support PointRCNN with PartA2 head

pcdet/models/detectors/__init__.py

@@ -3,13 +3,15 @@ from .second_net import SECONDNet
 from .PartA2_net import PartA2Net
 from .pv_rcnn import PVRCNN
 from .pointpillar import PointPillar
+from .point_rcnn import PointRCNN
 
 __all__ = {
     'Detector3DTemplate': Detector3DTemplate,
     'SECONDNet': SECONDNet,
     'PartA2Net': PartA2Net,
     'PVRCNN': PVRCNN,
-    'PointPillar': PointPillar
+    'PointPillar': PointPillar,
+    'PointRCNN': PointRCNN
 }
 
 

pcdet/models/detectors/point_rcnn.py

+from .detector3d_template import Detector3DTemplate
+
+
+class PointRCNN(Detector3DTemplate):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__(model_cfg=model_cfg, num_class=num_class, dataset=dataset)
+        self.module_list = self.build_networks()
+
+    def forward(self, batch_dict):
+        for cur_module in self.module_list:
+            batch_dict = cur_module(batch_dict)
+
+        if self.training:
+            loss, tb_dict, disp_dict = self.get_training_loss()
+
+            ret_dict = {
+                'loss': loss
+            }
+            return ret_dict, tb_dict, disp_dict
+        else:
+            pred_dicts, recall_dicts = self.post_processing(batch_dict)
+            return pred_dicts, recall_dicts
+
+    def get_training_loss(self):
+        disp_dict = {}
+        loss_point, tb_dict = self.point_head.get_loss()
+        loss_rcnn, tb_dict = self.roi_head.get_loss(tb_dict)
+
+        loss = loss_point + loss_rcnn
+        return loss, tb_dict, disp_dict

tools/cfgs/kitti_models/pointrcnn.yaml

+CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist']
+
+DATA_CONFIG:
+    _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml
+
+    DATA_PROCESSOR:
+        -   NAME: mask_points_and_boxes_outside_range
+            REMOVE_OUTSIDE_BOXES: True
+
+        -   NAME: shuffle_points
+            SHUFFLE_ENABLED: {
+                'train': True,
+                'test': False
+            }
+
+MODEL:
+    NAME: PointRCNN
+
+    BACKBONE_3D:
+        NAME: PointNet2Backbone
+        SA_CONFIG:
+            NPOINTS: [4096, 1024, 256, 64]
+            RADIUS: [[0.1, 0.5], [0.5, 1.0], [1.0, 2.0], [2.0, 4.0]]
+            NSAMPLE: [[16, 32], [16, 32], [16, 32], [16, 32]]
+            MLPS: [[[16, 16, 32], [32, 32, 64]],
+                   [[64, 64, 128], [64, 96, 128]],
+                   [[128, 196, 256], [128, 196, 256]],
+                   [[256, 256, 512], [256, 384, 512]]]
+        FP_MLPS: [[128, 128], [256, 256], [512, 512], [512, 512]]
+
+    POINT_HEAD:
+        NAME: PointHeadBox
+        CLS_FC: [256, 256]
+        REG_FC: [256, 256]
+        CLASS_AGNOSTIC: False
+        USE_POINT_FEATURES_BEFORE_FUSION: False
+        TARGET_CONFIG:
+            GT_EXTRA_WIDTH: [0.2, 0.2, 0.2]
+            BOX_CODER: PointResidualCoder
+            BOX_CODER_CONFIG: {
+                'use_mean_size': False ,
+                'mean_size': [
+                    [3.9, 1.6, 1.56],
+                    [0.8, 0.6, 1.73],
+                    [1.76, 0.6, 1.73]
+                ]
+            }
+
+        LOSS_CONFIG:
+            LOSS_REG: WeightedSmoothL1Loss
+            LOSS_WEIGHTS: {
+                'point_cls_weight': 1.0,
+                'point_box_weight': 1.0,
+                'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+            }
+
+    ROI_HEAD:
+        NAME: PartA2FCHead
+        CLASS_AGNOSTIC: True
+
+        SHARED_FC: [256, 256, 256]
+        CLS_FC: [256, 256]
+        REG_FC: [256, 256]
+        DP_RATIO: 0.3
+        DISABLE_PART: True
+        SEG_MASK_SCORE_THRESH: 0.0
+
+        NMS_CONFIG:
+            TRAIN:
+                NMS_TYPE: nms_gpu
+                MULTI_CLASSES_NMS: False
+                NMS_PRE_MAXSIZE: 9000
+                NMS_POST_MAXSIZE: 512
+                NMS_THRESH: 0.8
+            TEST:
+                NMS_TYPE: nms_gpu
+                MULTI_CLASSES_NMS: False
+                NMS_PRE_MAXSIZE: 1024
+                NMS_POST_MAXSIZE: 100
+                NMS_THRESH: 0.7
+
+        ROI_AWARE_POOL:
+            POOL_SIZE: 12
+            NUM_FEATURES: 128
+            MAX_POINTS_PER_VOXEL: 128
+
+        TARGET_CONFIG:
+            BOX_CODER: ResidualCoder
+            ROI_PER_IMAGE: 128
+            FG_RATIO: 0.5
+
+            SAMPLE_ROI_BY_EACH_CLASS: True
+            CLS_SCORE_TYPE: roi_iou
+
+            CLS_FG_THRESH: 0.75
+            CLS_BG_THRESH: 0.25
+            CLS_BG_THRESH_LO: 0.1
+            HARD_BG_RATIO: 0.8
+
+            REG_FG_THRESH: 0.65
+
+        LOSS_CONFIG:
+            CLS_LOSS: BinaryCrossEntropy
+            REG_LOSS: smooth-l1
+            CORNER_LOSS_REGULARIZATION: True
+            LOSS_WEIGHTS: {
+                'rcnn_cls_weight': 1.0,
+                'rcnn_reg_weight': 1.0,
+                'rcnn_corner_weight': 1.0,
+                'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+            }
+
+    POST_PROCESSING:
+        RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
+        SCORE_THRESH: 0.1
+        OUTPUT_RAW_SCORE: False
+
+        EVAL_METRIC: kitti
+
+        NMS_CONFIG:
+            MULTI_CLASSES_NMS: False
+            NMS_TYPE: nms_gpu
+            NMS_THRESH: 0.1
+            NMS_PRE_MAXSIZE: 4096
+            NMS_POST_MAXSIZE: 500
+
+
+OPTIMIZATION:
+    OPTIMIZER: adam_onecycle
+    LR: 0.01
+    WEIGHT_DECAY: 0.01
+    MOMENTUM: 0.9
+
+    MOMS: [0.95, 0.85]
+    PCT_START: 0.4
+    DIV_FACTOR: 10
+    DECAY_STEP_LIST: [35, 45]
+    LR_DECAY: 0.1
+    LR_CLIP: 0.0000001
+
+    LR_WARMUP: False
+    WARMUP_EPOCH: 1
+
+    GRAD_NORM_CLIP: 10