add debug ddp cfg

configs/mvp/nusc_centerpoint_voxelnet_0075voxel_fix_bn_z_scale_debug.py

@@ -0,0 +1,226 @@
+import itertools
+import logging
+
+from det3d.utils.config_tool import get_downsample_factor
+
+tasks = [
+    dict(num_class=1, class_names=["car"]),
+    dict(num_class=2, class_names=["truck", "construction_vehicle"]),
+    dict(num_class=2, class_names=["bus", "trailer"]),
+    dict(num_class=1, class_names=["barrier"]),
+    dict(num_class=2, class_names=["motorcycle", "bicycle"]),
+    dict(num_class=2, class_names=["pedestrian", "traffic_cone"]),
+]
+
+class_names = list(itertools.chain(*[t["class_names"] for t in tasks]))
+
+# training and testing settings
+target_assigner = dict(
+    tasks=tasks,
+)
+
+# model settings
+model = dict(
+    type="VoxelNet",
+    pretrained=None,
+    reader=dict(
+        type="DynamicVoxelEncoder",
+        pc_range=[-54, -54, -5.0, 54, 54, 3.0],
+        voxel_size=[0.075, 0.075, 0.2],
+    ),
+    backbone=dict(
+        type="SpMiddleResNetFHD", num_input_features=5, ds_factor=8
+    ),
+    neck=dict(
+        type="RPN",
+        layer_nums=[5, 5],
+        ds_layer_strides=[1, 2],
+        ds_num_filters=[128, 256],
+        us_layer_strides=[1, 2],
+        us_num_filters=[256, 256],
+        num_input_features=256,
+        logger=logging.getLogger("RPN"),
+    ),
+    bbox_head=dict(
+        type="CenterHead",
+        in_channels=sum([256, 256]),
+        tasks=tasks,
+        dataset='nuscenes',
+        weight=0.25,
+        code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2, 1.0, 1.0],
+        common_heads={'reg': (2, 2), 'height': (1, 2), 'dim':(3, 2), 'rot':(2, 2), 'vel': (2, 2)},
+        share_conv_channel=64,
+        dcn_head=False
+    ),
+)
+
+assigner = dict(
+    target_assigner=target_assigner,
+    out_size_factor=get_downsample_factor(model),
+    dense_reg=1,
+    gaussian_overlap=0.1,
+    max_objs=500,
+    min_radius=2,
+    pc_range=[-54, -54, -5.0, 54, 54, 3.0],
+    voxel_size=[0.075, 0.075, 0.2]
+)
+
+
+train_cfg = dict(assigner=assigner)
+
+test_cfg = dict(
+    post_center_limit_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
+    max_per_img=500,
+    nms=dict(
+        use_rotate_nms=True,
+        use_multi_class_nms=False,
+        nms_pre_max_size=1000,
+        nms_post_max_size=83,
+        nms_iou_threshold=0.2,
+    ),
+    score_threshold=0.1,
+    pc_range=[-54, -54],
+    out_size_factor=get_downsample_factor(model),
+    voxel_size=[0.075, 0.075]
+)
+
+# dataset settings
+dataset_type = "NuScenesDataset"
+nsweeps = 10
+data_root = "data/nuScenes"
+
+db_sampler = dict(
+    type="GT-AUG",
+    enable=False,
+    db_info_path="data/nuScenes/dbinfos_train_10sweeps_withvelo.pkl",
+    sample_groups=[
+        dict(car=2),
+        dict(truck=3),
+        dict(construction_vehicle=7),
+        dict(bus=4),
+        dict(trailer=6),
+        dict(barrier=2),
+        dict(motorcycle=6),
+        dict(bicycle=6),
+        dict(pedestrian=2),
+        dict(traffic_cone=2),
+    ],
+    db_prep_steps=[
+        dict(
+            filter_by_min_num_points=dict(
+                car=5,
+                truck=5,
+                bus=5,
+                trailer=5,
+                construction_vehicle=5,
+                traffic_cone=5,
+                barrier=5,
+                motorcycle=5,
+                bicycle=5,
+                pedestrian=5,
+            )
+        ),
+        dict(filter_by_difficulty=[-1],),
+    ],
+    global_random_rotation_range_per_object=[0, 0],
+    rate=1.0,
+)
+train_preprocessor = dict(
+    mode="train",
+    shuffle_points=True,
+    global_rot_noise=[-0.78539816, 0.78539816],
+    global_scale_noise=[0.9, 1.1],
+    global_translate_std=0.5,
+    db_sampler=db_sampler,
+    class_names=class_names,
+)
+
+val_preprocessor = dict(
+    mode="val",
+    shuffle_points=False,
+)
+
+train_pipeline = [
+    dict(type="LoadPointCloudFromFile", dataset=dataset_type),
+    dict(type="LoadPointCloudAnnotations", with_bbox=True),
+    dict(type="Preprocess", cfg=train_preprocessor),
+    dict(type="AssignLabel", cfg=train_cfg["assigner"]),
+    dict(type="Reformat"),
+    # dict(type='PointCloudCollect', keys=['points', 'voxels', 'annotations', 'calib']),
+]
+test_pipeline = [
+    dict(type="LoadPointCloudFromFile", dataset=dataset_type),
+    dict(type="LoadPointCloudAnnotations", with_bbox=True),
+    dict(type="Preprocess", cfg=val_preprocessor),
+    dict(type="AssignLabel", cfg=train_cfg["assigner"]),
+    dict(type="Reformat"),
+]
+
+train_anno = "data/nuScenes/infos_train_10sweeps_withvelo_filter_True.pkl"
+val_anno = "data/nuScenes/infos_val_10sweeps_withvelo_filter_True.pkl"
+test_anno = None
+
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset_type,
+        root_path=data_root,
+        info_path=train_anno,
+        ann_file=train_anno,
+        nsweeps=nsweeps,
+        load_interval=1000,
+        class_names=class_names,
+        pipeline=train_pipeline,
+    ),
+    val=dict(
+        type=dataset_type,
+        root_path=data_root,
+        info_path=val_anno,
+        test_mode=True,
+        ann_file=val_anno,
+        nsweeps=nsweeps,
+        class_names=class_names,
+        pipeline=test_pipeline,
+    ),
+    test=dict(
+        type=dataset_type,
+        root_path=data_root,
+        info_path=test_anno,
+        ann_file=test_anno,
+        nsweeps=nsweeps,
+        class_names=class_names,
+        pipeline=test_pipeline,
+    ),
+)
+
+
+
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+# optimizer
+optimizer = dict(
+    type="adam", amsgrad=0.0, wd=0.01, fixed_wd=True, moving_average=False,
+)
+lr_config = dict(
+    type="one_cycle", lr_max=0.001, moms=[0.95, 0.85], div_factor=10.0, pct_start=0.4,
+)
+
+checkpoint_config = dict(interval=1)
+# yapf:disable
+log_config = dict(
+    interval=5,
+    hooks=[
+        dict(type="TextLoggerHook"),
+        # dict(type='TensorboardLoggerHook')
+    ],
+)
+# yapf:enable
+# runtime settings
+total_epochs = 20
+device_ids = range(8)
+dist_params = dict(backend="nccl", init_method="env://")
+log_level = "INFO"
+work_dir = './work_dirs/{}/'.format(__file__[__file__.rfind('/') + 1:-3])
+load_from = None
+resume_from = None 
+workflow = [('train', 1)]

det3d/datasets/nuscenes/nuscenes.py

@@ -40,8 +40,10 @@ def __init__(
         class_names=None,
         test_mode=False,
         version="v1.0-trainval",
+        load_interval=1,
         **kwargs,
     ):
+        self.load_interval = load_interval 
         super(NuScenesDataset, self).__init__(
             root_path, info_path, pipeline, test_mode=test_mode, class_names=class_names
         )
@@ -76,6 +78,8 @@ def load_infos(self, info_path):
         with open(self._info_path, "rb") as f:
             _nusc_infos_all = pickle.load(f)
 
+        _nusc_infos_all = _nusc_infos_all[::self.load_interval]
+
         if not self.test_mode:  # if training
             self.frac = int(len(_nusc_infos_all) * 0.25)