengine.data import InstanceData
from mmdet3d.core import Det3DDataSample
from mmdet3d.core.bbox import LiDARInstance3DBoxes
from mmdet3d.metrics import KittiMetric
data_root = 'tests/data/kitti'
def _init_evaluate_input():
data_batch = [dict(data_sample=dict(sample_idx=0))]
predictions = Det3DDataSample()
pred_instances_3d = InstanceData()
pred_instances_3d.bboxes_3d = LiDARInstance3DBoxes(
torch.tensor(
[[8.7314, -1.8559, -1.5997, 0.4800, 1.2000, 1.8900, 0.0100]]))
pred_instances_3d.scores_3d = torch.Tensor([0.9])
pred_instances_3d.labels_3d = torch.Tensor([0])
predictions.pred_instances_3d = pred_instances_3d
predictions = predictions.to_dict()
return data_batch, [predictions]
def _init_multi_modal_evaluate_input():
data_batch = [dict(data_sample=dict(sample_idx=0))]
predictions = Det3DDataSample()
pred_instances_3d = InstanceData()
pred_instances = InstanceData()
pred_instances.bboxes = torch.tensor([[712.4, 143, 810.7, 307.92]])
pred_instances.scores = torch.Tensor([0.9])
pred_instances.labels = torch.Tensor([0])
pred_instances_3d.bboxes_3d = LiDARInstance3DBoxes(
torch.tensor(
[[8.7314, -1.8559, -1.5997, 0.4800, 1.2000, 1.8900, 0.0100]]))
pred_instances_3d.scores_3d = torch.Tensor([0.9])
pred_instances_3d.labels_3d = torch.Tensor([0])
predictions.pred_instances_3d = pred_instances_3d
predictions.pred_instances = pred_instances
predictions = predictions.to_dict()
return data_batch, [predictions]
def test_multi_modal_kitti_metric():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
kittimetric = KittiMetric(
data_root + '/kitti_infos_train.pkl', metric=['mAP'])
kittimetric.dataset_meta = dict(CLASSES=['Car', 'Pedestrian', 'Cyclist'])
data_batch, predictions = _init_multi_modal_evaluate_input()
kittimetric.process(data_batch, predictions)
ap_dict = kittimetric.compute_metrics(kittimetric.results)
assert np.isclose(ap_dict['pred_instances_3d/KITTI/Overall_3D_AP11_easy'],
3.0303030303030307)
assert np.isclose(ap_dict['pred_instances_3d/KITTI/Overall_BEV_AP11_easy'],
3.0303030303030307)
assert np.isclose(ap_dict['pred_instances_3d/KITTI/Overall_2D_AP11_easy'],
3.0303030303030307)
assert np.isclose(ap_dict['pred_instances/KITTI/Overall_2D_AP11_easy'],
3.0303030303030307)
assert np.isclose(ap_dict['pred_instances/KITTI/Overall_2D_AP11_moderate'],
3.0303030303030307)
assert np.isclose(ap_dict['pred_instances/KITTI/Overall_2D_AP11_hard'],
3.0303030303030307)
def test_kitti_metric_mAP():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
kittimetric = KittiMetric(
data_root + '/kitti_infos_train.pkl', metric=['mAP'])
kittimetric.dataset_meta = dict(CLASSES=['Car', 'Pedestrian', 'Cyclist'])
data_batch, predictions = _init_evaluate_input()
kittimetric.process(data_batch, predictions)
ap_dict = kittimetric.compute_metrics(kittimetric.results)
assert np.isclose(ap_dict['pred_instances_3d/KITTI/Overall_3D_AP11_easy'],
3.0303030303030307)
assert np.isclose(
ap_dict['pred_instances_3d/KITTI/Overall_3D_AP11_moderate'],
3.0303030303030307)
assert np.isclose(ap_dict['pred_instances_3d/KITTI/Overall_3D_AP11_hard'],
3.0303030303030307)
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