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inference.py
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inference.py
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import argparse
import glob
from pathlib import Path
import numpy as np
import torch
from pcdet.config import cfg, cfg_from_yaml_file
from pcdet.datasets import DatasetTemplate
from pcdet.models import build_network, load_data_to_gpu
from pcdet.utils import common_utils
import pickle
from tqdm import tqdm
class DemoDataset(DatasetTemplate):
def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None, ext='.bin'):
"""
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.root_path = root_path
self.ext = ext
data_file_list = glob.glob(str(root_path / f'*{self.ext}')) if self.root_path.is_dir() else [self.root_path]
data_file_list.sort()
self.sample_file_list = data_file_list
def __len__(self):
return len(self.sample_file_list)
def __getitem__(self, index):
if self.ext == '.bin':
points = np.fromfile(self.sample_file_list[index], dtype=np.float32).reshape(-1, 4)
frame_id = self.sample_file_list[index].split('.')[-2].split('/')[-1]
elif self.ext == '.npy':
points = np.load(self.sample_file_list[index])
else:
raise NotImplementedError
input_dict = {
'points': points,
'frame_id': frame_id
}
data_dict = self.prepare_data(data_dict=input_dict)
return data_dict
def parse_config():
parser = argparse.ArgumentParser(description='arg parser')
parser.add_argument('--cfg_file', type=str, default='cfgs/kitti_models/second.yaml',
help='specify the config for demo')
parser.add_argument('--data_path', type=str, default=None,
help='specify the point cloud data file or directory')
parser.add_argument('--ckpt', type=str, default='/OpenPCDet/checkpoints/second_7862.pth', help='specify the pretrained model')
parser.add_argument('--ext', type=str, default='.bin', help='specify the extension of your point cloud data file')
args = parser.parse_args()
cfg_from_yaml_file(args.cfg_file, cfg)
return args, cfg
def main():
args, cfg = parse_config()
logger = common_utils.create_logger()
logger.info('-----------------Quick Demo of OpenPCDet-------------------------')
demo_dataset = DemoDataset(
dataset_cfg=cfg.DATA_CONFIG, class_names=cfg.CLASS_NAMES, training=False,
root_path=Path(args.data_path), ext=args.ext, logger=logger
)
logger.info(f'Total number of samples: \t{len(demo_dataset)}')
model = build_network(model_cfg=cfg.MODEL, num_class=len(cfg.CLASS_NAMES), dataset=demo_dataset)
model.load_params_from_file(filename=args.ckpt, logger=logger, to_cpu=True)
model.cuda()
model.eval()
class_names = cfg.CLASS_NAMES
det_annos = []
result_dir = Path('/OpenPCDet/demo_output')
result_dir.mkdir(parents=True, exist_ok=True)
with torch.no_grad():
for idx, data_dict in enumerate(demo_dataset):
data_dict = demo_dataset.collate_batch([data_dict])
load_data_to_gpu(data_dict)
pred_dicts, _ = model.forward(data_dict)
annos = generate_prediction_dicts(data_dict, pred_dicts, class_names)
det_annos += annos
file = result_dir / 'result.pkl'
file.touch(exist_ok=True)
with open(file, 'wb') as f:
pickle.dump(det_annos, f)
logger.info('Demo done.')
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_dict['name'] = np.array(class_names)[pred_labels - 1]
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)
return annos
if __name__ == '__main__':
import os
cur_dir, cwd = Path(__file__).parent, Path.cwd()
if cur_dir != cwd:
os.chdir(cur_dir)
main()