[Refactor]Refactor lyft

configs/_base_/datasets/lyft-3d.py

@@ -10,12 +10,8 @@ dataset_type = 'LyftDataset'
 data_root = 'data/lyft/'
 # Input modality for Lyft dataset, this is consistent with the submission
 # format which requires the information in input_modality.
-input_modality = dict(
-    use_lidar=True,
-    use_camera=False,
-    use_radar=False,
-    use_map=False,
-    use_external=False)
+input_modality = dict(use_lidar=True, use_camera=False)
+data_prefix = dict(pts='samples/LIDAR_TOP', img='')
 file_client_args = dict(backend='disk')
 # Uncomment the following if use ceph or other file clients.
 # See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
@@ -47,8 +43,9 @@ train_pipeline = [
     dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
     dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
     dict(type='PointShuffle'),
-    dict(type='DefaultFormatBundle3D', class_names=class_names),
-    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+    dict(
+        type='Pack3DDetInputs',
+        keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
 ]
 test_pipeline = [
     dict(
@@ -74,13 +71,9 @@ test_pipeline = [
                 translation_std=[0, 0, 0]),
             dict(type='RandomFlip3D'),
             dict(
-                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['points'])
-        ])
+                type='PointsRangeFilter', point_cloud_range=point_cloud_range)
+        ]),
+    dict(type='Pack3DDetInputs', keys=['points'])
 ]
 # construct a pipeline for data and gt loading in show function
 # please keep its loading function consistent with test_pipeline (e.g. client)
@@ -95,42 +88,61 @@ eval_pipeline = [
         type='LoadPointsFromMultiSweeps',
         sweeps_num=10,
         file_client_args=file_client_args),
-    dict(
-        type='DefaultFormatBundle3D',
-        class_names=class_names,
-        with_label=False),
-    dict(type='Collect3D', keys=['points'])
+    dict(type='Pack3DDetInputs', keys=['points'])
 ]
-
-data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=2,
-    train=dict(
+train_dataloader = dict(
+    batch_size=2,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
         type=dataset_type,
         data_root=data_root,
-        ann_file=data_root + 'lyft_infos_train.pkl',
+        ann_file='lyft_infos_train.pkl',
         pipeline=train_pipeline,
-        classes=class_names,
+        metainfo=dict(CLASSES=class_names),
         modality=input_modality,
-        test_mode=False),
-    val=dict(
+        data_prefix=data_prefix,
+        test_mode=False,
+        box_type_3d='LiDAR'))
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
         type=dataset_type,
         data_root=data_root,
-        ann_file=data_root + 'lyft_infos_val.pkl',
+        ann_file='lyft_infos_val.pkl',
         pipeline=test_pipeline,
-        classes=class_names,
+        metainfo=dict(CLASSES=class_names),
         modality=input_modality,
-        test_mode=True),
-    test=dict(
+        data_prefix=data_prefix,
+        test_mode=True,
+        box_type_3d='LiDAR'))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
         type=dataset_type,
         data_root=data_root,
-        ann_file=data_root + 'lyft_infos_test.pkl',
+        ann_file='lyft_infos_val.pkl',
         pipeline=test_pipeline,
-        classes=class_names,
+        metainfo=dict(CLASSES=class_names),
         modality=input_modality,
-        test_mode=True))
-# For Lyft dataset, we usually evaluate the model at the end of training.
-# Since the models are trained by 24 epochs by default, we set evaluation
-# interval to be 24. Please change the interval accordingly if you do not
-# use a default schedule.
-evaluation = dict(interval=24, pipeline=eval_pipeline)
+        test_mode=True,
+        data_prefix=data_prefix,
+        box_type_3d='LiDAR'))
+
+val_evaluator = dict(
+    type='LyftMetric',
+    ann_file=data_root + 'lyft_infos_val.pkl',
+    metric='bbox')
+test_evaluator = dict(
+    type='LyftMetric',
+    ann_file=data_root + 'lyft_infos_val.pkl',
+    metric='bbox')

configs/pointpillars/hv_pointpillars_secfpn_sbn-all_2x8_2x_lyft-3d.py

@@ -41,3 +41,9 @@ model = dict(
             ],
             rotations=[0, 1.57],
             reshape_out=True)))
+
+# For Lyft dataset, we usually evaluate the model at the end of training.
+# Since the models are trained by 24 epochs by default, we set evaluation
+# interval to be 24. Please change the interval accordingly if you do not
+# use a default schedule.
+train_cfg = dict(val_interval=24)

tests/test_data/test_datasets/test_lyft_dataset.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+from mmcv.transforms.base import BaseTransform
+from mmengine.data import InstanceData
+from mmengine.registry import TRANSFORMS
+
+from mmdet3d.core.bbox import LiDARInstance3DBoxes
+from mmdet3d.core.data_structures import Det3DDataSample
+from mmdet3d.datasets import LyftDataset
+
+
+def _generate_nus_dataset_config():
+    data_root = 'tests/data/lyft'
+    ann_file = 'lyft_infos.pkl'
+    classes = [
+        'car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle',
+        'motorcycle', 'bicycle', 'pedestrian', 'animal'
+    ]
+    if 'Identity' not in TRANSFORMS:
+
+        @TRANSFORMS.register_module()
+        class Identity(BaseTransform):
+
+            def transform(self, info):
+                packed_input = dict(data_sample=Det3DDataSample())
+                if 'ann_info' in info:
+                    packed_input['data_sample'].gt_instances_3d = InstanceData(
+                    )
+                    packed_input[
+                        'data_sample'].gt_instances_3d.labels_3d = info[
+                            'ann_info']['gt_labels_3d']
+                return packed_input
+
+    pipeline = [
+        dict(type='Identity'),
+    ]
+    modality = dict(use_lidar=True, use_camera=False)
+    data_prefix = dict(pts='lidar', img='')
+    return data_root, ann_file, classes, data_prefix, pipeline, modality
+
+
+def test_getitem():
+    np.random.seed(0)
+    data_root, ann_file, classes, data_prefix, pipeline, modality = \
+        _generate_nus_dataset_config()
+
+    lyft_dataset = LyftDataset(
+        data_root,
+        ann_file,
+        data_prefix=data_prefix,
+        pipeline=pipeline,
+        metainfo=dict(CLASSES=classes),
+        modality=modality)
+
+    lyft_dataset.prepare_data(0)
+    input_dict = lyft_dataset.get_data_info(0)
+    # assert the the path should contains data_prefix and data_root
+    assert input_dict['lidar_points'][
+        'lidar_path'] == 'tests/data/lyft/lidar/host-a017_lidar1_' \
+                         '1236118886901125926.bin'
+
+    ann_info = lyft_dataset.parse_ann_info(input_dict)
+
+    # assert the keys in ann_info and the type
+    assert 'gt_labels_3d' in ann_info
+    assert ann_info['gt_labels_3d'].dtype == np.int64
+    assert len(ann_info['gt_labels_3d']) == 3
+
+    assert 'gt_bboxes_3d' in ann_info
+    assert isinstance(ann_info['gt_bboxes_3d'], LiDARInstance3DBoxes)
+
+    assert len(lyft_dataset.metainfo['CLASSES']) == 9

tools/data_converter/update_infos_to_v2.py

@@ -561,6 +561,110 @@ def update_sunrgbd_infos(pkl_path, out_dir):
     mmcv.dump(converted_data_info, out_path, 'pkl')
 
 
+def update_lyft_infos(pkl_path, out_dir):
+    print(f'{pkl_path} will be modified.')
+    if out_dir in pkl_path:
+        print(f'Warning, you may overwriting '
+              f'the original data {pkl_path}.')
+    print(f'Reading from input file: {pkl_path}.')
+    data_list = mmcv.load(pkl_path)
+    METAINFO = {
+        'CLASSES':
+        ('car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle',
+         'motorcycle', 'bicycle', 'pedestrian', 'animal'),
+        'DATASET':
+        'Nuscenes',
+        'version':
+        data_list['metadata']['version']
+    }
+    print('Start updating:')
+    converted_list = []
+    for i, ori_info_dict in enumerate(
+            mmcv.track_iter_progress(data_list['infos'])):
+        temp_data_info = get_empty_standard_data_info()
+        temp_data_info['sample_idx'] = i
+        temp_data_info['token'] = ori_info_dict['token']
+        temp_data_info['ego2global'] = convert_quaternion_to_matrix(
+            ori_info_dict['ego2global_rotation'],
+            ori_info_dict['ego2global_translation'])
+        temp_data_info['lidar_points']['lidar_path'] = ori_info_dict[
+            'lidar_path'].split('/')[-1]
+        temp_data_info['lidar_points'][
+            'lidar2ego'] = convert_quaternion_to_matrix(
+                ori_info_dict['lidar2ego_rotation'],
+                ori_info_dict['lidar2ego_translation'])
+        # bc-breaking: Timestamp has divided 1e6 in pkl infos.
+        temp_data_info['timestamp'] = ori_info_dict['timestamp'] / 1e6
+        for ori_sweep in ori_info_dict['sweeps']:
+            temp_lidar_sweep = get_single_lidar_sweep()
+            temp_lidar_sweep['lidar_points'][
+                'lidar2ego'] = convert_quaternion_to_matrix(
+                    ori_sweep['sensor2ego_rotation'],
+                    ori_sweep['sensor2ego_translation'])
+            temp_lidar_sweep['ego2global'] = convert_quaternion_to_matrix(
+                ori_sweep['ego2global_rotation'],
+                ori_sweep['ego2global_translation'])
+            lidar2sensor = np.eye(4)
+            lidar2sensor[:3, :3] = ori_sweep['sensor2lidar_rotation'].T
+            lidar2sensor[:3, 3] = -ori_sweep['sensor2lidar_translation']
+            temp_lidar_sweep['lidar_points'][
+                'lidar2sensor'] = lidar2sensor.astype(np.float32).tolist()
+            # bc-breaking: Timestamp has divided 1e6 in pkl infos.
+            temp_lidar_sweep['timestamp'] = ori_sweep['timestamp'] / 1e6
+            temp_lidar_sweep['lidar_points']['lidar_path'] = ori_sweep[
+                'data_path']
+            temp_lidar_sweep['sample_data_token'] = ori_sweep[
+                'sample_data_token']
+            temp_data_info['lidar_sweeps'].append(temp_lidar_sweep)
+        temp_data_info['images'] = {}
+        for cam in ori_info_dict['cams']:
+            empty_img_info = get_empty_img_info()
+            empty_img_info['img_path'] = ori_info_dict['cams'][cam][
+                'data_path'].split('/')[-1]
+            empty_img_info['cam2img'] = ori_info_dict['cams'][cam][
+                'cam_intrinsic'].tolist()
+            empty_img_info['sample_data_token'] = ori_info_dict['cams'][cam][
+                'sample_data_token']
+            empty_img_info[
+                'timestamp'] = ori_info_dict['cams'][cam]['timestamp'] / 1e6
+            empty_img_info['cam2ego'] = convert_quaternion_to_matrix(
+                ori_info_dict['cams'][cam]['sensor2ego_rotation'],
+                ori_info_dict['cams'][cam]['sensor2ego_translation'])
+            lidar2sensor = np.eye(4)
+            lidar2sensor[:3, :3] = ori_info_dict['cams'][cam][
+                'sensor2lidar_rotation'].T
+            lidar2sensor[:3, 3] = -ori_info_dict['cams'][cam][
+                'sensor2lidar_translation']
+            empty_img_info['lidar2cam'] = lidar2sensor.astype(
+                np.float32).tolist()
+            temp_data_info['images'][cam] = empty_img_info
+        num_instances = ori_info_dict['gt_boxes'].shape[0]
+        ignore_class_name = set()
+        for i in range(num_instances):
+            empty_instance = get_empty_instance()
+            empty_instance['bbox_3d'] = ori_info_dict['gt_boxes'][
+                i, :].tolist()
+            if ori_info_dict['gt_names'][i] in METAINFO['CLASSES']:
+                empty_instance['bbox_label'] = METAINFO['CLASSES'].index(
+                    ori_info_dict['gt_names'][i])
+            else:
+                ignore_class_name.add(ori_info_dict['gt_names'][i])
+                empty_instance['bbox_label'] = -1
+            empty_instance['bbox_label_3d'] = copy.deepcopy(
+                empty_instance['bbox_label'])
+            empty_instance = clear_instance_unused_keys(empty_instance)
+            temp_data_info['instances'].append(empty_instance)
+        temp_data_info, _ = clear_data_info_unused_keys(temp_data_info)
+        converted_list.append(temp_data_info)
+    pkl_name = pkl_path.split('/')[-1]
+    out_path = osp.join(out_dir, pkl_name)
+    print(f'Writing to output file: {out_path}.')
+    print(f'ignore classes: {ignore_class_name}')
+    converted_data_info = dict(metainfo=METAINFO, data_list=converted_list)
+
+    mmcv.dump(converted_data_info, out_path, 'pkl')
+
+
 def parse_args():
     parser = argparse.ArgumentParser(description='Arg parser for data coords '
                                      'update due to coords sys refactor.')
@@ -592,6 +696,8 @@ def main():
         update_scannet_infos(pkl_path=args.pkl, out_dir=args.out_dir)
     elif args.dataset.lower() == 'sunrgbd':
         update_sunrgbd_infos(pkl_path=args.pkl, out_dir=args.out_dir)
+    elif args.dataset.lower() == 'lyft':
+        update_lyft_infos(pkl_path=args.pkl, out_dir=args.out_dir)
     elif args.dataset.lower() == 'nuscenes':
         update_nuscenes_infos(pkl_path=args.pkl, out_dir=args.out_dir)
     else: