[Refactor]Refactor pp cfg

configs/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py

@@ -8,6 +8,7 @@ point_cloud_range = [0, -39.68, -3, 69.12, 39.68, 1]
 # dataset settings
 data_root = 'data/kitti/'
 class_names = ['Pedestrian', 'Cyclist', 'Car']
+metainfo = dict(CLASSES=class_names)
 # PointPillars adopted a different sampling strategies among classes
 db_sampler = dict(
     data_root=data_root,
@@ -32,8 +33,9 @@ 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_labels_3d', 'gt_bboxes_3d'])
 ]
 test_pipeline = [
     dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
@@ -51,31 +53,57 @@ test_pipeline = [
             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'])
+            dict(type='Pack3DDetInputs', keys=['points'])
         ])
 ]
 
-data = dict(
-    train=dict(dataset=dict(pipeline=train_pipeline, classes=class_names)),
-    val=dict(pipeline=test_pipeline, classes=class_names),
-    test=dict(pipeline=test_pipeline, classes=class_names))
-
+train_dataloader = dict(
+    dataset=dict(dataset=dict(pipeline=train_pipeline, metainfo=metainfo)))
+test_dataloader = dict(dataset=dict(metainfo=metainfo))
+val_dataloader = dict(dataset=dict(metainfo=metainfo))
 # In practice PointPillars also uses a different schedule
 # optimizer
 lr = 0.001
-optimizer = dict(lr=lr)
+epoch_num = 80
+iter_num_in_epoch = 3712
+optim_wrapper = dict(
+    optimizer=dict(lr=lr), clip_grad=dict(max_norm=35, norm_type=2))
+param_scheduler = [
+    dict(
+        type='CosineAnnealingLR',
+        T_max=epoch_num * 0.4 * iter_num_in_epoch,
+        eta_min=lr * 10,
+        by_epoch=False,
+        begin=0,
+        end=epoch_num * 0.4 * iter_num_in_epoch),
+    dict(
+        type='CosineAnnealingLR',
+        T_max=epoch_num * 0.6 * iter_num_in_epoch,
+        eta_min=lr * 1e-4,
+        by_epoch=False,
+        begin=epoch_num * 0.4 * iter_num_in_epoch,
+        end=epoch_num * 1 * iter_num_in_epoch),
+    dict(
+        type='CosineAnnealingBetas',
+        T_max=epoch_num * 0.4 * iter_num_in_epoch,
+        eta_min=0.85 / 0.95,
+        by_epoch=False,
+        begin=0,
+        end=epoch_num * 0.4 * iter_num_in_epoch),
+    dict(
+        type='CosineAnnealingBetas',
+        T_max=epoch_num * 0.6 * iter_num_in_epoch,
+        eta_min=1,
+        by_epoch=False,
+        begin=epoch_num * 0.4 * iter_num_in_epoch,
+        end=epoch_num * 1 * iter_num_in_epoch)
+]
 # max_norm=35 is slightly better than 10 for PointPillars in the earlier
 # development of the codebase thus we keep the setting. But we does not
 # specifically tune this parameter.
-optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
 # PointPillars usually need longer schedule than second, we simply double
 # the training schedule. Do remind that since we use RepeatDataset and
 # repeat factor is 2, so we actually train 160 epochs.
-runner = dict(max_epochs=80)
-
-# Use evaluation interval=2 reduce the number of evaluation timese
-evaluation = dict(interval=2)
+train_cfg = dict(by_epoch=True, max_epochs=epoch_num)
+val_cfg = dict(interval=2)
+test_cfg = dict()

configs/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car.py

@@ -31,6 +31,7 @@ model = dict(
 dataset_type = 'KittiDataset'
 data_root = 'data/kitti/'
 class_names = ['Car']
+metainfo = dict(CLASSES=class_names)
 db_sampler = dict(
     data_root=data_root,
     info_path=data_root + 'kitti_dbinfos_train.pkl',
@@ -52,7 +53,9 @@ train_pipeline = [
     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_labels_3d', 'gt_bboxes_3d'])
 ]
 test_pipeline = [
     dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
@@ -70,18 +73,13 @@ test_pipeline = [
             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'])
+            dict(type='Pack3DDetInputs', keys=['points'])
         ])
 ]
 
-data = dict(
-    train=dict(
+train_dataloader = dict(
+    dataset=dict(
         type='RepeatDataset',
         times=2,
-        dataset=dict(pipeline=train_pipeline, classes=class_names)),
-    val=dict(pipeline=test_pipeline, classes=class_names),
-    test=dict(pipeline=test_pipeline, classes=class_names))
+        dataset=dict(pipeline=train_pipeline, metainfo=metainfo)))
+test_dataloader = dict(dataset=dict(pipeline=test_pipeline, metainfo=metainfo))