support separate regression heads for different properties

pcdet/models/dense_heads/anchor_head_multi.py

@@ -6,20 +6,39 @@ import torch
 
 
 class SingleHead(BaseBEVBackbone):
-    def __init__(self, model_cfg, input_channels, num_class, num_anchors_per_location, code_size, encode_conv_cfg=None,
-                 head_label_indices=None):
-        super().__init__(encode_conv_cfg, input_channels)
+    def __init__(self, model_cfg, input_channels, num_class, num_anchors_per_location, code_size, rpn_head_cfg=None,
+                 head_label_indices=None, separate_reg_config=None):
+        super().__init__(rpn_head_cfg, input_channels)
 
         self.num_anchors_per_location = num_anchors_per_location
         self.num_class = num_class
         self.code_size = code_size
         self.model_cfg = model_cfg
+        self.separate_reg_config = separate_reg_config
         self.register_buffer('head_label_indices', head_label_indices)
 
         self.conv_cls = nn.Conv2d(
             input_channels, self.num_anchors_per_location * self.num_class,
             kernel_size=1
         )
+        if self.separate_reg_config is not None:
+            code_size_cnt = 0
+            self.conv_box = nn.ModuleDict()
+            self.conv_box_names = []
+            for reg_config in self.separate_reg_config:
+                reg_name, reg_channel = reg_config.split(':')
+                cur_conv = nn.Conv2d(
+                    input_channels, self.num_anchors_per_location * reg_channel,
+                    kernel_size=3, stride=1, padding=1, bias=True
+                )
+                nn.init.kaiming_normal_(cur_conv.weight, mode='fan_out', nonlinearity='relu')
+                nn.init.constant_(cur_conv.bias, 0)
+                code_size_cnt += reg_channel
+                self.conv_box[f'conv_{reg_name}'] = cur_conv
+                self.conv_box_names.append(f'conv_{reg_name}')
+
+            assert code_size_cnt == code_size, f'Code size does not match: {code_size_cnt}:{code_size}'
+        else:
             self.conv_box = nn.Conv2d(
                 input_channels, self.num_anchors_per_location * self.code_size,
                 kernel_size=1
@@ -45,7 +64,14 @@ class SingleHead(BaseBEVBackbone):
         spatial_features_2d = super().forward({'spatial_features': spatial_features_2d})['spatial_features_2d']
 
         cls_preds = self.conv_cls(spatial_features_2d)
+
+        if self.separate_reg_config is not None:
             box_preds = self.conv_box(spatial_features_2d)
+        else:
+            box_preds_list = []
+            for reg_name in self.conv_box_names:
+                box_preds_list.append(self.conv_box[f'conv_{reg_name}'](spatial_features_2d))
+            box_preds = torch.cat(box_preds_list, dim=1)
 
         if not self.use_multihead:
             box_preds = box_preds.permute(0, 2, 3, 1).contiguous()
@@ -121,7 +147,8 @@ class AnchorHeadMulti(AnchorHeadTemplate):
                 self.model_cfg, input_channels,
                 len(rpn_head_cfg['HEAD_CLS_NAME']) if self.separate_multihead else self.num_class,
                 num_anchors_per_location, self.box_coder.code_size, rpn_head_cfg,
-                head_label_indices=head_label_indices
+                head_label_indices=head_label_indices,
+                separate_reg_config=self.model_cfg.get('SEPARATE_REG_CONFIG', None)
             )
             rpn_heads.append(rpn_head)
         self.rpn_heads = nn.ModuleList(rpn_heads)

pcdet/utils/box_coder_utils.py

@@ -2,17 +2,18 @@ import torch
 
 
 class ResidualCoder(object):
-    def __init__(self, code_size=7, **kwargs):
+    def __init__(self, code_size=7, encode_angle_by_sincos=False, **kwargs):
         super().__init__()
         self.code_size = code_size
-        assert code_size in [7, 9]
+        self.encode_angle_by_sincos = encode_angle_by_sincos
+        if self.encode_angle_by_sincos:
+            self.code_size += 1
 
-    @staticmethod
-    def encode_torch(boxes, anchors):
+    def encode_torch(self, boxes, anchors):
         """
         Args:
             boxes: (N, 7 + C) [x, y, z, dx, dy, dz, heading, ...]
-            anchors: (N, 7 + C) [x, y, z, dx, dy, dz, heading, ...]
+            anchors: (N, 7 + C) [x, y, z, dx, dy, dz, heading or *[cos, sin], ...]
 
         Returns:
 
@@ -30,23 +31,30 @@ class ResidualCoder(object):
         dxt = torch.log(dxg / dxa)
         dyt = torch.log(dyg / dya)
         dzt = torch.log(dzg / dza)
-        rt = rg - ra
+        if self.encode_angle_by_sincos:
+            rt_cos = torch.cos(rg) - torch.cos(ra)
+            rt_sin = torch.sin(rg) - torch.sin(ra)
+            rts = [rt_cos, rt_sin]
+        else:
+            rts = [rg - ra]
 
         cts = [g - a for g, a in zip(cgs, cas)]
-        return torch.cat([xt, yt, zt, dxt, dyt, dzt, rt, *cts], dim=-1)
+        return torch.cat([xt, yt, zt, dxt, dyt, dzt, *rts, *cts], dim=-1)
 
-    @staticmethod
-    def decode_torch(box_encodings, anchors):
+    def decode_torch(self, box_encodings, anchors):
         """
         Args:
-            box_encodings: (B, N, 7 + C) or (N, 7 + C) [x, y, z, dx, dy, dz, heading, ...]
+            box_encodings: (B, N, 7 + C) or (N, 7 + C) [x, y, z, dx, dy, dz, heading or *[cos, sin], ...]
             anchors: (B, N, 7 + C) or (N, 7 + C) [x, y, z, dx, dy, dz, heading, ...]
 
         Returns:
 
         """
         xa, ya, za, dxa, dya, dza, ra, *cas = torch.split(anchors, 1, dim=-1)
+        if self.encode_angle_by_sincos:
             xt, yt, zt, dxt, dyt, dzt, rt, *cts = torch.split(box_encodings, 1, dim=-1)
+        else:
+            xt, yt, zt, dxt, dyt, dzt, cost, sint, *cts = torch.split(box_encodings, 1, dim=-1)
 
         diagonal = torch.sqrt(dxa ** 2 + dya ** 2)
         xg = xt * diagonal + xa
@@ -56,6 +64,12 @@ class ResidualCoder(object):
         dxg = torch.exp(dxt) * dxa
         dyg = torch.exp(dyt) * dya
         dzg = torch.exp(dzt) * dza
+
+        if self.encode_angle_by_sincos:
+            rg_cos = cost + torch.cos(ra)
+            rg_sin = sint + torch.sin(ra)
+            rg = torch.atan2(rg_sin, rg_cos)
+        else:
             rg = rt + ra
 
         cgs = [t + a for t, a in zip(cts, cas)]

tools/cfgs/nuscenes_models/cbgs_1conv_sepreg.yaml

+CLASS_NAMES: ['car','truck', 'construction_vehicle', 'bus', 'trailer',
+              'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone']
+
+DATA_CONFIG:
+    _BASE_CONFIG_: cfgs/dataset_configs/nuscenes_dataset.yaml
+
+
+MODEL:
+    NAME: SECONDNet
+
+    VFE:
+        NAME: MeanVFE
+
+    BACKBONE_3D:
+        NAME: VoxelResBackBone8x
+
+    MAP_TO_BEV:
+        NAME: HeightCompression
+        NUM_BEV_FEATURES: 256
+
+    BACKBONE_2D:
+        NAME: BaseBEVBackbone
+
+        LAYER_NUMS: [5, 5]
+        LAYER_STRIDES: [1, 2]
+        NUM_FILTERS: [128, 256]
+        UPSAMPLE_STRIDES: [1, 2]
+        NUM_UPSAMPLE_FILTERS: [256, 256]
+
+    DENSE_HEAD:
+        NAME: AnchorHeadMulti
+        CLASS_AGNOSTIC: False
+
+        USE_DIRECTION_CLASSIFIER: True
+        DIR_OFFSET: 0.78539
+        DIR_LIMIT_OFFSET: 0.0
+        NUM_DIR_BINS: 2
+
+        USE_MULTIHEAD: True
+        SEPARATE_MULTIHEAD: True
+        ANCHOR_GENERATOR_CONFIG: [
+            {
+                'class_name': car,
+                'anchor_sizes': [[4.63, 1.97, 1.74]],
+                'anchor_rotations': [0, 1.57],
+                'anchor_bottom_heights': [-0.95],
+                'align_center': False,
+                'feature_map_stride': 8,
+                'matched_threshold': 0.6,
+                'unmatched_threshold': 0.45
+            },
+            {
+                'class_name': truck,
+                'anchor_sizes': [[6.93, 2.51, 2.84]],
+                'anchor_rotations': [0, 1.57],
+                'anchor_bottom_heights': [-0.6],
+                'align_center': False,
+                'feature_map_stride': 8,
+                'matched_threshold': 0.55,
+                'unmatched_threshold': 0.4
+            },
+            {
+                'class_name': construction_vehicle,
+                'anchor_sizes': [[6.37, 2.85, 3.19]],
+                'anchor_rotations': [0, 1.57],
+                'anchor_bottom_heights': [-0.225],
+                'align_center': False,
+                'feature_map_stride': 8,
+                'matched_threshold': 0.5,
+                'unmatched_threshold': 0.35
+            },
+            {
+                'class_name': bus,
+                'anchor_sizes': [[10.5, 2.94, 3.47]],
+                'anchor_rotations': [0, 1.57],
+                'anchor_bottom_heights': [-0.085],
+                'align_center': False,
+                'feature_map_stride': 8,
+                'matched_threshold': 0.55,
+                'unmatched_threshold': 0.4
+            },
+            {
+                'class_name': trailer,
+                'anchor_sizes': [[12.29, 2.90, 3.87]],
+                'anchor_rotations': [0, 1.57],
+                'anchor_bottom_heights': [0.115],
+                'align_center': False,
+                'feature_map_stride': 8,
+                'matched_threshold': 0.5,
+                'unmatched_threshold': 0.35
+            },
+            {
+                'class_name': barrier,
+                'anchor_sizes': [[0.50, 2.53, 0.98]],
+                'anchor_rotations': [0, 1.57],
+                'anchor_bottom_heights': [-1.33],
+                'align_center': False,
+                'feature_map_stride': 8,
+                'matched_threshold': 0.55,
+                'unmatched_threshold': 0.4
+            },
+            {
+                'class_name': motorcycle,
+                'anchor_sizes': [[2.11, 0.77, 1.47]],
+                'anchor_rotations': [0, 1.57],
+                'anchor_bottom_heights': [-1.085],
+                'align_center': False,
+                'feature_map_stride': 8,
+                'matched_threshold': 0.5,
+                'unmatched_threshold': 0.3
+            },
+            {
+                'class_name': bicycle,
+                'anchor_sizes': [[1.70, 0.60, 1.28]],
+                'anchor_rotations': [0, 1.57],
+                'anchor_bottom_heights': [-1.18],
+                'align_center': False,
+                'feature_map_stride': 8,
+                'matched_threshold': 0.5,
+                'unmatched_threshold': 0.35
+            },
+            {
+                'class_name': pedestrian,
+                'anchor_sizes': [[0.73, 0.67, 1.77]],
+                'anchor_rotations': [0, 1.57],
+                'anchor_bottom_heights': [-0.935],
+                'align_center': False,
+                'feature_map_stride': 8,
+                'matched_threshold': 0.6,
+                'unmatched_threshold': 0.4
+            },
+            {
+                'class_name': traffic_cone,
+                'anchor_sizes': [[0.41, 0.41, 1.07]],
+                'anchor_rotations': [0, 1.57],
+                'anchor_bottom_heights': [-1.285],
+                'align_center': False,
+                'feature_map_stride': 8,
+                'matched_threshold': 0.6,
+                'unmatched_threshold': 0.4
+            },
+        ]
+
+        SHARED_CONV_NUM_FILTER: 64
+        RPN_HEAD_CFGS: [
+            {
+                'HEAD_CLS_NAME': ['car'],
+                'LAYER_NUMS': [0],
+                'LAYER_STRIDES': [1],
+                'NUM_FILTERS': [64],
+            },
+            {
+                'HEAD_CLS_NAME': ['truck', 'construction_vehicle'],
+                'LAYER_NUMS': [0],
+                'LAYER_STRIDES': [1],
+                'NUM_FILTERS': [64],
+            },
+            {
+                'HEAD_CLS_NAME': ['bus', 'trailer'],
+                'LAYER_NUMS': [0],
+                'LAYER_STRIDES': [1],
+                'NUM_FILTERS': [64],
+            },
+            {
+                'HEAD_CLS_NAME': ['barrier'],
+                'LAYER_NUMS': [0],
+                'LAYER_STRIDES': [1],
+                'NUM_FILTERS': [64],
+            },
+            {
+                'HEAD_CLS_NAME': ['motorcycle', 'bicycle'],
+                'LAYER_NUMS': [0],
+                'LAYER_STRIDES': [1],
+                'NUM_FILTERS': [64],
+            },
+            {
+                'HEAD_CLS_NAME': ['pedestrian', 'traffic_cone'],
+                'LAYER_NUMS': [0],
+                'LAYER_STRIDES': [1],
+                'NUM_FILTERS': [64],
+            },
+        ]
+
+        SEPARATE_REG_CONFIG: ['reg:2', 'height:1', 'size:3', 'angle:2', 'velo:2']
+        TARGET_ASSIGNER_CONFIG:
+            NAME: AxisAlignedTargetAssigner
+            POS_FRACTION: -1.0
+            SAMPLE_SIZE: 512
+            NORM_BY_NUM_EXAMPLES: False
+            MATCH_HEIGHT: False
+            BOX_CODER: ResidualCoder
+            BOX_CODER_CONFIG: {
+                'code_size': 9,
+                'encode_angle_by_sincos': True
+            }
+
+
+        LOSS_CONFIG:
+            REG_LOSS_TYPE: WeightedL1Loss
+            LOSS_WEIGHTS: {
+                'pos_cls_weight': 1.0,
+                'neg_cls_weight': 2.0,
+                'cls_weight': 1.0,
+                'loc_weight': 0.25,
+                'dir_weight': 0.2,
+                'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2]
+            }
+
+    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: 0.2
+            NMS_PRE_MAXSIZE: 1000
+            NMS_POST_MAXSIZE: 100
+
+
+OPTIMIZATION:
+    OPTIMIZER: adam_onecycle
+    LR: 0.003
+    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

tools/cfgs/nuscenes_models/pp_multihead_sepreg.yaml

+CLASS_NAMES: ['car','truck', 'construction_vehicle', 'bus', 'trailer',
+              'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone']
+
+DATA_CONFIG:
+    _BASE_CONFIG_: cfgs/dataset_configs/nuscenes_dataset.yaml
+
+    POINT_CLOUD_RANGE: [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]
+    DATA_PROCESSOR:
+        -   NAME: mask_points_and_boxes_outside_range
+            REMOVE_OUTSIDE_BOXES: True
+
+        -   NAME: shuffle_points
+            SHUFFLE_ENABLED: {
+                'train': True,
+                'test': True
+            }
+
+        -   NAME: transform_points_to_voxels
+            VOXEL_SIZE: [0.2, 0.2, 8.0]
+            MAX_POINTS_PER_VOXEL: 20
+            MAX_NUMBER_OF_VOXELS: {
+                'train': 30000,
+                'test': 30000
+            }
+
+MODEL:
+    NAME: PointPillar
+
+    VFE:
+        NAME: PillarVFE
+        WITH_DISTANCE: False
+        USE_ABSLOTE_XYZ: True
+        USE_NORM: True
+        NUM_FILTERS: [64]
+
+    MAP_TO_BEV:
+        NAME: PointPillarScatter
+        NUM_BEV_FEATURES: 64
+
+    BACKBONE_2D:
+        NAME: BaseBEVBackbone
+        LAYER_NUMS: [3, 5, 5]
+        LAYER_STRIDES: [2, 2, 2]
+        NUM_FILTERS: [64, 128, 256]
+        UPSAMPLE_STRIDES: [0.5, 1, 2]
+        NUM_UPSAMPLE_FILTERS: [128, 128, 128]
+
+    DENSE_HEAD:
+        NAME: AnchorHeadMulti
+        CLASS_AGNOSTIC: False
+
+        USE_DIRECTION_CLASSIFIER: True
+        DIR_OFFSET: 0.78539
+        DIR_LIMIT_OFFSET: 0.0
+        NUM_DIR_BINS: 2
+
+        USE_MULTIHEAD: True
+        SEPARATE_MULTIHEAD: True
+        ANCHOR_GENERATOR_CONFIG: [
+            {
+                'class_name': car,
+                'anchor_sizes': [[4.63, 1.97, 1.74]],
+                'anchor_rotations': [0, 1.57],
+                'anchor_bottom_heights': [-0.95],
+                'align_center': False,
+                'feature_map_stride': 4,
+                'matched_threshold': 0.6,
+                'unmatched_threshold': 0.45
+            },
+            {
+                'class_name': truck,
+                'anchor_sizes': [[6.93, 2.51, 2.84]],
+                'anchor_rotations': [0, 1.57],
+                'anchor_bottom_heights': [-0.6],
+                'align_center': False,
+                'feature_map_stride': 4,
+                'matched_threshold': 0.55,
+                'unmatched_threshold': 0.4
+            },
+            {
+                'class_name': construction_vehicle,
+                'anchor_sizes': [[6.37, 2.85, 3.19]],
+                'anchor_rotations': [0, 1.57],
+                'anchor_bottom_heights': [-0.225],
+                'align_center': False,
+                'feature_map_stride': 4,
+                'matched_threshold': 0.5,
+                'unmatched_threshold': 0.35
+            },
+            {
+                'class_name': bus,
+                'anchor_sizes': [[10.5, 2.94, 3.47]],
+                'anchor_rotations': [0, 1.57],
+                'anchor_bottom_heights': [-0.085],
+                'align_center': False,
+                'feature_map_stride': 4,
+                'matched_threshold': 0.55,
+                'unmatched_threshold': 0.4
+            },
+            {
+                'class_name': trailer,
+                'anchor_sizes': [[12.29, 2.90, 3.87]],
+                'anchor_rotations': [0, 1.57],
+                'anchor_bottom_heights': [0.115],
+                'align_center': False,
+                'feature_map_stride': 4,
+                'matched_threshold': 0.5,
+                'unmatched_threshold': 0.35
+            },
+            {
+                'class_name': barrier,
+                'anchor_sizes': [[0.50, 2.53, 0.98]],
+                'anchor_rotations': [0, 1.57],
+                'anchor_bottom_heights': [-1.33],
+                'align_center': False,
+                'feature_map_stride': 4,
+                'matched_threshold': 0.55,
+                'unmatched_threshold': 0.4
+            },
+            {
+                'class_name': motorcycle,
+                'anchor_sizes': [[2.11, 0.77, 1.47]],
+                'anchor_rotations': [0, 1.57],
+                'anchor_bottom_heights': [-1.085],
+                'align_center': False,
+                'feature_map_stride': 4,
+                'matched_threshold': 0.5,
+                'unmatched_threshold': 0.3
+            },
+            {
+                'class_name': bicycle,
+                'anchor_sizes': [[1.70, 0.60, 1.28]],
+                'anchor_rotations': [0, 1.57],
+                'anchor_bottom_heights': [-1.18],
+                'align_center': False,
+                'feature_map_stride': 4,
+                'matched_threshold': 0.5,
+                'unmatched_threshold': 0.35
+            },
+            {
+                'class_name': pedestrian,
+                'anchor_sizes': [[0.73, 0.67, 1.77]],
+                'anchor_rotations': [0, 1.57],
+                'anchor_bottom_heights': [-0.935],
+                'align_center': False,
+                'feature_map_stride': 4,
+                'matched_threshold': 0.6,
+                'unmatched_threshold': 0.4
+            },
+            {
+                'class_name': traffic_cone,
+                'anchor_sizes': [[0.41, 0.41, 1.07]],
+                'anchor_rotations': [0, 1.57],
+                'anchor_bottom_heights': [-1.285],
+                'align_center': False,
+                'feature_map_stride': 4,
+                'matched_threshold': 0.6,
+                'unmatched_threshold': 0.4
+            },
+        ]
+
+        SHARED_CONV_NUM_FILTER: 64
+
+        RPN_HEAD_CFGS: [
+            {
+                'HEAD_CLS_NAME': ['car'],
+                'LAYER_NUMS': [0],
+                'LAYER_STRIDES': [1],
+                'NUM_FILTERS': [64],
+            },
+            {
+                'HEAD_CLS_NAME': ['truck', 'construction_vehicle'],
+                'LAYER_NUMS': [0],
+                'LAYER_STRIDES': [1],
+                'NUM_FILTERS': [64],
+            },
+            {
+                'HEAD_CLS_NAME': ['bus', 'trailer'],
+                'LAYER_NUMS': [0],
+                'LAYER_STRIDES': [1],
+                'NUM_FILTERS': [64],
+            },
+            {
+                'HEAD_CLS_NAME': ['barrier'],
+                'LAYER_NUMS': [0],
+                'LAYER_STRIDES': [1],
+                'NUM_FILTERS': [64],
+            },
+            {
+                'HEAD_CLS_NAME': ['motorcycle', 'bicycle'],
+                'LAYER_NUMS': [0],
+                'LAYER_STRIDES': [1],
+                'NUM_FILTERS': [64],
+            },
+            {
+                'HEAD_CLS_NAME': ['pedestrian', 'traffic_cone'],
+                'LAYER_NUMS': [0],
+                'LAYER_STRIDES': [1],
+                'NUM_FILTERS': [64],
+            },
+        ]
+        SEPARATE_REG_CONFIG: ['reg:2', 'height:1', 'size:3', 'angle:2', 'velo:2']
+
+        TARGET_ASSIGNER_CONFIG:
+            NAME: AxisAlignedTargetAssigner
+            POS_FRACTION: -1.0
+            SAMPLE_SIZE: 512
+            NORM_BY_NUM_EXAMPLES: False
+            MATCH_HEIGHT: False
+            BOX_CODER: ResidualCoder
+            BOX_CODER_CONFIG: {
+                'code_size': 9,
+                'encode_angle_by_sincos': True
+            }
+
+
+        LOSS_CONFIG:
+            REG_LOSS_TYPE: WeightedL1Loss
+            LOSS_WEIGHTS: {
+                'pos_cls_weight': 1.0,
+                'neg_cls_weight': 2.0,
+                'cls_weight': 1.0,
+                'loc_weight': 0.25,
+                'dir_weight': 0.2,
+                'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2]
+            }
+
+    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: 0.2
+            NMS_PRE_MAXSIZE: 4096
+            NMS_POST_MAXSIZE: 100
+
+
+OPTIMIZATION:
+    OPTIMIZER: adam_onecycle
+    LR: 0.001
+    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