【Hackathon No.166】Paddle3D目标检测结果可视化

demo/bev_demo.py

@@ -0,0 +1,97 @@
+import argparse
+import os
+import numpy as np
+
+from paddle3d.apis.infer import Infer
+from paddle3d.apis.config import Config
+from paddle3d.slim import get_qat_config
+from paddle3d.utils.checkpoint import load_pretrained_model
+
+
+def parse_args():
+    """
+    """
+    parser = argparse.ArgumentParser(description='Model evaluation')
+    # params of training
+    parser.add_argument(
+        "--config", dest="cfg", help="The config file.", default=None, type=str)
+    parser.add_argument(
+        '--batch_size',
+        dest='batch_size',
+        help='Mini batch size of one gpu or cpu',
+        type=int,
+        default=None)
+    parser.add_argument(
+        '--model',
+        dest='model',
+        help='pretrained parameters of the model',
+        type=str,
+        default=None)
+    parser.add_argument(
+        '--num_workers',
+        dest='num_workers',
+        help='Num workers for data loader',
+        type=int,
+        default=2)
+    parser.add_argument(
+        '--quant_config',
+        dest='quant_config',
+        help='Config for quant model.',
+        default=None,
+        type=str)
+
+    return parser.parse_args()
+
+
+def worker_init_fn(worker_id):
+    np.random.seed(1024)
+
+
+def main(args):
+    """
+    """
+    if args.cfg is None:
+        raise RuntimeError("No configuration file specified!")
+
+    if not os.path.exists(args.cfg):
+        raise RuntimeError("Config file `{}` does not exist!".format(args.cfg))
+
+    cfg = Config(path=args.cfg, batch_size=args.batch_size)
+    print(args.cfg)
+    if cfg.val_dataset is None:
+        raise RuntimeError(
+            'The validation dataset is not specified in the configuration file!'
+        )
+    elif len(cfg.val_dataset) == 0:
+        raise ValueError(
+            'The length of validation dataset is 0. Please check if your dataset is valid!'
+        )
+
+    dic = cfg.to_dict()
+    batch_size = dic.pop('batch_size')
+    dic.update({
+        'dataloader_fn': {
+            'batch_size': batch_size,
+            'num_workers': args.num_workers,
+            'worker_init_fn': worker_init_fn
+        }
+    })
+
+    if args.quant_config:
+        quant_config = get_qat_config(args.quant_config)
+        cfg.model.build_slim_model(quant_config['quant_config'])
+
+    if args.model is not None:
+        load_pretrained_model(cfg.model, args.model)
+        dic['checkpoint'] = None
+        dic['resume'] = False
+    else:
+        dic['resume'] = True
+
+    infer = Infer(**dic)
+    infer.infer('bev')
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    main(args)

demo/bev_demo_deploy.py

@@ -0,0 +1,174 @@
+import argparse
+import numpy as np
+import paddle
+from paddle.inference import Config, create_predictor
+from paddle3d.ops.iou3d_nms_cuda import nms_gpu
+from utils import preprocess, Calibration, show_bev_with_boxes
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_file",
+        type=str,
+        help="Model filename, Specify this when your model is a combined model.",
+        required=True)
+    parser.add_argument(
+        "--params_file",
+        type=str,
+        help=
+        "Parameter filename, Specify this when your model is a combined model.",
+        required=True)
+    parser.add_argument(
+        '--lidar_file', type=str, help='The lidar path.', required=True)
+    parser.add_argument(
+        '--calib_file', type=str, help='The lidar path.', required=True)
+    parser.add_argument(
+        "--num_point_dim",
+        type=int,
+        default=4,
+        help="Dimension of a point in the lidar file.")
+    parser.add_argument(
+        "--point_cloud_range",
+        dest='point_cloud_range',
+        nargs='+',
+        help="Range of point cloud for voxelize operation.",
+        type=float,
+        default=None)
+    parser.add_argument(
+        "--voxel_size",
+        dest='voxel_size',
+        nargs='+',
+        help="Size of voxels for voxelize operation.",
+        type=float,
+        default=None)
+    parser.add_argument(
+        "--max_points_in_voxel",
+        type=int,
+        default=100,
+        help="Maximum number of points in a voxel.")
+    parser.add_argument(
+        "--max_voxel_num",
+        type=int,
+        default=12000,
+        help="Maximum number of voxels.")
+    parser.add_argument("--gpu_id", type=int, default=0, help="GPU card id.")
+    parser.add_argument(
+        "--use_trt",
+        type=int,
+        default=0,
+        help="Whether to use tensorrt to accelerate when using gpu.")
+    parser.add_argument(
+        "--trt_precision",
+        type=int,
+        default=0,
+        help="Precision type of tensorrt, 0: kFloat32, 1: kHalf.")
+    parser.add_argument(
+        "--trt_use_static",
+        type=int,
+        default=0,
+        help="Whether to load the tensorrt graph optimization from a disk path."
+    )
+    parser.add_argument(
+        "--trt_static_dir",
+        type=str,
+        help="Path of a tensorrt graph optimization directory.")
+    parser.add_argument(
+        "--collect_shape_info",
+        type=int,
+        default=0,
+        help="Whether to collect dynamic shape before using tensorrt.")
+    parser.add_argument(
+        "--dynamic_shape_file",
+        type=str,
+        default="",
+        help="Path of a dynamic shape file for tensorrt.")
+
+    return parser.parse_args()
+
+
+def init_predictor(model_file,
+                   params_file,
+                   gpu_id=0,
+                   use_trt=False,
+                   trt_precision=0,
+                   trt_use_static=False,
+                   trt_static_dir=None,
+                   collect_shape_info=False,
+                   dynamic_shape_file=None):
+    config = Config(model_file, params_file)
+    config.enable_memory_optim()
+    config.enable_use_gpu(1000, gpu_id)
+    if use_trt:
+        precision_mode = paddle.inference.PrecisionType.Float32
+        if trt_precision == 1:
+            precision_mode = paddle.inference.PrecisionType.Half
+        config.enable_tensorrt_engine(
+            workspace_size=1 << 30,
+            max_batch_size=1,
+            min_subgraph_size=10,
+            precision_mode=precision_mode,
+            use_static=trt_use_static,
+            use_calib_mode=False)
+        if collect_shape_info:
+            config.collect_shape_range_info(dynamic_shape_file)
+        else:
+            config.enable_tuned_tensorrt_dynamic_shape(dynamic_shape_file, True)
+        if trt_use_static:
+            config.set_optim_cache_dir(trt_static_dir)
+
+    predictor = create_predictor(config)
+    return predictor
+
+
+def run(predictor, voxels, coords, num_points_per_voxel):
+    input_names = predictor.get_input_names()
+    for i, name in enumerate(input_names):
+        input_tensor = predictor.get_input_handle(name)
+        if name == "voxels":
+            input_tensor.reshape(voxels.shape)
+            input_tensor.copy_from_cpu(voxels.copy())
+        elif name == "coords":
+            input_tensor.reshape(coords.shape)
+            input_tensor.copy_from_cpu(coords.copy())
+        elif name == "num_points_per_voxel":
+            input_tensor.reshape(num_points_per_voxel.shape)
+            input_tensor.copy_from_cpu(num_points_per_voxel.copy())
+
+    # do the inference
+    predictor.run()
+
+    # get out data from output tensor
+    output_names = predictor.get_output_names()
+    for i, name in enumerate(output_names):
+        output_tensor = predictor.get_output_handle(name)
+        if i == 0:
+            box3d_lidar = output_tensor.copy_to_cpu()
+        elif i == 1:
+            label_preds = output_tensor.copy_to_cpu()
+        elif i == 2:
+            scores = output_tensor.copy_to_cpu()
+    return box3d_lidar, label_preds, scores
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    predictor = init_predictor(args.model_file, args.params_file, args.gpu_id,
+                               args.use_trt, args.trt_precision,
+                               args.trt_use_static, args.trt_static_dir,
+                               args.collect_shape_info, args.dynamic_shape_file)
+    voxels, coords, num_points_per_voxel = preprocess(
+        args.lidar_file, args.num_point_dim, args.point_cloud_range,
+        args.voxel_size, args.max_points_in_voxel, args.max_voxel_num)
+    box3d_lidar, label_preds, scores = run(predictor, voxels, coords,
+                                           num_points_per_voxel)
+
+    scan = np.fromfile(args.lidar_file, dtype=np.float32)
+    pc_velo = scan.reshape((-1, 4))
+
+    # Obtain calibration information about Kitti
+    calib = Calibration(args.calib_file)
+
+    # Plot box in lidar cloud
+    show_bev_with_boxes(pc_velo, box3d_lidar, scores, calib)

demo/mono_demo.py

@@ -0,0 +1,101 @@
+import argparse
+import os
+import cv2
+import numpy as np
+
+from utils import make_imgpts_list, draw_mono_3d, total_imgpred_by_conf_to_kitti_records
+
+from paddle3d.apis.infer import Infer
+from paddle3d.apis.config import Config
+from paddle3d.slim import get_qat_config
+from paddle3d.utils.checkpoint import load_pretrained_model
+from paddle3d.datasets.kitti.kitti_utils import camera_record_to_object
+
+
+def parse_args():
+    """
+    """
+    parser = argparse.ArgumentParser(description='Model evaluation')
+    # params of training
+    parser.add_argument(
+        "--config", dest="cfg", help="The config file.", default=None, type=str)
+    parser.add_argument(
+        '--batch_size',
+        dest='batch_size',
+        help='Mini batch size of one gpu or cpu',
+        type=int,
+        default=None)
+    parser.add_argument(
+        '--model',
+        dest='model',
+        help='pretrained parameters of the model',
+        type=str,
+        default=None)
+    parser.add_argument(
+        '--num_workers',
+        dest='num_workers',
+        help='Num workers for data loader',
+        type=int,
+        default=2)
+    parser.add_argument(
+        '--quant_config',
+        dest='quant_config',
+        help='Config for quant model.',
+        default=None,
+        type=str)
+
+    return parser.parse_args()
+
+
+def worker_init_fn(worker_id):
+    np.random.seed(1024)
+
+
+def main(args):
+    """
+    """
+    if args.cfg is None:
+        raise RuntimeError("No configuration file specified!")
+
+    if not os.path.exists(args.cfg):
+        raise RuntimeError("Config file `{}` does not exist!".format(args.cfg))
+
+    cfg = Config(path=args.cfg, batch_size=args.batch_size)
+    print(args.cfg)
+    if cfg.val_dataset is None:
+        raise RuntimeError(
+            'The validation dataset is not specified in the configuration file!'
+        )
+    elif len(cfg.val_dataset) == 0:
+        raise ValueError(
+            'The length of validation dataset is 0. Please check if your dataset is valid!'
+        )
+
+    dic = cfg.to_dict()
+    batch_size = dic.pop('batch_size')
+    dic.update({
+        'dataloader_fn': {
+            'batch_size': batch_size,
+            'num_workers': args.num_workers,
+            'worker_init_fn': worker_init_fn
+        }
+    })
+
+    if args.quant_config:
+        quant_config = get_qat_config(args.quant_config)
+        cfg.model.build_slim_model(quant_config['quant_config'])
+
+    if args.model is not None:
+        load_pretrained_model(cfg.model, args.model)
+        dic['checkpoint'] = None
+        dic['resume'] = False
+    else:
+        dic['resume'] = True
+
+    infer = Infer(**dic)
+    infer.infer('image')
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    main(args)