init

projects/mmdet3d_plugin/__init__.py

+from .core.bbox.assigners.hungarian_assigner_3d import HungarianAssigner3D
+from .core.bbox.coders.nms_free_coder import NMSFreeCoder
+from .core.bbox.match_costs import BBox3DL1Cost
+from .core.evaluation.eval_hooks import CustomDistEvalHook
+from .datasets.pipelines import (
+  PhotoMetricDistortionMultiViewImage, PadMultiViewImage, 
+  NormalizeMultiviewImage,  CustomCollect3D)
+from .models.utils import *
+from .models.opt.adamw import AdamW2
+from .bevformer import *
+from .dd3d import *

projects/mmdet3d_plugin/bevformer/__init__.py

+
+from .dense_heads import *
+from .detectors import *
+from .modules import *
+from .runner import *
+from .hooks import *

projects/mmdet3d_plugin/bevformer/apis/__init__.py

+from .train import custom_train_model
+from .mmdet_train import custom_train_detector
+# from .test import custom_multi_gpu_test
\ No newline at end of file

projects/mmdet3d_plugin/bevformer/apis/train.py

+# ---------------------------------------------
+# Copyright (c) OpenMMLab. All rights reserved.
+# ---------------------------------------------
+#  Modified by Zhiqi Li
+# ---------------------------------------------
+
+from .mmdet_train import custom_train_detector
+from mmseg.apis import train_segmentor
+from mmdet.apis import train_detector
+
+def custom_train_model(model,
+                dataset,
+                cfg,
+                distributed=False,
+                validate=False,
+                timestamp=None,
+                eval_model=None,
+                meta=None):
+    """A function wrapper for launching model training according to cfg.
+
+    Because we need different eval_hook in runner. Should be deprecated in the
+    future.
+    """
+    if cfg.model.type in ['EncoderDecoder3D']:
+        assert False
+    else:
+        custom_train_detector(
+            model,
+            dataset,
+            cfg,
+            distributed=distributed,
+            validate=validate,
+            timestamp=timestamp,
+            eval_model=eval_model,
+            meta=meta)
+
+
+def train_model(model,
+                dataset,
+                cfg,
+                distributed=False,
+                validate=False,
+                timestamp=None,
+                meta=None):
+    """A function wrapper for launching model training according to cfg.
+
+    Because we need different eval_hook in runner. Should be deprecated in the
+    future.
+    """
+    if cfg.model.type in ['EncoderDecoder3D']:
+        train_segmentor(
+            model,
+            dataset,
+            cfg,
+            distributed=distributed,
+            validate=validate,
+            timestamp=timestamp,
+            meta=meta)
+    else:
+        train_detector(
+            model,
+            dataset,
+            cfg,
+            distributed=distributed,
+            validate=validate,
+            timestamp=timestamp,
+            meta=meta)