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import copy
import pickle
import argparse
import os
from os import path as osp
import torch
from av2.utils.io import read_feather
import numpy as np
import multiprocessing as mp
import pickle as pkl
from pathlib import Path
import pandas as pd
from ..dataset import DatasetTemplate
from .argo2_utils.so3 import yaw_to_quat, quat_to_yaw
from .argo2_utils.constants import LABEL_ATTR
def process_single_segment(segment_path, split, info_list, ts2idx, output_dir, save_bin):
test_mode = 'test' in split
if not test_mode:
segment_anno = read_feather(Path(osp.join(segment_path, 'annotations.feather')))
segname = segment_path.split('/')[-1]
frame_path_list = os.listdir(osp.join(segment_path, 'sensors/lidar/'))
for frame_name in frame_path_list:
ts = int(osp.basename(frame_name).split('.')[0])
if not test_mode:
frame_anno = segment_anno[segment_anno['timestamp_ns'] == ts]
else:
frame_anno = None
frame_path = osp.join(segment_path, 'sensors/lidar/', frame_name)
frame_info = process_and_save_frame(frame_path, frame_anno, ts2idx, segname, output_dir, save_bin)
info_list.append(frame_info)
def process_and_save_frame(frame_path, frame_anno, ts2idx, segname, output_dir, save_bin):
frame_info = {}
frame_info['uuid'] = segname + '/' + frame_path.split('/')[-1].split('.')[0]
frame_info['sample_idx'] = ts2idx[frame_info['uuid']]
frame_info['image'] = dict()
frame_info['point_cloud'] = dict(
num_features=4,
velodyne_path=None,
)
frame_info['calib'] = dict() # not need for lidar-only
frame_info['pose'] = dict() # not need for single frame
frame_info['annos'] = dict(
name=None,
truncated=None,
occluded=None,
alpha=None,
bbox=None, # not need for lidar-only
dimensions=None,
location=None,
rotation_y=None,
index=None,
group_ids=None,
camera_id=None,
difficulty=None,
num_points_in_gt=None,
)
frame_info['sweeps'] = [] # not need for single frame
if frame_anno is not None:
frame_anno = frame_anno[frame_anno['num_interior_pts'] > 0]
cuboid_params = frame_anno.loc[:, list(LABEL_ATTR)].to_numpy()
cuboid_params = torch.from_numpy(cuboid_params)
yaw = quat_to_yaw(cuboid_params[:, -4:])
xyz = cuboid_params[:, :3]
lwh = cuboid_params[:, [3, 4, 5]]
cat = frame_anno['category'].to_numpy().tolist()
cat = [c.lower().capitalize() for c in cat]
cat = np.array(cat)
num_obj = len(cat)
annos = frame_info['annos']
annos['name'] = cat
annos['truncated'] = np.zeros(num_obj, dtype=np.float64)
annos['occluded'] = np.zeros(num_obj, dtype=np.int64)
annos['alpha'] = -10 * np.ones(num_obj, dtype=np.float64)
annos['dimensions'] = lwh.numpy().astype(np.float64)
annos['location'] = xyz.numpy().astype(np.float64)
annos['rotation_y'] = yaw.numpy().astype(np.float64)
annos['index'] = np.arange(num_obj, dtype=np.int32)
annos['num_points_in_gt'] = frame_anno['num_interior_pts'].to_numpy().astype(np.int32)
# frame_info['group_ids'] = np.arange(num_obj, dtype=np.int32)
prefix2split = {'0': 'training', '1': 'training', '2': 'testing'}
sample_idx = frame_info['sample_idx']
split = prefix2split[sample_idx[0]]
abs_save_path = osp.join(output_dir, split, 'velodyne', f'{sample_idx}.bin')
rel_save_path = osp.join(split, 'velodyne', f'{sample_idx}.bin')
frame_info['point_cloud']['velodyne_path'] = rel_save_path
if save_bin:
save_point_cloud(frame_path, abs_save_path)
return frame_info
def save_point_cloud(frame_path, save_path):
lidar = read_feather(Path(frame_path))
lidar = lidar.loc[:, ['x', 'y', 'z', 'intensity']].to_numpy().astype(np.float32)
lidar.tofile(save_path)
def prepare(root):
ts2idx = {}
ts_list = []
bin_idx_list = []
seg_path_list = []
seg_split_list = []
assert root.split('/')[-1] == 'sensor'
# include test if you need it
splits = ['train', 'val'] # , 'test']
num_train_samples = 0
num_val_samples = 0
num_test_samples = 0
# 0 for training, 1 for validation and 2 for testing.
prefixes = [0, 1, ] # 2]
for i in range(len(splits)):
split = splits[i]
prefix = prefixes[i]
split_root = osp.join(root, split)
seg_file_list = os.listdir(split_root)
print(f'num of {split} segments:', len(seg_file_list))
for seg_idx, seg_name in enumerate(seg_file_list):
seg_path = osp.join(split_root, seg_name)
seg_path_list.append(seg_path)
seg_split_list.append(split)
assert seg_idx < 1000
frame_path_list = os.listdir(osp.join(seg_path, 'sensors/lidar/'))
for frame_idx, frame_path in enumerate(frame_path_list):
assert frame_idx < 1000
bin_idx = str(prefix) + str(seg_idx).zfill(3) + str(frame_idx).zfill(3)
ts = frame_path.split('/')[-1].split('.')[0]
ts = seg_name + '/' + ts # ts is not unique, so add seg_name
ts2idx[ts] = bin_idx
ts_list.append(ts)
bin_idx_list.append(bin_idx)
if split == 'train':
num_train_samples = len(ts_list)
elif split == 'val':
num_val_samples = len(ts_list) - num_train_samples
else:
num_test_samples = len(ts_list) - num_train_samples - num_val_samples
# print three num samples
print('num of train samples:', num_train_samples)
print('num of val samples:', num_val_samples)
print('num of test samples:', num_test_samples)
assert len(ts_list) == len(set(ts_list))
assert len(bin_idx_list) == len(set(bin_idx_list))
return ts2idx, seg_path_list, seg_split_list
def create_argo2_infos(seg_path_list, seg_split_list, info_list, ts2idx, output_dir, save_bin, token, num_process):
for seg_i, seg_path in enumerate(seg_path_list):
if seg_i % num_process != token:
continue
print(f'processing segment: {seg_i}/{len(seg_path_list)}')
split = seg_split_list[seg_i]
process_single_segment(seg_path, split, info_list, ts2idx, output_dir, save_bin)
class Argo2Dataset(DatasetTemplate):
def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None):
"""
Args:
root_path:
dataset_cfg:
class_names:
training:
logger:
"""
super().__init__(
dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=root_path, logger=logger
)
self.split = self.dataset_cfg.DATA_SPLIT[self.mode]
self.root_split_path = self.root_path / ('training' if self.split != 'test' else 'testing')
split_dir = self.root_path / 'ImageSets' / (self.split + '.txt')
self.sample_id_list = [x.strip() for x in open(split_dir).readlines()] if split_dir.exists() else None
self.argo2_infos = []
self.include_argo2_data(self.mode)
self.evaluate_range = dataset_cfg.get("EVALUATE_RANGE", 200.0)
def include_argo2_data(self, mode):
if self.logger is not None:
self.logger.info('Loading Argoverse2 dataset')
argo2_infos = []
for info_path in self.dataset_cfg.INFO_PATH[mode]:
info_path = self.root_path / info_path
if not info_path.exists():
continue
with open(info_path, 'rb') as f:
infos = pickle.load(f)
argo2_infos.extend(infos)
self.argo2_infos.extend(argo2_infos)
if self.logger is not None:
self.logger.info('Total samples for Argo2 dataset: %d' % (len(argo2_infos)))
def set_split(self, split):
super().__init__(
dataset_cfg=self.dataset_cfg, class_names=self.class_names, training=self.training, root_path=self.root_path, logger=self.logger
)
self.split = split
self.root_split_path = self.root_path / ('training' if self.split != 'test' else 'testing')
split_dir = self.root_path / 'ImageSets' / (self.split + '.txt')
self.sample_id_list = [x.strip() for x in open(split_dir).readlines()] if split_dir.exists() else None
def get_lidar(self, idx):
lidar_file = self.root_split_path / 'velodyne' / ('%s.bin' % idx)
assert lidar_file.exists()
return np.fromfile(str(lidar_file), dtype=np.float32).reshape(-1, 4)
@staticmethod
def generate_prediction_dicts(batch_dict, pred_dicts, class_names, output_path=None):
"""
Args:
batch_dict:
frame_id:
pred_dicts: list of pred_dicts
pred_boxes: (N, 7), Tensor
pred_scores: (N), Tensor
pred_labels: (N), Tensor
class_names:
output_path:
Returns:
"""
def get_template_prediction(num_samples):
ret_dict = {
'name': np.zeros(num_samples), 'truncated': np.zeros(num_samples),
'occluded': np.zeros(num_samples), 'alpha': np.zeros(num_samples),
'bbox': np.zeros([num_samples, 4]), 'dimensions': np.zeros([num_samples, 3]),
'location': np.zeros([num_samples, 3]), 'rotation_y': np.zeros(num_samples),
'score': np.zeros(num_samples), 'boxes_lidar': np.zeros([num_samples, 7])
}
return ret_dict
def generate_single_sample_dict(batch_index, box_dict):
pred_scores = box_dict['pred_scores'].cpu().numpy()
pred_boxes = box_dict['pred_boxes'].cpu().numpy()
pred_labels = box_dict['pred_labels'].cpu().numpy()
pred_dict = get_template_prediction(pred_scores.shape[0])
if pred_scores.shape[0] == 0:
return pred_dict
pred_boxes_img = pred_boxes
pred_boxes_camera = pred_boxes
pred_dict['name'] = np.array(class_names)[pred_labels - 1]
pred_dict['alpha'] = -np.arctan2(-pred_boxes[:, 1], pred_boxes[:, 0]) + pred_boxes_camera[:, 6]
pred_dict['bbox'] = pred_boxes_img
pred_dict['dimensions'] = pred_boxes_camera[:, 3:6]
pred_dict['location'] = pred_boxes_camera[:, 0:3]
pred_dict['rotation_y'] = pred_boxes_camera[:, 6]
pred_dict['score'] = pred_scores
pred_dict['boxes_lidar'] = pred_boxes
return pred_dict
annos = []
for index, box_dict in enumerate(pred_dicts):
frame_id = batch_dict['frame_id'][index]
single_pred_dict = generate_single_sample_dict(index, box_dict)
single_pred_dict['frame_id'] = frame_id
annos.append(single_pred_dict)
if output_path is not None:
cur_det_file = output_path / ('%s.txt' % frame_id)
with open(cur_det_file, 'w') as f:
bbox = single_pred_dict['bbox']
loc = single_pred_dict['location']
dims = single_pred_dict['dimensions'] # lhw -> hwl
for idx in range(len(bbox)):
print('%s -1 -1 %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f'
% (single_pred_dict['name'][idx], single_pred_dict['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], single_pred_dict['rotation_y'][idx],
single_pred_dict['score'][idx]), file=f)
return annos
def __len__(self):
if self._merge_all_iters_to_one_epoch:
return len(self.argo2_infos) * self.total_epochs
return len(self.argo2_infos)
def __getitem__(self, index):
# index = 4
if self._merge_all_iters_to_one_epoch:
index = index % len(self.argo2_infos)
info = copy.deepcopy(self.argo2_infos[index])
sample_idx = info['point_cloud']['velodyne_path'].split('/')[-1].rstrip('.bin')
calib = None
get_item_list = self.dataset_cfg.get('GET_ITEM_LIST', ['points'])
input_dict = {
'frame_id': sample_idx,
'calib': calib,
}
if 'annos' in info:
annos = info['annos']
loc, dims, rots = annos['location'], annos['dimensions'], annos['rotation_y']
gt_names = annos['name']
gt_bboxes_3d = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1).astype(np.float32)
input_dict.update({
'gt_names': gt_names,
'gt_boxes': gt_bboxes_3d
})
if "points" in get_item_list:
points = self.get_lidar(sample_idx)
input_dict['points'] = points
input_dict['calib'] = calib
data_dict = self.prepare_data(data_dict=input_dict)
return data_dict
def format_results(self,
outputs,
class_names,
pklfile_prefix=None,
submission_prefix=None,
):
"""Format the results to .feather file with argo2 format.
Args:
outputs (list[dict]): Testing results of the dataset.
pklfile_prefix (str | None): 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 | None): 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.
Returns:
tuple: (result_files, tmp_dir), result_files is a dict containing
the json filepaths, tmp_dir is the temporal directory created
for saving json files when jsonfile_prefix is not specified.
"""
import pandas as pd
assert len(self.argo2_infos) == len(outputs)
num_samples = len(outputs)
print('\nGot {} samples'.format(num_samples))
serialized_dts_list = []
print('\nConvert predictions to Argoverse 2 format')
for i in range(num_samples):
out_i = outputs[i]
log_id, ts = self.argo2_infos[i]['uuid'].split('/')
track_uuid = None
#cat_id = out_i['labels_3d'].numpy().tolist()
#category = [class_names[i].upper() for i in cat_id]
category = [class_name.upper() for class_name in out_i['name']]
serialized_dts = pd.DataFrame(
self.lidar_box_to_argo2(out_i['bbox']).numpy(), columns=list(LABEL_ATTR)
)
serialized_dts["score"] = out_i['score']
serialized_dts["log_id"] = log_id
serialized_dts["timestamp_ns"] = int(ts)
serialized_dts["category"] = category
serialized_dts_list.append(serialized_dts)
dts = (
pd.concat(serialized_dts_list)
.set_index(["log_id", "timestamp_ns"])
.sort_index()
)
dts = dts.sort_values("score", ascending=False).reset_index()
if pklfile_prefix is not None:
if not pklfile_prefix.endswith(('.feather')):
pklfile_prefix = f'{pklfile_prefix}.feather'
dts.to_feather(pklfile_prefix)
print(f'Result is saved to {pklfile_prefix}.')
dts = dts.set_index(["log_id", "timestamp_ns"]).sort_index()
return dts
def lidar_box_to_argo2(self, boxes):
boxes = torch.Tensor(boxes)
cnt_xyz = boxes[:, :3]
lwh = boxes[:, [3, 4, 5]]
yaw = boxes[:, 6]
quat = yaw_to_quat(yaw)
argo_cuboid = torch.cat([cnt_xyz, lwh, quat], dim=1)
return argo_cuboid
def evaluation(self,
results,
class_names,
eval_metric='waymo',
logger=None,
pklfile_prefix=None,
submission_prefix=None,
show=False,
output_path=None,
pipeline=None):
"""Evaluation in Argo2 protocol.
Args:
results (list[dict]): Testing results of the dataset.
metric (str | list[str]): Metrics to be evaluated.
Default: 'waymo'. Another supported metric is 'Argo2'.
logger (logging.Logger | str | None): Logger used for printing
related information during evaluation. Default: None.
pklfile_prefix (str | None): 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 | None): The prefix of submission datas.
If not specified, the submission data will not be generated.
show (bool): Whether to visualize.
Default: False.
out_dir (str): Path to save the visualization results.
Default: None.
pipeline (list[dict], optional): raw data loading for showing.
Default: None.
Returns:
dict[str: float]: results of each evaluation metric
"""
from av2.evaluation.detection.constants import CompetitionCategories
from av2.evaluation.detection.utils import DetectionCfg
from av2.evaluation.detection.eval import evaluate
from av2.utils.io import read_feather
dts = self.format_results(results, class_names, pklfile_prefix, submission_prefix)
argo2_root = self.root_path
val_anno_path = osp.join(argo2_root, 'val_anno.feather')
gts = read_feather(Path(val_anno_path))
gts = gts.set_index(["log_id", "timestamp_ns"]).sort_values("category")
valid_uuids_gts = gts.index.tolist()
valid_uuids_dts = dts.index.tolist()
valid_uuids = set(valid_uuids_gts) & set(valid_uuids_dts)
gts = gts.loc[list(valid_uuids)].sort_index()
categories = set(x.value for x in CompetitionCategories)
categories &= set(gts["category"].unique().tolist())
dataset_dir = Path(argo2_root) / 'sensor' / 'val'
cfg = DetectionCfg(
dataset_dir=dataset_dir,
categories=tuple(sorted(categories)),
max_range_m=self.evaluate_range,
eval_only_roi_instances=True,
)
# Evaluate using Argoverse detection API.
eval_dts, eval_gts, metrics = evaluate(
dts.reset_index(), gts.reset_index(), cfg
)
valid_categories = sorted(categories) + ["AVERAGE_METRICS"]
ap_dict = {}
for index, row in metrics.iterrows():
ap_dict[index] = row.to_json()
return metrics.loc[valid_categories], ap_dict
def parse_config():
parser = argparse.ArgumentParser(description='arg parser')
parser.add_argument('--root_path', type=str, default="/data/argo2/sensor")
parser.add_argument('--output_dir', type=str, default="/data/argo2/processed")
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_config()
root = args.root_path
output_dir = args.output_dir
save_bin = True
ts2idx, seg_path_list, seg_split_list = prepare(root)
velodyne_dir = Path(output_dir) / 'training' / 'velodyne'
if not velodyne_dir.exists():
velodyne_dir.mkdir(parents=True, exist_ok=True)
info_list = []
create_argo2_infos(seg_path_list, seg_split_list, info_list, ts2idx, output_dir, save_bin, 0, 1)
assert len(info_list) > 0
train_info = [e for e in info_list if e['sample_idx'][0] == '0']
val_info = [e for e in info_list if e['sample_idx'][0] == '1']
test_info = [e for e in info_list if e['sample_idx'][0] == '2']
trainval_info = train_info + val_info
assert len(train_info) + len(val_info) + len(test_info) == len(info_list)
# save info_list in under the output_dir as pickle file
with open(osp.join(output_dir, 'argo2_infos_train.pkl'), 'wb') as f:
pkl.dump(train_info, f)
with open(osp.join(output_dir, 'argo2_infos_val.pkl'), 'wb') as f:
pkl.dump(val_info, f)
# save validation anno feather
save_feather_path = os.path.join(output_dir, 'val_anno.feather')
val_seg_path_list = [seg_path for seg_path in seg_path_list if 'val' in seg_path]
assert len(val_seg_path_list) == len([i for i in seg_split_list if i == 'val'])
seg_anno_list = []
for seg_path in val_seg_path_list:
seg_anno = read_feather(osp.join(seg_path, 'annotations.feather'))
log_id = seg_path.split('/')[-1]
seg_anno["log_id"] = log_id
seg_anno_list.append(seg_anno)
gts = pd.concat(seg_anno_list).reset_index()
gts.to_feather(save_feather_path)