From 3a7e3b16e99688923835e7c05a21dae948814239 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Wed, 2 Aug 2023 13:44:47 +0000 Subject: [PATCH 01/93] Add pv_rcnn_plusplus config for KITTI --- tools/cfgs/kitti_models/pv_rcnn_plusplus.yaml | 301 ++++++++++++++++++ 1 file changed, 301 insertions(+) create mode 100644 tools/cfgs/kitti_models/pv_rcnn_plusplus.yaml diff --git a/tools/cfgs/kitti_models/pv_rcnn_plusplus.yaml b/tools/cfgs/kitti_models/pv_rcnn_plusplus.yaml new file mode 100644 index 000000000..c1b550917 --- /dev/null +++ b/tools/cfgs/kitti_models/pv_rcnn_plusplus.yaml @@ -0,0 +1,301 @@ +CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist'] + +DATA_CONFIG: + _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml + DATA_AUGMENTOR: + DISABLE_AUG_LIST: ['placeholder'] + AUG_CONFIG_LIST: + - NAME: gt_sampling + USE_ROAD_PLANE: True + DB_INFO_PATH: + - kitti_dbinfos_train.pkl + PREPARE: { + filter_by_min_points: ['Car:5', 'Pedestrian:5', 'Cyclist:5'], + filter_by_difficulty: [-1], + } + + SAMPLE_GROUPS: ['Car:15','Pedestrian:10', 'Cyclist:10'] + NUM_POINT_FEATURES: 4 + DATABASE_WITH_FAKELIDAR: False + REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0] + LIMIT_WHOLE_SCENE: False + - NAME: random_world_flip + ALONG_AXIS_LIST: ['x'] + + - NAME: random_world_rotation + WORLD_ROT_ANGLE: [-0.78539816, 0.78539816] + + - NAME: random_world_scaling + WORLD_SCALE_RANGE: [0.95, 1.05] + +MODEL: + NAME: PVRCNNPlusPlus + + VFE: + NAME: MeanVFE + + BACKBONE_3D: + NAME: VoxelBackBone8x + + 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: CenterHead + CLASS_AGNOSTIC: False + + CLASS_NAMES_EACH_HEAD: [ + [ 'Car', 'Pedestrian', 'Cyclist' ] + ] + + SHARED_CONV_CHANNEL: 64 + USE_BIAS_BEFORE_NORM: True + NUM_HM_CONV: 2 + SEPARATE_HEAD_CFG: + HEAD_ORDER: [ 'center', 'center_z', 'dim', 'rot' ] + HEAD_DICT: { + 'center': { 'out_channels': 2, 'num_conv': 2 }, + 'center_z': { 'out_channels': 1, 'num_conv': 2 }, + 'dim': { 'out_channels': 3, 'num_conv': 2 }, + 'rot': { 'out_channels': 2, 'num_conv': 2 }, + } + + TARGET_ASSIGNER_CONFIG: + FEATURE_MAP_STRIDE: 8 + NUM_MAX_OBJS: 500 + GAUSSIAN_OVERLAP: 0.1 + MIN_RADIUS: 2 + + LOSS_CONFIG: + LOSS_WEIGHTS: { + 'cls_weight': 1.0, + 'loc_weight': 2.0, + 'code_weights': [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] + } + + POST_PROCESSING: + SCORE_THRESH: 0.01 + POST_CENTER_LIMIT_RANGE: [ -75.2, -75.2, -2, 75.2, 75.2, 4 ] + MAX_OBJ_PER_SAMPLE: 500 + NMS_CONFIG: + NMS_TYPE: nms_gpu + NMS_THRESH: 0.7 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 + + PFE: + NAME: VoxelSetAbstraction + POINT_SOURCE: raw_points + NUM_KEYPOINTS: 4096 + NUM_OUTPUT_FEATURES: 90 + SAMPLE_METHOD: SPC + SPC_SAMPLING: + NUM_SECTORS: 6 + SAMPLE_RADIUS_WITH_ROI: 1.6 + + FEATURES_SOURCE: ['bev', 'x_conv3', 'x_conv4', 'raw_points'] + SA_LAYER: + raw_points: + NAME: VectorPoolAggregationModuleMSG + NUM_GROUPS: 2 + LOCAL_AGGREGATION_TYPE: local_interpolation + NUM_REDUCED_CHANNELS: 1 + NUM_CHANNELS_OF_LOCAL_AGGREGATION: 32 + MSG_POST_MLPS: [ 32 ] + FILTER_NEIGHBOR_WITH_ROI: True + RADIUS_OF_NEIGHBOR_WITH_ROI: 2.4 + + GROUP_CFG_0: + NUM_LOCAL_VOXEL: [ 2, 2, 2 ] + MAX_NEIGHBOR_DISTANCE: 0.2 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [ 32, 32 ] + GROUP_CFG_1: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 0.4 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [ 32, 32 ] + + x_conv3: + DOWNSAMPLE_FACTOR: 4 + INPUT_CHANNELS: 64 + + NAME: VectorPoolAggregationModuleMSG + NUM_GROUPS: 2 + LOCAL_AGGREGATION_TYPE: local_interpolation + NUM_REDUCED_CHANNELS: 32 + NUM_CHANNELS_OF_LOCAL_AGGREGATION: 32 + MSG_POST_MLPS: [128] + FILTER_NEIGHBOR_WITH_ROI: True + RADIUS_OF_NEIGHBOR_WITH_ROI: 4.0 + + GROUP_CFG_0: + NUM_LOCAL_VOXEL: [3, 3, 3] + MAX_NEIGHBOR_DISTANCE: 1.2 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [64, 64] + GROUP_CFG_1: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 2.4 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [ 64, 64 ] + + x_conv4: + DOWNSAMPLE_FACTOR: 8 + INPUT_CHANNELS: 64 + + NAME: VectorPoolAggregationModuleMSG + NUM_GROUPS: 2 + LOCAL_AGGREGATION_TYPE: local_interpolation + NUM_REDUCED_CHANNELS: 32 + NUM_CHANNELS_OF_LOCAL_AGGREGATION: 32 + MSG_POST_MLPS: [ 128 ] + FILTER_NEIGHBOR_WITH_ROI: True + RADIUS_OF_NEIGHBOR_WITH_ROI: 6.4 + + GROUP_CFG_0: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 2.4 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [ 64, 64 ] + GROUP_CFG_1: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 4.8 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [ 64, 64 ] + + + POINT_HEAD: + NAME: PointHeadSimple + CLS_FC: [256, 256] + CLASS_AGNOSTIC: True + USE_POINT_FEATURES_BEFORE_FUSION: True + TARGET_CONFIG: + GT_EXTRA_WIDTH: [0.2, 0.2, 0.2] + LOSS_CONFIG: + LOSS_REG: smooth-l1 + LOSS_WEIGHTS: { + 'point_cls_weight': 1.0, + } + + ROI_HEAD: + NAME: PVRCNNHead + CLASS_AGNOSTIC: True + + SHARED_FC: [256, 256] + CLS_FC: [256, 256] + REG_FC: [256, 256] + DP_RATIO: 0.3 + + 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 + SCORE_THRESH: 0.1 + +# NMS_PRE_MAXSIZE: 4096 +# NMS_POST_MAXSIZE: 500 +# NMS_THRESH: 0.85 + + + ROI_GRID_POOL: + GRID_SIZE: 6 + + NAME: VectorPoolAggregationModuleMSG + NUM_GROUPS: 2 + LOCAL_AGGREGATION_TYPE: voxel_random_choice + NUM_REDUCED_CHANNELS: 30 + NUM_CHANNELS_OF_LOCAL_AGGREGATION: 32 + MSG_POST_MLPS: [ 128 ] + + GROUP_CFG_0: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 0.8 + NEIGHBOR_NSAMPLE: 32 + POST_MLPS: [ 64, 64 ] + GROUP_CFG_1: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 1.6 + NEIGHBOR_NSAMPLE: 32 + POST_MLPS: [ 64, 64 ] + + 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: 0.55 + + 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: 0.7 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 + + +OPTIMIZATION: + BATCH_SIZE_PER_GPU: 2 + NUM_EPOCHS: 140 + + OPTIMIZER: adam_onecycle + LR: 0.01 + WEIGHT_DECAY: 0.001 + 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 \ No newline at end of file From 6bba173ae713b3bef75511b6deccdcc2adc5fc53 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Fri, 4 Aug 2023 07:32:40 +0000 Subject: [PATCH 02/93] Rename pv_rcnn_plusplus config file --- ...lusplus.yaml => pv_rcnn_plusplus_reproduced_by_community.yaml} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename tools/cfgs/kitti_models/{pv_rcnn_plusplus.yaml => pv_rcnn_plusplus_reproduced_by_community.yaml} (100%) diff --git a/tools/cfgs/kitti_models/pv_rcnn_plusplus.yaml b/tools/cfgs/kitti_models/pv_rcnn_plusplus_reproduced_by_community.yaml similarity index 100% rename from tools/cfgs/kitti_models/pv_rcnn_plusplus.yaml rename to tools/cfgs/kitti_models/pv_rcnn_plusplus_reproduced_by_community.yaml From 4df43ef925290c36f38dcaf26d311184aac230a0 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Thu, 10 Aug 2023 09:58:58 +0000 Subject: [PATCH 03/93] Add class specific score filtering --- pcdet/models/detectors/detector3d_template.py | 3 ++- pcdet/models/model_utils/model_nms_utils.py | 21 ++++++++++++++----- 2 files changed, 18 insertions(+), 6 deletions(-) diff --git a/pcdet/models/detectors/detector3d_template.py b/pcdet/models/detectors/detector3d_template.py index 91e44bd46..0482fb34b 100644 --- a/pcdet/models/detectors/detector3d_template.py +++ b/pcdet/models/detectors/detector3d_template.py @@ -255,7 +255,8 @@ def post_processing(self, batch_dict): else: label_preds = label_preds + 1 selected, selected_scores = model_nms_utils.class_agnostic_nms( - box_scores=cls_preds, box_preds=box_preds, + box_scores=cls_preds, box_preds=box_preds, + label_preds=label_preds, nms_config=post_process_cfg.NMS_CONFIG, score_thresh=post_process_cfg.SCORE_THRESH ) diff --git a/pcdet/models/model_utils/model_nms_utils.py b/pcdet/models/model_utils/model_nms_utils.py index 8be1097e9..cd5c5d586 100644 --- a/pcdet/models/model_utils/model_nms_utils.py +++ b/pcdet/models/model_utils/model_nms_utils.py @@ -1,15 +1,26 @@ import torch +from functools import reduce from ...ops.iou3d_nms import iou3d_nms_utils -def class_agnostic_nms(box_scores, box_preds, nms_config, score_thresh=None): +def class_agnostic_nms(box_scores, box_preds, nms_config, label_preds=None, score_thresh=None): src_box_scores = box_scores if score_thresh is not None: - scores_mask = (box_scores >= score_thresh) - box_scores = box_scores[scores_mask] - box_preds = box_preds[scores_mask] - + if isinstance(score_thresh, list): + assert label_preds is not None, 'label preds have to be provided for class specific threshold' + scores_mask_list = [] + for class_index, class_score_threshold in enumerate(score_thresh): + cur_scores_mask = torch.logical_and(label_preds == (class_index + 1), box_scores >= class_score_threshold) + scores_mask_list.append(cur_scores_mask) + scores_mask = reduce(torch.logical_or, scores_mask_list) + box_scores = box_scores[scores_mask] + box_preds = box_preds[scores_mask] + else: + scores_mask = (box_scores >= score_thresh) + box_scores = box_scores[scores_mask] + box_preds = box_preds[scores_mask] + selected = [] if box_scores.shape[0] > 0: box_scores_nms, indices = torch.topk(box_scores, k=min(nms_config.NMS_PRE_MAXSIZE, box_scores.shape[0])) From d3f57e7d3f9fa5afe9f41da3b51f407262e574e0 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Mon, 14 Aug 2023 11:07:00 +0000 Subject: [PATCH 04/93] Add pv_rcnn_relation code --- pcdet/models/detectors/__init__.py | 2 + pcdet/models/detectors/pv_rcnn_relation.py | 58 ++++ pcdet/models/object_relation/__init__.py | 0 pcdet/models/object_relation/gnn.py | 97 +++++++ pcdet/models/roi_heads/__init__.py | 2 + .../models/roi_heads/pvrcnn_head_relation.py | 207 ++++++++++++++ tools/cfgs/kitti_models/pv_rcnn_relation.yaml | 267 ++++++++++++++++++ 7 files changed, 633 insertions(+) create mode 100644 pcdet/models/detectors/pv_rcnn_relation.py create mode 100644 pcdet/models/object_relation/__init__.py create mode 100644 pcdet/models/object_relation/gnn.py create mode 100644 pcdet/models/roi_heads/pvrcnn_head_relation.py create mode 100644 tools/cfgs/kitti_models/pv_rcnn_relation.yaml diff --git a/pcdet/models/detectors/__init__.py b/pcdet/models/detectors/__init__.py index 1af193078..cbf79145b 100644 --- a/pcdet/models/detectors/__init__.py +++ b/pcdet/models/detectors/__init__.py @@ -15,6 +15,7 @@ from .voxelnext import VoxelNeXt from .transfusion import TransFusion from .bevfusion import BevFusion +from .pv_rcnn_relation import PVRCNNRelation __all__ = { 'Detector3DTemplate': Detector3DTemplate, @@ -35,6 +36,7 @@ 'VoxelNeXt': VoxelNeXt, 'TransFusion': TransFusion, 'BevFusion': BevFusion, + 'PVRCNNRelation': PVRCNNRelation, } diff --git a/pcdet/models/detectors/pv_rcnn_relation.py b/pcdet/models/detectors/pv_rcnn_relation.py new file mode 100644 index 000000000..7268b7eb8 --- /dev/null +++ b/pcdet/models/detectors/pv_rcnn_relation.py @@ -0,0 +1,58 @@ +from .detector3d_template import Detector3DTemplate + +from pcdet.models.object_relation.gnn import GNN + + +class PVRCNNRelation(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() + self.gnn = GNN(model_cfg.ROI_HEAD.SHARED_FC[-1], model_cfg.OBJECT_RELATION.RADIUS, model_cfg.OBJECT_RELATION.LAYERS) + + def forward(self, batch_dict): + # MeanVFE: Voxelisation + batch_dict = self.vfe(batch_dict) + # VoxelBackBone8x: 3D Backbone + batch_dict = self.backbone_3d(batch_dict) + # HeightCompression(): 3D to BEV + batch_dict = self.map_to_bev_module(batch_dict) + # VoxelSetAbstraction: Aggregation of raw points with 3D features and BEV features + batch_dict = self.pfe(batch_dict) + # BaseBEVBackbone: 2D Backbone + batch_dict = self.backbone_2d(batch_dict) + # AnchorHeadSingle: Proposal generation for each voxel + batch_dict = self.dense_head(batch_dict) + # PointHeadSimple: prediction of a + batch_dict = self.point_head(batch_dict) + # PVRCNNHead: Proposal refinement + batch_dict = self.roi_head(batch_dict) + # GNN: Object relation + batch_dict = self.gnn(batch_dict) + + batch_dict = self.roi_head.final_predictions(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_rpn, tb_dict = self.dense_head.get_loss() + loss_point, tb_dict = self.point_head.get_loss(tb_dict) + loss_rcnn, tb_dict = self.roi_head.get_loss(tb_dict) + + loss = loss_rpn + loss_point + loss_rcnn + + if hasattr(self.backbone_3d, 'get_loss'): + loss_backbone3d, tb_dict = self.backbone_3d.get_loss(tb_dict) + loss += loss_backbone3d + + return loss, tb_dict, disp_dict diff --git a/pcdet/models/object_relation/__init__.py b/pcdet/models/object_relation/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py new file mode 100644 index 000000000..10c7b636c --- /dev/null +++ b/pcdet/models/object_relation/gnn.py @@ -0,0 +1,97 @@ +import torch +import torch.nn as nn +import torch_geometric as tg +import torch.nn.functional as F +import torch_scatter + +# custom implementation for EdgeConv +# class EdgeWeightingNetwork(nn.Module): +# def __init__(self, dimension): +# super(EdgeWeightingNetwork, self).__init__() +# self.fc = nn.Linear(dimension*2, dimension) + +# def forward(self, x, edge_index): +# from_node, to_node = edge_index +# x_i, x_j = x[from_node], x[to_node] +# e_ij = torch.cat((x_j - x_i, x_i), dim=-1) +# e_ij = self.fc(e_ij) +# e_ij = F.relu(e_ij) +# # why from node here????? +# out, _ = torch_scatter.scatter_max(e_ij, from_node, dim=0) +# return out + +# similar to EdgeConv https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.conv.EdgeConv.html +class EdgeConv(tg.nn.MessagePassing): + def __init__(self, in_dim, out_dim): + super(EdgeConv, self).__init__(aggr='max') + self.fc = nn.Linear(in_dim, out_dim) + + def forward(self, x, edge_index): + return self.propagate(edge_index, x=x) + + def message(self, x_i, x_j): + x = torch.cat((x_j - x_i, x_i), dim=-1) + x = self.fc(x) + x = F.relu(x) + return x + + +class GNN(nn.Module): + def __init__(self, dim, radius, layers): + super(GNN, self).__init__() + self.radius = radius + conv_list = [] + for i in range(len(layers)): + if i == 0: + conv_list.append(EdgeConv(2*(256+7), layers[i])) + else: + conv_list.append(EdgeConv(2*layers[i-1], layers[i])) + self.conv_list = nn.ModuleList(conv_list) + self.radius_transform = tg.transforms.RadiusGraph(r=self.radius) + self.init_weights() + + def init_weights(self, weight_init='xavier'): + if weight_init == 'xavier': + init_func = nn.init.xavier_uniform_ + for m in self.conv_list: + init_func(m.fc.weight) + + def forward(self, batch_dict): + # BxNx7 + proposal_boxes = batch_dict['rois'] + # BxNxC + pooled_features = batch_dict['pooled_features'] + B,N,C = pooled_features.shape + + global_pooled_features = torch.cat((pooled_features, proposal_boxes), dim=-1) + global_pooled_features = global_pooled_features.view(-1, C+7) + + batch_vector = torch.arange(B, device=pooled_features.device).repeat_interleave(N) + edge_index = tg.nn.radius_graph(proposal_boxes[:,:,:3].view(-1, 3), r=self.radius, batch=batch_vector, loop=False) + batch_dict['gnn_edges'] = edge_index + + gnn_features = [global_pooled_features] + x = global_pooled_features + for i in range(len(self.conv_list)): + x = self.conv_list[i](x, edge_index) + gnn_features.append(x) + + batch_dict['related_features'] = torch.cat(gnn_features, dim=-1) + + return batch_dict + +if __name__ == '__main__': + rois = torch.tensor([ + [[0, 0, 0], [2, 2, 2], [4, 4, 4]] + ], dtype=torch.float32) + pooled_features = torch.tensor([ + [[1, 2], [2, 3], [3, 4]] + ], dtype=torch.float32) + batch_dict = { + 'rois': rois, # Random positions for 10 batches of 100 proposals each + 'pooled_features': pooled_features # Random 16-dimensional features for 10 batches of 100 proposals each + } + + + model = GNN(2, 4) + print(model(batch_dict)) diff --git a/pcdet/models/roi_heads/__init__.py b/pcdet/models/roi_heads/__init__.py index 693cec426..0dcc717a8 100644 --- a/pcdet/models/roi_heads/__init__.py +++ b/pcdet/models/roi_heads/__init__.py @@ -1,6 +1,7 @@ from .partA2_head import PartA2FCHead from .pointrcnn_head import PointRCNNHead from .pvrcnn_head import PVRCNNHead +from .pvrcnn_head_relation import PVRCNNHeadRelation from .second_head import SECONDHead from .voxelrcnn_head import VoxelRCNNHead from .roi_head_template import RoIHeadTemplate @@ -16,4 +17,5 @@ 'VoxelRCNNHead': VoxelRCNNHead, 'MPPNetHead': MPPNetHead, 'MPPNetHeadE2E': MPPNetHeadE2E, + 'PVRCNNHeadRelation': PVRCNNHeadRelation, } diff --git a/pcdet/models/roi_heads/pvrcnn_head_relation.py b/pcdet/models/roi_heads/pvrcnn_head_relation.py new file mode 100644 index 000000000..1b5faa52d --- /dev/null +++ b/pcdet/models/roi_heads/pvrcnn_head_relation.py @@ -0,0 +1,207 @@ +import torch.nn as nn + +from ...ops.pointnet2.pointnet2_stack import pointnet2_modules as pointnet2_stack_modules +from ...utils import common_utils +from .roi_head_template import RoIHeadTemplate + + +class PVRCNNHeadRelation(RoIHeadTemplate): + def __init__(self, input_channels, model_cfg, num_class=1, **kwargs): + super().__init__(num_class=num_class, model_cfg=model_cfg) + self.model_cfg = model_cfg + + self.roi_grid_pool_layer, num_c_out = pointnet2_stack_modules.build_local_aggregation_module( + input_channels=input_channels, config=self.model_cfg.ROI_GRID_POOL + ) + + GRID_SIZE = self.model_cfg.ROI_GRID_POOL.GRID_SIZE + pre_channel = GRID_SIZE * GRID_SIZE * GRID_SIZE * num_c_out + + shared_fc_list = [] + for k in range(0, self.model_cfg.SHARED_FC.__len__()): + shared_fc_list.extend([ + nn.Conv1d(pre_channel, self.model_cfg.SHARED_FC[k], kernel_size=1, bias=False), + nn.BatchNorm1d(self.model_cfg.SHARED_FC[k]), + nn.ReLU() + ]) + pre_channel = self.model_cfg.SHARED_FC[k] + + if k != self.model_cfg.SHARED_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0: + shared_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO)) + + self.shared_fc_layer = nn.Sequential(*shared_fc_list) + + # TODO update this + self.head_input_channels = self.model_cfg.SHARED_FC[-1] * 5 + 7 + + self.cls_layers = self.make_fc_layers( + input_channels=self.head_input_channels, output_channels=self.num_class, fc_list=self.model_cfg.CLS_FC + ) + self.reg_layers = self.make_fc_layers( + input_channels=self.head_input_channels, + output_channels=self.box_coder.code_size * self.num_class, + fc_list=self.model_cfg.REG_FC + ) + self.init_weights(weight_init='xavier') + + def init_weights(self, weight_init='xavier'): + if weight_init == 'kaiming': + init_func = nn.init.kaiming_normal_ + elif weight_init == 'xavier': + init_func = nn.init.xavier_normal_ + elif weight_init == 'normal': + init_func = nn.init.normal_ + else: + raise NotImplementedError + + for m in self.modules(): + if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d): + if weight_init == 'normal': + init_func(m.weight, mean=0, std=0.001) + else: + init_func(m.weight) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + nn.init.normal_(self.reg_layers[-1].weight, mean=0, std=0.001) + + def roi_grid_pool(self, batch_dict): + """ + Args: + batch_dict: + batch_size: + rois: (B, num_rois, 7 + C) + point_coords: (num_points, 4) [bs_idx, x, y, z] + point_features: (num_points, C) + point_cls_scores: (N1 + N2 + N3 + ..., 1) + point_part_offset: (N1 + N2 + N3 + ..., 3) + Returns: + + """ + batch_size = batch_dict['batch_size'] + rois = batch_dict['rois'] + point_coords = batch_dict['point_coords'] + point_features = batch_dict['point_features'] + + point_features = point_features * batch_dict['point_cls_scores'].view(-1, 1) + + global_roi_grid_points, local_roi_grid_points = self.get_global_grid_points_of_roi( + rois, grid_size=self.model_cfg.ROI_GRID_POOL.GRID_SIZE + ) # (BxN, 6x6x6, 3) + global_roi_grid_points = global_roi_grid_points.view(batch_size, -1, 3) # (B, Nx6x6x6, 3) + + xyz = point_coords[:, 1:4] + xyz_batch_cnt = xyz.new_zeros(batch_size).int() + batch_idx = point_coords[:, 0] + for k in range(batch_size): + xyz_batch_cnt[k] = (batch_idx == k).sum() + + new_xyz = global_roi_grid_points.view(-1, 3) + new_xyz_batch_cnt = xyz.new_zeros(batch_size).int().fill_(global_roi_grid_points.shape[1]) + pooled_points, pooled_features = self.roi_grid_pool_layer( + xyz=xyz.contiguous(), + xyz_batch_cnt=xyz_batch_cnt, + new_xyz=new_xyz, + new_xyz_batch_cnt=new_xyz_batch_cnt, + features=point_features.contiguous(), + ) # (M1 + M2 ..., C) + + pooled_features = pooled_features.view( + -1, self.model_cfg.ROI_GRID_POOL.GRID_SIZE ** 3, + pooled_features.shape[-1] + ) # (BxN, 6x6x6, C) + return pooled_features + + def get_global_grid_points_of_roi(self, rois, grid_size): + rois = rois.view(-1, rois.shape[-1]) + batch_size_rcnn = rois.shape[0] + + local_roi_grid_points = self.get_dense_grid_points(rois, batch_size_rcnn, grid_size) # (B, 6x6x6, 3) + global_roi_grid_points = common_utils.rotate_points_along_z( + local_roi_grid_points.clone(), rois[:, 6] + ).squeeze(dim=1) + global_center = rois[:, 0:3].clone() + global_roi_grid_points += global_center.unsqueeze(dim=1) + return global_roi_grid_points, local_roi_grid_points + + @staticmethod + def get_dense_grid_points(rois, batch_size_rcnn, grid_size): + faked_features = rois.new_ones((grid_size, grid_size, grid_size)) + dense_idx = faked_features.nonzero() # (N, 3) [x_idx, y_idx, z_idx] + dense_idx = dense_idx.repeat(batch_size_rcnn, 1, 1).float() # (B, 6x6x6, 3) + + local_roi_size = rois.view(batch_size_rcnn, -1)[:, 3:6] + roi_grid_points = (dense_idx + 0.5) / grid_size * local_roi_size.unsqueeze(dim=1) \ + - (local_roi_size.unsqueeze(dim=1) / 2) # (B, 6x6x6, 3) + return roi_grid_points + + def forward(self, batch_dict): + """ + :param input_data: input dict + :return: + """ + # TODO fix that num_proposals might night be nms_post_max_size + # this might fail if we have less than nms_post_max_size proposals + num_proposals = self.model_cfg.TARGET_CONFIG.ROI_PER_IMAGE if self.training else self.model_cfg.NMS_CONFIG.TEST.NMS_POST_MAXSIZE + + targets_dict = self.proposal_layer( + batch_dict, nms_config=self.model_cfg.NMS_CONFIG['TRAIN' if self.training else 'TEST'] + ) + if self.training: + targets_dict = batch_dict.get('roi_targets_dict', None) + if targets_dict is None: + targets_dict = self.assign_targets(batch_dict) + batch_dict['rois'] = targets_dict['rois'] + batch_dict['roi_labels'] = targets_dict['roi_labels'] + + # RoI aware pooling + pooled_features = self.roi_grid_pool(batch_dict) # (BxN, 6x6x6, C) + _, num_grid_points, C = pooled_features.shape + + grid_size = self.model_cfg.ROI_GRID_POOL.GRID_SIZE + batch_size_rcnn = pooled_features.shape[0] + pooled_features = pooled_features.permute(0, 2, 1).\ + contiguous().view(batch_size_rcnn, -1, grid_size, grid_size, grid_size) # (BxN, C, 6, 6, 6) + + shared_features = self.shared_fc_layer(pooled_features.view(batch_size_rcnn, -1, 1)) + batch_dict['pooled_features'] = shared_features.view(-1, num_proposals, self.model_cfg.SHARED_FC[-1]) + + self.forward_ret_dict = targets_dict + + + # rcnn_cls = self.cls_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1) # (B, 1 or 2) + # rcnn_reg = self.reg_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1) # (B, C) + + # if not self.training: + # batch_cls_preds, batch_box_preds = self.generate_predicted_boxes( + # batch_size=batch_dict['batch_size'], rois=batch_dict['rois'], cls_preds=rcnn_cls, box_preds=rcnn_reg + # ) + # batch_dict['batch_cls_preds'] = batch_cls_preds + # batch_dict['batch_box_preds'] = batch_box_preds + # batch_dict['cls_preds_normalized'] = False + # else: + # targets_dict['rcnn_cls'] = rcnn_cls + # targets_dict['rcnn_reg'] = rcnn_reg + + # self.forward_ret_dict = targets_dict + + return batch_dict + + def final_predictions(self, batch_dict): + shared_features = batch_dict['related_features'] + shared_features = shared_features.view(-1, self.head_input_channels, 1) + rcnn_cls = self.cls_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1) # (B, 1 or 2) + rcnn_reg = self.reg_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1) # (B, C) + + if not self.training: + batch_cls_preds, batch_box_preds = self.generate_predicted_boxes( + batch_size=batch_dict['batch_size'], rois=batch_dict['rois'], cls_preds=rcnn_cls, box_preds=rcnn_reg + ) + batch_dict['batch_cls_preds'] = batch_cls_preds + batch_dict['batch_box_preds'] = batch_box_preds + batch_dict['cls_preds_normalized'] = False + else: + self.forward_ret_dict['rcnn_cls'] = rcnn_cls + self.forward_ret_dict['rcnn_reg'] = rcnn_reg + + return batch_dict + diff --git a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml new file mode 100644 index 000000000..9d0adaf0f --- /dev/null +++ b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml @@ -0,0 +1,267 @@ +CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist'] + +DATA_CONFIG: + _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml + DATA_AUGMENTOR: + DISABLE_AUG_LIST: ['placeholder'] + AUG_CONFIG_LIST: + - NAME: gt_sampling + USE_ROAD_PLANE: True + DB_INFO_PATH: + - kitti_dbinfos_train.pkl + PREPARE: { + filter_by_min_points: ['Car:5', 'Pedestrian:5', 'Cyclist:5'], + filter_by_difficulty: [-1], + } + + SAMPLE_GROUPS: ['Car:15','Pedestrian:10', 'Cyclist:10'] + NUM_POINT_FEATURES: 4 + DATABASE_WITH_FAKELIDAR: False + REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0] + LIMIT_WHOLE_SCENE: False + + - NAME: random_world_flip + ALONG_AXIS_LIST: ['x'] + + - NAME: random_world_rotation + WORLD_ROT_ANGLE: [-0.78539816, 0.78539816] + + - NAME: random_world_scaling + WORLD_SCALE_RANGE: [0.95, 1.05] + +MODEL: + NAME: PVRCNNRelation + + VFE: + NAME: MeanVFE + + BACKBONE_3D: + NAME: VoxelBackBone8x + + 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: AnchorHeadSingle + CLASS_AGNOSTIC: False + + USE_DIRECTION_CLASSIFIER: True + DIR_OFFSET: 0.78539 + DIR_LIMIT_OFFSET: 0.0 + NUM_DIR_BINS: 2 + + ANCHOR_GENERATOR_CONFIG: [ + { + 'class_name': 'Car', + 'anchor_sizes': [[3.9, 1.6, 1.56]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [-1.78], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.6, + 'unmatched_threshold': 0.45 + }, + { + 'class_name': 'Pedestrian', + 'anchor_sizes': [[0.8, 0.6, 1.73]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [-0.6], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.5, + 'unmatched_threshold': 0.35 + }, + { + 'class_name': 'Cyclist', + 'anchor_sizes': [[1.76, 0.6, 1.73]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [-0.6], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.5, + 'unmatched_threshold': 0.35 + } + ] + + TARGET_ASSIGNER_CONFIG: + NAME: AxisAlignedTargetAssigner + POS_FRACTION: -1.0 + SAMPLE_SIZE: 512 + NORM_BY_NUM_EXAMPLES: False + MATCH_HEIGHT: False + BOX_CODER: ResidualCoder + + LOSS_CONFIG: + LOSS_WEIGHTS: { + 'cls_weight': 1.0, + 'loc_weight': 2.0, + 'dir_weight': 0.2, + 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + + PFE: + NAME: VoxelSetAbstraction + POINT_SOURCE: raw_points + NUM_KEYPOINTS: 2048 + NUM_OUTPUT_FEATURES: 128 + SAMPLE_METHOD: FPS + + FEATURES_SOURCE: ['bev', 'x_conv1', 'x_conv2', 'x_conv3', 'x_conv4', 'raw_points'] + SA_LAYER: + raw_points: + MLPS: [[16, 16], [16, 16]] + POOL_RADIUS: [0.4, 0.8] + NSAMPLE: [16, 16] + x_conv1: + DOWNSAMPLE_FACTOR: 1 + MLPS: [[16, 16], [16, 16]] + POOL_RADIUS: [0.4, 0.8] + NSAMPLE: [16, 16] + x_conv2: + DOWNSAMPLE_FACTOR: 2 + MLPS: [[32, 32], [32, 32]] + POOL_RADIUS: [0.8, 1.2] + NSAMPLE: [16, 32] + x_conv3: + DOWNSAMPLE_FACTOR: 4 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [1.2, 2.4] + NSAMPLE: [16, 32] + x_conv4: + DOWNSAMPLE_FACTOR: 8 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [2.4, 4.8] + NSAMPLE: [16, 32] + + POINT_HEAD: + NAME: PointHeadSimple + CLS_FC: [256, 256] + CLASS_AGNOSTIC: True + USE_POINT_FEATURES_BEFORE_FUSION: True + TARGET_CONFIG: + GT_EXTRA_WIDTH: [0.2, 0.2, 0.2] + LOSS_CONFIG: + LOSS_REG: smooth-l1 + LOSS_WEIGHTS: { + 'point_cls_weight': 1.0, + } + + ROI_HEAD: + NAME: PVRCNNHeadRelation + CLASS_AGNOSTIC: True + + SHARED_FC: [256, 256] + CLS_FC: [256, 256] + REG_FC: [256, 256] + DP_RATIO: 0.3 + + NMS_CONFIG: + TRAIN: + NMS_TYPE: nms_gpu + MULTI_CLASSES_NMS: False + # the proposals are soreted according to scores and then we take the NMS_PRE_MAXSIZE first + NMS_PRE_MAXSIZE: 9000 + # proposals with more than NMS_THRESH overlap with others will be removed + NMS_THRESH: 0.8 + # the proposals after NMS will be limited to NMS_POST_MAXSIZE + NMS_POST_MAXSIZE: 512 + # values are different for testing + TEST: + NMS_TYPE: nms_gpu + MULTI_CLASSES_NMS: False + NMS_PRE_MAXSIZE: 1024 + NMS_POST_MAXSIZE: 100 + NMS_THRESH: 0.7 + + ROI_GRID_POOL: + GRID_SIZE: 6 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [0.8, 1.6] + NSAMPLE: [16, 16] + POOL_METHOD: max_pool + + TARGET_CONFIG: + BOX_CODER: ResidualCoder + # number of proposal after the sampling + ROI_PER_IMAGE: 128 + # max number of foreground proposal + FG_RATIO: 0.5 + + SAMPLE_ROI_BY_EACH_CLASS: True + CLS_SCORE_TYPE: roi_iou + + # thresholds defining the foregrouns, easy background and hard background + CLS_FG_THRESH: 0.75 + CLS_BG_THRESH: 0.25 + CLS_BG_THRESH_LO: 0.1 + # from the background proposals, HARD_BG_RATIO are ones with very small iou with gt (1-HARD_BG_RATIO are with large iou with gt -> still background) + HARD_BG_RATIO: 0.8 + + REG_FG_THRESH: 0.55 + + 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] + } + + OBJECT_RELATION: + NAME: GNN + RADIUS: 4 + LAYERS: [256, 256, 256, 256] + + + POST_PROCESSING: + RECALL_THRESH_LIST: [0.3, 0.5, 0.7] + # Car, Pedestrian, Cyclist + SCORE_THRESH: [0.7, 0.4, 0.3] + # SCORE_THRESH: 0.1 + # SCORE_THRESH: 0.0 + OUTPUT_RAW_SCORE: False + + EVAL_METRIC: kitti + + NMS_CONFIG: + MULTI_CLASSES_NMS: False + NMS_TYPE: nms_gpu + NMS_THRESH: 0.1 + # NMS_THRESH: 1.0 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 + + +OPTIMIZATION: + BATCH_SIZE_PER_GPU: 2 + NUM_EPOCHS: 10 + + 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 From ca33bc722b2f63829ee5967b89549096616355d5 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Sat, 19 Aug 2023 09:21:08 +0000 Subject: [PATCH 05/93] Update object-object relation module and add knn --- pcdet/models/detectors/detector3d_template.py | 12 ++++- pcdet/models/detectors/pv_rcnn_relation.py | 2 +- pcdet/models/object_relation/gnn.py | 51 ++++++++++++++----- .../models/roi_heads/pvrcnn_head_relation.py | 2 +- tools/cfgs/kitti_models/pv_rcnn_relation.yaml | 15 ++++-- 5 files changed, 61 insertions(+), 21 deletions(-) diff --git a/pcdet/models/detectors/detector3d_template.py b/pcdet/models/detectors/detector3d_template.py index 0482fb34b..0293c5e62 100644 --- a/pcdet/models/detectors/detector3d_template.py +++ b/pcdet/models/detectors/detector3d_template.py @@ -196,6 +196,7 @@ def post_processing(self, batch_dict): batch_size = batch_dict['batch_size'] recall_dict = {} pred_dicts = [] + edges_list = [] for index in range(batch_size): if batch_dict.get('batch_index', None) is not None: assert batch_dict['batch_box_preds'].shape.__len__() == 2 @@ -268,6 +269,13 @@ def post_processing(self, batch_dict): final_scores = selected_scores final_labels = label_preds[selected] final_boxes = box_preds[selected] + + if "OBJECT_RELATION" in self.model_cfg: + edges = batch_dict['gnn_edges'] + from_node, to_node = edges + edges_mask = torch.isin(from_node, selected) & torch.isin(to_node, selected) + final_edges = edges[:, edges_mask] + edge_to_pred = {selected[i].item(): i for i in list(range(len(selected)))} recall_dict = self.generate_recall_record( box_preds=final_boxes if 'rois' not in batch_dict else src_box_preds, @@ -278,7 +286,9 @@ def post_processing(self, batch_dict): record_dict = { 'pred_boxes': final_boxes, 'pred_scores': final_scores, - 'pred_labels': final_labels + 'pred_labels': final_labels, + 'gnn_edges_final': final_edges if "OBJECT_RELATION" in self.model_cfg else None, + 'edge_to_pred': edge_to_pred if "OBJECT_RELATION" in self.model_cfg else None, } pred_dicts.append(record_dict) diff --git a/pcdet/models/detectors/pv_rcnn_relation.py b/pcdet/models/detectors/pv_rcnn_relation.py index 7268b7eb8..5196e3130 100644 --- a/pcdet/models/detectors/pv_rcnn_relation.py +++ b/pcdet/models/detectors/pv_rcnn_relation.py @@ -7,7 +7,7 @@ class PVRCNNRelation(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() - self.gnn = GNN(model_cfg.ROI_HEAD.SHARED_FC[-1], model_cfg.OBJECT_RELATION.RADIUS, model_cfg.OBJECT_RELATION.LAYERS) + self.gnn = GNN(model_cfg.OBJECT_RELATION) def forward(self, batch_dict): # MeanVFE: Voxelisation diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py index 10c7b636c..697dfacfa 100644 --- a/pcdet/models/object_relation/gnn.py +++ b/pcdet/models/object_relation/gnn.py @@ -37,24 +37,40 @@ def message(self, x_i, x_j): class GNN(nn.Module): - def __init__(self, dim, radius, layers): + def __init__(self, object_relation_cfg): super(GNN, self).__init__() - self.radius = radius - conv_list = [] - for i in range(len(layers)): + self.graph_cfg = object_relation_cfg.GRAPH + self.gnn_layers = object_relation_cfg.LAYERS + self.mlp_layers = object_relation_cfg.MLP_GLOBAL_LAYERS + + mlp_layer_list = [] + for i in range(len(self.mlp_layers)): + if i == 0: + mlp_layer_list.append(nn.Linear(7, self.mlp_layers[i])) + else: + mlp_layer_list.append(nn.Linear(self.mlp_layers[i-1], self.mlp_layers[i])) + mlp_layer_list.append(nn.ReLU()) + self.mlp = nn.Sequential(*mlp_layer_list) + + self.gnn_input_dim = self.mlp_layers[-1] + 256 + conv_layer_list = [] + for i in range(len(self.gnn_layers)): if i == 0: - conv_list.append(EdgeConv(2*(256+7), layers[i])) + conv_layer_list.append(EdgeConv(2*self.gnn_input_dim, self.gnn_layers[i])) else: - conv_list.append(EdgeConv(2*layers[i-1], layers[i])) - self.conv_list = nn.ModuleList(conv_list) - self.radius_transform = tg.transforms.RadiusGraph(r=self.radius) + conv_layer_list.append(EdgeConv(2*self.gnn_layers[i-1], self.gnn_layers[i])) + self.gnn = nn.ModuleList(conv_layer_list) self.init_weights() + def init_weights(self, weight_init='xavier'): if weight_init == 'xavier': init_func = nn.init.xavier_uniform_ - for m in self.conv_list: + for m in self.gnn: init_func(m.fc.weight) + for m in self.mlp: + if isinstance(m, nn.Linear): + init_func(m.weight) def forward(self, batch_dict): # BxNx7 @@ -63,17 +79,24 @@ def forward(self, batch_dict): pooled_features = batch_dict['pooled_features'] B,N,C = pooled_features.shape - global_pooled_features = torch.cat((pooled_features, proposal_boxes), dim=-1) - global_pooled_features = global_pooled_features.view(-1, C+7) + embedded_global_features = self.mlp(proposal_boxes) + + + global_pooled_features = torch.cat((pooled_features, embedded_global_features), dim=-1) + global_pooled_features = global_pooled_features.view(-1, self.gnn_input_dim) batch_vector = torch.arange(B, device=pooled_features.device).repeat_interleave(N) - edge_index = tg.nn.radius_graph(proposal_boxes[:,:,:3].view(-1, 3), r=self.radius, batch=batch_vector, loop=False) + if self.graph_cfg.NAME == 'radius_graph': + edge_index = tg.nn.radius_graph(proposal_boxes[:,:,:3].view(-1, 3), r=self.graph_cfg.RADIUS, batch=batch_vector, loop=False) + elif self.graph_cfg.NAME == 'knn': + edge_index = tg.nn.knn_graph(proposal_boxes[:,:,:3].view(-1, 3), k=self.graph_cfg.K, batch=batch_vector, loop=False) + batch_dict['gnn_edges'] = edge_index gnn_features = [global_pooled_features] x = global_pooled_features - for i in range(len(self.conv_list)): - x = self.conv_list[i](x, edge_index) + for i in range(len(self.gnn)): + x = self.gnn[i](x, edge_index) gnn_features.append(x) batch_dict['related_features'] = torch.cat(gnn_features, dim=-1) diff --git a/pcdet/models/roi_heads/pvrcnn_head_relation.py b/pcdet/models/roi_heads/pvrcnn_head_relation.py index 1b5faa52d..856fb7201 100644 --- a/pcdet/models/roi_heads/pvrcnn_head_relation.py +++ b/pcdet/models/roi_heads/pvrcnn_head_relation.py @@ -32,7 +32,7 @@ def __init__(self, input_channels, model_cfg, num_class=1, **kwargs): self.shared_fc_layer = nn.Sequential(*shared_fc_list) # TODO update this - self.head_input_channels = self.model_cfg.SHARED_FC[-1] * 5 + 7 + self.head_input_channels = self.model_cfg.SHARED_FC[-1] * 5 + 256 self.cls_layers = self.make_fc_layers( input_channels=self.head_input_channels, output_channels=self.num_class, fc_list=self.model_cfg.CLS_FC diff --git a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml index 9d0adaf0f..5dc44c924 100644 --- a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml +++ b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml @@ -222,15 +222,22 @@ MODEL: OBJECT_RELATION: NAME: GNN - RADIUS: 4 + # GRAPH: { + # NAME: radius_graph, + # RADIUS: 6 + # } + GRAPH: { + NAME: knn, + K: 16, + } LAYERS: [256, 256, 256, 256] + MLP_GLOBAL_LAYERS: [256, 256] POST_PROCESSING: RECALL_THRESH_LIST: [0.3, 0.5, 0.7] # Car, Pedestrian, Cyclist - SCORE_THRESH: [0.7, 0.4, 0.3] - # SCORE_THRESH: 0.1 + SCORE_THRESH: 0.1 # SCORE_THRESH: 0.0 OUTPUT_RAW_SCORE: False @@ -247,7 +254,7 @@ MODEL: OPTIMIZATION: BATCH_SIZE_PER_GPU: 2 - NUM_EPOCHS: 10 + NUM_EPOCHS: 100 OPTIMIZER: adam_onecycle LR: 0.01 From 343894a2829769037c93b77b131193f1308d7c5d Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Tue, 22 Aug 2023 13:21:42 +0000 Subject: [PATCH 06/93] Update GNN to be able to not include global info --- pcdet/models/object_relation/gnn.py | 43 ++++++++++--------- .../models/roi_heads/pvrcnn_head_relation.py | 2 +- tools/cfgs/kitti_models/pv_rcnn_relation.yaml | 19 ++++---- 3 files changed, 35 insertions(+), 29 deletions(-) diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py index 697dfacfa..a2e025e66 100644 --- a/pcdet/models/object_relation/gnn.py +++ b/pcdet/models/object_relation/gnn.py @@ -41,18 +41,20 @@ def __init__(self, object_relation_cfg): super(GNN, self).__init__() self.graph_cfg = object_relation_cfg.GRAPH self.gnn_layers = object_relation_cfg.LAYERS - self.mlp_layers = object_relation_cfg.MLP_GLOBAL_LAYERS - - mlp_layer_list = [] - for i in range(len(self.mlp_layers)): - if i == 0: - mlp_layer_list.append(nn.Linear(7, self.mlp_layers[i])) - else: - mlp_layer_list.append(nn.Linear(self.mlp_layers[i-1], self.mlp_layers[i])) - mlp_layer_list.append(nn.ReLU()) - self.mlp = nn.Sequential(*mlp_layer_list) + self.global_information = object_relation_cfg.GOBAL_INFORMATOIN if 'GLOBAL_INFORMATION' in object_relation_cfg else None + + if self.global_information: + self.global_mlp = self.global_information.MLP_LAYERS + mlp_layer_list = [] + for i in range(len(self.global_mlp)): + if i == 0: + mlp_layer_list.append(nn.Linear(7, self.global_mlp[i])) + else: + mlp_layer_list.append(nn.Linear(self.global_mlp[i-1], self.global_mlp[i])) + mlp_layer_list.append(nn.ReLU()) + self.global_info_mlp = nn.Sequential(*mlp_layer_list) - self.gnn_input_dim = self.mlp_layers[-1] + 256 + self.gnn_input_dim = (self.global_mlp[-1] if self.global_information else 0) + 256 conv_layer_list = [] for i in range(len(self.gnn_layers)): if i == 0: @@ -68,9 +70,10 @@ def init_weights(self, weight_init='xavier'): init_func = nn.init.xavier_uniform_ for m in self.gnn: init_func(m.fc.weight) - for m in self.mlp: - if isinstance(m, nn.Linear): - init_func(m.weight) + if self.global_information: + for m in self.global_info_mlp: + if isinstance(m, nn.Linear): + init_func(m.weight) def forward(self, batch_dict): # BxNx7 @@ -79,11 +82,11 @@ def forward(self, batch_dict): pooled_features = batch_dict['pooled_features'] B,N,C = pooled_features.shape - embedded_global_features = self.mlp(proposal_boxes) + if self.global_information: + embedded_global_features = self.global_info_mlp(proposal_boxes) + pooled_features = torch.cat((pooled_features, embedded_global_features), dim=-1) - - global_pooled_features = torch.cat((pooled_features, embedded_global_features), dim=-1) - global_pooled_features = global_pooled_features.view(-1, self.gnn_input_dim) + pooled_features = pooled_features.view(-1, self.gnn_input_dim) batch_vector = torch.arange(B, device=pooled_features.device).repeat_interleave(N) if self.graph_cfg.NAME == 'radius_graph': @@ -93,8 +96,8 @@ def forward(self, batch_dict): batch_dict['gnn_edges'] = edge_index - gnn_features = [global_pooled_features] - x = global_pooled_features + gnn_features = [pooled_features] + x = pooled_features for i in range(len(self.gnn)): x = self.gnn[i](x, edge_index) gnn_features.append(x) diff --git a/pcdet/models/roi_heads/pvrcnn_head_relation.py b/pcdet/models/roi_heads/pvrcnn_head_relation.py index 856fb7201..fe326cfcd 100644 --- a/pcdet/models/roi_heads/pvrcnn_head_relation.py +++ b/pcdet/models/roi_heads/pvrcnn_head_relation.py @@ -32,7 +32,7 @@ def __init__(self, input_channels, model_cfg, num_class=1, **kwargs): self.shared_fc_layer = nn.Sequential(*shared_fc_list) # TODO update this - self.head_input_channels = self.model_cfg.SHARED_FC[-1] * 5 + 256 + self.head_input_channels = self.model_cfg.SHARED_FC[-1] * 5 + 0 self.cls_layers = self.make_fc_layers( input_channels=self.head_input_channels, output_channels=self.num_class, fc_list=self.model_cfg.CLS_FC diff --git a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml index 5dc44c924..6f2745a51 100644 --- a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml +++ b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml @@ -222,16 +222,19 @@ MODEL: OBJECT_RELATION: NAME: GNN - # GRAPH: { - # NAME: radius_graph, - # RADIUS: 6 - # } GRAPH: { - NAME: knn, - K: 16, + NAME: radius_graph, + RADIUS: 6 } + # GRAPH: { + # NAME: knn, + # K: 16, + # } LAYERS: [256, 256, 256, 256] - MLP_GLOBAL_LAYERS: [256, 256] + # GLOBAL_INFORMATION: { + # MLP_LAYERS: [256, 256] + # } + # MLP_GLOBAL_LAYERS: [256, 256 POST_PROCESSING: @@ -253,7 +256,7 @@ MODEL: OPTIMIZATION: - BATCH_SIZE_PER_GPU: 2 + BATCH_SIZE_PER_GPU: 6 NUM_EPOCHS: 100 OPTIMIZER: adam_onecycle From e22c028a75332a5325fa873e19372770dfe53665 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Tue, 22 Aug 2023 13:23:05 +0000 Subject: [PATCH 07/93] Add logging of model config to train and test --- tools/process_tools/logger.py | 8 ++++++++ tools/test.py | 5 ++++- tools/train.py | 4 +++- 3 files changed, 15 insertions(+), 2 deletions(-) create mode 100644 tools/process_tools/logger.py diff --git a/tools/process_tools/logger.py b/tools/process_tools/logger.py new file mode 100644 index 000000000..c699844f3 --- /dev/null +++ b/tools/process_tools/logger.py @@ -0,0 +1,8 @@ +import json +from pathlib import Path + +class CustomEncoder(json.JSONEncoder): + def default(self, obj): + if isinstance(obj, Path): + return str(obj) + return super().default(obj) \ No newline at end of file diff --git a/tools/test.py b/tools/test.py index 51b7178c6..d6f3e6475 100644 --- a/tools/test.py +++ b/tools/test.py @@ -6,6 +6,7 @@ import re import time from pathlib import Path +import json import numpy as np import torch @@ -16,7 +17,7 @@ from pcdet.datasets import build_dataloader from pcdet.models import build_network from pcdet.utils import common_utils - +from tools.process_tools.logger import CustomEncoder def parse_config(): parser = argparse.ArgumentParser(description='arg parser') @@ -95,6 +96,8 @@ def repeat_eval_ckpt(model, test_loader, args, eval_output_dir, logger, ckpt_dir # tensorboard log if cfg.LOCAL_RANK == 0: tb_log = SummaryWriter(log_dir=str(eval_output_dir / ('tensorboard_%s' % cfg.DATA_CONFIG.DATA_SPLIT['test']))) + tb_log.add_text('Configurations', json.dumps(cfg, cls=CustomEncoder, indent=2)) + total_time = 0 first_eval = True diff --git a/tools/train.py b/tools/train.py index 29a88bde9..8f50e25a7 100644 --- a/tools/train.py +++ b/tools/train.py @@ -5,6 +5,7 @@ import os from pathlib import Path from test import repeat_eval_ckpt +import json import torch import torch.nn as nn @@ -16,7 +17,7 @@ from pcdet.utils import common_utils from train_utils.optimization import build_optimizer, build_scheduler from train_utils.train_utils import train_model - +from tools.process_tools.logger import CustomEncoder def parse_config(): parser = argparse.ArgumentParser(description='arg parser') @@ -112,6 +113,7 @@ def main(): os.system('cp %s %s' % (args.cfg_file, output_dir)) tb_log = SummaryWriter(log_dir=str(output_dir / 'tensorboard')) if cfg.LOCAL_RANK == 0 else None + tb_log.add_text('Configurations', json.dumps(cfg, cls=CustomEncoder, indent=2)) logger.info("----------- Create dataloader & network & optimizer -----------") train_set, train_loader, train_sampler = build_dataloader( From 150cbe6b1c9c9021eb7fe3ed4d965b8a1128c8dd Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Wed, 23 Aug 2023 14:30:56 +0000 Subject: [PATCH 08/93] Update epochs and batch size for pv_rcnn_relation --- tools/cfgs/kitti_models/pv_rcnn_relation.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml index 6f2745a51..46c09f089 100644 --- a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml +++ b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml @@ -256,8 +256,8 @@ MODEL: OPTIMIZATION: - BATCH_SIZE_PER_GPU: 6 - NUM_EPOCHS: 100 + BATCH_SIZE_PER_GPU: 2 + NUM_EPOCHS: 80 OPTIMIZER: adam_onecycle LR: 0.01 From 3dcffa7142844aab5c680dcd74dfbd9bf5ba0374 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Thu, 24 Aug 2023 08:43:02 +0000 Subject: [PATCH 09/93] Update --- pcdet/datasets/__init__.py | 4 ++-- pcdet/models/object_relation/gnn.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pcdet/datasets/__init__.py b/pcdet/datasets/__init__.py index 47c3900bf..7bcd7ab17 100644 --- a/pcdet/datasets/__init__.py +++ b/pcdet/datasets/__init__.py @@ -12,7 +12,7 @@ from .pandaset.pandaset_dataset import PandasetDataset from .lyft.lyft_dataset import LyftDataset from .once.once_dataset import ONCEDataset -from .argo2.argo2_dataset import Argo2Dataset +# from .argo2.argo2_dataset import Argo2Dataset from .custom.custom_dataset import CustomDataset __all__ = { @@ -24,7 +24,7 @@ 'LyftDataset': LyftDataset, 'ONCEDataset': ONCEDataset, 'CustomDataset': CustomDataset, - 'Argo2Dataset': Argo2Dataset + # 'Argo2Dataset': Argo2Dataset } diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py index a2e025e66..d4dfbde7a 100644 --- a/pcdet/models/object_relation/gnn.py +++ b/pcdet/models/object_relation/gnn.py @@ -2,7 +2,7 @@ import torch.nn as nn import torch_geometric as tg import torch.nn.functional as F -import torch_scatter +# import torch_scatter # custom implementation for EdgeConv # class EdgeWeightingNetwork(nn.Module): From 4debe5188c83465e48588c3407627ddb72c690bc Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Thu, 24 Aug 2023 13:19:47 +0000 Subject: [PATCH 10/93] Minor fixes --- pcdet/models/object_relation/gnn.py | 2 +- pcdet/models/roi_heads/pvrcnn_head_relation.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py index a2e025e66..78874d0f4 100644 --- a/pcdet/models/object_relation/gnn.py +++ b/pcdet/models/object_relation/gnn.py @@ -41,7 +41,7 @@ def __init__(self, object_relation_cfg): super(GNN, self).__init__() self.graph_cfg = object_relation_cfg.GRAPH self.gnn_layers = object_relation_cfg.LAYERS - self.global_information = object_relation_cfg.GOBAL_INFORMATOIN if 'GLOBAL_INFORMATION' in object_relation_cfg else None + self.global_information = object_relation_cfg.GLOBAL_INFORMATION if 'GLOBAL_INFORMATION' in object_relation_cfg else None if self.global_information: self.global_mlp = self.global_information.MLP_LAYERS diff --git a/pcdet/models/roi_heads/pvrcnn_head_relation.py b/pcdet/models/roi_heads/pvrcnn_head_relation.py index fe326cfcd..856fb7201 100644 --- a/pcdet/models/roi_heads/pvrcnn_head_relation.py +++ b/pcdet/models/roi_heads/pvrcnn_head_relation.py @@ -32,7 +32,7 @@ def __init__(self, input_channels, model_cfg, num_class=1, **kwargs): self.shared_fc_layer = nn.Sequential(*shared_fc_list) # TODO update this - self.head_input_channels = self.model_cfg.SHARED_FC[-1] * 5 + 0 + self.head_input_channels = self.model_cfg.SHARED_FC[-1] * 5 + 256 self.cls_layers = self.make_fc_layers( input_channels=self.head_input_channels, output_channels=self.num_class, fc_list=self.model_cfg.CLS_FC From bc0f9dadf19ee1a0f248144f7d89bff0e20badec Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Fri, 25 Aug 2023 09:31:49 +0000 Subject: [PATCH 11/93] Add scripts to start training and evaluation --- tools/scripts/eval_with_tensorboard.sh | 8 ++++++++ tools/scripts/train_with_tensorboard.sh | 18 ++++++++++++++++++ 2 files changed, 26 insertions(+) create mode 100755 tools/scripts/eval_with_tensorboard.sh create mode 100755 tools/scripts/train_with_tensorboard.sh diff --git a/tools/scripts/eval_with_tensorboard.sh b/tools/scripts/eval_with_tensorboard.sh new file mode 100755 index 000000000..07e03d090 --- /dev/null +++ b/tools/scripts/eval_with_tensorboard.sh @@ -0,0 +1,8 @@ +date='put date here' +architecture='pv_rcnn_relation' + +tmux send-keys -t 0 "python test.py --cfg ./cfgs/kitti_models/$architecture.yaml --eval_all --extra_tag $date" C-m + +sleep 2 + +tmux send-keys -t 1 "(cd output/cfgs/kitti_models/$architecture/$date/eval/eval_all_default/default; tensorboard dev upload --logdir tensorboard_val --name \"${architecture//-/ } KITTI Evaluation $date\")" C-m \ No newline at end of file diff --git a/tools/scripts/train_with_tensorboard.sh b/tools/scripts/train_with_tensorboard.sh new file mode 100755 index 000000000..4d435921a --- /dev/null +++ b/tools/scripts/train_with_tensorboard.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +# Getting the current date +date=$(date "+%Y-%m-%d_%H-%M-%S") + +# Defining the architecture variable +architecture="pv_rcnn_relation" + +# Sending the training command to tmux session 0 +tmux send-keys -t 0 "python train.py --cfg_file ./cfgs/kitti_models/$architecture.yaml --extra_tag $date" C-m + +# Giving the command time to start +sleep 2 + +# Navigating to the output directory and starting tensorboard in tmux session 1 +# Note: the architecture name is converted to uppercase with "${architecture^^}" +tmux send-keys -t 1 "(cd output/cfgs/kitti_models/$architecture/default/; tensorboard dev upload --logdir tensorboard --name \"${architecture^^} KITTI $date\")" C-m + From 1147f418dd82eaf2b7f53b5c96467bd4b1546487 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Fri, 25 Aug 2023 10:08:15 +0000 Subject: [PATCH 12/93] Add class specific graph option --- pcdet/models/object_relation/gnn.py | 7 ++++++- tools/cfgs/kitti_models/pv_rcnn_relation.yaml | 9 +++++---- 2 files changed, 11 insertions(+), 5 deletions(-) diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py index 992725b22..530951f8d 100644 --- a/pcdet/models/object_relation/gnn.py +++ b/pcdet/models/object_relation/gnn.py @@ -37,11 +37,12 @@ def message(self, x_i, x_j): class GNN(nn.Module): - def __init__(self, object_relation_cfg): + def __init__(self, object_relation_cfg, number_classes=3): super(GNN, self).__init__() self.graph_cfg = object_relation_cfg.GRAPH self.gnn_layers = object_relation_cfg.LAYERS self.global_information = object_relation_cfg.GLOBAL_INFORMATION if 'GLOBAL_INFORMATION' in object_relation_cfg else None + self.number_classes = number_classes if self.global_information: self.global_mlp = self.global_information.MLP_LAYERS @@ -89,6 +90,10 @@ def forward(self, batch_dict): pooled_features = pooled_features.view(-1, self.gnn_input_dim) batch_vector = torch.arange(B, device=pooled_features.device).repeat_interleave(N) + + if self.graph_cfg.CONNECT_ONLY_SAME_CLASS: + batch_vector = (batch_dict['roi_labels'] + torch.arange(0, B*self.number_classes, self.number_classes, device=batch_dict['roi_labels'].device).view(B, 1)).view(-1) + if self.graph_cfg.NAME == 'radius_graph': edge_index = tg.nn.radius_graph(proposal_boxes[:,:,:3].view(-1, 3), r=self.graph_cfg.RADIUS, batch=batch_vector, loop=False) elif self.graph_cfg.NAME == 'knn': diff --git a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml index 46c09f089..5bd3eca0d 100644 --- a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml +++ b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml @@ -224,16 +224,17 @@ MODEL: NAME: GNN GRAPH: { NAME: radius_graph, - RADIUS: 6 + RADIUS: 6, + CONNECT_ONLY_SAME_CLASS: False } # GRAPH: { # NAME: knn, # K: 16, # } LAYERS: [256, 256, 256, 256] - # GLOBAL_INFORMATION: { - # MLP_LAYERS: [256, 256] - # } + GLOBAL_INFORMATION: { + MLP_LAYERS: [256, 256] + } # MLP_GLOBAL_LAYERS: [256, 256 From c337cbc645358e4dd599b7ae3720bf41f0f478f6 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Fri, 25 Aug 2023 10:11:56 +0000 Subject: [PATCH 13/93] Minor update for same class graph --- pcdet/models/object_relation/gnn.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py index 530951f8d..853309fd2 100644 --- a/pcdet/models/object_relation/gnn.py +++ b/pcdet/models/object_relation/gnn.py @@ -79,8 +79,11 @@ def init_weights(self, weight_init='xavier'): def forward(self, batch_dict): # BxNx7 proposal_boxes = batch_dict['rois'] + # BxN + proposal_labels = batch_dict['roi_labels'] # BxNxC pooled_features = batch_dict['pooled_features'] + B,N,C = pooled_features.shape if self.global_information: @@ -92,7 +95,7 @@ def forward(self, batch_dict): batch_vector = torch.arange(B, device=pooled_features.device).repeat_interleave(N) if self.graph_cfg.CONNECT_ONLY_SAME_CLASS: - batch_vector = (batch_dict['roi_labels'] + torch.arange(0, B*self.number_classes, self.number_classes, device=batch_dict['roi_labels'].device).view(B, 1)).view(-1) + batch_vector = (proposal_labels + torch.arange(0, B*self.number_classes, self.number_classes, device=proposal_labels.device).view(B, 1)).view(-1) if self.graph_cfg.NAME == 'radius_graph': edge_index = tg.nn.radius_graph(proposal_boxes[:,:,:3].view(-1, 3), r=self.graph_cfg.RADIUS, batch=batch_vector, loop=False) From 5eeb86547f01274ce3ddb88480166aa60337ceff Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Fri, 25 Aug 2023 10:38:00 +0000 Subject: [PATCH 14/93] Update training scripts --- tools/scripts/eval_with_tensorboard.sh | 9 ++++----- tools/scripts/train_with_tensorboard.sh | 7 ++----- 2 files changed, 6 insertions(+), 10 deletions(-) diff --git a/tools/scripts/eval_with_tensorboard.sh b/tools/scripts/eval_with_tensorboard.sh index 07e03d090..1aa63ad94 100755 --- a/tools/scripts/eval_with_tensorboard.sh +++ b/tools/scripts/eval_with_tensorboard.sh @@ -1,8 +1,7 @@ -date='put date here' +date='2023-08-24_13-25-38' architecture='pv_rcnn_relation' -tmux send-keys -t 0 "python test.py --cfg ./cfgs/kitti_models/$architecture.yaml --eval_all --extra_tag $date" C-m +tmux send-keys -t 0 "(cd tools/; python test.py --cfg ./cfgs/kitti_models/$architecture.yaml --eval_all --extra_tag $date)" C-m -sleep 2 - -tmux send-keys -t 1 "(cd output/cfgs/kitti_models/$architecture/$date/eval/eval_all_default/default; tensorboard dev upload --logdir tensorboard_val --name \"${architecture//-/ } KITTI Evaluation $date\")" C-m \ No newline at end of file +tmux new-window -n tensorboard +tmux send-keys -t 1 "(cd output/cfgs/kitti_models/$architecture/$date/eval/eval_all_default/default; tensorboard dev upload --logdir tensorboard_val --name \"${architecture//-/ } KITTI Evaluation $date\")" C-m diff --git a/tools/scripts/train_with_tensorboard.sh b/tools/scripts/train_with_tensorboard.sh index 4d435921a..46686990c 100755 --- a/tools/scripts/train_with_tensorboard.sh +++ b/tools/scripts/train_with_tensorboard.sh @@ -7,12 +7,9 @@ date=$(date "+%Y-%m-%d_%H-%M-%S") architecture="pv_rcnn_relation" # Sending the training command to tmux session 0 -tmux send-keys -t 0 "python train.py --cfg_file ./cfgs/kitti_models/$architecture.yaml --extra_tag $date" C-m +tmux send-keys -t 0 "(cd tools/; python train.py --cfg_file ./cfgs/kitti_models/$architecture.yaml --extra_tag $date)" C-m -# Giving the command time to start -sleep 2 -# Navigating to the output directory and starting tensorboard in tmux session 1 -# Note: the architecture name is converted to uppercase with "${architecture^^}" +tmux new-window -n tensorboard tmux send-keys -t 1 "(cd output/cfgs/kitti_models/$architecture/default/; tensorboard dev upload --logdir tensorboard --name \"${architecture^^} KITTI $date\")" C-m From 3e75d1d0156e45f752cbabef54d6fe4345157bb7 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Sat, 26 Aug 2023 09:36:21 +0000 Subject: [PATCH 15/93] Update evaluation and training scripts --- tools/scripts/eval_with_tensorboard.sh | 15 +++++++++++---- tools/scripts/train_with_tensorboard.sh | 12 +++++------- 2 files changed, 16 insertions(+), 11 deletions(-) diff --git a/tools/scripts/eval_with_tensorboard.sh b/tools/scripts/eval_with_tensorboard.sh index 1aa63ad94..110a149db 100755 --- a/tools/scripts/eval_with_tensorboard.sh +++ b/tools/scripts/eval_with_tensorboard.sh @@ -1,7 +1,14 @@ -date='2023-08-24_13-25-38' +#!/bin/bash +date='2023-08-25_13-03-43' architecture='pv_rcnn_relation' -tmux send-keys -t 0 "(cd tools/; python test.py --cfg ./cfgs/kitti_models/$architecture.yaml --eval_all --extra_tag $date)" C-m +# Create the first tmux session and run a command +tmux new-session -d -s Session1 +tmux send-keys -t Session1 "(cd tools/; python test.py --cfg ./cfgs/kitti_models/$architecture.yaml --eval_all --extra_tag $date)" C-m + +sleep 300 + +# Create the second tmux session and run another command +tmux new-session -d -s Session2 +tmux send-keys -t Session2 "(cd output/cfgs/kitti_models/$architecture/$date/eval/eval_all_default/default; tensorboard dev upload --logdir tensorboard_val --name \"${architecture//-/ } KITTI Evaluation $date\")" C-m -tmux new-window -n tensorboard -tmux send-keys -t 1 "(cd output/cfgs/kitti_models/$architecture/$date/eval/eval_all_default/default; tensorboard dev upload --logdir tensorboard_val --name \"${architecture//-/ } KITTI Evaluation $date\")" C-m diff --git a/tools/scripts/train_with_tensorboard.sh b/tools/scripts/train_with_tensorboard.sh index 46686990c..6bb8c85b1 100755 --- a/tools/scripts/train_with_tensorboard.sh +++ b/tools/scripts/train_with_tensorboard.sh @@ -1,15 +1,13 @@ #!/bin/bash - -# Getting the current date date=$(date "+%Y-%m-%d_%H-%M-%S") - -# Defining the architecture variable architecture="pv_rcnn_relation" # Sending the training command to tmux session 0 -tmux send-keys -t 0 "(cd tools/; python train.py --cfg_file ./cfgs/kitti_models/$architecture.yaml --extra_tag $date)" C-m +tmux new-session -d -s Session1 +tmux send-keys -t Session1 "(cd tools/; python train.py --cfg_file ./cfgs/kitti_models/$architecture.yaml --extra_tag $date)" C-m +sleep 300 -tmux new-window -n tensorboard -tmux send-keys -t 1 "(cd output/cfgs/kitti_models/$architecture/default/; tensorboard dev upload --logdir tensorboard --name \"${architecture^^} KITTI $date\")" C-m +tmux new-session -d -s Session2 +tmux send-keys -t Session2 "(cd output/cfgs/kitti_models/$architecture/default/; tensorboard dev upload --logdir tensorboard --name \"${architecture^^} KITTI $date\")" C-m From fbb4327af886170e94bea628f0d2870ec5486730 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Sat, 26 Aug 2023 09:41:39 +0000 Subject: [PATCH 16/93] Update training scripts --- tools/scripts/eval_with_tensorboard.sh | 2 ++ tools/scripts/train_with_tensorboard.sh | 2 ++ 2 files changed, 4 insertions(+) diff --git a/tools/scripts/eval_with_tensorboard.sh b/tools/scripts/eval_with_tensorboard.sh index 110a149db..71aec9e68 100755 --- a/tools/scripts/eval_with_tensorboard.sh +++ b/tools/scripts/eval_with_tensorboard.sh @@ -2,6 +2,8 @@ date='2023-08-25_13-03-43' architecture='pv_rcnn_relation' +tmux kill-server + # Create the first tmux session and run a command tmux new-session -d -s Session1 tmux send-keys -t Session1 "(cd tools/; python test.py --cfg ./cfgs/kitti_models/$architecture.yaml --eval_all --extra_tag $date)" C-m diff --git a/tools/scripts/train_with_tensorboard.sh b/tools/scripts/train_with_tensorboard.sh index 6bb8c85b1..34eb2b900 100755 --- a/tools/scripts/train_with_tensorboard.sh +++ b/tools/scripts/train_with_tensorboard.sh @@ -2,6 +2,8 @@ date=$(date "+%Y-%m-%d_%H-%M-%S") architecture="pv_rcnn_relation" +tmux kill-server + # Sending the training command to tmux session 0 tmux new-session -d -s Session1 tmux send-keys -t Session1 "(cd tools/; python train.py --cfg_file ./cfgs/kitti_models/$architecture.yaml --extra_tag $date)" C-m From 059d4c539b723f4c7abba0bf4c16557ffd25f40b Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Sat, 26 Aug 2023 10:19:40 +0000 Subject: [PATCH 17/93] Update train and eval script --- tools/scripts/eval_with_tensorboard.sh | 4 +--- tools/scripts/train_with_tensorboard.sh | 2 -- 2 files changed, 1 insertion(+), 5 deletions(-) diff --git a/tools/scripts/eval_with_tensorboard.sh b/tools/scripts/eval_with_tensorboard.sh index 71aec9e68..9caf47709 100755 --- a/tools/scripts/eval_with_tensorboard.sh +++ b/tools/scripts/eval_with_tensorboard.sh @@ -1,9 +1,7 @@ #!/bin/bash -date='2023-08-25_13-03-43' +date='2023-08-25_13-47-22' architecture='pv_rcnn_relation' -tmux kill-server - # Create the first tmux session and run a command tmux new-session -d -s Session1 tmux send-keys -t Session1 "(cd tools/; python test.py --cfg ./cfgs/kitti_models/$architecture.yaml --eval_all --extra_tag $date)" C-m diff --git a/tools/scripts/train_with_tensorboard.sh b/tools/scripts/train_with_tensorboard.sh index 34eb2b900..6bb8c85b1 100755 --- a/tools/scripts/train_with_tensorboard.sh +++ b/tools/scripts/train_with_tensorboard.sh @@ -2,8 +2,6 @@ date=$(date "+%Y-%m-%d_%H-%M-%S") architecture="pv_rcnn_relation" -tmux kill-server - # Sending the training command to tmux session 0 tmux new-session -d -s Session1 tmux send-keys -t Session1 "(cd tools/; python train.py --cfg_file ./cfgs/kitti_models/$architecture.yaml --extra_tag $date)" C-m From 34e02740fb802b157cc2be5b81d4d0bd8c898ee4 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Sat, 26 Aug 2023 18:59:58 +0000 Subject: [PATCH 18/93] Update eval and train script --- tools/scripts/eval_with_tensorboard.sh | 2 +- tools/scripts/train_with_tensorboard.sh | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/tools/scripts/eval_with_tensorboard.sh b/tools/scripts/eval_with_tensorboard.sh index 71aec9e68..743fe6bc9 100755 --- a/tools/scripts/eval_with_tensorboard.sh +++ b/tools/scripts/eval_with_tensorboard.sh @@ -2,7 +2,7 @@ date='2023-08-25_13-03-43' architecture='pv_rcnn_relation' -tmux kill-server +# tmux kill-server # Create the first tmux session and run a command tmux new-session -d -s Session1 diff --git a/tools/scripts/train_with_tensorboard.sh b/tools/scripts/train_with_tensorboard.sh index 34eb2b900..fd62d863e 100755 --- a/tools/scripts/train_with_tensorboard.sh +++ b/tools/scripts/train_with_tensorboard.sh @@ -2,7 +2,7 @@ date=$(date "+%Y-%m-%d_%H-%M-%S") architecture="pv_rcnn_relation" -tmux kill-server +# tmux kill-server # Sending the training command to tmux session 0 tmux new-session -d -s Session1 @@ -11,5 +11,5 @@ tmux send-keys -t Session1 "(cd tools/; python train.py --cfg_file ./cfgs/kitti_ sleep 300 tmux new-session -d -s Session2 -tmux send-keys -t Session2 "(cd output/cfgs/kitti_models/$architecture/default/; tensorboard dev upload --logdir tensorboard --name \"${architecture^^} KITTI $date\")" C-m +tmux send-keys -t Session2 "(cd output/cfgs/kitti_models/$architecture/$date/; tensorboard dev upload --logdir tensorboard --name \"${architecture^^} KITTI $date\")" C-m From ae283c205273c416b3508ac66490756b7388f325 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Sat, 26 Aug 2023 19:06:56 +0000 Subject: [PATCH 19/93] Fix same class only edges --- pcdet/models/object_relation/gnn.py | 74 +++++++++++++++++++++-------- 1 file changed, 54 insertions(+), 20 deletions(-) diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py index 853309fd2..f8b601ae2 100644 --- a/pcdet/models/object_relation/gnn.py +++ b/pcdet/models/object_relation/gnn.py @@ -83,8 +83,7 @@ def forward(self, batch_dict): proposal_labels = batch_dict['roi_labels'] # BxNxC pooled_features = batch_dict['pooled_features'] - - B,N,C = pooled_features.shape + (B, N, C) = pooled_features.shape if self.global_information: embedded_global_features = self.global_info_mlp(proposal_boxes) @@ -92,16 +91,7 @@ def forward(self, batch_dict): pooled_features = pooled_features.view(-1, self.gnn_input_dim) - batch_vector = torch.arange(B, device=pooled_features.device).repeat_interleave(N) - - if self.graph_cfg.CONNECT_ONLY_SAME_CLASS: - batch_vector = (proposal_labels + torch.arange(0, B*self.number_classes, self.number_classes, device=proposal_labels.device).view(B, 1)).view(-1) - - if self.graph_cfg.NAME == 'radius_graph': - edge_index = tg.nn.radius_graph(proposal_boxes[:,:,:3].view(-1, 3), r=self.graph_cfg.RADIUS, batch=batch_vector, loop=False) - elif self.graph_cfg.NAME == 'knn': - edge_index = tg.nn.knn_graph(proposal_boxes[:,:,:3].view(-1, 3), k=self.graph_cfg.K, batch=batch_vector, loop=False) - + edge_index = self.get_edges(proposal_boxes[:,:,:3].view(-1,3), proposal_labels.view(-1), (B, N, C)) batch_dict['gnn_edges'] = edge_index gnn_features = [pooled_features] @@ -114,18 +104,62 @@ def forward(self, batch_dict): return batch_dict + def get_edges(self, proposal_boxes, proposal_labels, shape): + B, N, _ = shape + f = getattr(tg.nn, self.graph_cfg.NAME) + a = (self.graph_cfg.RADIUS if self.graph_cfg.NAME == 'radius_graph' else self.graph_cfg.K) + batch_vector = torch.arange(B, device=proposal_boxes.device).repeat_interleave(N) + + if self.graph_cfg.CONNECT_ONLY_SAME_CLASS: + final_edge_indices = [] + for predicted_class in range(1, self.number_classes + 1): + label_indices = torch.where(proposal_labels == predicted_class)[0] + label_proposal_boxes = proposal_boxes[label_indices] + label_batch_vector = batch_vector[label_indices] + + label_edge_index = f(label_proposal_boxes, a, batch=label_batch_vector, loop=False) + label_edge_index[0] = label_indices[label_edge_index[0]] + label_edge_index[1] = label_indices[label_edge_index[1]] + final_edge_indices.append(label_edge_index) + edge_index = torch.cat(final_edge_indices, dim=-1) + else: + edge_index = f(proposal_boxes, a, batch=batch_vector, loop=False) + return edge_index + + + if __name__ == '__main__': + from easydict import EasyDict as edict rois = torch.tensor([ - [[0, 0, 0], [2, 2, 2], [4, 4, 4]] - ], dtype=torch.float32) - pooled_features = torch.tensor([ - [[1, 2], [2, 3], [3, 4]] + [[0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 2, 2, 2, 2], [3, 3, 3, 3, 3, 3, 3]] ], dtype=torch.float32) + # 1x3x512 + pooled_features = torch.rand((1, 3, 256), dtype=torch.float32) + proposal_labels = torch.tensor([ + [0, 0, 1] + ], dtype=torch.int64) + batch_dict = { 'rois': rois, # Random positions for 10 batches of 100 proposals each - 'pooled_features': pooled_features # Random 16-dimensional features for 10 batches of 100 proposals each + 'pooled_features': pooled_features, # Random 16-dimensional features for 10 batches of 100 proposals each + 'roi_labels': proposal_labels # Random labels for 10 batches of 100 proposals each } - - model = GNN(2, 4) - print(model(batch_dict)) + cfg = edict({ + 'GRAPH': { + 'NAME': 'radius_graph', + 'RADIUS': 3, + 'CONNECT_ONLY_SAME_CLASS': True + }, + 'LAYERS': [256, 256, 256], + 'GLOBAL_INFORMATION': { + 'MLP_LAYERS': [256, 256, 256] + } + }) + + model = GNN(cfg) + + batch_dict = model(batch_dict) + edges = batch_dict['gnn_edges'] + assert edges.shape[0] == 2 + assert edges.shape[1] == 6 From 2bc3b824d6860a0c154f7a0098ba7af99a2ca7d6 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Sun, 27 Aug 2023 08:00:52 +0000 Subject: [PATCH 20/93] Update evaluation script --- tools/scripts/eval_with_tensorboard.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/scripts/eval_with_tensorboard.sh b/tools/scripts/eval_with_tensorboard.sh index 743fe6bc9..45ab3bcde 100755 --- a/tools/scripts/eval_with_tensorboard.sh +++ b/tools/scripts/eval_with_tensorboard.sh @@ -6,7 +6,7 @@ architecture='pv_rcnn_relation' # Create the first tmux session and run a command tmux new-session -d -s Session1 -tmux send-keys -t Session1 "(cd tools/; python test.py --cfg ./cfgs/kitti_models/$architecture.yaml --eval_all --extra_tag $date)" C-m +tmux send-keys -t Session1 "(cd tools/; python test.py --cfg ./cfgs/kitti_models/$architecture/$date/$architecture.yaml --eval_all --extra_tag $date)" C-m sleep 300 From 4ee66839279ef4665b454df02c24ca5ceb863f6e Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Sun, 27 Aug 2023 11:53:12 +0000 Subject: [PATCH 21/93] Reorder global mlp and dropout and bn --- pcdet/models/object_relation/gnn.py | 28 +++++++++++-------- .../models/roi_heads/pvrcnn_head_relation.py | 2 +- tools/cfgs/kitti_models/pv_rcnn_relation.yaml | 15 ++++++---- 3 files changed, 27 insertions(+), 18 deletions(-) diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py index f8b601ae2..596b8bfbb 100644 --- a/pcdet/models/object_relation/gnn.py +++ b/pcdet/models/object_relation/gnn.py @@ -43,19 +43,25 @@ def __init__(self, object_relation_cfg, number_classes=3): self.gnn_layers = object_relation_cfg.LAYERS self.global_information = object_relation_cfg.GLOBAL_INFORMATION if 'GLOBAL_INFORMATION' in object_relation_cfg else None self.number_classes = number_classes + self.drop_out = object_relation_cfg.DP_RATIO if self.global_information: self.global_mlp = self.global_information.MLP_LAYERS mlp_layer_list = [] for i in range(len(self.global_mlp)): if i == 0: - mlp_layer_list.append(nn.Linear(7, self.global_mlp[i])) + mlp_layer_list.append(nn.Linear(256 + 7, self.global_mlp[i])) else: mlp_layer_list.append(nn.Linear(self.global_mlp[i-1], self.global_mlp[i])) + + mlp_layer_list.append(nn.BatchNorm1d(self.global_mlp[i])) mlp_layer_list.append(nn.ReLU()) + if self.drop_out > 0: + mlp_layer_list.append(nn.Dropout(self.drop_out)) + self.global_info_mlp = nn.Sequential(*mlp_layer_list) - self.gnn_input_dim = (self.global_mlp[-1] if self.global_information else 0) + 256 + self.gnn_input_dim = 256 conv_layer_list = [] for i in range(len(self.gnn_layers)): if i == 0: @@ -77,21 +83,21 @@ def init_weights(self, weight_init='xavier'): init_func(m.weight) def forward(self, batch_dict): + (B, N, C) = batch_dict['pooled_features'].shape + # BxNx7 - proposal_boxes = batch_dict['rois'] + proposal_boxes = batch_dict['rois'].view(B*N,7) # BxN - proposal_labels = batch_dict['roi_labels'] + proposal_labels = batch_dict['roi_labels'].view(B*N) # BxNxC - pooled_features = batch_dict['pooled_features'] - (B, N, C) = pooled_features.shape + pooled_features = batch_dict['pooled_features'].view(B*N,C) + if self.global_information: - embedded_global_features = self.global_info_mlp(proposal_boxes) - pooled_features = torch.cat((pooled_features, embedded_global_features), dim=-1) - - pooled_features = pooled_features.view(-1, self.gnn_input_dim) + pooled_features_with_global_info = torch.cat([pooled_features, proposal_boxes], dim=1) + pooled_features = self.global_info_mlp(pooled_features_with_global_info) - edge_index = self.get_edges(proposal_boxes[:,:,:3].view(-1,3), proposal_labels.view(-1), (B, N, C)) + edge_index = self.get_edges(proposal_boxes[:,:3], proposal_labels, (B, N, C)) batch_dict['gnn_edges'] = edge_index gnn_features = [pooled_features] diff --git a/pcdet/models/roi_heads/pvrcnn_head_relation.py b/pcdet/models/roi_heads/pvrcnn_head_relation.py index 856fb7201..487624c25 100644 --- a/pcdet/models/roi_heads/pvrcnn_head_relation.py +++ b/pcdet/models/roi_heads/pvrcnn_head_relation.py @@ -32,7 +32,7 @@ def __init__(self, input_channels, model_cfg, num_class=1, **kwargs): self.shared_fc_layer = nn.Sequential(*shared_fc_list) # TODO update this - self.head_input_channels = self.model_cfg.SHARED_FC[-1] * 5 + 256 + self.head_input_channels = self.model_cfg.SHARED_FC[-1] * 5 self.cls_layers = self.make_fc_layers( input_channels=self.head_input_channels, output_channels=self.num_class, fc_list=self.model_cfg.CLS_FC diff --git a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml index 5bd3eca0d..d3f29c7b2 100644 --- a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml +++ b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml @@ -222,23 +222,26 @@ MODEL: OBJECT_RELATION: NAME: GNN + # GRAPH: { + # NAME: radius_graph, + # RADIUS: 6, + # CONNECT_ONLY_SAME_CLASS: False + # } GRAPH: { - NAME: radius_graph, - RADIUS: 6, + NAME: knn_graph, + K: 16, CONNECT_ONLY_SAME_CLASS: False } - # GRAPH: { - # NAME: knn, - # K: 16, - # } LAYERS: [256, 256, 256, 256] GLOBAL_INFORMATION: { MLP_LAYERS: [256, 256] } + DP_RATIO: 0.3 # MLP_GLOBAL_LAYERS: [256, 256 POST_PROCESSING: + # IoU Threshold RECALL_THRESH_LIST: [0.3, 0.5, 0.7] # Car, Pedestrian, Cyclist SCORE_THRESH: 0.1 From 820426d1f5a1dc843d7f70b20acdb25b729d8c12 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Mon, 28 Aug 2023 09:36:35 +0000 Subject: [PATCH 22/93] git update eval script --- tools/scripts/eval_with_tensorboard.sh | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/tools/scripts/eval_with_tensorboard.sh b/tools/scripts/eval_with_tensorboard.sh index 45ab3bcde..7d459010b 100755 --- a/tools/scripts/eval_with_tensorboard.sh +++ b/tools/scripts/eval_with_tensorboard.sh @@ -1,16 +1,15 @@ #!/bin/bash -date='2023-08-25_13-03-43' +date='2023-08-27_11-56-11' architecture='pv_rcnn_relation' # tmux kill-server # Create the first tmux session and run a command tmux new-session -d -s Session1 -tmux send-keys -t Session1 "(cd tools/; python test.py --cfg ./cfgs/kitti_models/$architecture/$date/$architecture.yaml --eval_all --extra_tag $date)" C-m +tmux send-keys -t Session1 "(cd tools/; python test.py --cfg ./cfgs/kitti_models/$architecture.yaml --eval_all --extra_tag $date)" C-m sleep 300 # Create the second tmux session and run another command tmux new-session -d -s Session2 -tmux send-keys -t Session2 "(cd output/cfgs/kitti_models/$architecture/$date/eval/eval_all_default/default; tensorboard dev upload --logdir tensorboard_val --name \"${architecture//-/ } KITTI Evaluation $date\")" C-m - +tmux send-keys -t Session2 "(cd output/cfgs/kitti_models/$architecture/$date/eval/eval_all_default/default; tensorboard dev upload --logdir tensorboard_val --name "${architecture//-/ }_KITTI_Evaluation_$date")" C-m From cf2bb56679f2ae468ddf1162ad96f8352bc2ae0d Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Tue, 29 Aug 2023 07:23:17 +0000 Subject: [PATCH 23/93] Add KITTI demo --- tools/kitti_demo.py | 78 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 78 insertions(+) create mode 100644 tools/kitti_demo.py diff --git a/tools/kitti_demo.py b/tools/kitti_demo.py new file mode 100644 index 000000000..0d04926a7 --- /dev/null +++ b/tools/kitti_demo.py @@ -0,0 +1,78 @@ +import os +import torch +import json + +from pcdet.models import build_network, load_data_to_gpu +from pathlib import Path +from pcdet.config import cfg, cfg_from_yaml_file +from pcdet.utils import common_utils +from pcdet.datasets.kitti.kitti_dataset import KittiDataset + +# import mayavi.mlab as mlab +# from visual_utils import visualize_utils as V + +NUMBER_OF_SCENES = 500 + +def main(cfg_path, model_path, save_3d=False, tag=None): + cfg_from_yaml_file(cfg_path, cfg) + logger = common_utils.create_logger() + logger.info('-----------------Creating data for visualization-------------------------') + kitti_dataset = KittiDataset( + dataset_cfg=cfg.DATA_CONFIG, class_names=cfg.CLASS_NAMES, training=False, root_path=Path(cfg.DATA_CONFIG.DATA_PATH), + ) + + logger.info(f'Total number of samples: \t{NUMBER_OF_SCENES}') + + model = build_network(model_cfg=cfg.MODEL, num_class=len(cfg.CLASS_NAMES), dataset=kitti_dataset) + model.load_params_from_file(filename=model_path, logger=logger, to_cpu=True) + model.cuda() + model.eval() + + # create folder for visualization + vis_path = '/'.join(os.path.normpath(model_path).split(os.path.sep)[:-2]) + '/visualization' + tag + os.makedirs(vis_path, exist_ok=True) + + with torch.no_grad(): + for idx, data_dict in enumerate(kitti_dataset): + if idx >= NUMBER_OF_SCENES: + break + logger.info(f'Visualized sample index: \t{idx + 1}') + data_dict = kitti_dataset.collate_batch([data_dict]) + load_data_to_gpu(data_dict) + pred_dicts, _ = model.forward(data_dict) + + if save_3d: + torch.save(data_dict['points'][:,1:], os.path.join(vis_path, 'points_{}.pt'.format(int(data_dict['frame_id'])))) + torch.save(pred_dicts[0]['pred_boxes'], os.path.join(vis_path, 'pred_boxes_{}.pt'.format(int(data_dict['frame_id'])))) + torch.save(pred_dicts[0]['pred_scores'], os.path.join(vis_path, 'pred_scores_{}.pt'.format(int(data_dict['frame_id'])))) + torch.save(pred_dicts[0]['pred_labels'], os.path.join(vis_path, 'pred_labels_{}.pt'.format(int(data_dict['frame_id'])))) + torch.save(data_dict['gt_boxes'], os.path.join(vis_path, 'gt_boxes_{}.pt'.format(int(data_dict['frame_id'])))) + if 'gnn_edges_final' in pred_dicts[0]: + torch.save(pred_dicts[0]['gnn_edges_final'],os.path.join(vis_path, 'gnn_edges{}.pt'.format(int(data_dict['frame_id'])))) + json.dump(pred_dicts[0]['edge_to_pred'] , open(os.path.join(vis_path, 'edge_to_predict{}.json'.format(int(data_dict['frame_id']))), 'w')) + + else: + # fig = V.draw_scenes( + # points=data_dict['points'][:, 1:], ref_boxes=pred_dicts[0]['pred_boxes'], + # ref_scores=pred_dicts[0]['pred_scores'], ref_labels=pred_dicts[0]['pred_labels'] + # ) + # mlab.savefig(os.path.join(vis_path, 'points_{}.pt'.format(int(data_dict['frame_id'])))) + pass + + logger.info('Demo done.') + +if __name__ == '__main__': + model_path = '../output/cfgs/kitti_models/pv_rcnn_relation/2023-08-25_13-47-22' + full_model_path = model_path + '/ckpt/checkpoint_epoch_73.pth' + # cfg_path = model_path + '/pv_rcnn_relation.yaml' + cfg_path = '../tools/cfgs/kitti_models/pv_rcnn_relation.yaml' + # /pv_rcnn_relation.yaml + tag = '/epoch_73/' + # tag = '/no_post_processing/' + # tag = '/no_post_processing-94_epoch/' + # tag = '/epoch_100_no_post_processing/' + + # model_path = '../output/cfgs/kitti_models/pv_rcnn/2023-08-01_20-06-45/ckpt/checkpoint_epoch_90.pth' + # model_path = '../output/cfgs/kitti_models/pv_rcnn/debug/ckpt/checkpoint_epoch_2.pth' + # model_path = '../output/kitti/pv_rcnn_8369.pth' + main(cfg_path, full_model_path, save_3d=True, tag=tag) \ No newline at end of file From 6c4a52cf4d74db056d646b1348e80f04b995057b Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Tue, 29 Aug 2023 14:14:01 +0000 Subject: [PATCH 24/93] Update gnn structure --- pcdet/models/detectors/detector3d_template.py | 1 + pcdet/models/object_relation/gnn.py | 2 +- pcdet/models/object_relation/gnn_old.py | 169 ++++++++++++++++++ .../models/roi_heads/pvrcnn_head_relation.py | 7 +- 4 files changed, 176 insertions(+), 3 deletions(-) create mode 100644 pcdet/models/object_relation/gnn_old.py diff --git a/pcdet/models/detectors/detector3d_template.py b/pcdet/models/detectors/detector3d_template.py index 0293c5e62..3b4aba9db 100644 --- a/pcdet/models/detectors/detector3d_template.py +++ b/pcdet/models/detectors/detector3d_template.py @@ -167,6 +167,7 @@ def build_roi_head(self, model_info_dict): point_cloud_range=model_info_dict['point_cloud_range'], voxel_size=model_info_dict['voxel_size'], num_class=self.num_class if not self.model_cfg.ROI_HEAD.CLASS_AGNOSTIC else 1, + object_relation_config=self.model_cfg.OBJECT_RELATION, ) model_info_dict['module_list'].append(point_head_module) diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py index 596b8bfbb..291d0b16d 100644 --- a/pcdet/models/object_relation/gnn.py +++ b/pcdet/models/object_relation/gnn.py @@ -56,7 +56,7 @@ def __init__(self, object_relation_cfg, number_classes=3): mlp_layer_list.append(nn.BatchNorm1d(self.global_mlp[i])) mlp_layer_list.append(nn.ReLU()) - if self.drop_out > 0: + if self.drop_out: mlp_layer_list.append(nn.Dropout(self.drop_out)) self.global_info_mlp = nn.Sequential(*mlp_layer_list) diff --git a/pcdet/models/object_relation/gnn_old.py b/pcdet/models/object_relation/gnn_old.py new file mode 100644 index 000000000..0a12ef666 --- /dev/null +++ b/pcdet/models/object_relation/gnn_old.py @@ -0,0 +1,169 @@ +import torch +import torch.nn as nn +import torch_geometric as tg +import torch.nn.functional as F +# import torch_scatter + +# custom implementation for EdgeConv +# class EdgeWeightingNetwork(nn.Module): +# def __init__(self, dimension): +# super(EdgeWeightingNetwork, self).__init__() +# self.fc = nn.Linear(dimension*2, dimension) + +# def forward(self, x, edge_index): +# from_node, to_node = edge_index +# x_i, x_j = x[from_node], x[to_node] +# e_ij = torch.cat((x_j - x_i, x_i), dim=-1) +# e_ij = self.fc(e_ij) +# e_ij = F.relu(e_ij) +# # why from node here????? +# out, _ = torch_scatter.scatter_max(e_ij, from_node, dim=0) +# return out + +# similar to EdgeConv https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.conv.EdgeConv.html +class EdgeConv(tg.nn.MessagePassing): + def __init__(self, in_dim, out_dim): + super(EdgeConv, self).__init__(aggr='max') + self.fc = nn.Linear(in_dim, out_dim) + + def forward(self, x, edge_index): + return self.propagate(edge_index, x=x) + + def message(self, x_i, x_j): + x = torch.cat((x_j - x_i, x_i), dim=-1) + x = self.fc(x) + x = F.relu(x) + return x + + +class GNN(nn.Module): + def __init__(self, object_relation_cfg, number_classes=3): + super(GNN, self).__init__() + self.graph_cfg = object_relation_cfg.GRAPH + self.gnn_layers = object_relation_cfg.LAYERS + self.global_information = object_relation_cfg.GLOBAL_INFORMATION if 'GLOBAL_INFORMATION' in object_relation_cfg else None + self.number_classes = number_classes + + if self.global_information: + self.global_mlp = self.global_information.MLP_LAYERS + mlp_layer_list = [] + for i in range(len(self.global_mlp)): + if i == 0: + mlp_layer_list.append(nn.Linear(7, self.global_mlp[i])) + else: + mlp_layer_list.append(nn.Linear(self.global_mlp[i-1], self.global_mlp[i])) + + mlp_layer_list.append(nn.ReLU()) + self.global_info_mlp = nn.Sequential(*mlp_layer_list) + + self.gnn_input_dim = (self.global_mlp[-1] if self.global_information else 0) + 256 + conv_layer_list = [] + for i in range(len(self.gnn_layers)): + if i == 0: + conv_layer_list.append(EdgeConv(2*self.gnn_input_dim, self.gnn_layers[i])) + else: + conv_layer_list.append(EdgeConv(2*self.gnn_layers[i-1], self.gnn_layers[i])) + self.gnn = nn.ModuleList(conv_layer_list) + self.init_weights() + + + def init_weights(self, weight_init='xavier'): + if weight_init == 'xavier': + init_func = nn.init.xavier_uniform_ + for m in self.gnn: + init_func(m.fc.weight) + if self.global_information: + for m in self.global_info_mlp: + if isinstance(m, nn.Linear): + init_func(m.weight) + + def forward(self, batch_dict): + (B, N, C) = batch_dict['pooled_features'].shape + + # BxNx7 + proposal_boxes = batch_dict['rois'] + # BxN + proposal_labels = batch_dict['roi_labels'] + # BxNxC + pooled_features = batch_dict['pooled_features'] + (B, N, C) = pooled_features.shape + + + if self.global_information: + embedded_global_features = self.global_info_mlp(proposal_boxes) + pooled_features = torch.cat((pooled_features, embedded_global_features), dim=-1) + + pooled_features = pooled_features.view(-1, self.gnn_input_dim) + + edge_index = self.get_edges(proposal_boxes[:,:,:3].view(-1,3), proposal_labels.view(-1), (B, N, C)) + batch_dict['gnn_edges'] = edge_index + + gnn_features = [pooled_features] + x = pooled_features + for i in range(len(self.gnn)): + x = self.gnn[i](x, edge_index) + gnn_features.append(x) + + batch_dict['related_features'] = torch.cat(gnn_features, dim=-1) + + return batch_dict + + def get_edges(self, proposal_boxes, proposal_labels, shape): + B, N, _ = shape + f = getattr(tg.nn, self.graph_cfg.NAME) + a = (self.graph_cfg.RADIUS if self.graph_cfg.NAME == 'radius_graph' else self.graph_cfg.K) + batch_vector = torch.arange(B, device=proposal_boxes.device).repeat_interleave(N) + + if self.graph_cfg.CONNECT_ONLY_SAME_CLASS: + final_edge_indices = [] + for predicted_class in range(1, self.number_classes + 1): + label_indices = torch.where(proposal_labels == predicted_class)[0] + label_proposal_boxes = proposal_boxes[label_indices] + label_batch_vector = batch_vector[label_indices] + + label_edge_index = f(label_proposal_boxes, a, batch=label_batch_vector, loop=False) + label_edge_index[0] = label_indices[label_edge_index[0]] + label_edge_index[1] = label_indices[label_edge_index[1]] + final_edge_indices.append(label_edge_index) + edge_index = torch.cat(final_edge_indices, dim=-1) + else: + edge_index = f(proposal_boxes, a, batch=batch_vector, loop=False) + return edge_index + + + +if __name__ == '__main__': + from easydict import EasyDict as edict + rois = torch.tensor([ + [[0, 0, 0, 0, 0, 0, 0], [2, 2, 2, 2, 2, 2, 2], [3, 3, 3, 3, 3, 3, 3]] + ], dtype=torch.float32) + # 1x3x512 + pooled_features = torch.rand((1, 3, 256), dtype=torch.float32) + proposal_labels = torch.tensor([ + [0, 0, 1] + ], dtype=torch.int64) + + batch_dict = { + 'rois': rois, # Random positions for 10 batches of 100 proposals each + 'pooled_features': pooled_features, # Random 16-dimensional features for 10 batches of 100 proposals each + 'roi_labels': proposal_labels # Random labels for 10 batches of 100 proposals each + } + + cfg = edict({ + 'GRAPH': { + 'NAME': 'radius_graph', + 'RADIUS': 3, + 'CONNECT_ONLY_SAME_CLASS': True + }, + 'LAYERS': [256, 256, 256], + 'GLOBAL_INFORMATION': { + 'MLP_LAYERS': [256, 256, 256] + } + }) + + model = GNN(cfg) + + batch_dict = model(batch_dict) + edges = batch_dict['gnn_edges'] + assert edges.shape[0] == 2 + assert edges.shape[1] == 6 diff --git a/pcdet/models/roi_heads/pvrcnn_head_relation.py b/pcdet/models/roi_heads/pvrcnn_head_relation.py index 487624c25..32599011b 100644 --- a/pcdet/models/roi_heads/pvrcnn_head_relation.py +++ b/pcdet/models/roi_heads/pvrcnn_head_relation.py @@ -6,7 +6,7 @@ class PVRCNNHeadRelation(RoIHeadTemplate): - def __init__(self, input_channels, model_cfg, num_class=1, **kwargs): + def __init__(self, input_channels, model_cfg, num_class=1, object_relation_config=None, **kwargs): super().__init__(num_class=num_class, model_cfg=model_cfg) self.model_cfg = model_cfg @@ -31,8 +31,11 @@ def __init__(self, input_channels, model_cfg, num_class=1, **kwargs): self.shared_fc_layer = nn.Sequential(*shared_fc_list) + # Input dim to heads depend on object relation + global_information_output_dim = 0 if not object_relation_config.GLOBAL_INFORMATION else object_relation_config.GLOBAL_INFORMATION.MLP_LAYERS[-1] + self.head_input_channels = global_information_output_dim + self.model_cfg.SHARED_FC[-1] + sum(object_relation_config.LAYERS) # TODO update this - self.head_input_channels = self.model_cfg.SHARED_FC[-1] * 5 + self.head_input_channels = self.model_cfg.SHARED_FC[-1]*5 self.cls_layers = self.make_fc_layers( input_channels=self.head_input_channels, output_channels=self.num_class, fc_list=self.model_cfg.CLS_FC From 3ccdd7ef4e6a468580a861eee82e8dcdcfea8b4c Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Tue, 29 Aug 2023 15:31:03 +0000 Subject: [PATCH 25/93] Updage gnn with edge embeddings --- pcdet/models/object_relation/gnn.py | 17 ++++++++++++----- tools/cfgs/kitti_models/pv_rcnn_relation.yaml | 3 ++- 2 files changed, 14 insertions(+), 6 deletions(-) diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py index 291d0b16d..6226b0cb9 100644 --- a/pcdet/models/object_relation/gnn.py +++ b/pcdet/models/object_relation/gnn.py @@ -26,11 +26,14 @@ def __init__(self, in_dim, out_dim): super(EdgeConv, self).__init__(aggr='max') self.fc = nn.Linear(in_dim, out_dim) - def forward(self, x, edge_index): - return self.propagate(edge_index, x=x) + def forward(self, x, edge_index, edge_attr=None): + return self.propagate(edge_index, x=x, edge_attr=edge_attr) - def message(self, x_i, x_j): - x = torch.cat((x_j - x_i, x_i), dim=-1) + def message(self, x_i, x_j, edge_attr=None): + if edge_attr is not None: + x = torch.cat((x_j - x_i, x_i, edge_attr), dim=-1) + else: + x = torch.cat((x_j - x_i, x_i), dim=-1) x = self.fc(x) x = F.relu(x) return x @@ -65,7 +68,8 @@ def __init__(self, object_relation_cfg, number_classes=3): conv_layer_list = [] for i in range(len(self.gnn_layers)): if i == 0: - conv_layer_list.append(EdgeConv(2*self.gnn_input_dim, self.gnn_layers[i])) + input_dim = 2*self.gnn_input_dim + (7 if self.graph_cfg.EDGE_EMBEDDING else 0) + conv_layer_list.append(EdgeConv(input_dim, self.gnn_layers[i])) else: conv_layer_list.append(EdgeConv(2*self.gnn_layers[i-1], self.gnn_layers[i])) self.gnn = nn.ModuleList(conv_layer_list) @@ -100,6 +104,9 @@ def forward(self, batch_dict): edge_index = self.get_edges(proposal_boxes[:,:3], proposal_labels, (B, N, C)) batch_dict['gnn_edges'] = edge_index + if self.graph_cfg.EDGE_EMBEDDING: + edge_embedding = None + gnn_features = [pooled_features] x = pooled_features for i in range(len(self.gnn)): diff --git a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml index d3f29c7b2..c08732cd5 100644 --- a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml +++ b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml @@ -230,7 +230,8 @@ MODEL: GRAPH: { NAME: knn_graph, K: 16, - CONNECT_ONLY_SAME_CLASS: False + CONNECT_ONLY_SAME_CLASS: False, + EDGE_EMBEDDING: True } LAYERS: [256, 256, 256, 256] GLOBAL_INFORMATION: { From 266684b90e4d13da4a33499a0539d5b9914f6f04 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Wed, 30 Aug 2023 06:49:38 +0000 Subject: [PATCH 26/93] Udpate GNN to vary position of global info mlp --- pcdet/models/object_relation/gnn.py | 25 ++++++++++++------- .../models/roi_heads/pvrcnn_head_relation.py | 15 ++++++----- tools/cfgs/kitti_models/pv_rcnn_relation.yaml | 5 ++-- 3 files changed, 28 insertions(+), 17 deletions(-) diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py index 6226b0cb9..ac814c775 100644 --- a/pcdet/models/object_relation/gnn.py +++ b/pcdet/models/object_relation/gnn.py @@ -40,20 +40,22 @@ def message(self, x_i, x_j, edge_attr=None): class GNN(nn.Module): - def __init__(self, object_relation_cfg, number_classes=3): + def __init__(self, object_relation_cfg, number_classes=3, pooled_feature_dim=256): super(GNN, self).__init__() self.graph_cfg = object_relation_cfg.GRAPH self.gnn_layers = object_relation_cfg.LAYERS self.global_information = object_relation_cfg.GLOBAL_INFORMATION if 'GLOBAL_INFORMATION' in object_relation_cfg else None self.number_classes = number_classes self.drop_out = object_relation_cfg.DP_RATIO + self.pooled_feature_dim = pooled_feature_dim if self.global_information: self.global_mlp = self.global_information.MLP_LAYERS mlp_layer_list = [] + global_mlp_input_dim = pooled_feature_dim + 7 if self.global_information.CONCATENATED else 7 for i in range(len(self.global_mlp)): if i == 0: - mlp_layer_list.append(nn.Linear(256 + 7, self.global_mlp[i])) + mlp_layer_list.append(nn.Linear(global_mlp_input_dim, self.global_mlp[i])) else: mlp_layer_list.append(nn.Linear(self.global_mlp[i-1], self.global_mlp[i])) @@ -64,12 +66,11 @@ def __init__(self, object_relation_cfg, number_classes=3): self.global_info_mlp = nn.Sequential(*mlp_layer_list) - self.gnn_input_dim = 256 + gnn_input_dim = self.global_mlp[-1] if self.global_information.CONCATENATED else (self.global_mlp[-1] + self.pooled_feature_dim) conv_layer_list = [] for i in range(len(self.gnn_layers)): if i == 0: - input_dim = 2*self.gnn_input_dim + (7 if self.graph_cfg.EDGE_EMBEDDING else 0) - conv_layer_list.append(EdgeConv(input_dim, self.gnn_layers[i])) + conv_layer_list.append(EdgeConv(2*gnn_input_dim + (7 if self.graph_cfg.EDGE_EMBEDDING else 0), self.gnn_layers[i])) else: conv_layer_list.append(EdgeConv(2*self.gnn_layers[i-1], self.gnn_layers[i])) self.gnn = nn.ModuleList(conv_layer_list) @@ -98,19 +99,25 @@ def forward(self, batch_dict): if self.global_information: - pooled_features_with_global_info = torch.cat([pooled_features, proposal_boxes], dim=1) - pooled_features = self.global_info_mlp(pooled_features_with_global_info) + if self.global_information.CONCATENATED: + pooled_features_with_global_info = torch.cat([pooled_features, proposal_boxes], dim=1) + pooled_features = self.global_info_mlp(pooled_features_with_global_info) + else: + embedded_global_information = self.global_info_mlp(proposal_boxes) + pooled_features = torch.cat([pooled_features, embedded_global_information], dim=1) edge_index = self.get_edges(proposal_boxes[:,:3], proposal_labels, (B, N, C)) batch_dict['gnn_edges'] = edge_index + edge_attr = None if self.graph_cfg.EDGE_EMBEDDING: - edge_embedding = None + from_node, to_node = edge_index + edge_attr = proposal_boxes[from_node] - proposal_boxes[to_node] gnn_features = [pooled_features] x = pooled_features for i in range(len(self.gnn)): - x = self.gnn[i](x, edge_index) + x = self.gnn[i](x, edge_index, edge_attr=edge_attr) gnn_features.append(x) batch_dict['related_features'] = torch.cat(gnn_features, dim=-1) diff --git a/pcdet/models/roi_heads/pvrcnn_head_relation.py b/pcdet/models/roi_heads/pvrcnn_head_relation.py index 32599011b..c651ea4aa 100644 --- a/pcdet/models/roi_heads/pvrcnn_head_relation.py +++ b/pcdet/models/roi_heads/pvrcnn_head_relation.py @@ -30,12 +30,15 @@ def __init__(self, input_channels, model_cfg, num_class=1, object_relation_confi shared_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO)) self.shared_fc_layer = nn.Sequential(*shared_fc_list) - - # Input dim to heads depend on object relation - global_information_output_dim = 0 if not object_relation_config.GLOBAL_INFORMATION else object_relation_config.GLOBAL_INFORMATION.MLP_LAYERS[-1] - self.head_input_channels = global_information_output_dim + self.model_cfg.SHARED_FC[-1] + sum(object_relation_config.LAYERS) - # TODO update this - self.head_input_channels = self.model_cfg.SHARED_FC[-1]*5 + + if object_relation_config.GLOBAL_INFORMATION: + initial_input_dim = object_relation_config.GLOBAL_INFORMATION.MLP_LAYERS[-1] + if not object_relation_config.GLOBAL_INFORMATION.CONCATENATED: + initial_input_dim += self.model_cfg.SHARED_FC[-1] + else: + initial_input_dim = self.model_cfg.SHARED_FC[-1] + + self.head_input_channels = initial_input_dim + sum(object_relation_config.LAYERS) self.cls_layers = self.make_fc_layers( input_channels=self.head_input_channels, output_channels=self.num_class, fc_list=self.model_cfg.CLS_FC diff --git a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml index c08732cd5..1b0441352 100644 --- a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml +++ b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml @@ -231,11 +231,12 @@ MODEL: NAME: knn_graph, K: 16, CONNECT_ONLY_SAME_CLASS: False, - EDGE_EMBEDDING: True + EDGE_EMBEDDING: False } LAYERS: [256, 256, 256, 256] GLOBAL_INFORMATION: { - MLP_LAYERS: [256, 256] + MLP_LAYERS: [256, 256], + CONCATENATED: False, } DP_RATIO: 0.3 # MLP_GLOBAL_LAYERS: [256, 256 From 236dcf1b6255904e926bc0ef0bc80e5eb5bc58b3 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Fri, 1 Sep 2023 09:56:14 +0000 Subject: [PATCH 27/93] Add launch.json file --- .vscode/launch.json | 109 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 109 insertions(+) create mode 100644 .vscode/launch.json diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 000000000..4df9d13dc --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,109 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python: Run File", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": true, + // "preLaunchTask": "Change working directory", + "cwd": "/root/OpenPCDet/tools/" + }, + { + "name": "Python: Train KITTI PV_RCNN", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": true, + "args": ["--cfg_file", "./cfgs/kitti_models/pv_rcnn.yaml", "--extra_tag", "debug"], + // "preLaunchTask": "Change working directory", + "cwd": "/root/OpenPCDet/tools/", + // "env": { + // "CUDA_VISIBLE_DEVICES": "1" + + // }, + }, + { + "name": "Python: Train KITTI PV_RCNN_RELATION", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": true, + "args": ["--cfg_file", "./cfgs/kitti_models/pv_rcnn_relation.yaml", "--extra_tag", "debug"], + // "preLaunchTask": "Change working directory", + "cwd": "/root/OpenPCDet/tools/", + "env": { + "CUDA_VISIBLE_DEVICES": "1" + + }, + }, + { + "name": "Python: Train KITTI PV_RCNN++", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": true, + "args": ["--cfg_file", "./cfgs/kitti_models/pv_rcnn_plusplus.yaml", "--extra_tag", "debug"], + "env": { + "CUDA_VISIBLE_DEVICES": "1" + + }, + // "preLaunchTask": "Change working directory", + "cwd": "/root/OpenPCDet/tools/" + }, + { + "name": "Python: Visualize KITTI PV_RCNN", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": true, + // "env": { + // "CUDA_VISIBLE_DEVICES": "1" + + // }, + // "args": ["--cfg_file", "./cfgs/kitti_models/pv_rcnn.yaml", "--ckpt", "../output/kitti/pv_rcnn_8369.pth", "--data_path", "../data/kitti/testing/velodyne/000008.bin"], + // "preLaunchTask": "Change working directory", + "cwd": "/root/OpenPCDet/tools/" + }, + { + "name": "Python: Evaluate KITTI PV_RCNN", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "args": ["--cfg_file", "./cfgs/kitti_models/pv_rcnn.yaml", "--ckpt", "../output/cfgs/kitti_models/pv_rcnn/2023-08-01_20-06-45/ckpt/checkpoint_epoch_90.pth", "--batch_size", "6"], + "cwd": "/root/OpenPCDet/tools/", + "env": { + "CUDA_VISIBLE_DEVICES": "1" + + }, + }, + { + "name": "Python: Evaluate KITTI PV_RCNN++ ALL", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "args": ["--cfg_file", "./cfgs/kitti_models/pv_rcnn_plusplus.yaml", "--batch_size", "8", "--extra_tag", "2023-07-31_15-59-33", "--eval_all"], + "cwd": "/root/OpenPCDet/tools/" + }, + { + "name": "Python: Train PV-RCNN++ with ckpt", + "type": "python", + "request": "launch", + "program": "${file}", + "args": ["--cfg_file", "./cfgs/kitti_models/pv_rcnn_plusplus.yaml", "--extra_tag", "2023-07-29_20-33-11_continued", "--ckpt", "../output/cfgs/kitti_models/pv_rcnn_plusplus/2023-07-29_20-33-11/ckpt/checkpoint_epoch_80.pth"], + "console": "integratedTerminal", + "cwd": "/root/OpenPCDet/tools/" + } + ] +} \ No newline at end of file From 3871cd99e83574e208b6379ad29bf7737a548790 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Fri, 1 Sep 2023 10:00:11 +0000 Subject: [PATCH 28/93] Update gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 9fd7ec8eb..053e30cb7 100644 --- a/.gitignore +++ b/.gitignore @@ -15,3 +15,4 @@ venv/ *.bin output version.py +.vscode/ From 452011f506327383c90cd4ec907e23b0de70fb4f Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Fri, 1 Sep 2023 10:00:48 +0000 Subject: [PATCH 29/93] Update pv_rcnn_relation config --- tools/cfgs/kitti_models/pv_rcnn_relation.yaml | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml index 1b0441352..d45644c8f 100644 --- a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml +++ b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml @@ -222,17 +222,18 @@ MODEL: OBJECT_RELATION: NAME: GNN - # GRAPH: { - # NAME: radius_graph, - # RADIUS: 6, - # CONNECT_ONLY_SAME_CLASS: False - # } GRAPH: { - NAME: knn_graph, - K: 16, + NAME: radius_graph, + RADIUS: 4, CONNECT_ONLY_SAME_CLASS: False, - EDGE_EMBEDDING: False + EDGE_EMBEDDING: False, } + # GRAPH: { + # NAME: radius, + # K: 16, + # CONNECT_ONLY_SAME_CLASS: False, + # EDGE_EMBEDDING: False + # } LAYERS: [256, 256, 256, 256] GLOBAL_INFORMATION: { MLP_LAYERS: [256, 256], From 0481edc868b5618cbc3156a3ccebb559d351e687 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Sat, 2 Sep 2023 10:17:11 +0000 Subject: [PATCH 30/93] Update gnn to include bn and dropout --- pcdet/models/object_relation/gnn.py | 26 ++++++++++++++----- tools/cfgs/kitti_models/pv_rcnn_relation.yaml | 1 - 2 files changed, 19 insertions(+), 8 deletions(-) diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py index ac814c775..9d7f06299 100644 --- a/pcdet/models/object_relation/gnn.py +++ b/pcdet/models/object_relation/gnn.py @@ -22,9 +22,19 @@ # similar to EdgeConv https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.conv.EdgeConv.html class EdgeConv(tg.nn.MessagePassing): - def __init__(self, in_dim, out_dim): + def __init__(self, in_dim, out_dim, drop_out=None): super(EdgeConv, self).__init__(aggr='max') - self.fc = nn.Linear(in_dim, out_dim) + # self.fc = nn.Linear(in_dim, out_dim) + fc_list = [] + fc_list.append(nn.Linear(in_dim, out_dim)) + # also try graph norm + # fc_list.append(tg.nn.GraphNorm(out_dim)) + fc_list.append(tg.nn.BatchNorm(out_dim)) + fc_list.append(nn.ReLU()) + if drop_out: + fc_list.append(nn.Dropout(drop_out)) + + self.fc = nn.Sequential(*fc_list) def forward(self, x, edge_index, edge_attr=None): return self.propagate(edge_index, x=x, edge_attr=edge_attr) @@ -35,7 +45,6 @@ def message(self, x_i, x_j, edge_attr=None): else: x = torch.cat((x_j - x_i, x_i), dim=-1) x = self.fc(x) - x = F.relu(x) return x @@ -70,9 +79,10 @@ def __init__(self, object_relation_cfg, number_classes=3, pooled_feature_dim=256 conv_layer_list = [] for i in range(len(self.gnn_layers)): if i == 0: - conv_layer_list.append(EdgeConv(2*gnn_input_dim + (7 if self.graph_cfg.EDGE_EMBEDDING else 0), self.gnn_layers[i])) + input_dim = 2*gnn_input_dim + (7 if self.graph_cfg.EDGE_EMBEDDING else 0) else: - conv_layer_list.append(EdgeConv(2*self.gnn_layers[i-1], self.gnn_layers[i])) + input_dim = 2*self.gnn_layers[i-1] + conv_layer_list.append(EdgeConv(input_dim, self.gnn_layers[i], drop_out=self.drop_out)) self.gnn = nn.ModuleList(conv_layer_list) self.init_weights() @@ -80,8 +90,10 @@ def __init__(self, object_relation_cfg, number_classes=3, pooled_feature_dim=256 def init_weights(self, weight_init='xavier'): if weight_init == 'xavier': init_func = nn.init.xavier_uniform_ - for m in self.gnn: - init_func(m.fc.weight) + for edge_convs in self.gnn: + for m in edge_convs.fc: + if isinstance(m, nn.Linear): + init_func(m.weight) if self.global_information: for m in self.global_info_mlp: if isinstance(m, nn.Linear): diff --git a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml index 1b0441352..8be958c7f 100644 --- a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml +++ b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml @@ -239,7 +239,6 @@ MODEL: CONCATENATED: False, } DP_RATIO: 0.3 - # MLP_GLOBAL_LAYERS: [256, 256 POST_PROCESSING: From e2d58ddd3e2e7c31bdbe8d8bdad97f95295fdcef Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Sun, 3 Sep 2023 15:18:02 +0000 Subject: [PATCH 31/93] Update gnn for edge embedding --- pcdet/models/object_relation/gnn.py | 85 +++++++++++-------- .../models/roi_heads/pvrcnn_head_relation.py | 2 +- tools/cfgs/kitti_models/pv_rcnn_relation.yaml | 25 +++--- 3 files changed, 61 insertions(+), 51 deletions(-) diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py index 9d7f06299..a96ca8690 100644 --- a/pcdet/models/object_relation/gnn.py +++ b/pcdet/models/object_relation/gnn.py @@ -22,19 +22,9 @@ # similar to EdgeConv https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.conv.EdgeConv.html class EdgeConv(tg.nn.MessagePassing): - def __init__(self, in_dim, out_dim, drop_out=None): + def __init__(self, dim_in, dim_out, drop_out=None): super(EdgeConv, self).__init__(aggr='max') - # self.fc = nn.Linear(in_dim, out_dim) - fc_list = [] - fc_list.append(nn.Linear(in_dim, out_dim)) - # also try graph norm - # fc_list.append(tg.nn.GraphNorm(out_dim)) - fc_list.append(tg.nn.BatchNorm(out_dim)) - fc_list.append(nn.ReLU()) - if drop_out: - fc_list.append(nn.Dropout(drop_out)) - - self.fc = nn.Sequential(*fc_list) + self.mlp = build_mlp(dim_in, [dim_out], activation="ReLU", bn=True, drop_out=drop_out) def forward(self, x, edge_index, edge_attr=None): return self.propagate(edge_index, x=x, edge_attr=edge_attr) @@ -44,7 +34,7 @@ def message(self, x_i, x_j, edge_attr=None): x = torch.cat((x_j - x_i, x_i, edge_attr), dim=-1) else: x = torch.cat((x_j - x_i, x_i), dim=-1) - x = self.fc(x) + x = self.mlp(x) return x @@ -53,36 +43,34 @@ def __init__(self, object_relation_cfg, number_classes=3, pooled_feature_dim=256 super(GNN, self).__init__() self.graph_cfg = object_relation_cfg.GRAPH self.gnn_layers = object_relation_cfg.LAYERS + self.in_between_layers = object_relation_cfg.IN_BETWEEN_MLP if 'IN_BETWEEN_MLP' in object_relation_cfg else None self.global_information = object_relation_cfg.GLOBAL_INFORMATION if 'GLOBAL_INFORMATION' in object_relation_cfg else None self.number_classes = number_classes self.drop_out = object_relation_cfg.DP_RATIO self.pooled_feature_dim = pooled_feature_dim if self.global_information: - self.global_mlp = self.global_information.MLP_LAYERS - mlp_layer_list = [] global_mlp_input_dim = pooled_feature_dim + 7 if self.global_information.CONCATENATED else 7 - for i in range(len(self.global_mlp)): - if i == 0: - mlp_layer_list.append(nn.Linear(global_mlp_input_dim, self.global_mlp[i])) - else: - mlp_layer_list.append(nn.Linear(self.global_mlp[i-1], self.global_mlp[i])) - - mlp_layer_list.append(nn.BatchNorm1d(self.global_mlp[i])) - mlp_layer_list.append(nn.ReLU()) - if self.drop_out: - mlp_layer_list.append(nn.Dropout(self.drop_out)) - - self.global_info_mlp = nn.Sequential(*mlp_layer_list) + self.global_info_mlp = build_mlp(global_mlp_input_dim, self.global_information.MLP_LAYERS, activation='ReLU', bn=True, drop_out=self.drop_out) + + if not self.global_information: + gnn_input_dim = pooled_feature_dim + else: + gnn_input_dim = self.global_mlp[-1] if self.global_information.CONCATENATED else (self.global_mlp[-1] + self.pooled_feature_dim) - gnn_input_dim = self.global_mlp[-1] if self.global_information.CONCATENATED else (self.global_mlp[-1] + self.pooled_feature_dim) conv_layer_list = [] for i in range(len(self.gnn_layers)): + curr_conv_layer_list = [] if i == 0: - input_dim = 2*gnn_input_dim + (7 if self.graph_cfg.EDGE_EMBEDDING else 0) + input_dim = 2*gnn_input_dim else: input_dim = 2*self.gnn_layers[i-1] - conv_layer_list.append(EdgeConv(input_dim, self.gnn_layers[i], drop_out=self.drop_out)) + input_dim += (7 if self.graph_cfg.EDGE_EMBEDDING else 0) + curr_conv_layer_list.append(EdgeConv(input_dim, self.gnn_layers[i], drop_out=self.drop_out)) + if self.in_between_layers: + curr_mlp = build_mlp(self.gnn_layers[i], [self.in_between_layers[i]], activation="ReLU", bn=True, drop_out=True) + curr_conv_layer_list.append(curr_mlp) + conv_layer_list.append(nn.ModuleList(curr_conv_layer_list)) self.gnn = nn.ModuleList(conv_layer_list) self.init_weights() @@ -90,10 +78,16 @@ def __init__(self, object_relation_cfg, number_classes=3, pooled_feature_dim=256 def init_weights(self, weight_init='xavier'): if weight_init == 'xavier': init_func = nn.init.xavier_uniform_ - for edge_convs in self.gnn: - for m in edge_convs.fc: - if isinstance(m, nn.Linear): - init_func(m.weight) + for seq in self.gnn: + for n in seq: + if isinstance(n, EdgeConv): + for m in n.mlp: + if isinstance(m, nn.Linear): + init_func(m.weight) + else: + for m in n: + if isinstance(m, nn.Linear): + init_func(m.weight) if self.global_information: for m in self.global_info_mlp: if isinstance(m, nn.Linear): @@ -122,14 +116,18 @@ def forward(self, batch_dict): batch_dict['gnn_edges'] = edge_index edge_attr = None - if self.graph_cfg.EDGE_EMBEDDING: + if 'EDGE_EMBEDDING' in self.graph_cfg and self.graph_cfg.EDGE_EMBEDDING: from_node, to_node = edge_index edge_attr = proposal_boxes[from_node] - proposal_boxes[to_node] gnn_features = [pooled_features] x = pooled_features - for i in range(len(self.gnn)): - x = self.gnn[i](x, edge_index, edge_attr=edge_attr) + for module_list in self.gnn: + for module in module_list: + if isinstance(module, EdgeConv): + x = module(x, edge_index, edge_attr=edge_attr) + else: + x = module(x) gnn_features.append(x) batch_dict['related_features'] = torch.cat(gnn_features, dim=-1) @@ -158,6 +156,19 @@ def get_edges(self, proposal_boxes, proposal_labels, shape): edge_index = f(proposal_boxes, a, batch=batch_vector, loop=False) return edge_index +def build_mlp(input_dim, hidden_dims, activation='ReLU', bn=False, drop_out=None): + mlp_list = [] + for i in range(len(hidden_dims)): + if i == 0: + mlp_list.append(nn.Linear(input_dim, hidden_dims[i])) + else: + mlp_list.append(nn.Linear(hidden_dims[i-1], hidden_dims[i])) + if bn: + mlp_list.append(nn.BatchNorm1d(hidden_dims[i])) + mlp_list.append(getattr(nn, activation)()) + if drop_out: + mlp_list.append(nn.Dropout(drop_out)) + return nn.Sequential(*mlp_list) if __name__ == '__main__': diff --git a/pcdet/models/roi_heads/pvrcnn_head_relation.py b/pcdet/models/roi_heads/pvrcnn_head_relation.py index c651ea4aa..ccd6d568d 100644 --- a/pcdet/models/roi_heads/pvrcnn_head_relation.py +++ b/pcdet/models/roi_heads/pvrcnn_head_relation.py @@ -31,7 +31,7 @@ def __init__(self, input_channels, model_cfg, num_class=1, object_relation_confi self.shared_fc_layer = nn.Sequential(*shared_fc_list) - if object_relation_config.GLOBAL_INFORMATION: + if 'GLOBAL_INFORMATION' in object_relation_config: initial_input_dim = object_relation_config.GLOBAL_INFORMATION.MLP_LAYERS[-1] if not object_relation_config.GLOBAL_INFORMATION.CONCATENATED: initial_input_dim += self.model_cfg.SHARED_FC[-1] diff --git a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml index 147438996..a97c8b88b 100644 --- a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml +++ b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml @@ -222,23 +222,22 @@ MODEL: OBJECT_RELATION: NAME: GNN + # NAME: radius_graph, + # RADIUS: 6, + # CONNECT_ONLY_SAME_CLASS: False + # } GRAPH: { - NAME: radius_graph, - RADIUS: 4, + NAME: knn_graph, + K: 16, CONNECT_ONLY_SAME_CLASS: False, - EDGE_EMBEDDING: False, + EDGE_EMBEDDING: True, } - # GRAPH: { - # NAME: radius, - # K: 16, - # CONNECT_ONLY_SAME_CLASS: False, - # EDGE_EMBEDDING: False - # } LAYERS: [256, 256, 256, 256] - GLOBAL_INFORMATION: { - MLP_LAYERS: [256, 256], - CONCATENATED: False, - } + IN_BETWEEN_MLP: [256, 256, 256, 256] + # GLOBAL_INFORMATION: { + # MLP_LAYERS: [256, 256], + # CONCATENATED: False, + # } DP_RATIO: 0.3 From f78c425426c1df67a894897bbb9e8c6627eb6331 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Mon, 4 Sep 2023 12:12:01 +0000 Subject: [PATCH 32/93] Add GATConv --- pcdet/models/object_relation/gnn.py | 27 ++++++++++++++----- tools/cfgs/kitti_models/pv_rcnn_relation.yaml | 7 ++++- 2 files changed, 26 insertions(+), 8 deletions(-) diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py index a96ca8690..d6e04da59 100644 --- a/pcdet/models/object_relation/gnn.py +++ b/pcdet/models/object_relation/gnn.py @@ -42,6 +42,7 @@ class GNN(nn.Module): def __init__(self, object_relation_cfg, number_classes=3, pooled_feature_dim=256): super(GNN, self).__init__() self.graph_cfg = object_relation_cfg.GRAPH + self.graph_conv = object_relation_cfg.GRAPH.CONV self.gnn_layers = object_relation_cfg.LAYERS self.in_between_layers = object_relation_cfg.IN_BETWEEN_MLP if 'IN_BETWEEN_MLP' in object_relation_cfg else None self.global_information = object_relation_cfg.GLOBAL_INFORMATION if 'GLOBAL_INFORMATION' in object_relation_cfg else None @@ -62,11 +63,18 @@ def __init__(self, object_relation_cfg, number_classes=3, pooled_feature_dim=256 for i in range(len(self.gnn_layers)): curr_conv_layer_list = [] if i == 0: - input_dim = 2*gnn_input_dim + input_dim = gnn_input_dim else: - input_dim = 2*self.gnn_layers[i-1] - input_dim += (7 if self.graph_cfg.EDGE_EMBEDDING else 0) - curr_conv_layer_list.append(EdgeConv(input_dim, self.gnn_layers[i], drop_out=self.drop_out)) + input_dim = self.gnn_layers[i-1] + + edge_dim = (7 if self.graph_conv.EDGE_EMBEDDING else 0) + if self.graph_conv.NAME == "EdgeConv": + curr_conv_layer_list.append(EdgeConv(2*input_dim+edge_dim, self.gnn_layers[i], drop_out=self.drop_out)) + elif self.graph_conv.NAME == "GATConv": + # layer according to tg example: https://github.com/pyg-team/pytorch_geometric/blob/master/examples/gat.py + curr_conv_layer_list.append(nn.Dropout(p=self.drop_out)) + curr_conv_layer_list.append(tg.nn.GATConv(input_dim, self.gnn_layers[i], self.graph_conv.HEADS, dropout=self.drop_out, edge_dim=edge_dim, concat=False)) + curr_conv_layer_list.append(nn.ELU()) if self.in_between_layers: curr_mlp = build_mlp(self.gnn_layers[i], [self.in_between_layers[i]], activation="ReLU", bn=True, drop_out=True) curr_conv_layer_list.append(curr_mlp) @@ -84,10 +92,15 @@ def init_weights(self, weight_init='xavier'): for m in n.mlp: if isinstance(m, nn.Linear): init_func(m.weight) - else: + elif isinstance(n, nn.Sequential): for m in n: if isinstance(m, nn.Linear): init_func(m.weight) + elif isinstance(n, tg.nn.GATConv): + # automatically initialized + continue + else: + continue if self.global_information: for m in self.global_info_mlp: if isinstance(m, nn.Linear): @@ -116,7 +129,7 @@ def forward(self, batch_dict): batch_dict['gnn_edges'] = edge_index edge_attr = None - if 'EDGE_EMBEDDING' in self.graph_cfg and self.graph_cfg.EDGE_EMBEDDING: + if 'EDGE_EMBEDDING' in self.graph_conv and self.graph_conv.EDGE_EMBEDDING: from_node, to_node = edge_index edge_attr = proposal_boxes[from_node] - proposal_boxes[to_node] @@ -124,7 +137,7 @@ def forward(self, batch_dict): x = pooled_features for module_list in self.gnn: for module in module_list: - if isinstance(module, EdgeConv): + if isinstance(module, (EdgeConv, tg.nn.GATConv)): x = module(x, edge_index, edge_attr=edge_attr) else: x = module(x) diff --git a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml index a97c8b88b..ee5abe890 100644 --- a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml +++ b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml @@ -227,10 +227,15 @@ MODEL: # CONNECT_ONLY_SAME_CLASS: False # } GRAPH: { + CONV: { + NAME: EdgeConv, + EDGE_EMBEDDING: True, + # HEADS: 4, + }, NAME: knn_graph, K: 16, CONNECT_ONLY_SAME_CLASS: False, - EDGE_EMBEDDING: True, + # EDGE_EMBEDDING: True, } LAYERS: [256, 256, 256, 256] IN_BETWEEN_MLP: [256, 256, 256, 256] From 19708e2d7ab684f9a0fa145ef589f8f0f4213863 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Tue, 5 Sep 2023 06:39:38 +0000 Subject: [PATCH 33/93] Update detector template to be able to run pvrcnn --- pcdet/models/detectors/detector3d_template.py | 22 +++++++++++-------- 1 file changed, 13 insertions(+), 9 deletions(-) diff --git a/pcdet/models/detectors/detector3d_template.py b/pcdet/models/detectors/detector3d_template.py index 3b4aba9db..b356c410a 100644 --- a/pcdet/models/detectors/detector3d_template.py +++ b/pcdet/models/detectors/detector3d_template.py @@ -160,15 +160,19 @@ def build_point_head(self, model_info_dict): def build_roi_head(self, model_info_dict): if self.model_cfg.get('ROI_HEAD', None) is None: return None, model_info_dict - point_head_module = roi_heads.__all__[self.model_cfg.ROI_HEAD.NAME]( - model_cfg=self.model_cfg.ROI_HEAD, - input_channels=model_info_dict['num_point_features'], - backbone_channels= model_info_dict.get('backbone_channels', None), - point_cloud_range=model_info_dict['point_cloud_range'], - voxel_size=model_info_dict['voxel_size'], - num_class=self.num_class if not self.model_cfg.ROI_HEAD.CLASS_AGNOSTIC else 1, - object_relation_config=self.model_cfg.OBJECT_RELATION, - ) + + common_args = { + 'model_cfg': self.model_cfg.ROI_HEAD, + 'input_channels': model_info_dict['num_point_features'], + 'backbone_channels': model_info_dict.get('backbone_channels', None), + 'point_cloud_range': model_info_dict['point_cloud_range'], + 'voxel_size': model_info_dict['voxel_size'], + 'num_class': self.num_class if not self.model_cfg.ROI_HEAD.CLASS_AGNOSTIC else 1, + } + if self.model_cfg.NAME == 'PVRCNNRelation': + common_args['object_relation_config'] = self.model_cfg.OBJECT_RELATION + + point_head_module = roi_heads.__all__[self.model_cfg.ROI_HEAD.NAME](**common_args) model_info_dict['module_list'].append(point_head_module) return point_head_module, model_info_dict From 66eac5c7c418fbdd7bf9621581a97703ba7c0b85 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Wed, 6 Sep 2023 07:04:33 +0000 Subject: [PATCH 34/93] Add dynamic graphs --- pcdet/models/object_relation/gnn.py | 25 +++++++++++++------ tools/cfgs/kitti_models/pv_rcnn_relation.yaml | 6 +++-- 2 files changed, 22 insertions(+), 9 deletions(-) diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py index d6e04da59..ed026f7eb 100644 --- a/pcdet/models/object_relation/gnn.py +++ b/pcdet/models/object_relation/gnn.py @@ -33,6 +33,8 @@ def message(self, x_i, x_j, edge_attr=None): if edge_attr is not None: x = torch.cat((x_j - x_i, x_i, edge_attr), dim=-1) else: + # one could also try this: + # x = torch.cat((x_j, x_i), dim=-1) x = torch.cat((x_j - x_i, x_i), dim=-1) x = self.mlp(x) return x @@ -124,8 +126,15 @@ def forward(self, batch_dict): else: embedded_global_information = self.global_info_mlp(proposal_boxes) pooled_features = torch.cat([pooled_features, embedded_global_information], dim=1) - - edge_index = self.get_edges(proposal_boxes[:,:3], proposal_labels, (B, N, C)) + + if self.graph_cfg.SPACE == 'R3': + assert self.graph_cfg.DYNAMIC == False, 'Distance should be measured in feature space if the graph is created dynamically' + edge_index = self.get_edges(proposal_boxes[:,:3], proposal_labels, (B, N, C)) + elif self.graph_cfg.SPACE == 'Feature': + edge_index = self.get_edges(pooled_features, proposal_labels, (B, N, C)) + else: + raise NotImplemented('Distance space was {} but should be R3 or FEATURE'.format(self.graph_cfg.SPACE)) + batch_dict['gnn_edges'] = edge_index edge_attr = None @@ -142,31 +151,33 @@ def forward(self, batch_dict): else: x = module(x) gnn_features.append(x) + if self.graph_cfg.DYNAMIC: + edge_index = self.get_edges(x, proposal_labels, (B, N, None)) batch_dict['related_features'] = torch.cat(gnn_features, dim=-1) return batch_dict - def get_edges(self, proposal_boxes, proposal_labels, shape): + def get_edges(self, edge_generating_tensor, proposal_labels, shape): B, N, _ = shape f = getattr(tg.nn, self.graph_cfg.NAME) a = (self.graph_cfg.RADIUS if self.graph_cfg.NAME == 'radius_graph' else self.graph_cfg.K) - batch_vector = torch.arange(B, device=proposal_boxes.device).repeat_interleave(N) + batch_vector = torch.arange(B, device=edge_generating_tensor.device).repeat_interleave(N) if self.graph_cfg.CONNECT_ONLY_SAME_CLASS: final_edge_indices = [] for predicted_class in range(1, self.number_classes + 1): label_indices = torch.where(proposal_labels == predicted_class)[0] - label_proposal_boxes = proposal_boxes[label_indices] + label_edge_generating_tensor = edge_generating_tensor[label_indices] label_batch_vector = batch_vector[label_indices] - label_edge_index = f(label_proposal_boxes, a, batch=label_batch_vector, loop=False) + label_edge_index = f(label_edge_generating_tensor, a, batch=label_batch_vector, loop=False) label_edge_index[0] = label_indices[label_edge_index[0]] label_edge_index[1] = label_indices[label_edge_index[1]] final_edge_indices.append(label_edge_index) edge_index = torch.cat(final_edge_indices, dim=-1) else: - edge_index = f(proposal_boxes, a, batch=batch_vector, loop=False) + edge_index = f(edge_generating_tensor, a, batch=batch_vector, loop=False) return edge_index def build_mlp(input_dim, hidden_dims, activation='ReLU', bn=False, drop_out=None): diff --git a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml index ee5abe890..a4220445d 100644 --- a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml +++ b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml @@ -229,16 +229,18 @@ MODEL: GRAPH: { CONV: { NAME: EdgeConv, - EDGE_EMBEDDING: True, + EDGE_EMBEDDING: False, # HEADS: 4, }, + DYNAMIC: False, NAME: knn_graph, K: 16, CONNECT_ONLY_SAME_CLASS: False, + SPACE: Feature, # EDGE_EMBEDDING: True, } LAYERS: [256, 256, 256, 256] - IN_BETWEEN_MLP: [256, 256, 256, 256] + # IN_BETWEEN_MLP: [256, 256, 256, 256] # GLOBAL_INFORMATION: { # MLP_LAYERS: [256, 256], # CONCATENATED: False, From 6ea08a0cf8a04f59fadf9108212172d2e07e22ca Mon Sep 17 00:00:00 2001 From: MarcBrede <34718040+MarcBrede@users.noreply.github.com> Date: Wed, 6 Sep 2023 10:04:15 +0200 Subject: [PATCH 35/93] Rename README.md to README_OpenPCDet.md --- README.md => README_OpenPCDet.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename README.md => README_OpenPCDet.md (100%) diff --git a/README.md b/README_OpenPCDet.md similarity index 100% rename from README.md rename to README_OpenPCDet.md From 8f52abd81dd156e05a31f11fd01016f7768728ba Mon Sep 17 00:00:00 2001 From: MarcBrede <34718040+MarcBrede@users.noreply.github.com> Date: Wed, 6 Sep 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zw}g+8G&0XQZTpIZZi5f(d~oUc5@4c=#TMzQClU${UiZ zD-K>;U*8+HXw{~#obeP84{qItMZSB%Sxq_v8uhe+Jbz*P`le(a`3mj!s)Y(m*P}rC z*xDuQd$dYap4R%$$mL%-=bu2w_wE1IzQ;thEpp-aZ`7yNcGpaHj{fhzin5Rd9=2U4 zu>ac$P-37%+_QfDubQ;mNp-avzm6^Y&j&^ysI1~88!ht(FE=5RS1yWO^_G|kpRdq; z4!Ha*>0*$AfGP1S>wL5ZSM`imC0GtR<*sJetls%iTR*C!p~~7)_^$Dlae$Q;_77Jr z(nC%_|Eu8BHmYzbAnt0BtMr>_nXEN^X+Z+duD9pE{2O%!57&issn6Q0l=Y0;MHx$_ zREK-)^v7AkZw~&)*Z^>NT#lc-oD239SxcLiMEoJf5-{JVmZZIAfx6 z5>4hwcZDv%8l>I$&Hwh`aRaKns1m=gEJCHPIZDSNlPy4&|M!2fgxNl!R??Ty5HO*) z0eSWC^_bl%ryPA=S8A>P)=Sj+aWYr^TlOQWXu^8)+q^e8YV3$$S7!zgrozR7So&{k m=uxE{zVU0xCf|?FFBDhMMiNVE8W(_nKRdYYFW&p>@Ba_7)d4*K literal 0 HcmV?d00001 From f3cbf8bb3c3e19079fc4ea51d974089557ac06fb Mon Sep 17 00:00:00 2001 From: MarcBrede <34718040+MarcBrede@users.noreply.github.com> Date: Wed, 6 Sep 2023 11:06:45 +0200 Subject: [PATCH 42/93] Update README.md --- README.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/README.md b/README.md index f9befe700..2a77df119 100644 --- a/README.md +++ b/README.md @@ -2,3 +2,11 @@ +3D object detectors usually predict objects independently from each other. Given the limited receptive field for each object, they are missing the required context. Especially when it comes to heavily occoluded or sparse objects, context is necessary to correctly predict 3D bounding boxes. Simply increasing the receptive field would not give enough context while still being computational feasible. Therefore, this project, comparable to previous work, proposes to model context efficiently with object relation. + +

+ +
Object Relation as an efficient Way to model Context
+
+ + From 8304a44c6ed8c8c696b49dc23597f0911ee22796 Mon Sep 17 00:00:00 2001 From: MarcBrede <34718040+MarcBrede@users.noreply.github.com> Date: Wed, 6 Sep 2023 12:16:00 +0200 Subject: [PATCH 43/93] Fixing typos in README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 2a77df119..b8415f2f6 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ -3D object detectors usually predict objects independently from each other. Given the limited receptive field for each object, they are missing the required context. Especially when it comes to heavily occoluded or sparse objects, context is necessary to correctly predict 3D bounding boxes. Simply increasing the receptive field would not give enough context while still being computational feasible. Therefore, this project, comparable to previous work, proposes to model context efficiently with object relation. +3D object detectors usually predict objects independently from each other. Given the limited receptive field for each object, they are missing the required context. Especially when it comes to heavily occluded or sparse objects, context is necessary to correctly predict 3D bounding boxes. Simply increasing the receptive field would not give enough context while still being computationally feasible. Therefore, this project, comparable to previous work, proposes to model context efficiently with object relation.
From 1b19cbd0900fe41b53c0f1d77ecbb706653fbb28 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Thu, 7 Sep 2023 11:14:00 +0000 Subject: [PATCH 44/93] Add centerpoint configs for KITTI --- tools/cfgs/kitti_models/centerpoint.yaml | 99 ++++++++++++++++++++++++ 1 file changed, 99 insertions(+) create mode 100644 tools/cfgs/kitti_models/centerpoint.yaml diff --git a/tools/cfgs/kitti_models/centerpoint.yaml b/tools/cfgs/kitti_models/centerpoint.yaml new file mode 100644 index 000000000..9d47851d9 --- /dev/null +++ b/tools/cfgs/kitti_models/centerpoint.yaml @@ -0,0 +1,99 @@ +CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist'] + +DATA_CONFIG: + _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml + + +MODEL: + NAME: CenterPoint + + VFE: + NAME: MeanVFE + + BACKBONE_3D: + NAME: VoxelBackBone8x + + MAP_TO_BEV: + NAME: HeightCompression + NUM_BEV_FEATURES: 256 + + BACKBONE_2D: + NAME: BaseBEVBackbone + + LAYER_NUMS: [5] + LAYER_STRIDES: [1] + NUM_FILTERS: [128] + UPSAMPLE_STRIDES: [2] + NUM_UPSAMPLE_FILTERS: [256] + + DENSE_HEAD: + NAME: CenterHead + CLASS_AGNOSTIC: False + + SHARED_CONV_CHANNEL: 64 + USE_BIAS_BEFORE_NORM: True + NUM_HM_CONV: 2 + SEPARATE_HEAD_CFG: + HEAD_ORDER: ['center', 'center_z', 'dim', 'rot'] + HEAD_DICT: { + 'center': {'out_channels': 2, 'num_conv': 2}, + 'center_z': {'out_channels': 1, 'num_conv': 2}, + 'dim': {'out_channels': 3, 'num_conv': 2}, + 'rot': {'out_channels': 2, 'num_conv': 2}, + } + + CLASS_NAMES_EACH_HEAD: [ + ['Car', 'Pedestrian', 'Cyclist'] + ] + + TARGET_ASSIGNER_CONFIG: + FEATURE_MAP_STRIDE: 8 + NUM_MAX_OBJS: 100 + VOXEL_SIZE: [0.05, 0.05, 0.1] + OUT_SIZE_FACTOR: 4 + GAUSSIAN_OVERLAP: 0.1 + MIN_RADIUS: 2 + BOX_CODER: ResidualCoder + + LOSS_CONFIG: + LOSS_WEIGHTS: { + 'cls_weight': 1.0, + 'loc_weight': 2.0, + 'code_weights': [1.0, 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: 0.01 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 + + +OPTIMIZATION: + BATCH_SIZE_PER_GPU: 8 + NUM_EPOCHS: 80 + + 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 \ No newline at end of file From 3fd2a90a5a26d55f7320074964faff1dfad5883d Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Fri, 8 Sep 2023 14:24:07 +0000 Subject: [PATCH 45/93] Fix minor bug in gnn with dynamic graph --- pcdet/models/object_relation/gnn.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py index ed026f7eb..0e544e377 100644 --- a/pcdet/models/object_relation/gnn.py +++ b/pcdet/models/object_relation/gnn.py @@ -153,6 +153,9 @@ def forward(self, batch_dict): gnn_features.append(x) if self.graph_cfg.DYNAMIC: edge_index = self.get_edges(x, proposal_labels, (B, N, None)) + if edge_attr: + from_node, to_node = edge_index + edge_attr = proposal_boxes[from_node] - proposal_boxes[to_node] batch_dict['related_features'] = torch.cat(gnn_features, dim=-1) From 99546626d8a230b674e9cc867b957a4373eab2dd Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Sat, 9 Sep 2023 13:48:38 +0000 Subject: [PATCH 46/93] Fix small bug for dynamic graphs --- pcdet/models/object_relation/gnn.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py index 0e544e377..7c999cc23 100644 --- a/pcdet/models/object_relation/gnn.py +++ b/pcdet/models/object_relation/gnn.py @@ -153,7 +153,7 @@ def forward(self, batch_dict): gnn_features.append(x) if self.graph_cfg.DYNAMIC: edge_index = self.get_edges(x, proposal_labels, (B, N, None)) - if edge_attr: + if edge_attr is not None: from_node, to_node = edge_index edge_attr = proposal_boxes[from_node] - proposal_boxes[to_node] From e9c693ce3c88af7971662cee37745c43b60c2aea Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Sat, 9 Sep 2023 13:57:32 +0000 Subject: [PATCH 47/93] Tmp --- pcdet/models/roi_heads/__init__.py | 2 + pcdet/models/roi_heads/roi_head.py | 163 +++++++++++ pcdet/utils/box_torch_ops.py | 277 ++++++++++++++++++ tools/cfgs/kitti_models/centerpoint.yaml | 26 +- .../kitti_models/centerpoint_twostage.yaml | 150 ++++++++++ 5 files changed, 612 insertions(+), 6 deletions(-) create mode 100644 pcdet/models/roi_heads/roi_head.py create mode 100644 pcdet/utils/box_torch_ops.py create mode 100644 tools/cfgs/kitti_models/centerpoint_twostage.yaml diff --git a/pcdet/models/roi_heads/__init__.py b/pcdet/models/roi_heads/__init__.py index 0dcc717a8..6d93f9c83 100644 --- a/pcdet/models/roi_heads/__init__.py +++ b/pcdet/models/roi_heads/__init__.py @@ -7,6 +7,7 @@ from .roi_head_template import RoIHeadTemplate from .mppnet_head import MPPNetHead from .mppnet_memory_bank_e2e import MPPNetHeadE2E +from .roi_head import RoIHead __all__ = { 'RoIHeadTemplate': RoIHeadTemplate, @@ -18,4 +19,5 @@ 'MPPNetHead': MPPNetHead, 'MPPNetHeadE2E': MPPNetHeadE2E, 'PVRCNNHeadRelation': PVRCNNHeadRelation, + 'ROIHead': RoIHead, } diff --git a/pcdet/models/roi_heads/roi_head.py b/pcdet/models/roi_heads/roi_head.py new file mode 100644 index 000000000..b626e5513 --- /dev/null +++ b/pcdet/models/roi_heads/roi_head.py @@ -0,0 +1,163 @@ +import torch +import torch.nn as nn +from ..backbones_3d.pfe.voxel_set_abstraction import bilinear_interpolate_torch +from ...utils.box_torch_ops import center_to_corner_box2d +from .roi_head_template import RoIHeadTemplate + +class RoIHead(RoIHeadTemplate): + def __init__(self, model_cfg): + super().__init__(model_cfg=model_cfg) + self.bev_feature_extractor = BEVFeatureExtractor(pc_start, voxel_size, out_stride) + + def forward(self, batch_dict): + + features = self.bev_feature_extractor(batch_dict, batch_centers, num_point) + + self.reorder_first_stage_pred_and_feature(first_pred=one_stage_pred, example=example, features=features) + + + batch_dict['batch_size'] = len(batch_dict['rois']) + if training: + targets_dict = self.assign_targets(batch_dict) + batch_dict['rois'] = targets_dict['rois'] + batch_dict['roi_labels'] = targets_dict['roi_labels'] + batch_dict['roi_features'] = targets_dict['roi_features'] + batch_dict['roi_scores'] = targets_dict['roi_scores'] + + # RoI aware pooling + if self.add_box_param: + batch_dict['roi_features'] = torch.cat([batch_dict['roi_features'], batch_dict['rois'], batch_dict['roi_scores'].unsqueeze(-1)], dim=-1) + + pooled_features = batch_dict['roi_features'].reshape(-1, 1, + batch_dict['roi_features'].shape[-1]).contiguous() # (BxN, 1, C) + + batch_size_rcnn = pooled_features.shape[0] + pooled_features = pooled_features.permute(0, 2, 1).contiguous() # (BxN, C, 1) + + shared_features = self.shared_fc_layer(pooled_features.view(batch_size_rcnn, -1, 1)) + rcnn_cls = self.cls_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1) # (B, 1 or 2) + rcnn_reg = self.reg_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1) # (B, C) + + if not training: + batch_cls_preds, batch_box_preds = self.generate_predicted_boxes( + batch_size=batch_dict['batch_size'], rois=batch_dict['rois'], cls_preds=rcnn_cls, box_preds=rcnn_reg + ) + batch_dict['batch_cls_preds'] = batch_cls_preds + batch_dict['batch_box_preds'] = batch_box_preds + batch_dict['cls_preds_normalized'] = False + else: + targets_dict['rcnn_cls'] = rcnn_cls + targets_dict['rcnn_reg'] = rcnn_reg + + self.forward_ret_dict = targets_dict + + return batch_dict + + + def reorder_first_stage_pred_and_feature(self, first_pred, example, features): + batch_size = len(first_pred) + box_length = first_pred[0]['box3d_lidar'].shape[1] + feature_vector_length = sum([feat[0].shape[-1] for feat in features]) + + rois = first_pred[0]['box3d_lidar'].new_zeros((batch_size, + self.NMS_POST_MAXSIZE, box_length + )) + roi_scores = first_pred[0]['scores'].new_zeros((batch_size, + self.NMS_POST_MAXSIZE + )) + roi_labels = first_pred[0]['label_preds'].new_zeros((batch_size, + self.NMS_POST_MAXSIZE), dtype=torch.long + ) + roi_features = features[0][0].new_zeros((batch_size, + self.NMS_POST_MAXSIZE, feature_vector_length + )) + + for i in range(batch_size): + num_obj = features[0][i].shape[0] + # basically move rotation to position 6, so now the box is 7 + C . C is 2 for nuscenes to + # include velocity target + + box_preds = first_pred[i]['box3d_lidar'] + + if self.roi_head.code_size == 9: + # x, y, z, w, l, h, rotation_y, velocity_x, velocity_y + box_preds = box_preds[:, [0, 1, 2, 3, 4, 5, 8, 6, 7]] + + rois[i, :num_obj] = box_preds + roi_labels[i, :num_obj] = first_pred[i]['label_preds'] + 1 + roi_scores[i, :num_obj] = first_pred[i]['scores'] + roi_features[i, :num_obj] = torch.cat([feat[i] for feat in features], dim=-1) + + example['rois'] = rois # used in pooled features + example['roi_labels'] = roi_labels + example['roi_scores'] = roi_scores # used in pooled features + example['roi_features'] = roi_features # used in pooled features + + example['has_class_labels']= True + + return example + + def get_box_center(self, boxes): + # box [List] + centers = [] + for box in boxes: + if self.num_point == 1 or len(box['box3d_lidar']) == 0: + centers.append(box['box3d_lidar'][:, :3]) + + elif self.num_point == 5: + center2d = box['box3d_lidar'][:, :2] + height = box['box3d_lidar'][:, 2:3] + dim2d = box['box3d_lidar'][:, 3:5] + rotation_y = box['box3d_lidar'][:, -1] + + corners = center_to_corner_box2d(center2d, dim2d, rotation_y) + + front_middle = torch.cat([(corners[:, 0] + corners[:, 1])/2, height], dim=-1) + back_middle = torch.cat([(corners[:, 2] + corners[:, 3])/2, height], dim=-1) + left_middle = torch.cat([(corners[:, 0] + corners[:, 3])/2, height], dim=-1) + right_middle = torch.cat([(corners[:, 1] + corners[:, 2])/2, height], dim=-1) + + points = torch.cat([box['box3d_lidar'][:, :3], front_middle, back_middle, left_middle, \ + right_middle], dim=0) + + centers.append(points) + else: + raise NotImplementedError() + + return centers + + + + +class BEVFeatureExtractor(nn.Module): + def __init__(self, pc_start, + voxel_size, out_stride): + super().__init__() + self.pc_start = pc_start + self.voxel_size = voxel_size + self.out_stride = out_stride + + def absl_to_relative(self, absolute): + a1 = (absolute[..., 0] - self.pc_start[0]) / self.voxel_size[0] / self.out_stride + a2 = (absolute[..., 1] - self.pc_start[1]) / self.voxel_size[1] / self.out_stride + + return a1, a2 + + def forward(self, example, batch_centers, num_point): + batch_size = len(example['bev_feature']) + ret_maps = [] + + for batch_idx in range(batch_size): + xs, ys = self.absl_to_relative(batch_centers[batch_idx]) + + # N x C + feature_map = bilinear_interpolate_torch(example['bev_feature'][batch_idx], + xs, ys) + + if num_point > 1: + section_size = len(feature_map) // num_point + feature_map = torch.cat([feature_map[i*section_size: (i+1)*section_size] for i in range(num_point)], dim=1) + + ret_maps.append(feature_map) + + return ret_maps \ No newline at end of file diff --git a/pcdet/utils/box_torch_ops.py b/pcdet/utils/box_torch_ops.py new file mode 100644 index 000000000..18a3b12ab --- /dev/null +++ b/pcdet/utils/box_torch_ops.py @@ -0,0 +1,277 @@ +import math +from functools import reduce + +import numpy as np +import torch +from torch import stack as tstack +try: + from det3d.ops.iou3d_nms import iou3d_nms_cuda, iou3d_nms_utils +except: + print("iou3d cuda not built. You don't need this if you use circle_nms. Otherwise, refer to the advanced installation part to build this cuda extension") + +def torch_to_np_dtype(ttype): + type_map = { + torch.float16: np.dtype(np.float16), + torch.float32: np.dtype(np.float32), + torch.float16: np.dtype(np.float64), + torch.int32: np.dtype(np.int32), + torch.int64: np.dtype(np.int64), + torch.uint8: np.dtype(np.uint8), + } + return type_map[ttype] + + +def corners_nd(dims, origin=0.5): + """generate relative box corners based on length per dim and + origin point. + + Args: + dims (float array, shape=[N, ndim]): array of length per dim + origin (list or array or float): origin point relate to smallest point. + dtype (output dtype, optional): Defaults to np.float32 + + Returns: + float array, shape=[N, 2 ** ndim, ndim]: returned corners. + point layout example: (2d) x0y0, x0y1, x1y0, x1y1; + (3d) x0y0z0, x0y0z1, x0y1z0, x0y1z1, x1y0z0, x1y0z1, x1y1z0, x1y1z1 + where x0 < x1, y0 < y1, z0 < z1 + """ + ndim = int(dims.shape[1]) + dtype = torch_to_np_dtype(dims.dtype) + if isinstance(origin, float): + origin = [origin] * ndim + corners_norm = np.stack( + np.unravel_index(np.arange(2 ** ndim), [2] * ndim), axis=1 + ).astype(dtype) + # now corners_norm has format: (2d) x0y0, x0y1, x1y0, x1y1 + # (3d) x0y0z0, x0y0z1, x0y1z0, x0y1z1, x1y0z0, x1y0z1, x1y1z0, x1y1z1 + # so need to convert to a format which is convenient to do other computing. + # for 2d boxes, format is clockwise start from minimum point + # for 3d boxes, please draw them by your hand. + if ndim == 2: + # generate clockwise box corners + corners_norm = corners_norm[[0, 1, 3, 2]] + elif ndim == 3: + corners_norm = corners_norm[[0, 1, 3, 2, 4, 5, 7, 6]] + corners_norm = corners_norm - np.array(origin, dtype=dtype) + corners_norm = torch.from_numpy(corners_norm).type_as(dims) + corners = dims.view(-1, 1, ndim) * corners_norm.view(1, 2 ** ndim, ndim) + return corners + + +def corners_2d(dims, origin=0.5): + """generate relative 2d box corners based on length per dim and + origin point. + + Args: + dims (float array, shape=[N, 2]): array of length per dim + origin (list or array or float): origin point relate to smallest point. + dtype (output dtype, optional): Defaults to np.float32 + + Returns: + float array, shape=[N, 4, 2]: returned corners. + point layout: x0y0, x0y1, x1y1, x1y0 + """ + return corners_nd(dims, origin) + + +def corner_to_standup_nd(boxes_corner): + ndim = boxes_corner.shape[2] + standup_boxes = [] + for i in range(ndim): + standup_boxes.append(torch.min(boxes_corner[:, :, i], dim=1)[0]) + for i in range(ndim): + standup_boxes.append(torch.max(boxes_corner[:, :, i], dim=1)[0]) + return torch.stack(standup_boxes, dim=1) + + +def rotation_3d_in_axis(points, angles, axis=0): + # points: [N, point_size, 3] + # angles: [N] + rot_sin = torch.sin(angles) + rot_cos = torch.cos(angles) + ones = torch.ones_like(rot_cos) + zeros = torch.zeros_like(rot_cos) + if axis == 1: + rot_mat_T = tstack( + [ + tstack([rot_cos, zeros, -rot_sin]), + tstack([zeros, ones, zeros]), + tstack([rot_sin, zeros, rot_cos]), + ] + ) + elif axis == 2 or axis == -1: + rot_mat_T = tstack( + [ + tstack([rot_cos, -rot_sin, zeros]), + tstack([rot_sin, rot_cos, zeros]), + tstack([zeros, zeros, ones]), + ] + ) + elif axis == 0: + rot_mat_T = tstack( + [ + tstack([zeros, rot_cos, -rot_sin]), + tstack([zeros, rot_sin, rot_cos]), + tstack([ones, zeros, zeros]), + ] + ) + else: + raise ValueError("axis should in range") + # print(points.shape, rot_mat_T.shape) + return torch.einsum("aij,jka->aik", points, rot_mat_T) + +def rotate_points_along_z(points, angle): + """ + Args: + points: (B, N, 3 + C) + angle: (B), angle along z-axis, angle increases x ==> y + Returns: + """ + cosa = torch.cos(angle) + sina = torch.sin(angle) + zeros = angle.new_zeros(points.shape[0]) + ones = angle.new_ones(points.shape[0]) + rot_matrix = torch.stack(( + cosa, -sina, zeros, + sina, cosa, zeros, + zeros, zeros, ones + ), dim=1).view(-1, 3, 3).float() + points_rot = torch.matmul(points[:, :, 0:3], rot_matrix) + points_rot = torch.cat((points_rot, points[:, :, 3:]), dim=-1) + return points_rot + + +def rotation_2d(points, angles): + """rotation 2d points based on origin point clockwise when angle positive. + + Args: + points (float array, shape=[N, point_size, 2]): points to be rotated. + angles (float array, shape=[N]): rotation angle. + + Returns: + float array: same shape as points + """ + rot_sin = torch.sin(angles) + rot_cos = torch.cos(angles) + rot_mat_T = torch.stack([tstack([rot_cos, -rot_sin]), tstack([rot_sin, rot_cos])]) + return torch.einsum("aij,jka->aik", (points, rot_mat_T)) + + +def center_to_corner_box3d(centers, dims, angles, origin=(0.5, 0.5, 0.5), axis=1): + """convert kitti locations, dimensions and angles to corners + + Args: + centers (float array, shape=[N, 3]): locations in kitti label file. + dims (float array, shape=[N, 3]): dimensions in kitti label file. + angles (float array, shape=[N]): rotation_y in kitti label file. + origin (list or array or float): origin point relate to smallest point. + use [0.5, 1.0, 0.5] in camera and [0.5, 0.5, 0] in lidar. + axis (int): rotation axis. 1 for camera and 2 for lidar. + Returns: + [type]: [description] + """ + # 'length' in kitti format is in x axis. + # yzx(hwl)(kitti label file)<->xyz(lhw)(camera)<->z(-x)(-y)(wlh)(lidar) + # center in kitti format is [0.5, 1.0, 0.5] in xyz. + corners = corners_nd(dims, origin=origin) + # corners: [N, 8, 3] + corners = rotation_3d_in_axis(corners, angles, axis=axis) + corners += centers.view(-1, 1, 3) + return corners + + +def center_to_corner_box2d(centers, dims, angles=None, origin=0.5): + """convert kitti locations, dimensions and angles to corners + + Args: + centers (float array, shape=[N, 2]): locations in kitti label file. + dims (float array, shape=[N, 2]): dimensions in kitti label file. + angles (float array, shape=[N]): rotation_y in kitti label file. + + Returns: + [type]: [description] + """ + # 'length' in kitti format is in x axis. + # xyz(hwl)(kitti label file)<->xyz(lhw)(camera)<->z(-x)(-y)(wlh)(lidar) + # center in kitti format is [0.5, 1.0, 0.5] in xyz. + corners = corners_nd(dims, origin=origin) + # corners: [N, 4, 2] + if angles is not None: + corners = rotation_2d(corners, angles) + corners += centers.view(-1, 1, 2) + return corners + + +def project_to_image(points_3d, proj_mat): + points_num = list(points_3d.shape)[:-1] + points_shape = np.concatenate([points_num, [1]], axis=0).tolist() + points_4 = torch.cat( + [points_3d, torch.ones(*points_shape).type_as(points_3d)], dim=-1 + ) + # point_2d = points_4 @ tf.transpose(proj_mat, [1, 0]) + point_2d = torch.matmul(points_4, proj_mat.t()) + point_2d_res = point_2d[..., :2] / point_2d[..., 2:3] + return point_2d_res + + +def camera_to_lidar(points, r_rect, velo2cam): + num_points = points.shape[0] + points = torch.cat([points, torch.ones(num_points, 1).type_as(points)], dim=-1) + lidar_points = points @ torch.inverse((r_rect @ velo2cam).t()) + return lidar_points[..., :3] + + +def lidar_to_camera(points, r_rect, velo2cam): + num_points = points.shape[0] + points = torch.cat([points, torch.ones(num_points, 1).type_as(points)], dim=-1) + camera_points = points @ (r_rect @ velo2cam).t() + return camera_points[..., :3] + + +def box_camera_to_lidar(data, r_rect, velo2cam): + xyz = data[..., 0:3] + l, h, w = data[..., 3:4], data[..., 4:5], data[..., 5:6] + r = data[..., 6:7] + xyz_lidar = camera_to_lidar(xyz, r_rect, velo2cam) + return torch.cat([xyz_lidar, w, l, h, r], dim=-1) + + +def box_lidar_to_camera(data, r_rect, velo2cam): + xyz_lidar = data[..., 0:3] + w, l, h = data[..., 3:4], data[..., 4:5], data[..., 5:6] + r = data[..., 6:7] + xyz = lidar_to_camera(xyz_lidar, r_rect, velo2cam) + return torch.cat([xyz, l, h, w, r], dim=-1) + + +def rotate_nms_pcdet(boxes, scores, thresh, pre_maxsize=None, post_max_size=None): + """ + :param boxes: (N, 5) [x, y, z, l, w, h, theta] + :param scores: (N) + :param thresh: + :return: + """ + # transform back to pcdet's coordinate + boxes = boxes[:, [0, 1, 2, 4, 3, 5, -1]] + boxes[:, -1] = -boxes[:, -1] - np.pi /2 + + order = scores.sort(0, descending=True)[1] + if pre_maxsize is not None: + order = order[:pre_maxsize] + + boxes = boxes[order].contiguous() + + keep = torch.LongTensor(boxes.size(0)) + + if len(boxes) == 0: + num_out =0 + else: + num_out = iou3d_nms_cuda.nms_gpu(boxes, keep, thresh) + + selected = order[keep[:num_out].cuda()].contiguous() + + if post_max_size is not None: + selected = selected[:post_max_size] + + return selected \ No newline at end of file diff --git a/tools/cfgs/kitti_models/centerpoint.yaml b/tools/cfgs/kitti_models/centerpoint.yaml index 9d47851d9..c4d9f74a4 100644 --- a/tools/cfgs/kitti_models/centerpoint.yaml +++ b/tools/cfgs/kitti_models/centerpoint.yaml @@ -1,3 +1,4 @@ +# Taken from here: https://github.com/open-mmlab/OpenPCDet/issues/750 CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist'] DATA_CONFIG: @@ -11,7 +12,7 @@ MODEL: NAME: MeanVFE BACKBONE_3D: - NAME: VoxelBackBone8x + NAME: VoxelResBackBone8x MAP_TO_BEV: NAME: HeightCompression @@ -30,11 +31,14 @@ MODEL: NAME: CenterHead CLASS_AGNOSTIC: False + USE_DIRECTION_CLASSIFIER: False + ############################## SHARED_CONV_CHANNEL: 64 USE_BIAS_BEFORE_NORM: True NUM_HM_CONV: 2 SEPARATE_HEAD_CFG: HEAD_ORDER: ['center', 'center_z', 'dim', 'rot'] + # this is what the head is predicting HEAD_DICT: { 'center': {'out_channels': 2, 'num_conv': 2}, 'center_z': {'out_channels': 1, 'num_conv': 2}, @@ -45,15 +49,16 @@ MODEL: CLASS_NAMES_EACH_HEAD: [ ['Car', 'Pedestrian', 'Cyclist'] ] + ############################## TARGET_ASSIGNER_CONFIG: - FEATURE_MAP_STRIDE: 8 - NUM_MAX_OBJS: 100 - VOXEL_SIZE: [0.05, 0.05, 0.1] - OUT_SIZE_FACTOR: 4 + FEATURE_MAP_STRIDE: 4 + NUM_MAX_OBJS: 500 + # VOXEL_SIZE: [0.05, 0.05, 0.1] + # OUT_SIZE_FACTOR: 4 GAUSSIAN_OVERLAP: 0.1 MIN_RADIUS: 2 - BOX_CODER: ResidualCoder + # BOX_CODER: ResidualCoder LOSS_CONFIG: LOSS_WEIGHTS: { @@ -61,6 +66,15 @@ MODEL: 'loc_weight': 2.0, 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] } + POST_PROCESSING: + SCORE_THRESH: 0.1 + POST_CENTER_LIMIT_RANGE: [-75.2, -75.2, -2, 75.2, 75.2, 4] + MAX_OBJ_PER_SAMPLE: 500 + NMS_CONFIG: + NMS_TYPE: nms_gpu + NMS_THRESH: 0.01 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 POST_PROCESSING: RECALL_THRESH_LIST: [0.3, 0.5, 0.7] diff --git a/tools/cfgs/kitti_models/centerpoint_twostage.yaml b/tools/cfgs/kitti_models/centerpoint_twostage.yaml new file mode 100644 index 000000000..763d05d87 --- /dev/null +++ b/tools/cfgs/kitti_models/centerpoint_twostage.yaml @@ -0,0 +1,150 @@ +# Taken from here: https://github.com/open-mmlab/OpenPCDet/issues/750 +CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist'] + +DATA_CONFIG: + _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml + + +MODEL: + NAME: CenterPoint + + VFE: + NAME: MeanVFE + + BACKBONE_3D: + NAME: VoxelResBackBone8x + + MAP_TO_BEV: + NAME: HeightCompression + NUM_BEV_FEATURES: 256 + + BACKBONE_2D: + NAME: BaseBEVBackbone + + LAYER_NUMS: [5] + LAYER_STRIDES: [1] + NUM_FILTERS: [128] + UPSAMPLE_STRIDES: [2] + NUM_UPSAMPLE_FILTERS: [256] + + DENSE_HEAD: + NAME: CenterHead + CLASS_AGNOSTIC: False + + USE_DIRECTION_CLASSIFIER: False + ############################## + SHARED_CONV_CHANNEL: 64 + USE_BIAS_BEFORE_NORM: True + NUM_HM_CONV: 2 + SEPARATE_HEAD_CFG: + HEAD_ORDER: ['center', 'center_z', 'dim', 'rot'] + # this is what the head is predicting + HEAD_DICT: { + 'center': {'out_channels': 2, 'num_conv': 2}, + 'center_z': {'out_channels': 1, 'num_conv': 2}, + 'dim': {'out_channels': 3, 'num_conv': 2}, + 'rot': {'out_channels': 2, 'num_conv': 2}, + } + + CLASS_NAMES_EACH_HEAD: [ + ['Car', 'Pedestrian', 'Cyclist'] + ] + ############################## + + TARGET_ASSIGNER_CONFIG: + FEATURE_MAP_STRIDE: 4 + NUM_MAX_OBJS: 500 + # VOXEL_SIZE: [0.05, 0.05, 0.1] + # OUT_SIZE_FACTOR: 4 + GAUSSIAN_OVERLAP: 0.1 + MIN_RADIUS: 2 + # BOX_CODER: ResidualCoder + + LOSS_CONFIG: + LOSS_WEIGHTS: { + 'cls_weight': 1.0, + 'loc_weight': 2.0, + 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + # POST_PROCESSING: + # SCORE_THRESH: 0.1 + # POST_CENTER_LIMIT_RANGE: [-75.2, -75.2, -2, 75.2, 75.2, 4] + # MAX_OBJ_PER_SAMPLE: 500 + # NMS_CONFIG: + # NMS_TYPE: nms_gpu + # NMS_THRESH: 0.01 + # NMS_PRE_MAXSIZE: 4096 + # NMS_POST_MAXSIZE: 500 + + ROI_HEAD: + NAME: ROIHead + NUM_POINT_FEATURES: 1920 # -> 128*3*5 + CLASS_AGNOSTIC: True + SHARED_FC: [256, 256] + CLS_FC: [256, 256] + REG_FC: [256, 256] + DP_RATIO: 0.3 + + 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: 0.55 + + + 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: 0.01 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 + + +OPTIMIZATION: + BATCH_SIZE_PER_GPU: 8 + NUM_EPOCHS: 80 + + 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 \ No newline at end of file From 530a7d128f80137bfd9857cfc77429f75ed83054 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Mon, 11 Sep 2023 06:58:19 +0000 Subject: [PATCH 48/93] tmp --- .vscode/launch.json | 14 +- pcdet/datasets/waymo/waymo_dataset.py | 4 + pcdet/models/roi_heads/__init__.py | 2 + pcdet/models/roi_heads/roi_head.py | 163 +++++++++++ pcdet/utils/box_torch_ops.py | 277 ++++++++++++++++++ tools/cfgs/kitti_models/centerpoint.yaml | 26 +- .../kitti_models/centerpoint_twostage.yaml | 150 ++++++++++ 7 files changed, 626 insertions(+), 10 deletions(-) create mode 100644 pcdet/models/roi_heads/roi_head.py create mode 100644 pcdet/utils/box_torch_ops.py create mode 100644 tools/cfgs/kitti_models/centerpoint_twostage.yaml diff --git a/.vscode/launch.json b/.vscode/launch.json index 4df9d13dc..d0d06555f 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -39,10 +39,6 @@ "args": ["--cfg_file", "./cfgs/kitti_models/pv_rcnn_relation.yaml", "--extra_tag", "debug"], // "preLaunchTask": "Change working directory", "cwd": "/root/OpenPCDet/tools/", - "env": { - "CUDA_VISIBLE_DEVICES": "1" - - }, }, { "name": "Python: Train KITTI PV_RCNN++", @@ -59,6 +55,16 @@ // "preLaunchTask": "Change working directory", "cwd": "/root/OpenPCDet/tools/" }, + { + "name": "Python: Train KITTI CenterPoint", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": true, + "args": ["--cfg_file", "./cfgs/kitti_models/centerpoint_twostage.yaml", "--extra_tag", "debug"], + "cwd": "/root/OpenPCDet/tools/", + }, { "name": "Python: Visualize KITTI PV_RCNN", "type": "python", diff --git a/pcdet/datasets/waymo/waymo_dataset.py b/pcdet/datasets/waymo/waymo_dataset.py index 44632d53f..8163bf1fb 100644 --- a/pcdet/datasets/waymo/waymo_dataset.py +++ b/pcdet/datasets/waymo/waymo_dataset.py @@ -714,6 +714,8 @@ def create_waymo_infos(dataset_cfg, class_names, data_path, save_path, os.environ["CUDA_VISIBLE_DEVICES"] = "-1" print('---------------Start to generate data infos---------------') + raw_data_tag = 'training' + dataset.set_split(train_split) waymo_infos_train = dataset.get_infos( raw_data_path=data_path / raw_data_tag, @@ -724,6 +726,8 @@ def create_waymo_infos(dataset_cfg, class_names, data_path, save_path, pickle.dump(waymo_infos_train, f) print('----------------Waymo info train file is saved to %s----------------' % train_filename) + raw_data_tag = 'validation' + dataset.set_split(val_split) waymo_infos_val = dataset.get_infos( raw_data_path=data_path / raw_data_tag, diff --git a/pcdet/models/roi_heads/__init__.py b/pcdet/models/roi_heads/__init__.py index 0dcc717a8..6d93f9c83 100644 --- a/pcdet/models/roi_heads/__init__.py +++ b/pcdet/models/roi_heads/__init__.py @@ -7,6 +7,7 @@ from .roi_head_template import RoIHeadTemplate from .mppnet_head import MPPNetHead from .mppnet_memory_bank_e2e import MPPNetHeadE2E +from .roi_head import RoIHead __all__ = { 'RoIHeadTemplate': RoIHeadTemplate, @@ -18,4 +19,5 @@ 'MPPNetHead': MPPNetHead, 'MPPNetHeadE2E': MPPNetHeadE2E, 'PVRCNNHeadRelation': PVRCNNHeadRelation, + 'ROIHead': RoIHead, } diff --git a/pcdet/models/roi_heads/roi_head.py b/pcdet/models/roi_heads/roi_head.py new file mode 100644 index 000000000..b626e5513 --- /dev/null +++ b/pcdet/models/roi_heads/roi_head.py @@ -0,0 +1,163 @@ +import torch +import torch.nn as nn +from ..backbones_3d.pfe.voxel_set_abstraction import bilinear_interpolate_torch +from ...utils.box_torch_ops import center_to_corner_box2d +from .roi_head_template import RoIHeadTemplate + +class RoIHead(RoIHeadTemplate): + def __init__(self, model_cfg): + super().__init__(model_cfg=model_cfg) + self.bev_feature_extractor = BEVFeatureExtractor(pc_start, voxel_size, out_stride) + + def forward(self, batch_dict): + + features = self.bev_feature_extractor(batch_dict, batch_centers, num_point) + + self.reorder_first_stage_pred_and_feature(first_pred=one_stage_pred, example=example, features=features) + + + batch_dict['batch_size'] = len(batch_dict['rois']) + if training: + targets_dict = self.assign_targets(batch_dict) + batch_dict['rois'] = targets_dict['rois'] + batch_dict['roi_labels'] = targets_dict['roi_labels'] + batch_dict['roi_features'] = targets_dict['roi_features'] + batch_dict['roi_scores'] = targets_dict['roi_scores'] + + # RoI aware pooling + if self.add_box_param: + batch_dict['roi_features'] = torch.cat([batch_dict['roi_features'], batch_dict['rois'], batch_dict['roi_scores'].unsqueeze(-1)], dim=-1) + + pooled_features = batch_dict['roi_features'].reshape(-1, 1, + batch_dict['roi_features'].shape[-1]).contiguous() # (BxN, 1, C) + + batch_size_rcnn = pooled_features.shape[0] + pooled_features = pooled_features.permute(0, 2, 1).contiguous() # (BxN, C, 1) + + shared_features = self.shared_fc_layer(pooled_features.view(batch_size_rcnn, -1, 1)) + rcnn_cls = self.cls_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1) # (B, 1 or 2) + rcnn_reg = self.reg_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1) # (B, C) + + if not training: + batch_cls_preds, batch_box_preds = self.generate_predicted_boxes( + batch_size=batch_dict['batch_size'], rois=batch_dict['rois'], cls_preds=rcnn_cls, box_preds=rcnn_reg + ) + batch_dict['batch_cls_preds'] = batch_cls_preds + batch_dict['batch_box_preds'] = batch_box_preds + batch_dict['cls_preds_normalized'] = False + else: + targets_dict['rcnn_cls'] = rcnn_cls + targets_dict['rcnn_reg'] = rcnn_reg + + self.forward_ret_dict = targets_dict + + return batch_dict + + + def reorder_first_stage_pred_and_feature(self, first_pred, example, features): + batch_size = len(first_pred) + box_length = first_pred[0]['box3d_lidar'].shape[1] + feature_vector_length = sum([feat[0].shape[-1] for feat in features]) + + rois = first_pred[0]['box3d_lidar'].new_zeros((batch_size, + self.NMS_POST_MAXSIZE, box_length + )) + roi_scores = first_pred[0]['scores'].new_zeros((batch_size, + self.NMS_POST_MAXSIZE + )) + roi_labels = first_pred[0]['label_preds'].new_zeros((batch_size, + self.NMS_POST_MAXSIZE), dtype=torch.long + ) + roi_features = features[0][0].new_zeros((batch_size, + self.NMS_POST_MAXSIZE, feature_vector_length + )) + + for i in range(batch_size): + num_obj = features[0][i].shape[0] + # basically move rotation to position 6, so now the box is 7 + C . C is 2 for nuscenes to + # include velocity target + + box_preds = first_pred[i]['box3d_lidar'] + + if self.roi_head.code_size == 9: + # x, y, z, w, l, h, rotation_y, velocity_x, velocity_y + box_preds = box_preds[:, [0, 1, 2, 3, 4, 5, 8, 6, 7]] + + rois[i, :num_obj] = box_preds + roi_labels[i, :num_obj] = first_pred[i]['label_preds'] + 1 + roi_scores[i, :num_obj] = first_pred[i]['scores'] + roi_features[i, :num_obj] = torch.cat([feat[i] for feat in features], dim=-1) + + example['rois'] = rois # used in pooled features + example['roi_labels'] = roi_labels + example['roi_scores'] = roi_scores # used in pooled features + example['roi_features'] = roi_features # used in pooled features + + example['has_class_labels']= True + + return example + + def get_box_center(self, boxes): + # box [List] + centers = [] + for box in boxes: + if self.num_point == 1 or len(box['box3d_lidar']) == 0: + centers.append(box['box3d_lidar'][:, :3]) + + elif self.num_point == 5: + center2d = box['box3d_lidar'][:, :2] + height = box['box3d_lidar'][:, 2:3] + dim2d = box['box3d_lidar'][:, 3:5] + rotation_y = box['box3d_lidar'][:, -1] + + corners = center_to_corner_box2d(center2d, dim2d, rotation_y) + + front_middle = torch.cat([(corners[:, 0] + corners[:, 1])/2, height], dim=-1) + back_middle = torch.cat([(corners[:, 2] + corners[:, 3])/2, height], dim=-1) + left_middle = torch.cat([(corners[:, 0] + corners[:, 3])/2, height], dim=-1) + right_middle = torch.cat([(corners[:, 1] + corners[:, 2])/2, height], dim=-1) + + points = torch.cat([box['box3d_lidar'][:, :3], front_middle, back_middle, left_middle, \ + right_middle], dim=0) + + centers.append(points) + else: + raise NotImplementedError() + + return centers + + + + +class BEVFeatureExtractor(nn.Module): + def __init__(self, pc_start, + voxel_size, out_stride): + super().__init__() + self.pc_start = pc_start + self.voxel_size = voxel_size + self.out_stride = out_stride + + def absl_to_relative(self, absolute): + a1 = (absolute[..., 0] - self.pc_start[0]) / self.voxel_size[0] / self.out_stride + a2 = (absolute[..., 1] - self.pc_start[1]) / self.voxel_size[1] / self.out_stride + + return a1, a2 + + def forward(self, example, batch_centers, num_point): + batch_size = len(example['bev_feature']) + ret_maps = [] + + for batch_idx in range(batch_size): + xs, ys = self.absl_to_relative(batch_centers[batch_idx]) + + # N x C + feature_map = bilinear_interpolate_torch(example['bev_feature'][batch_idx], + xs, ys) + + if num_point > 1: + section_size = len(feature_map) // num_point + feature_map = torch.cat([feature_map[i*section_size: (i+1)*section_size] for i in range(num_point)], dim=1) + + ret_maps.append(feature_map) + + return ret_maps \ No newline at end of file diff --git a/pcdet/utils/box_torch_ops.py b/pcdet/utils/box_torch_ops.py new file mode 100644 index 000000000..18a3b12ab --- /dev/null +++ b/pcdet/utils/box_torch_ops.py @@ -0,0 +1,277 @@ +import math +from functools import reduce + +import numpy as np +import torch +from torch import stack as tstack +try: + from det3d.ops.iou3d_nms import iou3d_nms_cuda, iou3d_nms_utils +except: + print("iou3d cuda not built. You don't need this if you use circle_nms. Otherwise, refer to the advanced installation part to build this cuda extension") + +def torch_to_np_dtype(ttype): + type_map = { + torch.float16: np.dtype(np.float16), + torch.float32: np.dtype(np.float32), + torch.float16: np.dtype(np.float64), + torch.int32: np.dtype(np.int32), + torch.int64: np.dtype(np.int64), + torch.uint8: np.dtype(np.uint8), + } + return type_map[ttype] + + +def corners_nd(dims, origin=0.5): + """generate relative box corners based on length per dim and + origin point. + + Args: + dims (float array, shape=[N, ndim]): array of length per dim + origin (list or array or float): origin point relate to smallest point. + dtype (output dtype, optional): Defaults to np.float32 + + Returns: + float array, shape=[N, 2 ** ndim, ndim]: returned corners. + point layout example: (2d) x0y0, x0y1, x1y0, x1y1; + (3d) x0y0z0, x0y0z1, x0y1z0, x0y1z1, x1y0z0, x1y0z1, x1y1z0, x1y1z1 + where x0 < x1, y0 < y1, z0 < z1 + """ + ndim = int(dims.shape[1]) + dtype = torch_to_np_dtype(dims.dtype) + if isinstance(origin, float): + origin = [origin] * ndim + corners_norm = np.stack( + np.unravel_index(np.arange(2 ** ndim), [2] * ndim), axis=1 + ).astype(dtype) + # now corners_norm has format: (2d) x0y0, x0y1, x1y0, x1y1 + # (3d) x0y0z0, x0y0z1, x0y1z0, x0y1z1, x1y0z0, x1y0z1, x1y1z0, x1y1z1 + # so need to convert to a format which is convenient to do other computing. + # for 2d boxes, format is clockwise start from minimum point + # for 3d boxes, please draw them by your hand. + if ndim == 2: + # generate clockwise box corners + corners_norm = corners_norm[[0, 1, 3, 2]] + elif ndim == 3: + corners_norm = corners_norm[[0, 1, 3, 2, 4, 5, 7, 6]] + corners_norm = corners_norm - np.array(origin, dtype=dtype) + corners_norm = torch.from_numpy(corners_norm).type_as(dims) + corners = dims.view(-1, 1, ndim) * corners_norm.view(1, 2 ** ndim, ndim) + return corners + + +def corners_2d(dims, origin=0.5): + """generate relative 2d box corners based on length per dim and + origin point. + + Args: + dims (float array, shape=[N, 2]): array of length per dim + origin (list or array or float): origin point relate to smallest point. + dtype (output dtype, optional): Defaults to np.float32 + + Returns: + float array, shape=[N, 4, 2]: returned corners. + point layout: x0y0, x0y1, x1y1, x1y0 + """ + return corners_nd(dims, origin) + + +def corner_to_standup_nd(boxes_corner): + ndim = boxes_corner.shape[2] + standup_boxes = [] + for i in range(ndim): + standup_boxes.append(torch.min(boxes_corner[:, :, i], dim=1)[0]) + for i in range(ndim): + standup_boxes.append(torch.max(boxes_corner[:, :, i], dim=1)[0]) + return torch.stack(standup_boxes, dim=1) + + +def rotation_3d_in_axis(points, angles, axis=0): + # points: [N, point_size, 3] + # angles: [N] + rot_sin = torch.sin(angles) + rot_cos = torch.cos(angles) + ones = torch.ones_like(rot_cos) + zeros = torch.zeros_like(rot_cos) + if axis == 1: + rot_mat_T = tstack( + [ + tstack([rot_cos, zeros, -rot_sin]), + tstack([zeros, ones, zeros]), + tstack([rot_sin, zeros, rot_cos]), + ] + ) + elif axis == 2 or axis == -1: + rot_mat_T = tstack( + [ + tstack([rot_cos, -rot_sin, zeros]), + tstack([rot_sin, rot_cos, zeros]), + tstack([zeros, zeros, ones]), + ] + ) + elif axis == 0: + rot_mat_T = tstack( + [ + tstack([zeros, rot_cos, -rot_sin]), + tstack([zeros, rot_sin, rot_cos]), + tstack([ones, zeros, zeros]), + ] + ) + else: + raise ValueError("axis should in range") + # print(points.shape, rot_mat_T.shape) + return torch.einsum("aij,jka->aik", points, rot_mat_T) + +def rotate_points_along_z(points, angle): + """ + Args: + points: (B, N, 3 + C) + angle: (B), angle along z-axis, angle increases x ==> y + Returns: + """ + cosa = torch.cos(angle) + sina = torch.sin(angle) + zeros = angle.new_zeros(points.shape[0]) + ones = angle.new_ones(points.shape[0]) + rot_matrix = torch.stack(( + cosa, -sina, zeros, + sina, cosa, zeros, + zeros, zeros, ones + ), dim=1).view(-1, 3, 3).float() + points_rot = torch.matmul(points[:, :, 0:3], rot_matrix) + points_rot = torch.cat((points_rot, points[:, :, 3:]), dim=-1) + return points_rot + + +def rotation_2d(points, angles): + """rotation 2d points based on origin point clockwise when angle positive. + + Args: + points (float array, shape=[N, point_size, 2]): points to be rotated. + angles (float array, shape=[N]): rotation angle. + + Returns: + float array: same shape as points + """ + rot_sin = torch.sin(angles) + rot_cos = torch.cos(angles) + rot_mat_T = torch.stack([tstack([rot_cos, -rot_sin]), tstack([rot_sin, rot_cos])]) + return torch.einsum("aij,jka->aik", (points, rot_mat_T)) + + +def center_to_corner_box3d(centers, dims, angles, origin=(0.5, 0.5, 0.5), axis=1): + """convert kitti locations, dimensions and angles to corners + + Args: + centers (float array, shape=[N, 3]): locations in kitti label file. + dims (float array, shape=[N, 3]): dimensions in kitti label file. + angles (float array, shape=[N]): rotation_y in kitti label file. + origin (list or array or float): origin point relate to smallest point. + use [0.5, 1.0, 0.5] in camera and [0.5, 0.5, 0] in lidar. + axis (int): rotation axis. 1 for camera and 2 for lidar. + Returns: + [type]: [description] + """ + # 'length' in kitti format is in x axis. + # yzx(hwl)(kitti label file)<->xyz(lhw)(camera)<->z(-x)(-y)(wlh)(lidar) + # center in kitti format is [0.5, 1.0, 0.5] in xyz. + corners = corners_nd(dims, origin=origin) + # corners: [N, 8, 3] + corners = rotation_3d_in_axis(corners, angles, axis=axis) + corners += centers.view(-1, 1, 3) + return corners + + +def center_to_corner_box2d(centers, dims, angles=None, origin=0.5): + """convert kitti locations, dimensions and angles to corners + + Args: + centers (float array, shape=[N, 2]): locations in kitti label file. + dims (float array, shape=[N, 2]): dimensions in kitti label file. + angles (float array, shape=[N]): rotation_y in kitti label file. + + Returns: + [type]: [description] + """ + # 'length' in kitti format is in x axis. + # xyz(hwl)(kitti label file)<->xyz(lhw)(camera)<->z(-x)(-y)(wlh)(lidar) + # center in kitti format is [0.5, 1.0, 0.5] in xyz. + corners = corners_nd(dims, origin=origin) + # corners: [N, 4, 2] + if angles is not None: + corners = rotation_2d(corners, angles) + corners += centers.view(-1, 1, 2) + return corners + + +def project_to_image(points_3d, proj_mat): + points_num = list(points_3d.shape)[:-1] + points_shape = np.concatenate([points_num, [1]], axis=0).tolist() + points_4 = torch.cat( + [points_3d, torch.ones(*points_shape).type_as(points_3d)], dim=-1 + ) + # point_2d = points_4 @ tf.transpose(proj_mat, [1, 0]) + point_2d = torch.matmul(points_4, proj_mat.t()) + point_2d_res = point_2d[..., :2] / point_2d[..., 2:3] + return point_2d_res + + +def camera_to_lidar(points, r_rect, velo2cam): + num_points = points.shape[0] + points = torch.cat([points, torch.ones(num_points, 1).type_as(points)], dim=-1) + lidar_points = points @ torch.inverse((r_rect @ velo2cam).t()) + return lidar_points[..., :3] + + +def lidar_to_camera(points, r_rect, velo2cam): + num_points = points.shape[0] + points = torch.cat([points, torch.ones(num_points, 1).type_as(points)], dim=-1) + camera_points = points @ (r_rect @ velo2cam).t() + return camera_points[..., :3] + + +def box_camera_to_lidar(data, r_rect, velo2cam): + xyz = data[..., 0:3] + l, h, w = data[..., 3:4], data[..., 4:5], data[..., 5:6] + r = data[..., 6:7] + xyz_lidar = camera_to_lidar(xyz, r_rect, velo2cam) + return torch.cat([xyz_lidar, w, l, h, r], dim=-1) + + +def box_lidar_to_camera(data, r_rect, velo2cam): + xyz_lidar = data[..., 0:3] + w, l, h = data[..., 3:4], data[..., 4:5], data[..., 5:6] + r = data[..., 6:7] + xyz = lidar_to_camera(xyz_lidar, r_rect, velo2cam) + return torch.cat([xyz, l, h, w, r], dim=-1) + + +def rotate_nms_pcdet(boxes, scores, thresh, pre_maxsize=None, post_max_size=None): + """ + :param boxes: (N, 5) [x, y, z, l, w, h, theta] + :param scores: (N) + :param thresh: + :return: + """ + # transform back to pcdet's coordinate + boxes = boxes[:, [0, 1, 2, 4, 3, 5, -1]] + boxes[:, -1] = -boxes[:, -1] - np.pi /2 + + order = scores.sort(0, descending=True)[1] + if pre_maxsize is not None: + order = order[:pre_maxsize] + + boxes = boxes[order].contiguous() + + keep = torch.LongTensor(boxes.size(0)) + + if len(boxes) == 0: + num_out =0 + else: + num_out = iou3d_nms_cuda.nms_gpu(boxes, keep, thresh) + + selected = order[keep[:num_out].cuda()].contiguous() + + if post_max_size is not None: + selected = selected[:post_max_size] + + return selected \ No newline at end of file diff --git a/tools/cfgs/kitti_models/centerpoint.yaml b/tools/cfgs/kitti_models/centerpoint.yaml index 9d47851d9..c4d9f74a4 100644 --- a/tools/cfgs/kitti_models/centerpoint.yaml +++ b/tools/cfgs/kitti_models/centerpoint.yaml @@ -1,3 +1,4 @@ +# Taken from here: https://github.com/open-mmlab/OpenPCDet/issues/750 CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist'] DATA_CONFIG: @@ -11,7 +12,7 @@ MODEL: NAME: MeanVFE BACKBONE_3D: - NAME: VoxelBackBone8x + NAME: VoxelResBackBone8x MAP_TO_BEV: NAME: HeightCompression @@ -30,11 +31,14 @@ MODEL: NAME: CenterHead CLASS_AGNOSTIC: False + USE_DIRECTION_CLASSIFIER: False + ############################## SHARED_CONV_CHANNEL: 64 USE_BIAS_BEFORE_NORM: True NUM_HM_CONV: 2 SEPARATE_HEAD_CFG: HEAD_ORDER: ['center', 'center_z', 'dim', 'rot'] + # this is what the head is predicting HEAD_DICT: { 'center': {'out_channels': 2, 'num_conv': 2}, 'center_z': {'out_channels': 1, 'num_conv': 2}, @@ -45,15 +49,16 @@ MODEL: CLASS_NAMES_EACH_HEAD: [ ['Car', 'Pedestrian', 'Cyclist'] ] + ############################## TARGET_ASSIGNER_CONFIG: - FEATURE_MAP_STRIDE: 8 - NUM_MAX_OBJS: 100 - VOXEL_SIZE: [0.05, 0.05, 0.1] - OUT_SIZE_FACTOR: 4 + FEATURE_MAP_STRIDE: 4 + NUM_MAX_OBJS: 500 + # VOXEL_SIZE: [0.05, 0.05, 0.1] + # OUT_SIZE_FACTOR: 4 GAUSSIAN_OVERLAP: 0.1 MIN_RADIUS: 2 - BOX_CODER: ResidualCoder + # BOX_CODER: ResidualCoder LOSS_CONFIG: LOSS_WEIGHTS: { @@ -61,6 +66,15 @@ MODEL: 'loc_weight': 2.0, 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] } + POST_PROCESSING: + SCORE_THRESH: 0.1 + POST_CENTER_LIMIT_RANGE: [-75.2, -75.2, -2, 75.2, 75.2, 4] + MAX_OBJ_PER_SAMPLE: 500 + NMS_CONFIG: + NMS_TYPE: nms_gpu + NMS_THRESH: 0.01 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 POST_PROCESSING: RECALL_THRESH_LIST: [0.3, 0.5, 0.7] diff --git a/tools/cfgs/kitti_models/centerpoint_twostage.yaml b/tools/cfgs/kitti_models/centerpoint_twostage.yaml new file mode 100644 index 000000000..763d05d87 --- /dev/null +++ b/tools/cfgs/kitti_models/centerpoint_twostage.yaml @@ -0,0 +1,150 @@ +# Taken from here: https://github.com/open-mmlab/OpenPCDet/issues/750 +CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist'] + +DATA_CONFIG: + _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml + + +MODEL: + NAME: CenterPoint + + VFE: + NAME: MeanVFE + + BACKBONE_3D: + NAME: VoxelResBackBone8x + + MAP_TO_BEV: + NAME: HeightCompression + NUM_BEV_FEATURES: 256 + + BACKBONE_2D: + NAME: BaseBEVBackbone + + LAYER_NUMS: [5] + LAYER_STRIDES: [1] + NUM_FILTERS: [128] + UPSAMPLE_STRIDES: [2] + NUM_UPSAMPLE_FILTERS: [256] + + DENSE_HEAD: + NAME: CenterHead + CLASS_AGNOSTIC: False + + USE_DIRECTION_CLASSIFIER: False + ############################## + SHARED_CONV_CHANNEL: 64 + USE_BIAS_BEFORE_NORM: True + NUM_HM_CONV: 2 + SEPARATE_HEAD_CFG: + HEAD_ORDER: ['center', 'center_z', 'dim', 'rot'] + # this is what the head is predicting + HEAD_DICT: { + 'center': {'out_channels': 2, 'num_conv': 2}, + 'center_z': {'out_channels': 1, 'num_conv': 2}, + 'dim': {'out_channels': 3, 'num_conv': 2}, + 'rot': {'out_channels': 2, 'num_conv': 2}, + } + + CLASS_NAMES_EACH_HEAD: [ + ['Car', 'Pedestrian', 'Cyclist'] + ] + ############################## + + TARGET_ASSIGNER_CONFIG: + FEATURE_MAP_STRIDE: 4 + NUM_MAX_OBJS: 500 + # VOXEL_SIZE: [0.05, 0.05, 0.1] + # OUT_SIZE_FACTOR: 4 + GAUSSIAN_OVERLAP: 0.1 + MIN_RADIUS: 2 + # BOX_CODER: ResidualCoder + + LOSS_CONFIG: + LOSS_WEIGHTS: { + 'cls_weight': 1.0, + 'loc_weight': 2.0, + 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + # POST_PROCESSING: + # SCORE_THRESH: 0.1 + # POST_CENTER_LIMIT_RANGE: [-75.2, -75.2, -2, 75.2, 75.2, 4] + # MAX_OBJ_PER_SAMPLE: 500 + # NMS_CONFIG: + # NMS_TYPE: nms_gpu + # NMS_THRESH: 0.01 + # NMS_PRE_MAXSIZE: 4096 + # NMS_POST_MAXSIZE: 500 + + ROI_HEAD: + NAME: ROIHead + NUM_POINT_FEATURES: 1920 # -> 128*3*5 + CLASS_AGNOSTIC: True + SHARED_FC: [256, 256] + CLS_FC: [256, 256] + REG_FC: [256, 256] + DP_RATIO: 0.3 + + 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: 0.55 + + + 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: 0.01 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 + + +OPTIMIZATION: + BATCH_SIZE_PER_GPU: 8 + NUM_EPOCHS: 80 + + 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 \ No newline at end of file From 2072e13406121e806ea3c9492e7bbeda45462a39 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Mon, 11 Sep 2023 09:17:49 +0000 Subject: [PATCH 49/93] Add pv_rcnn_relation_plusplus --- .vscode/launch.json | 15 + pcdet/models/detectors/__init__.py | 2 + pcdet/models/detectors/detector3d_template.py | 2 +- .../detectors/pv_rcnn_plusplus_relation.py | 61 ++++ .../pv_rcnn_plusplus_relation.yaml | 328 ++++++++++++++++++ 5 files changed, 407 insertions(+), 1 deletion(-) create mode 100644 pcdet/models/detectors/pv_rcnn_plusplus_relation.py create mode 100644 tools/cfgs/kitti_models/pv_rcnn_plusplus_relation.yaml diff --git a/.vscode/launch.json b/.vscode/launch.json index d0d06555f..874e8b484 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -29,6 +29,21 @@ // }, }, + { + "name": "Python: Train KITTI PV_RCNN_PLUSPLUS_RELATION", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": true, + "args": ["--cfg_file", "./cfgs/kitti_models/pv_rcnn_plusplus_relation.yaml", "--extra_tag", "debug"], + // "preLaunchTask": "Change working directory", + "cwd": "/root/OpenPCDet/tools/", + // "env": { + // "CUDA_VISIBLE_DEVICES": "1" + + // }, + }, { "name": "Python: Train KITTI PV_RCNN_RELATION", "type": "python", diff --git a/pcdet/models/detectors/__init__.py b/pcdet/models/detectors/__init__.py index cbf79145b..b8cacad47 100644 --- a/pcdet/models/detectors/__init__.py +++ b/pcdet/models/detectors/__init__.py @@ -16,6 +16,7 @@ from .transfusion import TransFusion from .bevfusion import BevFusion from .pv_rcnn_relation import PVRCNNRelation +from .pv_rcnn_plusplus_relation import PVRCNNPlusPlusRelation __all__ = { 'Detector3DTemplate': Detector3DTemplate, @@ -37,6 +38,7 @@ 'TransFusion': TransFusion, 'BevFusion': BevFusion, 'PVRCNNRelation': PVRCNNRelation, + 'PVRCNNPlusPlusRelation': PVRCNNPlusPlusRelation, } diff --git a/pcdet/models/detectors/detector3d_template.py b/pcdet/models/detectors/detector3d_template.py index b356c410a..672a2c963 100644 --- a/pcdet/models/detectors/detector3d_template.py +++ b/pcdet/models/detectors/detector3d_template.py @@ -169,7 +169,7 @@ def build_roi_head(self, model_info_dict): 'voxel_size': model_info_dict['voxel_size'], 'num_class': self.num_class if not self.model_cfg.ROI_HEAD.CLASS_AGNOSTIC else 1, } - if self.model_cfg.NAME == 'PVRCNNRelation': + if 'Relation' in self.model_cfg.NAME: common_args['object_relation_config'] = self.model_cfg.OBJECT_RELATION point_head_module = roi_heads.__all__[self.model_cfg.ROI_HEAD.NAME](**common_args) diff --git a/pcdet/models/detectors/pv_rcnn_plusplus_relation.py b/pcdet/models/detectors/pv_rcnn_plusplus_relation.py new file mode 100644 index 000000000..de373ae2b --- /dev/null +++ b/pcdet/models/detectors/pv_rcnn_plusplus_relation.py @@ -0,0 +1,61 @@ +from .detector3d_template import Detector3DTemplate + +from pcdet.models.object_relation.gnn import GNN + + +class PVRCNNPlusPlusRelation(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() + self.gnn = GNN(model_cfg.OBJECT_RELATION) + + def forward(self, batch_dict): + batch_dict = self.vfe(batch_dict) + batch_dict = self.backbone_3d(batch_dict) + batch_dict = self.map_to_bev_module(batch_dict) + batch_dict = self.backbone_2d(batch_dict) + batch_dict = self.dense_head(batch_dict) + + batch_dict = self.roi_head.proposal_layer( + batch_dict, nms_config=self.roi_head.model_cfg.NMS_CONFIG['TRAIN' if self.training else 'TEST'] + ) + if self.training: + targets_dict = self.roi_head.assign_targets(batch_dict) + batch_dict['rois'] = targets_dict['rois'] + batch_dict['roi_labels'] = targets_dict['roi_labels'] + batch_dict['roi_targets_dict'] = targets_dict + num_rois_per_scene = targets_dict['rois'].shape[1] + if 'roi_valid_num' in batch_dict: + batch_dict['roi_valid_num'] = [num_rois_per_scene for _ in range(batch_dict['batch_size'])] + + batch_dict = self.pfe(batch_dict) + batch_dict = self.point_head(batch_dict) + batch_dict = self.roi_head(batch_dict) + + # GNN: Object relation + batch_dict = self.gnn(batch_dict) + batch_dict = self.roi_head.final_predictions(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_rpn, tb_dict = self.dense_head.get_loss() + if self.point_head is not None: + loss_point, tb_dict = self.point_head.get_loss(tb_dict) + else: + loss_point = 0 + loss_rcnn, tb_dict = self.roi_head.get_loss(tb_dict) + + loss = loss_rpn + loss_point + loss_rcnn + return loss, tb_dict, disp_dict diff --git a/tools/cfgs/kitti_models/pv_rcnn_plusplus_relation.yaml b/tools/cfgs/kitti_models/pv_rcnn_plusplus_relation.yaml new file mode 100644 index 000000000..c85727cd0 --- /dev/null +++ b/tools/cfgs/kitti_models/pv_rcnn_plusplus_relation.yaml @@ -0,0 +1,328 @@ +CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist'] + +DATA_CONFIG: + _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml + DATA_AUGMENTOR: + DISABLE_AUG_LIST: ['placeholder'] + AUG_CONFIG_LIST: + - NAME: gt_sampling + USE_ROAD_PLANE: True + DB_INFO_PATH: + - kitti_dbinfos_train.pkl + PREPARE: { + filter_by_min_points: ['Car:5', 'Pedestrian:5', 'Cyclist:5'], + filter_by_difficulty: [-1], + } + + SAMPLE_GROUPS: ['Car:15','Pedestrian:10', 'Cyclist:10'] + NUM_POINT_FEATURES: 4 + DATABASE_WITH_FAKELIDAR: False + REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0] + LIMIT_WHOLE_SCENE: False + - NAME: random_world_flip + ALONG_AXIS_LIST: ['x'] + + - NAME: random_world_rotation + WORLD_ROT_ANGLE: [-0.78539816, 0.78539816] + + - NAME: random_world_scaling + WORLD_SCALE_RANGE: [0.95, 1.05] + +MODEL: + NAME: PVRCNNPlusPlusRelation + + VFE: + NAME: MeanVFE + + BACKBONE_3D: + NAME: VoxelBackBone8x + + 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: CenterHead + CLASS_AGNOSTIC: False + + CLASS_NAMES_EACH_HEAD: [ + [ 'Car', 'Pedestrian', 'Cyclist' ] + ] + + SHARED_CONV_CHANNEL: 64 + USE_BIAS_BEFORE_NORM: True + NUM_HM_CONV: 2 + SEPARATE_HEAD_CFG: + HEAD_ORDER: [ 'center', 'center_z', 'dim', 'rot' ] + HEAD_DICT: { + 'center': { 'out_channels': 2, 'num_conv': 2 }, + 'center_z': { 'out_channels': 1, 'num_conv': 2 }, + 'dim': { 'out_channels': 3, 'num_conv': 2 }, + 'rot': { 'out_channels': 2, 'num_conv': 2 }, + } + + TARGET_ASSIGNER_CONFIG: + FEATURE_MAP_STRIDE: 8 + NUM_MAX_OBJS: 500 + GAUSSIAN_OVERLAP: 0.1 + MIN_RADIUS: 2 + + LOSS_CONFIG: + LOSS_WEIGHTS: { + 'cls_weight': 1.0, + 'loc_weight': 2.0, + 'code_weights': [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] + } + + POST_PROCESSING: + SCORE_THRESH: 0.01 + POST_CENTER_LIMIT_RANGE: [ -75.2, -75.2, -2, 75.2, 75.2, 4 ] + MAX_OBJ_PER_SAMPLE: 500 + NMS_CONFIG: + NMS_TYPE: nms_gpu + NMS_THRESH: 0.7 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 + + PFE: + NAME: VoxelSetAbstraction + POINT_SOURCE: raw_points + NUM_KEYPOINTS: 4096 + NUM_OUTPUT_FEATURES: 90 + SAMPLE_METHOD: SPC + SPC_SAMPLING: + NUM_SECTORS: 6 + SAMPLE_RADIUS_WITH_ROI: 1.6 + + FEATURES_SOURCE: ['bev', 'x_conv3', 'x_conv4', 'raw_points'] + SA_LAYER: + raw_points: + NAME: VectorPoolAggregationModuleMSG + NUM_GROUPS: 2 + LOCAL_AGGREGATION_TYPE: local_interpolation + NUM_REDUCED_CHANNELS: 1 + NUM_CHANNELS_OF_LOCAL_AGGREGATION: 32 + MSG_POST_MLPS: [ 32 ] + FILTER_NEIGHBOR_WITH_ROI: True + RADIUS_OF_NEIGHBOR_WITH_ROI: 2.4 + + GROUP_CFG_0: + NUM_LOCAL_VOXEL: [ 2, 2, 2 ] + MAX_NEIGHBOR_DISTANCE: 0.2 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [ 32, 32 ] + GROUP_CFG_1: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 0.4 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [ 32, 32 ] + + x_conv3: + DOWNSAMPLE_FACTOR: 4 + INPUT_CHANNELS: 64 + + NAME: VectorPoolAggregationModuleMSG + NUM_GROUPS: 2 + LOCAL_AGGREGATION_TYPE: local_interpolation + NUM_REDUCED_CHANNELS: 32 + NUM_CHANNELS_OF_LOCAL_AGGREGATION: 32 + MSG_POST_MLPS: [128] + FILTER_NEIGHBOR_WITH_ROI: True + RADIUS_OF_NEIGHBOR_WITH_ROI: 4.0 + + GROUP_CFG_0: + NUM_LOCAL_VOXEL: [3, 3, 3] + MAX_NEIGHBOR_DISTANCE: 1.2 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [64, 64] + GROUP_CFG_1: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 2.4 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [ 64, 64 ] + + x_conv4: + DOWNSAMPLE_FACTOR: 8 + INPUT_CHANNELS: 64 + + NAME: VectorPoolAggregationModuleMSG + NUM_GROUPS: 2 + LOCAL_AGGREGATION_TYPE: local_interpolation + NUM_REDUCED_CHANNELS: 32 + NUM_CHANNELS_OF_LOCAL_AGGREGATION: 32 + MSG_POST_MLPS: [ 128 ] + FILTER_NEIGHBOR_WITH_ROI: True + RADIUS_OF_NEIGHBOR_WITH_ROI: 6.4 + + GROUP_CFG_0: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 2.4 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [ 64, 64 ] + GROUP_CFG_1: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 4.8 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [ 64, 64 ] + + + POINT_HEAD: + NAME: PointHeadSimple + CLS_FC: [256, 256] + CLASS_AGNOSTIC: True + USE_POINT_FEATURES_BEFORE_FUSION: True + TARGET_CONFIG: + GT_EXTRA_WIDTH: [0.2, 0.2, 0.2] + LOSS_CONFIG: + LOSS_REG: smooth-l1 + LOSS_WEIGHTS: { + 'point_cls_weight': 1.0, + } + + ROI_HEAD: + NAME: PVRCNNHeadRelation + CLASS_AGNOSTIC: True + + SHARED_FC: [256, 256] + CLS_FC: [256, 256] + REG_FC: [256, 256] + DP_RATIO: 0.3 + + 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 + SCORE_THRESH: 0.1 + +# NMS_PRE_MAXSIZE: 4096 +# NMS_POST_MAXSIZE: 500 +# NMS_THRESH: 0.85 + + + ROI_GRID_POOL: + GRID_SIZE: 6 + + NAME: VectorPoolAggregationModuleMSG + NUM_GROUPS: 2 + LOCAL_AGGREGATION_TYPE: voxel_random_choice + NUM_REDUCED_CHANNELS: 30 + NUM_CHANNELS_OF_LOCAL_AGGREGATION: 32 + MSG_POST_MLPS: [ 128 ] + + GROUP_CFG_0: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 0.8 + NEIGHBOR_NSAMPLE: 32 + POST_MLPS: [ 64, 64 ] + GROUP_CFG_1: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 1.6 + NEIGHBOR_NSAMPLE: 32 + POST_MLPS: [ 64, 64 ] + + 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: 0.55 + + 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] + } + + OBJECT_RELATION: + NAME: GNN + # NAME: radius_graph, + # RADIUS: 6, + # CONNECT_ONLY_SAME_CLASS: False + # } + GRAPH: { + CONV: { + NAME: EdgeConv, + EDGE_EMBEDDING: False, + # HEADS: 4, + }, + DYNAMIC: False, + NAME: knn_graph, + K: 16, + CONNECT_ONLY_SAME_CLASS: False, + SPACE: R3, + # EDGE_EMBEDDING: True, + } + LAYERS: [256, 256, 256, 256] + # IN_BETWEEN_MLP: [256, 256, 256, 256] + # GLOBAL_INFORMATION: { + # MLP_LAYERS: [256, 256], + # CONCATENATED: False, + # } + DP_RATIO: 0.3 + + 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: 0.7 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 + + +OPTIMIZATION: + BATCH_SIZE_PER_GPU: 2 + NUM_EPOCHS: 140 + + OPTIMIZER: adam_onecycle + LR: 0.01 + WEIGHT_DECAY: 0.001 + 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 \ No newline at end of file From aacb7fc27980b0a4825a1eb338d84ce23f017fac Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Mon, 11 Sep 2023 14:08:24 +0000 Subject: [PATCH 50/93] Update ReadMe --- README.md | 14 ++++++++++++-- .../With_and_without_context.jpeg | Bin resources/architecture.jpeg | Bin 0 -> 186922 bytes .../object_relation.png | Bin .../seen_in_context.jpeg | Bin .../with_context.jpeg | Bin .../without_context.jpeg | Bin 7 files changed, 12 insertions(+), 2 deletions(-) rename With_and_without_context.jpeg => resources/With_and_without_context.jpeg (100%) create mode 100644 resources/architecture.jpeg rename object_relation.png => resources/object_relation.png (100%) rename seen_in_context.jpeg => resources/seen_in_context.jpeg (100%) rename with_context.jpeg => resources/with_context.jpeg (100%) rename without_context.jpeg => resources/without_context.jpeg (100%) diff --git a/README.md b/README.md index b8415f2f6..b6aa06218 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,22 @@ # Object Relation for 3D Object Detection - + 3D object detectors usually predict objects independently from each other. Given the limited receptive field for each object, they are missing the required context. Especially when it comes to heavily occluded or sparse objects, context is necessary to correctly predict 3D bounding boxes. Simply increasing the receptive field would not give enough context while still being computationally feasible. Therefore, this project, comparable to previous work, proposes to model context efficiently with object relation.
- +
Object Relation as an efficient Way to model Context
+## Architecture + +To model object relation, this project introduces a GNN. The nodes in the graph are the detected objects; their features are obtained by the second stage of the model (refined features). The edges of the graph are obatined via a radius graph or knn (distance can be measured in R3 or in feature space). Global information, how the the objects relate to each other, are infused into the edges. After the GNN is applied the model obtains a feature vector for each object that contains information about all the other objects. + + + + + + + diff --git a/With_and_without_context.jpeg b/resources/With_and_without_context.jpeg similarity index 100% rename from With_and_without_context.jpeg rename to resources/With_and_without_context.jpeg diff --git a/resources/architecture.jpeg b/resources/architecture.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..3b67942914c4e0798dc60f45ae462e0cd1d55c1f GIT binary patch literal 186922 zcmeFZc~}$Ko<1H#jDQdk1VJIKsHli(6+|(nR2orfj7tm3mR1_s#Iy}a)R01veRG8t z8Ui9BB1U#aOoXts(#pOCB@3;rkt&5)l1lL{`_7$v?>xWx&D^<8Kl6w1011JUI-fe{ zv%K&7InzDU62jcwJ9h0LsHqVMJMmA#w3Kkjf#rXUKyY>@tRfHyvk5cQ<`LBKM{4*d zA;zCD^N+^_!gBm4fuNqP_P1BFXZ-b5jmB*CzdlwU`t*m>a>B;Fe#{VNupjfsuhy^m zny_*EF6Wt_M#De;c)aY7hjTkj6MIt#B{PR3G=CmLwsWSR5cFoN4{0>2tLYJD=&7me zsZG}r$oLg$sQvNr$G_p<)Mlv9)R;w_t*NDr|3Ud&{L9e->j?S*`o?ekuHt_rFH-kgNBk;TT 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zC;4-)uKgOd0RG%3`E#$X{fAb8{+ssoA6eV^Z(7GcvSRb!w2psd9p=Aj9h?3KVEPj1 literal 0 HcmV?d00001 diff --git a/object_relation.png b/resources/object_relation.png similarity index 100% rename from object_relation.png rename to resources/object_relation.png diff --git a/seen_in_context.jpeg b/resources/seen_in_context.jpeg similarity index 100% rename from seen_in_context.jpeg rename to resources/seen_in_context.jpeg diff --git a/with_context.jpeg b/resources/with_context.jpeg similarity index 100% rename from with_context.jpeg rename to resources/with_context.jpeg diff --git a/without_context.jpeg b/resources/without_context.jpeg similarity index 100% rename from without_context.jpeg rename to resources/without_context.jpeg From ad90e0b4dcf215575c3569bad57cd57683b6018f Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Mon, 11 Sep 2023 14:24:16 +0000 Subject: [PATCH 51/93] Update ReadMe --- README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index b6aa06218..2de3a137a 100644 --- a/README.md +++ b/README.md @@ -11,10 +11,13 @@ ## Architecture -To model object relation, this project introduces a GNN. The nodes in the graph are the detected objects; their features are obtained by the second stage of the model (refined features). The edges of the graph are obatined via a radius graph or knn (distance can be measured in R3 or in feature space). Global information, how the the objects relate to each other, are infused into the edges. After the GNN is applied the model obtains a feature vector for each object that contains information about all the other objects. +To model object relation, this project introduces a GNN. The nodes in the graph are the detected objects; their features are obtained by the second stage of the model (refined features). The edges of the graph are obtained via a radius graph or knn (distance can be measured in R3 or in feature space). Global information, how the objects relate to each other, are infused into the edges. After the GNN, is applied the model obtains a feature vector for each object that contains information about all the other objects. +## Results + + From 61b960149c2f9ac6e88bb9aa8403f593ed88f8e1 Mon Sep 17 00:00:00 2001 From: MarcBrede <34718040+MarcBrede@users.noreply.github.com> Date: Mon, 11 Sep 2023 16:52:53 +0200 Subject: [PATCH 52/93] Add results image --- resources/results.png | Bin 0 -> 156638 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 resources/results.png diff --git a/resources/results.png b/resources/results.png new file mode 100644 index 0000000000000000000000000000000000000000..5180dc4b00a6820b329dd38d681c547a3118edc3 GIT binary patch literal 156638 zcmeFZ1z1(xw>L^jhzNoL(t?zf(j8JFE!`j`-3=QM6_svjq(i!MBT9F7NcWc9vZ*__ z@B5zr|D1dN&v)K)zvtfXe$NNbW-r!WbFMYV7;}u@sQp1jNfz%W*-aD_6g;_SQZGJ$&Os zu#S^cp^jqA*(P|uU_^jd_7l4#nbee9c>58BV5jO)$}cZPV-)Hi6evH<0>v3H1GL|- z>TErI@*>z1!^GtSlhW&-1a0AM+JV?8ghW5y%E{TRPZ61*n>F7x`HsrB&B4>|UqpS9 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22de936728a659749bd738b106c4d4bbff7dfa86 Mon Sep 17 00:00:00 2001 From: MarcBrede <34718040+MarcBrede@users.noreply.github.com> Date: Mon, 25 Sep 2023 16:24:40 +0200 Subject: [PATCH 67/93] Update README.md --- README.md | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 94525fbe0..c68083467 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,17 @@ # Object Relation for 3D Object Detection - +**Can you spot the vehicle in this point-cloud and can you precisely draw a bounding box around it?** +
+ +
-3D object detectors usually predict objects independently from each other. Given the limited receptive field for each object, they are missing the required context. Especially when it comes to heavily occluded or sparse objects, context is necessary to correctly predict 3D bounding boxes. Simply increasing the receptive field would not give enough context while still being computationally feasible. Therefore, this project, comparable to previous work, proposes to model context efficiently with object relation. +In this example it seems difficult to detect the object. However, that's exactly what we expecet of current SoTA object detectors, given their limited receptive field. This project hypothesises that the receptive field of different architectures does not capture enough context to deal with occlusions and sparsity of 3D data. + +
+ +
+ +If we look at the full scene, it becomes clear that context can help us detect the vehicle correctly. However, simply increasing the receptive field would not give enough context while still being computationally feasible. Therefore, this project, comparable to previous work, proposes to model context efficiently with object relation.
From 4b64994717690b356cb61dab6c537098ca83e84b Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Wed, 4 Oct 2023 06:41:14 +0000 Subject: [PATCH 68/93] Update GNN --- pcdet/models/object_relation/gnn.py | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py index 8409b0919..e9fd26dbd 100644 --- a/pcdet/models/object_relation/gnn.py +++ b/pcdet/models/object_relation/gnn.py @@ -23,19 +23,23 @@ # similar to EdgeConv https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.conv.EdgeConv.html class EdgeConv(tg.nn.MessagePassing): - def __init__(self, dim_in, dim_out, drop_out=None): + def __init__(self, dim_in, dim_out, drop_out=None, skip_connection=False): super(EdgeConv, self).__init__(aggr='max') + self.skip_connection = skip_connection self.mlp = build_mlp(dim_in, [dim_out], activation="ReLU", bn=True, drop_out=drop_out) + self.batch_norm = nn.BatchNorm1d(dim_out) def forward(self, x, edge_index, edge_attr=None): - return self.propagate(edge_index, x=x, edge_attr=edge_attr) + out = self.propagate(edge_index, x=x, edge_attr=edge_attr) + out = self.batch_norm(out) + if self.skip_connection: + return out + x + return out def message(self, x_i, x_j, edge_attr=None): if edge_attr is not None: x = torch.cat((x_j - x_i, x_i, edge_attr), dim=-1) else: - # one could also try this: - # x = torch.cat((x_j, x_i), dim=-1) x = torch.cat((x_j - x_i, x_i), dim=-1) x = self.mlp(x) return x @@ -73,7 +77,8 @@ def __init__(self, object_relation_cfg, number_classes=3, pooled_feature_dim=256 edge_dim = (7 if self.graph_conv.EDGE_EMBEDDING else 0) if self.graph_conv.NAME == "EdgeConv": - curr_conv_layer_list.append(EdgeConv(2*input_dim+edge_dim, self.gnn_layers[i], drop_out=self.drop_out)) + edge_conv_skip_connection = False if "SKIP_CONNECTION" not in self.graph_conv else self.graph_conv.SKIP_CONNECTION + curr_conv_layer_list.append(EdgeConv(2*input_dim+edge_dim, self.gnn_layers[i], drop_out=self.drop_out, skip_connection=edge_conv_skip_connection)) elif self.graph_conv.NAME == "GATConv": # layer according to tg example: https://github.com/pyg-team/pytorch_geometric/blob/master/examples/gat.py curr_conv_layer_list.append(nn.Dropout(p=self.drop_out)) From 248a95988cdecc0409251047a3f108c3243c64d8 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Wed, 4 Oct 2023 07:27:48 +0000 Subject: [PATCH 69/93] Add partA2 relation model --- pcdet/models/detectors/PartA2_relation_net.py | 36 +++ pcdet/models/detectors/__init__.py | 2 + pcdet/models/roi_heads/__init__.py | 3 + .../models/roi_heads/partA2_relation_head.py | 265 ++++++++++++++++++ .../PartA2_relation_car_class_only.yaml | 191 +++++++++++++ 5 files changed, 497 insertions(+) create mode 100644 pcdet/models/detectors/PartA2_relation_net.py create mode 100644 pcdet/models/roi_heads/partA2_relation_head.py create mode 100644 tools/cfgs/kitti_models/PartA2_relation_car_class_only.yaml diff --git a/pcdet/models/detectors/PartA2_relation_net.py b/pcdet/models/detectors/PartA2_relation_net.py new file mode 100644 index 000000000..30e68dd2d --- /dev/null +++ b/pcdet/models/detectors/PartA2_relation_net.py @@ -0,0 +1,36 @@ +from .detector3d_template import Detector3DTemplate +from ..object_relation import build_object_relation_module + + +class PartA2NetRelation(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() + self.object_relation = build_object_relation_module(model_cfg.OBJECT_RELATION) + + def forward(self, batch_dict): + for cur_module in self.module_list: + batch_dict = cur_module(batch_dict) + + batch_dict = self.object_relation(batch_dict) + batch_dict = self.roi_head.final_predictions(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_rpn, tb_dict = self.dense_head.get_loss() + loss_point, tb_dict = self.point_head.get_loss(tb_dict) + loss_rcnn, tb_dict = self.roi_head.get_loss(tb_dict) + + loss = loss_rpn + loss_point + loss_rcnn + return loss, tb_dict, disp_dict \ No newline at end of file diff --git a/pcdet/models/detectors/__init__.py b/pcdet/models/detectors/__init__.py index 6bcedb61b..bb4b55744 100644 --- a/pcdet/models/detectors/__init__.py +++ b/pcdet/models/detectors/__init__.py @@ -18,6 +18,7 @@ from .pv_rcnn_relation import PVRCNNRelation from .pv_rcnn_plusplus_relation import PVRCNNPlusPlusRelation from .centerpoint_twostage import CenterPointTwoStage +from .PartA2_relation_net import PartA2NetRelation __all__ = { 'Detector3DTemplate': Detector3DTemplate, @@ -41,6 +42,7 @@ 'PVRCNNRelation': PVRCNNRelation, 'PVRCNNPlusPlusRelation': PVRCNNPlusPlusRelation, 'CenterPointTwoStage': CenterPointTwoStage, + 'PartA2NetRelation': PartA2NetRelation, } diff --git a/pcdet/models/roi_heads/__init__.py b/pcdet/models/roi_heads/__init__.py index 6d93f9c83..62ea5ce42 100644 --- a/pcdet/models/roi_heads/__init__.py +++ b/pcdet/models/roi_heads/__init__.py @@ -8,6 +8,8 @@ from .mppnet_head import MPPNetHead from .mppnet_memory_bank_e2e import MPPNetHeadE2E from .roi_head import RoIHead +from .partA2_relation_head import PartA2RelationFCHead + __all__ = { 'RoIHeadTemplate': RoIHeadTemplate, @@ -20,4 +22,5 @@ 'MPPNetHeadE2E': MPPNetHeadE2E, 'PVRCNNHeadRelation': PVRCNNHeadRelation, 'ROIHead': RoIHead, + 'PartA2RelationFCHead': PartA2RelationFCHead, } diff --git a/pcdet/models/roi_heads/partA2_relation_head.py b/pcdet/models/roi_heads/partA2_relation_head.py new file mode 100644 index 000000000..50aef6f5c --- /dev/null +++ b/pcdet/models/roi_heads/partA2_relation_head.py @@ -0,0 +1,265 @@ +import numpy as np +import torch +import torch.nn as nn + +from ...ops.roiaware_pool3d import roiaware_pool3d_utils +from ...utils.spconv_utils import spconv +from .roi_head_template import RoIHeadTemplate + + +class PartA2RelationFCHead(RoIHeadTemplate): + def __init__(self, input_channels, model_cfg, num_class=1, object_relation_config=None, **kwargs): + super().__init__(num_class=num_class, model_cfg=model_cfg) + self.model_cfg = model_cfg + + self.SA_modules = nn.ModuleList() + block = self.post_act_block + + c0 = self.model_cfg.ROI_AWARE_POOL.NUM_FEATURES // 2 + self.conv_part = spconv.SparseSequential( + block(4, 64, 3, padding=1, indice_key='rcnn_subm1'), + block(64, c0, 3, padding=1, indice_key='rcnn_subm1_1'), + ) + self.conv_rpn = spconv.SparseSequential( + block(input_channels, 64, 3, padding=1, indice_key='rcnn_subm2'), + block(64, c0, 3, padding=1, indice_key='rcnn_subm1_2'), + ) + + shared_fc_list = [] + pool_size = self.model_cfg.ROI_AWARE_POOL.POOL_SIZE + pre_channel = self.model_cfg.ROI_AWARE_POOL.NUM_FEATURES * pool_size * pool_size * pool_size + for k in range(0, self.model_cfg.SHARED_FC.__len__()): + shared_fc_list.extend([ + nn.Conv1d(pre_channel, self.model_cfg.SHARED_FC[k], kernel_size=1, bias=False), + nn.BatchNorm1d(self.model_cfg.SHARED_FC[k]), + nn.ReLU() + ]) + pre_channel = self.model_cfg.SHARED_FC[k] + + if k != self.model_cfg.SHARED_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0: + shared_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO)) + + self.shared_fc_layer = nn.Sequential(*shared_fc_list) + + if object_relation_config.NAME == 'GNN': + if 'GLOBAL_INFORMATION' in object_relation_config: + initial_input_dim = object_relation_config.GLOBAL_INFORMATION.MLP_LAYERS[-1] + if not object_relation_config.GLOBAL_INFORMATION.CONCATENATED: + initial_input_dim += self.model_cfg.SHARED_FC[-1] + else: + initial_input_dim = self.model_cfg.SHARED_FC[-1] + + if object_relation_config.SKIP_CONNECTION: + self.head_input_channels = initial_input_dim + sum(object_relation_config.LAYERS) + else: + self.head_input_channels = object_relation_config.LAYERS[-1] + elif object_relation_config.NAME == 'CGNLNet': + # TODO: udpate this + self.head_input_channels = self.model_cfg.SHARED_FC[-1] + 256 + 256 + else: + raise NotImplementedError + + self.cls_layers = self.make_fc_layers( + input_channels=self.head_input_channels, output_channels=self.num_class, fc_list=self.model_cfg.CLS_FC + ) + self.reg_layers = self.make_fc_layers( + input_channels=self.head_input_channels, + output_channels=self.box_coder.code_size * self.num_class, + fc_list=self.model_cfg.REG_FC + ) + + self.roiaware_pool3d_layer = roiaware_pool3d_utils.RoIAwarePool3d( + out_size=self.model_cfg.ROI_AWARE_POOL.POOL_SIZE, + max_pts_each_voxel=self.model_cfg.ROI_AWARE_POOL.MAX_POINTS_PER_VOXEL + ) + self.init_weights(weight_init='xavier') + + def init_weights(self, weight_init='xavier'): + if weight_init == 'kaiming': + init_func = nn.init.kaiming_normal_ + elif weight_init == 'xavier': + init_func = nn.init.xavier_normal_ + elif weight_init == 'normal': + init_func = nn.init.normal_ + else: + raise NotImplementedError + + for m in self.modules(): + if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d): + if weight_init == 'normal': + init_func(m.weight, mean=0, std=0.001) + else: + init_func(m.weight) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + nn.init.normal_(self.reg_layers[-1].weight, mean=0, std=0.001) + + def post_act_block(self, in_channels, out_channels, kernel_size, indice_key, stride=1, padding=0, conv_type='subm'): + if conv_type == 'subm': + m = spconv.SparseSequential( + spconv.SubMConv3d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key), + nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01), + nn.ReLU(), + ) + elif conv_type == 'spconv': + m = spconv.SparseSequential( + spconv.SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, + bias=False, indice_key=indice_key), + nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01), + nn.ReLU(), + ) + elif conv_type == 'inverseconv': + m = spconv.SparseSequential( + spconv.SparseInverseConv3d(in_channels, out_channels, kernel_size, + indice_key=indice_key, bias=False), + nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01), + nn.ReLU(), + ) + else: + raise NotImplementedError + return m + + def roiaware_pool(self, batch_dict): + """ + Args: + batch_dict: + batch_size: + rois: (B, num_rois, 7 + C) + point_coords: (num_points, 4) [bs_idx, x, y, z] + point_features: (num_points, C) + point_cls_scores: (N1 + N2 + N3 + ..., 1) + point_part_offset: (N1 + N2 + N3 + ..., 3) + Returns: + + """ + batch_size = batch_dict['batch_size'] + batch_idx = batch_dict['point_coords'][:, 0] + point_coords = batch_dict['point_coords'][:, 1:4] + point_features = batch_dict['point_features'] + part_features = torch.cat(( + batch_dict['point_part_offset'] if not self.model_cfg.get('DISABLE_PART', False) else point_coords, + batch_dict['point_cls_scores'].view(-1, 1).detach() + ), dim=1) + part_features[part_features[:, -1] < self.model_cfg.SEG_MASK_SCORE_THRESH, 0:3] = 0 + + rois = batch_dict['rois'] + + pooled_part_features_list, pooled_rpn_features_list = [], [] + + for bs_idx in range(batch_size): + bs_mask = (batch_idx == bs_idx) + cur_point_coords = point_coords[bs_mask] + cur_part_features = part_features[bs_mask] + cur_rpn_features = point_features[bs_mask] + cur_roi = rois[bs_idx][:, 0:7].contiguous() # (N, 7) + + pooled_part_features = self.roiaware_pool3d_layer.forward( + cur_roi, cur_point_coords, cur_part_features, pool_method='avg' + ) # (N, out_x, out_y, out_z, 4) + pooled_rpn_features = self.roiaware_pool3d_layer.forward( + cur_roi, cur_point_coords, cur_rpn_features, pool_method='max' + ) # (N, out_x, out_y, out_z, C) + + pooled_part_features_list.append(pooled_part_features) + pooled_rpn_features_list.append(pooled_rpn_features) + + pooled_part_features = torch.cat(pooled_part_features_list, dim=0) # (B * N, out_x, out_y, out_z, 4) + pooled_rpn_features = torch.cat(pooled_rpn_features_list, dim=0) # (B * N, out_x, out_y, out_z, C) + + return pooled_part_features, pooled_rpn_features + + @staticmethod + def fake_sparse_idx(sparse_idx, batch_size_rcnn): + print('Warning: Sparse_Idx_Shape(%s) \r' % (str(sparse_idx.shape)), end='', flush=True) + # at most one sample is non-empty, then fake the first voxels of each sample(BN needs at least + # two values each channel) as non-empty for the below calculation + sparse_idx = sparse_idx.new_zeros((batch_size_rcnn, 3)) + bs_idxs = torch.arange(batch_size_rcnn).type_as(sparse_idx).view(-1, 1) + sparse_idx = torch.cat((bs_idxs, sparse_idx), dim=1) + return sparse_idx + + def forward(self, batch_dict): + """ + Args: + batch_dict: + + Returns: + + """ + targets_dict = self.proposal_layer( + batch_dict, nms_config=self.model_cfg.NMS_CONFIG['TRAIN' if self.training else 'TEST'] + ) + if self.training: + targets_dict = self.assign_targets(batch_dict) + batch_dict['rois'] = targets_dict['rois'] + batch_dict['roi_labels'] = targets_dict['roi_labels'] + + B,N,_ = batch_dict['rois'].shape + # RoI aware pooling + pooled_part_features, pooled_rpn_features = self.roiaware_pool(batch_dict) + batch_size_rcnn = pooled_part_features.shape[0] # (B * N, out_x, out_y, out_z, 4) + + # transform to sparse tensors + sparse_shape = np.array(pooled_part_features.shape[1:4], dtype=np.int32) + sparse_idx = pooled_part_features.sum(dim=-1).nonzero() # (non_empty_num, 4) ==> [bs_idx, x_idx, y_idx, z_idx] + if sparse_idx.shape[0] < 3: + sparse_idx = self.fake_sparse_idx(sparse_idx, batch_size_rcnn) + if self.training: + # these are invalid samples + targets_dict['rcnn_cls_labels'].fill_(-1) + targets_dict['reg_valid_mask'].fill_(-1) + + part_features = pooled_part_features[sparse_idx[:, 0], sparse_idx[:, 1], sparse_idx[:, 2], sparse_idx[:, 3]] + rpn_features = pooled_rpn_features[sparse_idx[:, 0], sparse_idx[:, 1], sparse_idx[:, 2], sparse_idx[:, 3]] + coords = sparse_idx.int().contiguous() + part_features = spconv.SparseConvTensor(part_features, coords, sparse_shape, batch_size_rcnn) + rpn_features = spconv.SparseConvTensor(rpn_features, coords, sparse_shape, batch_size_rcnn) + + # forward rcnn network + x_part = self.conv_part(part_features) + x_rpn = self.conv_rpn(rpn_features) + + merged_feature = torch.cat((x_rpn.features, x_part.features), dim=1) # (N, C) + shared_feature = spconv.SparseConvTensor(merged_feature, coords, sparse_shape, batch_size_rcnn) + shared_feature = shared_feature.dense().view(batch_size_rcnn, -1, 1) + + shared_feature = self.shared_fc_layer(shared_feature) + batch_dict['pooled_features'] = shared_feature.view(B, N, self.model_cfg.SHARED_FC[-1]) + self.forward_ret_dict = targets_dict + + + # rcnn_cls = self.cls_layers(shared_feature).transpose(1, 2).contiguous().squeeze(dim=1) # (B, 1 or 2) + # rcnn_reg = self.reg_layers(shared_feature).transpose(1, 2).contiguous().squeeze(dim=1) # (B, C) + + # if not self.training: + # batch_cls_preds, batch_box_preds = self.generate_predicted_boxes( + # batch_size=batch_dict['batch_size'], rois=batch_dict['rois'], cls_preds=rcnn_cls, box_preds=rcnn_reg + # ) + # batch_dict['batch_cls_preds'] = batch_cls_preds + # batch_dict['batch_box_preds'] = batch_box_preds + # batch_dict['cls_preds_normalized'] = False + # else: + # targets_dict['rcnn_cls'] = rcnn_cls + # targets_dict['rcnn_reg'] = rcnn_reg + + # self.forward_ret_dict = targets_dict + return batch_dict + + def final_predictions(self, batch_dict): + shared_features = batch_dict['related_features'] + shared_features = shared_features.view(-1, self.head_input_channels, 1) + rcnn_cls = self.cls_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1) # (B, 1 or 2) + rcnn_reg = self.reg_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1) # (B, C) + + if not self.training: + batch_cls_preds, batch_box_preds = self.generate_predicted_boxes( + batch_size=batch_dict['batch_size'], rois=batch_dict['rois'], cls_preds=rcnn_cls, box_preds=rcnn_reg + ) + batch_dict['batch_cls_preds'] = batch_cls_preds + batch_dict['batch_box_preds'] = batch_box_preds + batch_dict['cls_preds_normalized'] = False + else: + self.forward_ret_dict['rcnn_cls'] = rcnn_cls + self.forward_ret_dict['rcnn_reg'] = rcnn_reg + + return batch_dict diff --git a/tools/cfgs/kitti_models/PartA2_relation_car_class_only.yaml b/tools/cfgs/kitti_models/PartA2_relation_car_class_only.yaml new file mode 100644 index 000000000..3cf732256 --- /dev/null +++ b/tools/cfgs/kitti_models/PartA2_relation_car_class_only.yaml @@ -0,0 +1,191 @@ +CLASS_NAMES: ['Car'] + +DATA_CONFIG: + _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml + + +MODEL: + NAME: PartA2NetRelation + + VFE: + NAME: MeanVFE + + BACKBONE_3D: + NAME: UNetV2 + + 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: AnchorHeadSingle + CLASS_AGNOSTIC: False + + USE_DIRECTION_CLASSIFIER: True + DIR_OFFSET: 0.78539 + DIR_LIMIT_OFFSET: 0.0 + NUM_DIR_BINS: 2 + + ANCHOR_GENERATOR_CONFIG: [ + { + 'class_name': 'Car', + 'anchor_sizes': [[3.9, 1.6, 1.56]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [-1.78], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.6, + 'unmatched_threshold': 0.45 + } + ] + + TARGET_ASSIGNER_CONFIG: + NAME: AxisAlignedTargetAssigner + POS_FRACTION: -1.0 + SAMPLE_SIZE: 512 + NORM_BY_NUM_EXAMPLES: False + MATCH_HEIGHT: False + BOX_CODER: ResidualCoder + + LOSS_CONFIG: + LOSS_WEIGHTS: { + 'cls_weight': 1.0, + 'loc_weight': 2.0, + 'dir_weight': 0.2, + 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + + POINT_HEAD: + NAME: PointIntraPartOffsetHead + CLS_FC: [] + PART_FC: [] + CLASS_AGNOSTIC: True + TARGET_CONFIG: + GT_EXTRA_WIDTH: [0.2, 0.2, 0.2] + LOSS_CONFIG: + LOSS_REG: smooth-l1 + LOSS_WEIGHTS: { + 'point_cls_weight': 1.0, + 'point_part_weight': 1.0 + } + + ROI_HEAD: + NAME: PartA2RelationFCHead + CLASS_AGNOSTIC: True + + SHARED_FC: [256, 256, 256] + CLS_FC: [256, 256] + REG_FC: [256, 256] + DP_RATIO: 0.3 + + SEG_MASK_SCORE_THRESH: 0.3 + + 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] + } + + OBJECT_RELATION: + NAME: GNN + GRAPH: { + CONV: { + NAME: EdgeConv, + EDGE_EMBEDDING: True, + SKIP_CONECTION: True + }, + DYNAMIC: False, + NAME: knn_graph, + K: 16, + CONNECT_ONLY_SAME_CLASS: False, + SPACE: R3, + } + LAYERS: [256, 256, 256, 256] + SKIP_CONNECTION: True + DP_RATIO: 0.3 + + + + 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: + BATCH_SIZE_PER_GPU: 4 + NUM_EPOCHS: 80 + + 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 From c7ca8f6c4c780f2fd22623b40adbe360a9358ee4 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Wed, 4 Oct 2023 08:24:22 +0000 Subject: [PATCH 70/93] Update launch configs --- .vscode/launch.json | 56 +++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 54 insertions(+), 2 deletions(-) diff --git a/.vscode/launch.json b/.vscode/launch.json index ec8d9ab36..fbc9c4d9a 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -29,6 +29,37 @@ // }, }, + { + "name": "Python: Train KITTI PV_RCNN_Relation Car Class only", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": true, + "args": ["--cfg_file", "./cfgs/kitti_models/pv_rcnn_relation_car_class_only.yaml", "--extra_tag", "debug"], + // "preLaunchTask": "Change working directory", + "cwd": "/root/OpenPCDet/tools/", + // "env": { + // "CUDA_VISIBLE_DEVICES": "1" + + // }, + }, + + { + "name": "Python: Train KITTI PV_RCNN_PlusPlus_Relation Car Class only", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": true, + "args": ["--cfg_file", "./cfgs/kitti_models/pv_rcnn_plusplus_reproduced_by_community_car_class_only.yaml", "--extra_tag", "debug"], + // "preLaunchTask": "Change working directory", + "cwd": "/root/OpenPCDet/tools/", + // "env": { + // "CUDA_VISIBLE_DEVICES": "1" + + // }, + }, { "name": "Python: Train KITTI PV_RCNN_PLUSPLUS_RELATION", "type": "python", @@ -90,6 +121,26 @@ "args": ["--cfg_file", "./cfgs/kitti_models/centerpoint_twostage.yaml", "--extra_tag", "debug"], "cwd": "/root/OpenPCDet/tools/", }, + { + "name": "Python: Train KITTI PartA2 Car Class Only", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": true, + "args": ["--cfg_file", "./cfgs/kitti_models/PartA2_car_class_only.yaml", "--extra_tag", "debug"], + "cwd": "/root/OpenPCDet/tools/", + }, + { + "name": "Python: Train KITTI PartA2 Relation Car Class Only", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": true, + "args": ["--cfg_file", "./cfgs/kitti_models/PartA2_relation_car_class_only.yaml", "--extra_tag", "debug"], + "cwd": "/root/OpenPCDet/tools/", + }, { "name": "Python: Visualize KITTI PV_RCNN", "type": "python", @@ -106,17 +157,18 @@ "cwd": "/root/OpenPCDet/tools/" }, { - "name": "Python: Evaluate KITTI PV_RCNN", + "name": "Python: Evaluate KITTI PV_RCNN_Relation_CAR_ONLY", "type": "python", "request": "launch", "program": "${file}", "console": "integratedTerminal", - "args": ["--cfg_file", "./cfgs/kitti_models/pv_rcnn.yaml", "--ckpt", "../output/cfgs/kitti_models/pv_rcnn/2023-08-01_20-06-45/ckpt/checkpoint_epoch_90.pth", "--batch_size", "6"], + "args": ["--cfg_file", "./cfgs/kitti_models/pv_rcnn_relation_car_class_only.yaml", "--ckpt", "../output/cfgs/kitti_models/pv_rcnn_relation_car_class_only/2023-09-29_07-21-48/ckpt/checkpoint_epoch_80.pth", "--extra_tag", "2023-09-29_07-21-48"], "cwd": "/root/OpenPCDet/tools/", "env": { "CUDA_VISIBLE_DEVICES": "1" }, + "justMyCode": false }, { "name": "Python: Evaluate KITTI PV_RCNN++ ALL", From df09f4ac0f413fd13b4d43f7102f037d54deacce Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Wed, 4 Oct 2023 08:30:40 +0000 Subject: [PATCH 71/93] Add configs for pv_rcnn_relation for only car --- .../pv_rcnn_relation_car_class_only.yaml | 271 ++++++++++++++++++ 1 file changed, 271 insertions(+) create mode 100644 tools/cfgs/kitti_models/pv_rcnn_relation_car_class_only.yaml diff --git a/tools/cfgs/kitti_models/pv_rcnn_relation_car_class_only.yaml b/tools/cfgs/kitti_models/pv_rcnn_relation_car_class_only.yaml new file mode 100644 index 000000000..0ce3d2f17 --- /dev/null +++ b/tools/cfgs/kitti_models/pv_rcnn_relation_car_class_only.yaml @@ -0,0 +1,271 @@ +CLASS_NAMES: ['Car'] + +DATA_CONFIG: + _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml + DATA_AUGMENTOR: + DISABLE_AUG_LIST: ['placeholder'] + AUG_CONFIG_LIST: + - NAME: gt_sampling + USE_ROAD_PLANE: True + DB_INFO_PATH: + - kitti_dbinfos_train.pkl + PREPARE: { + filter_by_min_points: ['Car:5'], + filter_by_difficulty: [-1], + } + + SAMPLE_GROUPS: ['Car:15'] + NUM_POINT_FEATURES: 4 + DATABASE_WITH_FAKELIDAR: False + REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0] + LIMIT_WHOLE_SCENE: False + + - NAME: random_world_flip + ALONG_AXIS_LIST: ['x'] + + - NAME: random_world_rotation + WORLD_ROT_ANGLE: [-0.78539816, 0.78539816] + + - NAME: random_world_scaling + WORLD_SCALE_RANGE: [0.95, 1.05] + +MODEL: + NAME: PVRCNNRelation + + VFE: + NAME: MeanVFE + + BACKBONE_3D: + NAME: VoxelBackBone8x + + 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: AnchorHeadSingle + CLASS_AGNOSTIC: False + + USE_DIRECTION_CLASSIFIER: True + DIR_OFFSET: 0.78539 + DIR_LIMIT_OFFSET: 0.0 + NUM_DIR_BINS: 2 + + ANCHOR_GENERATOR_CONFIG: [ + { + 'class_name': 'Car', + 'anchor_sizes': [[3.9, 1.6, 1.56]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [-1.78], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.6, + 'unmatched_threshold': 0.45 + }, + ] + + TARGET_ASSIGNER_CONFIG: + NAME: AxisAlignedTargetAssigner + POS_FRACTION: -1.0 + SAMPLE_SIZE: 512 + NORM_BY_NUM_EXAMPLES: False + MATCH_HEIGHT: False + BOX_CODER: ResidualCoder + + LOSS_CONFIG: + LOSS_WEIGHTS: { + 'cls_weight': 1.0, + 'loc_weight': 2.0, + 'dir_weight': 0.2, + 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + + PFE: + NAME: VoxelSetAbstraction + POINT_SOURCE: raw_points + NUM_KEYPOINTS: 2048 + NUM_OUTPUT_FEATURES: 128 + SAMPLE_METHOD: FPS + + FEATURES_SOURCE: ['bev', 'x_conv1', 'x_conv2', 'x_conv3', 'x_conv4', 'raw_points'] + SA_LAYER: + raw_points: + MLPS: [[16, 16], [16, 16]] + POOL_RADIUS: [0.4, 0.8] + NSAMPLE: [16, 16] + x_conv1: + DOWNSAMPLE_FACTOR: 1 + MLPS: [[16, 16], [16, 16]] + POOL_RADIUS: [0.4, 0.8] + NSAMPLE: [16, 16] + x_conv2: + DOWNSAMPLE_FACTOR: 2 + MLPS: [[32, 32], [32, 32]] + POOL_RADIUS: [0.8, 1.2] + NSAMPLE: [16, 32] + x_conv3: + DOWNSAMPLE_FACTOR: 4 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [1.2, 2.4] + NSAMPLE: [16, 32] + x_conv4: + DOWNSAMPLE_FACTOR: 8 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [2.4, 4.8] + NSAMPLE: [16, 32] + + POINT_HEAD: + NAME: PointHeadSimple + CLS_FC: [256, 256] + CLASS_AGNOSTIC: True + USE_POINT_FEATURES_BEFORE_FUSION: True + TARGET_CONFIG: + GT_EXTRA_WIDTH: [0.2, 0.2, 0.2] + LOSS_CONFIG: + LOSS_REG: smooth-l1 + LOSS_WEIGHTS: { + 'point_cls_weight': 1.0, + } + + ROI_HEAD: + NAME: PVRCNNHeadRelation + CLASS_AGNOSTIC: True + + SHARED_FC: [256, 256] + CLS_FC: [256, 256] + REG_FC: [256, 256] + DP_RATIO: 0.3 + + NMS_CONFIG: + TRAIN: + NMS_TYPE: nms_gpu + MULTI_CLASSES_NMS: False + # the proposals are soreted according to scores and then we take the NMS_PRE_MAXSIZE first + NMS_PRE_MAXSIZE: 9000 + # proposals with more than NMS_THRESH overlap with others will be removed + NMS_THRESH: 0.8 + # the proposals after NMS will be limited to NMS_POST_MAXSIZE + NMS_POST_MAXSIZE: 512 + # values are different for testing + TEST: + NMS_TYPE: nms_gpu + MULTI_CLASSES_NMS: False + NMS_PRE_MAXSIZE: 1024 + NMS_POST_MAXSIZE: 100 + NMS_THRESH: 0.7 + + ROI_GRID_POOL: + GRID_SIZE: 6 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [0.8, 1.6] + NSAMPLE: [16, 16] + POOL_METHOD: max_pool + + TARGET_CONFIG: + BOX_CODER: ResidualCoder + # number of proposal after the sampling + ROI_PER_IMAGE: 128 + # max number of foreground proposal + FG_RATIO: 0.5 + + SAMPLE_ROI_BY_EACH_CLASS: True + CLS_SCORE_TYPE: roi_iou + + # thresholds defining the foregrouns, easy background and hard background + CLS_FG_THRESH: 0.75 + CLS_BG_THRESH: 0.25 + CLS_BG_THRESH_LO: 0.1 + # from the background proposals, HARD_BG_RATIO are ones with very small iou with gt (1-HARD_BG_RATIO are with large iou with gt -> still background) + HARD_BG_RATIO: 0.8 + + REG_FG_THRESH: 0.55 + + 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] + } + + OBJECT_RELATION: + NAME: GNN + # NAME: radius_graph, + # RADIUS: 6, + # CONNECT_ONLY_SAME_CLASS: False + # } + GRAPH: { + CONV: { + NAME: EdgeConv, + EDGE_EMBEDDING: True, + # HEADS: 4, + SKIP_CONECTION: True + }, + DYNAMIC: False, + NAME: knn_graph, + K: 16, + CONNECT_ONLY_SAME_CLASS: False, + SPACE: R3, + # EDGE_EMBEDDING: True, + } + LAYERS: [256, 256, 256, 256] + SKIP_CONNECTION: True + # IN_BETWEEN_MLP: [256, 256, 256, 256] + # GLOBAL_INFORMATION: { + # MLP_LAYERS: [256, 256], + # CONCATENATED: False, + # } + DP_RATIO: 0.3 + + + POST_PROCESSING: + # IoU Threshold + RECALL_THRESH_LIST: [0.3, 0.5, 0.7] + # Car, Pedestrian, Cyclist + SCORE_THRESH: 0.1 + # SCORE_THRESH: 0.0 + OUTPUT_RAW_SCORE: False + + EVAL_METRIC: kitti + + NMS_CONFIG: + MULTI_CLASSES_NMS: False + NMS_TYPE: nms_gpu + NMS_THRESH: 0.1 + # NMS_THRESH: 1.0 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 + + +OPTIMIZATION: + BATCH_SIZE_PER_GPU: 2 + NUM_EPOCHS: 80 + + 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 From f7f06ee8b0590a28054478183636f49aee4cd5ca Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Thu, 5 Oct 2023 07:43:30 +0000 Subject: [PATCH 72/93] Fix bug in configs and gnn --- pcdet/models/object_relation/gnn.py | 11 ++++++----- pcdet/models/roi_heads/pvrcnn_head_relation.py | 2 +- tools/cfgs/kitti_models/pv_rcnn_relation.yaml | 5 ++++- .../kitti_models/pv_rcnn_relation_car_class_only.yaml | 4 +++- 4 files changed, 14 insertions(+), 8 deletions(-) diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py index e9fd26dbd..7711cce4c 100644 --- a/pcdet/models/object_relation/gnn.py +++ b/pcdet/models/object_relation/gnn.py @@ -51,8 +51,10 @@ def __init__(self, object_relation_cfg, number_classes=3, pooled_feature_dim=256 self.graph_cfg = object_relation_cfg.GRAPH self.graph_conv = object_relation_cfg.GRAPH.CONV self.gnn_layers = object_relation_cfg.LAYERS - self.in_between_layers = object_relation_cfg.IN_BETWEEN_MLP if 'IN_BETWEEN_MLP' in object_relation_cfg else None - self.global_information = object_relation_cfg.GLOBAL_INFORMATION if 'GLOBAL_INFORMATION' in object_relation_cfg else None + self.in_between_layers = object_relation_cfg.IN_BETWEEN_MLP + # self.in_between_layers = object_relation_cfg.IN_BETWEEN_MLP if 'IN_BETWEEN_MLP' in object_relation_cfg else None + self.global_information = object_relation_cfg.GLOBAL_INFORMATION + # self.global_information = object_relation_cfg.GLOBAL_INFORMATION if 'GLOBAL_INFORMATION' in object_relation_cfg else None self.number_classes = number_classes self.drop_out = object_relation_cfg.DP_RATIO self.skip_connection = object_relation_cfg.SKIP_CONNECTION @@ -77,8 +79,7 @@ def __init__(self, object_relation_cfg, number_classes=3, pooled_feature_dim=256 edge_dim = (7 if self.graph_conv.EDGE_EMBEDDING else 0) if self.graph_conv.NAME == "EdgeConv": - edge_conv_skip_connection = False if "SKIP_CONNECTION" not in self.graph_conv else self.graph_conv.SKIP_CONNECTION - curr_conv_layer_list.append(EdgeConv(2*input_dim+edge_dim, self.gnn_layers[i], drop_out=self.drop_out, skip_connection=edge_conv_skip_connection)) + curr_conv_layer_list.append(EdgeConv(2*input_dim+edge_dim, self.gnn_layers[i], drop_out=self.drop_out, skip_connection=self.graph_conv.SKIP_CONNECTION)) elif self.graph_conv.NAME == "GATConv": # layer according to tg example: https://github.com/pyg-team/pytorch_geometric/blob/master/examples/gat.py curr_conv_layer_list.append(nn.Dropout(p=self.drop_out)) @@ -145,7 +146,7 @@ def forward(self, batch_dict): batch_dict['gnn_edges'] = edge_index edge_attr = None - if 'EDGE_EMBEDDING' in self.graph_conv and self.graph_conv.EDGE_EMBEDDING: + if self.graph_conv.EDGE_EMBEDDING: from_node, to_node = edge_index edge_attr = proposal_boxes[from_node] - proposal_boxes[to_node] diff --git a/pcdet/models/roi_heads/pvrcnn_head_relation.py b/pcdet/models/roi_heads/pvrcnn_head_relation.py index 4fefcbc6b..7b499b37b 100644 --- a/pcdet/models/roi_heads/pvrcnn_head_relation.py +++ b/pcdet/models/roi_heads/pvrcnn_head_relation.py @@ -32,7 +32,7 @@ def __init__(self, input_channels, model_cfg, num_class=1, object_relation_confi self.shared_fc_layer = nn.Sequential(*shared_fc_list) if object_relation_config.NAME == 'GNN': - if 'GLOBAL_INFORMATION' in object_relation_config: + if object_relation_config.GLOBAL_INFORMATION: initial_input_dim = object_relation_config.GLOBAL_INFORMATION.MLP_LAYERS[-1] if not object_relation_config.GLOBAL_INFORMATION.CONCATENATED: initial_input_dim += self.model_cfg.SHARED_FC[-1] diff --git a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml index efb1b0bc5..eff359b58 100644 --- a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml +++ b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml @@ -231,6 +231,7 @@ MODEL: NAME: EdgeConv, EDGE_EMBEDDING: True, # HEADS: 4, + SKIP_CONNECTION: True }, DYNAMIC: False, NAME: knn_graph, @@ -240,12 +241,14 @@ MODEL: # EDGE_EMBEDDING: True, } LAYERS: [256, 256, 256, 256] - SKIP_CONNECTION: False + SKIP_CONNECTION: True # IN_BETWEEN_MLP: [256, 256, 256, 256] + IN_BETWEEN_MLP: False # GLOBAL_INFORMATION: { # MLP_LAYERS: [256, 256], # CONCATENATED: False, # } + GLOBAL_INFORMATION: False DP_RATIO: 0.3 diff --git a/tools/cfgs/kitti_models/pv_rcnn_relation_car_class_only.yaml b/tools/cfgs/kitti_models/pv_rcnn_relation_car_class_only.yaml index 0ce3d2f17..837b60e75 100644 --- a/tools/cfgs/kitti_models/pv_rcnn_relation_car_class_only.yaml +++ b/tools/cfgs/kitti_models/pv_rcnn_relation_car_class_only.yaml @@ -211,7 +211,7 @@ MODEL: NAME: EdgeConv, EDGE_EMBEDDING: True, # HEADS: 4, - SKIP_CONECTION: True + SKIP_CONNECTION: True }, DYNAMIC: False, NAME: knn_graph, @@ -223,10 +223,12 @@ MODEL: LAYERS: [256, 256, 256, 256] SKIP_CONNECTION: True # IN_BETWEEN_MLP: [256, 256, 256, 256] + IN_BETWEEN_MLP: False # GLOBAL_INFORMATION: { # MLP_LAYERS: [256, 256], # CONCATENATED: False, # } + GLOBAL_INFORMATION: False DP_RATIO: 0.3 From 5b5428311eed148015dda64ebca8634aa97a9894 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Tue, 10 Oct 2023 14:09:52 +0000 Subject: [PATCH 73/93] Update parta2 relation model --- pcdet/models/roi_heads/partA2_relation_head.py | 2 +- tools/cfgs/kitti_models/PartA2_relation_car_class_only.yaml | 4 +++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/pcdet/models/roi_heads/partA2_relation_head.py b/pcdet/models/roi_heads/partA2_relation_head.py index 50aef6f5c..dca2471e9 100644 --- a/pcdet/models/roi_heads/partA2_relation_head.py +++ b/pcdet/models/roi_heads/partA2_relation_head.py @@ -42,7 +42,7 @@ def __init__(self, input_channels, model_cfg, num_class=1, object_relation_confi self.shared_fc_layer = nn.Sequential(*shared_fc_list) if object_relation_config.NAME == 'GNN': - if 'GLOBAL_INFORMATION' in object_relation_config: + if object_relation_config.GLOBAL_INFORMATION: initial_input_dim = object_relation_config.GLOBAL_INFORMATION.MLP_LAYERS[-1] if not object_relation_config.GLOBAL_INFORMATION.CONCATENATED: initial_input_dim += self.model_cfg.SHARED_FC[-1] diff --git a/tools/cfgs/kitti_models/PartA2_relation_car_class_only.yaml b/tools/cfgs/kitti_models/PartA2_relation_car_class_only.yaml index 3cf732256..1c99aacf6 100644 --- a/tools/cfgs/kitti_models/PartA2_relation_car_class_only.yaml +++ b/tools/cfgs/kitti_models/PartA2_relation_car_class_only.yaml @@ -140,7 +140,7 @@ MODEL: CONV: { NAME: EdgeConv, EDGE_EMBEDDING: True, - SKIP_CONECTION: True + SKIP_CONNECTION: True }, DYNAMIC: False, NAME: knn_graph, @@ -150,6 +150,8 @@ MODEL: } LAYERS: [256, 256, 256, 256] SKIP_CONNECTION: True + IN_BETWEEN_MLP: False + GLOBAL_INFORMATION: False DP_RATIO: 0.3 From a9e8ade4f319b48dbb677454008adde8b3e3219d Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Tue, 10 Oct 2023 15:11:04 +0000 Subject: [PATCH 74/93] Add voxel_rcnn_relation code --- pcdet/models/detectors/__init__.py | 2 + pcdet/models/detectors/voxel_rcnn_relation.py | 42 +++ pcdet/models/roi_heads/__init__.py | 2 + .../roi_heads/voxelrcnn_relation_head.py | 304 ++++++++++++++++++ .../kitti_models/voxel_rcnn_relation_car.yaml | 232 +++++++++++++ 5 files changed, 582 insertions(+) create mode 100644 pcdet/models/detectors/voxel_rcnn_relation.py create mode 100644 pcdet/models/roi_heads/voxelrcnn_relation_head.py create mode 100644 tools/cfgs/kitti_models/voxel_rcnn_relation_car.yaml diff --git a/pcdet/models/detectors/__init__.py b/pcdet/models/detectors/__init__.py index bb4b55744..30484ae9c 100644 --- a/pcdet/models/detectors/__init__.py +++ b/pcdet/models/detectors/__init__.py @@ -19,6 +19,7 @@ from .pv_rcnn_plusplus_relation import PVRCNNPlusPlusRelation from .centerpoint_twostage import CenterPointTwoStage from .PartA2_relation_net import PartA2NetRelation +from .voxel_rcnn_relation import VoxelRCNNRelation __all__ = { 'Detector3DTemplate': Detector3DTemplate, @@ -43,6 +44,7 @@ 'PVRCNNPlusPlusRelation': PVRCNNPlusPlusRelation, 'CenterPointTwoStage': CenterPointTwoStage, 'PartA2NetRelation': PartA2NetRelation, + 'VoxelRCNNRelation': VoxelRCNNRelation, } diff --git a/pcdet/models/detectors/voxel_rcnn_relation.py b/pcdet/models/detectors/voxel_rcnn_relation.py new file mode 100644 index 000000000..9353936f0 --- /dev/null +++ b/pcdet/models/detectors/voxel_rcnn_relation.py @@ -0,0 +1,42 @@ +from .detector3d_template import Detector3DTemplate +from ..object_relation import build_object_relation_module + + +class VoxelRCNNRelation(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() + self.object_relation = build_object_relation_module(model_cfg.OBJECT_RELATION) + + def forward(self, batch_dict): + for cur_module in self.module_list: + batch_dict = cur_module(batch_dict) + + batch_dict = self.object_relation(batch_dict) + batch_dict = self.roi_head.final_predictions(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 = 0 + + loss_rpn, tb_dict = self.dense_head.get_loss() + loss_rcnn, tb_dict = self.roi_head.get_loss(tb_dict) + + loss = loss + loss_rpn + loss_rcnn + + if hasattr(self.backbone_3d, 'get_loss'): + loss_backbone3d, tb_dict = self.backbone_3d.get_loss(tb_dict) + loss += loss_backbone3d + + return loss, tb_dict, disp_dict diff --git a/pcdet/models/roi_heads/__init__.py b/pcdet/models/roi_heads/__init__.py index 62ea5ce42..0be2a8b00 100644 --- a/pcdet/models/roi_heads/__init__.py +++ b/pcdet/models/roi_heads/__init__.py @@ -9,6 +9,7 @@ from .mppnet_memory_bank_e2e import MPPNetHeadE2E from .roi_head import RoIHead from .partA2_relation_head import PartA2RelationFCHead +from .voxelrcnn_relation_head import VoxelRCNNRelationHead __all__ = { @@ -23,4 +24,5 @@ 'PVRCNNHeadRelation': PVRCNNHeadRelation, 'ROIHead': RoIHead, 'PartA2RelationFCHead': PartA2RelationFCHead, + 'VoxelRCNNRelationHead': VoxelRCNNRelationHead, } diff --git a/pcdet/models/roi_heads/voxelrcnn_relation_head.py b/pcdet/models/roi_heads/voxelrcnn_relation_head.py new file mode 100644 index 000000000..5e76ee73d --- /dev/null +++ b/pcdet/models/roi_heads/voxelrcnn_relation_head.py @@ -0,0 +1,304 @@ +import torch +import torch.nn as nn +from ...ops.pointnet2.pointnet2_stack import voxel_pool_modules as voxelpool_stack_modules +from ...utils import common_utils +from .roi_head_template import RoIHeadTemplate + + +class VoxelRCNNRelationHead(RoIHeadTemplate): + def __init__(self, backbone_channels, model_cfg, point_cloud_range, voxel_size, num_class=1, object_relation_config=None, **kwargs): + super().__init__(num_class=num_class, model_cfg=model_cfg) + self.model_cfg = model_cfg + self.pool_cfg = model_cfg.ROI_GRID_POOL + LAYER_cfg = self.pool_cfg.POOL_LAYERS + self.point_cloud_range = point_cloud_range + self.voxel_size = voxel_size + + c_out = 0 + self.roi_grid_pool_layers = nn.ModuleList() + for src_name in self.pool_cfg.FEATURES_SOURCE: + mlps = LAYER_cfg[src_name].MLPS + for k in range(len(mlps)): + mlps[k] = [backbone_channels[src_name]] + mlps[k] + pool_layer = voxelpool_stack_modules.NeighborVoxelSAModuleMSG( + query_ranges=LAYER_cfg[src_name].QUERY_RANGES, + nsamples=LAYER_cfg[src_name].NSAMPLE, + radii=LAYER_cfg[src_name].POOL_RADIUS, + mlps=mlps, + pool_method=LAYER_cfg[src_name].POOL_METHOD, + ) + + self.roi_grid_pool_layers.append(pool_layer) + + c_out += sum([x[-1] for x in mlps]) + + + GRID_SIZE = self.model_cfg.ROI_GRID_POOL.GRID_SIZE + # c_out = sum([x[-1] for x in mlps]) + pre_channel = GRID_SIZE * GRID_SIZE * GRID_SIZE * c_out + + shared_fc_list = [] + for k in range(0, self.model_cfg.SHARED_FC.__len__()): + shared_fc_list.extend([ + nn.Linear(pre_channel, self.model_cfg.SHARED_FC[k], bias=False), + nn.BatchNorm1d(self.model_cfg.SHARED_FC[k]), + nn.ReLU(inplace=True) + ]) + pre_channel = self.model_cfg.SHARED_FC[k] + + if k != self.model_cfg.SHARED_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0: + shared_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO)) + self.shared_fc_layer = nn.Sequential(*shared_fc_list) + + if object_relation_config.NAME == 'GNN': + if object_relation_config.GLOBAL_INFORMATION: + initial_input_dim = object_relation_config.GLOBAL_INFORMATION.MLP_LAYERS[-1] + if not object_relation_config.GLOBAL_INFORMATION.CONCATENATED: + initial_input_dim += self.model_cfg.SHARED_FC[-1] + else: + initial_input_dim = self.model_cfg.SHARED_FC[-1] + + if object_relation_config.SKIP_CONNECTION: + self.head_input_channels = initial_input_dim + sum(object_relation_config.LAYERS) + else: + self.head_input_channels = object_relation_config.LAYERS[-1] + elif object_relation_config.NAME == 'CGNLNet': + # TODO: udpate this + self.head_input_channels = self.model_cfg.SHARED_FC[-1] + 256 + 256 + else: + raise NotImplementedError + + cls_fc_list = [] + for k in range(0, self.model_cfg.CLS_FC.__len__()): + cls_fc_list.extend([ + nn.Linear(self.head_input_channels if k==0 else pre_channel, self.model_cfg.CLS_FC[k], bias=False), + nn.BatchNorm1d(self.model_cfg.CLS_FC[k]), + nn.ReLU() + ]) + pre_channel = self.model_cfg.CLS_FC[k] + + if k != self.model_cfg.CLS_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0: + cls_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO)) + self.cls_fc_layers = nn.Sequential(*cls_fc_list) + self.cls_pred_layer = nn.Linear(pre_channel, self.num_class, bias=True) + + reg_fc_list = [] + for k in range(0, self.model_cfg.REG_FC.__len__()): + reg_fc_list.extend([ + nn.Linear(self.head_input_channels if k==0 else pre_channel, self.model_cfg.REG_FC[k], bias=False), + nn.BatchNorm1d(self.model_cfg.REG_FC[k]), + nn.ReLU() + ]) + pre_channel = self.model_cfg.REG_FC[k] + + if k != self.model_cfg.REG_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0: + reg_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO)) + self.reg_fc_layers = nn.Sequential(*reg_fc_list) + self.reg_pred_layer = nn.Linear(pre_channel, self.box_coder.code_size * self.num_class, bias=True) + + self.init_weights() + + def init_weights(self): + init_func = nn.init.xavier_normal_ + for module_list in [self.shared_fc_layer, self.cls_fc_layers, self.reg_fc_layers]: + for m in module_list.modules(): + if isinstance(m, nn.Linear): + init_func(m.weight) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + nn.init.normal_(self.cls_pred_layer.weight, 0, 0.01) + nn.init.constant_(self.cls_pred_layer.bias, 0) + nn.init.normal_(self.reg_pred_layer.weight, mean=0, std=0.001) + nn.init.constant_(self.reg_pred_layer.bias, 0) + + # def _init_weights(self): + # init_func = nn.init.xavier_normal_ + # for m in self.modules(): + # if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d) or isinstance(m, nn.Linear): + # init_func(m.weight) + # if m.bias is not None: + # nn.init.constant_(m.bias, 0) + # nn.init.normal_(self.reg_layers[-1].weight, mean=0, std=0.001) + + def roi_grid_pool(self, batch_dict): + """ + Args: + batch_dict: + batch_size: + rois: (B, num_rois, 7 + C) + point_coords: (num_points, 4) [bs_idx, x, y, z] + point_features: (num_points, C) + point_cls_scores: (N1 + N2 + N3 + ..., 1) + point_part_offset: (N1 + N2 + N3 + ..., 3) + Returns: + + """ + rois = batch_dict['rois'] + batch_size = batch_dict['batch_size'] + with_vf_transform = batch_dict.get('with_voxel_feature_transform', False) + + roi_grid_xyz, _ = self.get_global_grid_points_of_roi( + rois, grid_size=self.pool_cfg.GRID_SIZE + ) # (BxN, 6x6x6, 3) + # roi_grid_xyz: (B, Nx6x6x6, 3) + roi_grid_xyz = roi_grid_xyz.view(batch_size, -1, 3) + + # compute the voxel coordinates of grid points + roi_grid_coords_x = (roi_grid_xyz[:, :, 0:1] - self.point_cloud_range[0]) // self.voxel_size[0] + roi_grid_coords_y = (roi_grid_xyz[:, :, 1:2] - self.point_cloud_range[1]) // self.voxel_size[1] + roi_grid_coords_z = (roi_grid_xyz[:, :, 2:3] - self.point_cloud_range[2]) // self.voxel_size[2] + # roi_grid_coords: (B, Nx6x6x6, 3) + roi_grid_coords = torch.cat([roi_grid_coords_x, roi_grid_coords_y, roi_grid_coords_z], dim=-1) + + batch_idx = rois.new_zeros(batch_size, roi_grid_coords.shape[1], 1) + for bs_idx in range(batch_size): + batch_idx[bs_idx, :, 0] = bs_idx + # roi_grid_coords: (B, Nx6x6x6, 4) + # roi_grid_coords = torch.cat([batch_idx, roi_grid_coords], dim=-1) + # roi_grid_coords = roi_grid_coords.int() + roi_grid_batch_cnt = rois.new_zeros(batch_size).int().fill_(roi_grid_coords.shape[1]) + + pooled_features_list = [] + for k, src_name in enumerate(self.pool_cfg.FEATURES_SOURCE): + pool_layer = self.roi_grid_pool_layers[k] + cur_stride = batch_dict['multi_scale_3d_strides'][src_name] + cur_sp_tensors = batch_dict['multi_scale_3d_features'][src_name] + + if with_vf_transform: + cur_sp_tensors = batch_dict['multi_scale_3d_features_post'][src_name] + else: + cur_sp_tensors = batch_dict['multi_scale_3d_features'][src_name] + + # compute voxel center xyz and batch_cnt + cur_coords = cur_sp_tensors.indices + cur_voxel_xyz = common_utils.get_voxel_centers( + cur_coords[:, 1:4], + downsample_times=cur_stride, + voxel_size=self.voxel_size, + point_cloud_range=self.point_cloud_range + ) + cur_voxel_xyz_batch_cnt = cur_voxel_xyz.new_zeros(batch_size).int() + for bs_idx in range(batch_size): + cur_voxel_xyz_batch_cnt[bs_idx] = (cur_coords[:, 0] == bs_idx).sum() + # get voxel2point tensor + v2p_ind_tensor = common_utils.generate_voxel2pinds(cur_sp_tensors) + # compute the grid coordinates in this scale, in [batch_idx, x y z] order + cur_roi_grid_coords = roi_grid_coords // cur_stride + cur_roi_grid_coords = torch.cat([batch_idx, cur_roi_grid_coords], dim=-1) + cur_roi_grid_coords = cur_roi_grid_coords.int() + # voxel neighbor aggregation + pooled_features = pool_layer( + xyz=cur_voxel_xyz.contiguous(), + xyz_batch_cnt=cur_voxel_xyz_batch_cnt, + new_xyz=roi_grid_xyz.contiguous().view(-1, 3), + new_xyz_batch_cnt=roi_grid_batch_cnt, + new_coords=cur_roi_grid_coords.contiguous().view(-1, 4), + features=cur_sp_tensors.features.contiguous(), + voxel2point_indices=v2p_ind_tensor + ) + + pooled_features = pooled_features.view( + -1, self.pool_cfg.GRID_SIZE ** 3, + pooled_features.shape[-1] + ) # (BxN, 6x6x6, C) + pooled_features_list.append(pooled_features) + + ms_pooled_features = torch.cat(pooled_features_list, dim=-1) + + return ms_pooled_features + + + def get_global_grid_points_of_roi(self, rois, grid_size): + rois = rois.view(-1, rois.shape[-1]) + batch_size_rcnn = rois.shape[0] + + local_roi_grid_points = self.get_dense_grid_points(rois, batch_size_rcnn, grid_size) # (B, 6x6x6, 3) + global_roi_grid_points = common_utils.rotate_points_along_z( + local_roi_grid_points.clone(), rois[:, 6] + ).squeeze(dim=1) + global_center = rois[:, 0:3].clone() + global_roi_grid_points += global_center.unsqueeze(dim=1) + return global_roi_grid_points, local_roi_grid_points + + @staticmethod + def get_dense_grid_points(rois, batch_size_rcnn, grid_size): + faked_features = rois.new_ones((grid_size, grid_size, grid_size)) + dense_idx = faked_features.nonzero() # (N, 3) [x_idx, y_idx, z_idx] + dense_idx = dense_idx.repeat(batch_size_rcnn, 1, 1).float() # (B, 6x6x6, 3) + + local_roi_size = rois.view(batch_size_rcnn, -1)[:, 3:6] + roi_grid_points = (dense_idx + 0.5) / grid_size * local_roi_size.unsqueeze(dim=1) \ + - (local_roi_size.unsqueeze(dim=1) / 2) # (B, 6x6x6, 3) + return roi_grid_points + + def forward(self, batch_dict): + """ + :param input_data: input dict + :return: + """ + + targets_dict = self.proposal_layer( + batch_dict, nms_config=self.model_cfg.NMS_CONFIG['TRAIN' if self.training else 'TEST'] + ) + if self.training: + targets_dict = self.assign_targets(batch_dict) + batch_dict['rois'] = targets_dict['rois'] + batch_dict['roi_labels'] = targets_dict['roi_labels'] + + B, N, C = batch_dict['rois'].shape + # RoI aware pooling + pooled_features = self.roi_grid_pool(batch_dict) # (BxN, 6x6x6, C) + + # Box Refinement + pooled_features = pooled_features.view(pooled_features.size(0), -1) + pooled_features = self.shared_fc_layer(pooled_features) + + batch_dict['pooled_features'] = pooled_features.view(B, N, self.model_cfg.SHARED_FC[-1]) + + self.forward_ret_dict = targets_dict + + + # rcnn_cls = self.cls_pred_layer(self.cls_fc_layers(shared_features)) + # rcnn_reg = self.reg_pred_layer(self.reg_fc_layers(shared_features)) + + # grid_size = self.model_cfg.ROI_GRID_POOL.GRID_SIZE + # batch_size_rcnn = pooled_features.shape[0] + # pooled_features = pooled_features.permute(0, 2, 1).\ + # contiguous().view(batch_size_rcnn, -1, grid_size, grid_size, grid_size) # (BxN, C, 6, 6, 6) + + # shared_features = self.shared_fc_layer(pooled_features.view(batch_size_rcnn, -1, 1)) + # rcnn_cls = self.cls_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1) # (B, 1 or 2) + # rcnn_reg = self.reg_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1) # (B, C) + + # if not self.training: + # batch_cls_preds, batch_box_preds = self.generate_predicted_boxes( + # batch_size=batch_dict['batch_size'], rois=batch_dict['rois'], cls_preds=rcnn_cls, box_preds=rcnn_reg + # ) + # batch_dict['batch_cls_preds'] = batch_cls_preds + # batch_dict['batch_box_preds'] = batch_box_preds + # batch_dict['cls_preds_normalized'] = False + # else: + # targets_dict['rcnn_cls'] = rcnn_cls + # targets_dict['rcnn_reg'] = rcnn_reg + + # self.forward_ret_dict = targets_dict + + return batch_dict + + def final_predictions(self, batch_dict): + shared_features = batch_dict['related_features'] + rcnn_cls = self.cls_pred_layer(self.cls_fc_layers(shared_features)) + rcnn_reg = self.reg_pred_layer(self.reg_fc_layers(shared_features)) + if not self.training: + batch_cls_preds, batch_box_preds = self.generate_predicted_boxes( + batch_size=batch_dict['batch_size'], rois=batch_dict['rois'], cls_preds=rcnn_cls, box_preds=rcnn_reg + ) + batch_dict['batch_cls_preds'] = batch_cls_preds + batch_dict['batch_box_preds'] = batch_box_preds + batch_dict['cls_preds_normalized'] = False + else: + self.forward_ret_dict['rcnn_cls'] = rcnn_cls + self.forward_ret_dict['rcnn_reg'] = rcnn_reg + + return batch_dict \ No newline at end of file diff --git a/tools/cfgs/kitti_models/voxel_rcnn_relation_car.yaml b/tools/cfgs/kitti_models/voxel_rcnn_relation_car.yaml new file mode 100644 index 000000000..5db51a4a1 --- /dev/null +++ b/tools/cfgs/kitti_models/voxel_rcnn_relation_car.yaml @@ -0,0 +1,232 @@ +CLASS_NAMES: ['Car'] + +DATA_CONFIG: + _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml + DATA_AUGMENTOR: + DISABLE_AUG_LIST: ['placeholder'] + AUG_CONFIG_LIST: + - NAME: gt_sampling + USE_ROAD_PLANE: True + DB_INFO_PATH: + - kitti_dbinfos_train.pkl + PREPARE: { + filter_by_min_points: ['Car:5'], + filter_by_difficulty: [-1], + } + + SAMPLE_GROUPS: ['Car:15'] + NUM_POINT_FEATURES: 4 + DATABASE_WITH_FAKELIDAR: False + REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0] + LIMIT_WHOLE_SCENE: False + + - NAME: random_world_flip + ALONG_AXIS_LIST: ['x'] + + - NAME: random_world_rotation + WORLD_ROT_ANGLE: [-0.78539816, 0.78539816] + + - NAME: random_world_scaling + WORLD_SCALE_RANGE: [0.95, 1.05] + +MODEL: + NAME: VoxelRCNNRelation + + VFE: + NAME: MeanVFE + + BACKBONE_3D: + NAME: VoxelBackBone8x + + MAP_TO_BEV: + NAME: HeightCompression + NUM_BEV_FEATURES: 256 + + BACKBONE_2D: + NAME: BaseBEVBackbone + + LAYER_NUMS: [5, 5] + LAYER_STRIDES: [1, 2] + NUM_FILTERS: [64, 128] + UPSAMPLE_STRIDES: [1, 2] + NUM_UPSAMPLE_FILTERS: [128, 128] + + DENSE_HEAD: + NAME: AnchorHeadSingle + CLASS_AGNOSTIC: False + + USE_DIRECTION_CLASSIFIER: True + DIR_OFFSET: 0.78539 + DIR_LIMIT_OFFSET: 0.0 + NUM_DIR_BINS: 2 + + ANCHOR_GENERATOR_CONFIG: [ + { + 'class_name': 'Car', + 'anchor_sizes': [[3.9, 1.6, 1.56]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [-1.78], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.6, + 'unmatched_threshold': 0.45 + }, + ] + + TARGET_ASSIGNER_CONFIG: + NAME: AxisAlignedTargetAssigner + POS_FRACTION: -1.0 + SAMPLE_SIZE: 512 + NORM_BY_NUM_EXAMPLES: False + MATCH_HEIGHT: False + BOX_CODER: ResidualCoder + + LOSS_CONFIG: + LOSS_WEIGHTS: { + 'cls_weight': 1.0, + 'loc_weight': 2.0, + 'dir_weight': 0.2, + 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + + ROI_HEAD: + NAME: VoxelRCNNRelationHead + CLASS_AGNOSTIC: True + + SHARED_FC: [256, 256] + CLS_FC: [256, 256] + REG_FC: [256, 256] + DP_RATIO: 0.3 + + 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 + USE_FAST_NMS: False + SCORE_THRESH: 0.0 + NMS_PRE_MAXSIZE: 2048 + NMS_POST_MAXSIZE: 100 + NMS_THRESH: 0.7 + + ROI_GRID_POOL: + FEATURES_SOURCE: ['x_conv2', 'x_conv3', 'x_conv4'] + PRE_MLP: True + GRID_SIZE: 6 + POOL_LAYERS: + x_conv2: + MLPS: [[32, 32]] + QUERY_RANGES: [[4, 4, 4]] + POOL_RADIUS: [0.4] + NSAMPLE: [16] + POOL_METHOD: max_pool + x_conv3: + MLPS: [[32, 32]] + QUERY_RANGES: [[4, 4, 4]] + POOL_RADIUS: [0.8] + NSAMPLE: [16] + POOL_METHOD: max_pool + x_conv4: + MLPS: [[32, 32]] + QUERY_RANGES: [[4, 4, 4]] + POOL_RADIUS: [1.6] + NSAMPLE: [16] + POOL_METHOD: max_pool + 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: 0.55 + + LOSS_CONFIG: + CLS_LOSS: BinaryCrossEntropy + REG_LOSS: smooth-l1 + CORNER_LOSS_REGULARIZATION: True + GRID_3D_IOU_LOSS: False + LOSS_WEIGHTS: { + 'rcnn_cls_weight': 1.0, + 'rcnn_reg_weight': 1.0, + 'rcnn_corner_weight': 1.0, + 'rcnn_iou3d_weight': 1.0, + 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + OBJECT_RELATION: + NAME: GNN + # NAME: radius_graph, + # RADIUS: 6, + # CONNECT_ONLY_SAME_CLASS: False + # } + GRAPH: { + CONV: { + NAME: EdgeConv, + EDGE_EMBEDDING: True, + # HEADS: 4, + SKIP_CONNECTION: True + }, + DYNAMIC: False, + NAME: knn_graph, + K: 16, + CONNECT_ONLY_SAME_CLASS: False, + SPACE: R3, + # EDGE_EMBEDDING: True, + } + LAYERS: [256, 256, 256, 256] + SKIP_CONNECTION: True + # IN_BETWEEN_MLP: [256, 256, 256, 256] + IN_BETWEEN_MLP: False + # GLOBAL_INFORMATION: { + # MLP_LAYERS: [256, 256], + # CONCATENATED: False, + # } + GLOBAL_INFORMATION: False + DP_RATIO: 0.3 + + POST_PROCESSING: + RECALL_THRESH_LIST: [0.3, 0.5, 0.7] + SCORE_THRESH: 0.3 + 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: + BATCH_SIZE_PER_GPU: 2 + NUM_EPOCHS: 80 + + 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 From 1ad6e3d02ac7de564e1e84ddee28c381de3b9678 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Sun, 15 Oct 2023 16:29:20 +0000 Subject: [PATCH 75/93] Add code for frozen backbone model --- pcdet/models/detectors/detector3d_template.py | 10 +- pcdet/models/detectors/pv_rcnn.py | 6 +- pcdet/models/detectors/pv_rcnn_relation.py | 6 +- tools/cfgs/kitti_models/pv_rcnn_frozen.yaml | 256 +++++++++++++++ .../pv_rcnn_frozen_car_class_only.yaml | 235 ++++++++++++++ .../kitti_models/pv_rcnn_frozen_relation.yaml | 299 ++++++++++++++++++ ...v_rcnn_frozen_relation_car_class_only.yaml | 279 ++++++++++++++++ tools/scripts/experiments.py | 63 ++++ 8 files changed, 1149 insertions(+), 5 deletions(-) create mode 100644 tools/cfgs/kitti_models/pv_rcnn_frozen.yaml create mode 100644 tools/cfgs/kitti_models/pv_rcnn_frozen_car_class_only.yaml create mode 100644 tools/cfgs/kitti_models/pv_rcnn_frozen_relation.yaml create mode 100644 tools/cfgs/kitti_models/pv_rcnn_frozen_relation_car_class_only.yaml create mode 100644 tools/scripts/experiments.py diff --git a/pcdet/models/detectors/detector3d_template.py b/pcdet/models/detectors/detector3d_template.py index cc2aaaa5c..25793f844 100644 --- a/pcdet/models/detectors/detector3d_template.py +++ b/pcdet/models/detectors/detector3d_template.py @@ -344,7 +344,7 @@ def generate_recall_record(box_preds, recall_dict, batch_index, data_dict=None, gt_iou = box_preds.new_zeros(box_preds.shape[0]) return recall_dict - def _load_state_dict(self, model_state_disk, *, strict=True): + def _load_state_dict(self, model_state_disk, *, strict=True, learnable_layer=None): state_dict = self.state_dict() # local cache of state_dict spconv_keys = find_all_spconv_keys(self) @@ -371,11 +371,15 @@ def _load_state_dict(self, model_state_disk, *, strict=True): if strict: self.load_state_dict(update_model_state) else: + if learnable_layer: + for key in list(update_model_state.keys()): + if any([(l in key) for l in learnable_layer]): + del update_model_state[key] state_dict.update(update_model_state) self.load_state_dict(state_dict) return state_dict, update_model_state - def load_params_from_file(self, filename, logger, to_cpu=False, pre_trained_path=None): + def load_params_from_file(self, filename, logger, to_cpu=False, pre_trained_path=None, learnable_layer=None): if not os.path.isfile(filename): raise FileNotFoundError @@ -392,7 +396,7 @@ def load_params_from_file(self, filename, logger, to_cpu=False, pre_trained_path if version is not None: logger.info('==> Checkpoint trained from version: %s' % version) - state_dict, update_model_state = self._load_state_dict(model_state_disk, strict=False) + state_dict, update_model_state = self._load_state_dict(model_state_disk, strict=False, learnable_layer=learnable_layer) for key in state_dict: if key not in update_model_state: diff --git a/pcdet/models/detectors/pv_rcnn.py b/pcdet/models/detectors/pv_rcnn.py index 4808513a2..45ea30506 100644 --- a/pcdet/models/detectors/pv_rcnn.py +++ b/pcdet/models/detectors/pv_rcnn.py @@ -5,6 +5,7 @@ class PVRCNN(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() + self.frozen = True if "FROZEN" in model_cfg.keys() else model_cfg.FROZEN def forward(self, batch_dict): for cur_module in self.module_list: @@ -27,7 +28,10 @@ def get_training_loss(self): loss_point, tb_dict = self.point_head.get_loss(tb_dict) loss_rcnn, tb_dict = self.roi_head.get_loss(tb_dict) - loss = loss_rpn + loss_point + loss_rcnn + if self.frozen: + loss = loss_rcnn + else: + loss = loss_rpn + loss_point + loss_rcnn if hasattr(self.backbone_3d, 'get_loss'): loss_backbone3d, tb_dict = self.backbone_3d.get_loss(tb_dict) diff --git a/pcdet/models/detectors/pv_rcnn_relation.py b/pcdet/models/detectors/pv_rcnn_relation.py index d3f5cbcd2..6d9e32085 100644 --- a/pcdet/models/detectors/pv_rcnn_relation.py +++ b/pcdet/models/detectors/pv_rcnn_relation.py @@ -9,6 +9,7 @@ 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() self.object_relation = build_object_relation_module(model_cfg.OBJECT_RELATION) + self.frozen = True if "FROZEN" in model_cfg.keys() else model_cfg.FROZEN def forward(self, batch_dict): # MeanVFE: Voxelisation @@ -50,7 +51,10 @@ def get_training_loss(self): loss_point, tb_dict = self.point_head.get_loss(tb_dict) loss_rcnn, tb_dict = self.roi_head.get_loss(tb_dict) - loss = loss_rpn + loss_point + loss_rcnn + if self.frozen: + loss = loss_rcnn + else: + loss = loss_rpn + loss_point + loss_rcnn if hasattr(self.backbone_3d, 'get_loss'): loss_backbone3d, tb_dict = self.backbone_3d.get_loss(tb_dict) diff --git a/tools/cfgs/kitti_models/pv_rcnn_frozen.yaml b/tools/cfgs/kitti_models/pv_rcnn_frozen.yaml new file mode 100644 index 000000000..20f66c454 --- /dev/null +++ b/tools/cfgs/kitti_models/pv_rcnn_frozen.yaml @@ -0,0 +1,256 @@ +PRE_TRAINED: + MODEL_PATH: ../output/cfgs/kitti_models/pv_rcnn/2023-09-04_09-52-39/ckpt/checkpoint_epoch_78.pth + # LEARNABLE_LAYER: ['roi_head.reg_layers', 'roi_head.cls_layers'] + LEARNABLE_LAYER: ['roi_head'] + + +CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist'] + +DATA_CONFIG: + _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml + DATA_AUGMENTOR: + DISABLE_AUG_LIST: ['placeholder'] + AUG_CONFIG_LIST: + - NAME: gt_sampling + USE_ROAD_PLANE: True + DB_INFO_PATH: + - kitti_dbinfos_train.pkl + PREPARE: { + filter_by_min_points: ['Car:5', 'Pedestrian:5', 'Cyclist:5'], + filter_by_difficulty: [-1], + } + + SAMPLE_GROUPS: ['Car:15','Pedestrian:10', 'Cyclist:10'] + NUM_POINT_FEATURES: 4 + DATABASE_WITH_FAKELIDAR: False + REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0] + LIMIT_WHOLE_SCENE: False + + - NAME: random_world_flip + ALONG_AXIS_LIST: ['x'] + + - NAME: random_world_rotation + WORLD_ROT_ANGLE: [-0.78539816, 0.78539816] + + - NAME: random_world_scaling + WORLD_SCALE_RANGE: [0.95, 1.05] + +MODEL: + NAME: PVRCNN + FROZEN: True + + VFE: + NAME: MeanVFE + + BACKBONE_3D: + NAME: VoxelBackBone8x + + 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: AnchorHeadSingle + CLASS_AGNOSTIC: False + + USE_DIRECTION_CLASSIFIER: True + DIR_OFFSET: 0.78539 + DIR_LIMIT_OFFSET: 0.0 + NUM_DIR_BINS: 2 + + ANCHOR_GENERATOR_CONFIG: [ + { + 'class_name': 'Car', + 'anchor_sizes': [[3.9, 1.6, 1.56]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [-1.78], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.6, + 'unmatched_threshold': 0.45 + }, + { + 'class_name': 'Pedestrian', + 'anchor_sizes': [[0.8, 0.6, 1.73]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [-0.6], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.5, + 'unmatched_threshold': 0.35 + }, + { + 'class_name': 'Cyclist', + 'anchor_sizes': [[1.76, 0.6, 1.73]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [-0.6], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.5, + 'unmatched_threshold': 0.35 + } + ] + + TARGET_ASSIGNER_CONFIG: + NAME: AxisAlignedTargetAssigner + POS_FRACTION: -1.0 + SAMPLE_SIZE: 512 + NORM_BY_NUM_EXAMPLES: False + MATCH_HEIGHT: False + BOX_CODER: ResidualCoder + + LOSS_CONFIG: + LOSS_WEIGHTS: { + 'cls_weight': 1.0, + 'loc_weight': 2.0, + 'dir_weight': 0.2, + 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + + PFE: + NAME: VoxelSetAbstraction + POINT_SOURCE: raw_points + NUM_KEYPOINTS: 2048 + NUM_OUTPUT_FEATURES: 128 + SAMPLE_METHOD: FPS + + FEATURES_SOURCE: ['bev', 'x_conv1', 'x_conv2', 'x_conv3', 'x_conv4', 'raw_points'] + SA_LAYER: + raw_points: + MLPS: [[16, 16], [16, 16]] + POOL_RADIUS: [0.4, 0.8] + NSAMPLE: [16, 16] + x_conv1: + DOWNSAMPLE_FACTOR: 1 + MLPS: [[16, 16], [16, 16]] + POOL_RADIUS: [0.4, 0.8] + NSAMPLE: [16, 16] + x_conv2: + DOWNSAMPLE_FACTOR: 2 + MLPS: [[32, 32], [32, 32]] + POOL_RADIUS: [0.8, 1.2] + NSAMPLE: [16, 32] + x_conv3: + DOWNSAMPLE_FACTOR: 4 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [1.2, 2.4] + NSAMPLE: [16, 32] + x_conv4: + DOWNSAMPLE_FACTOR: 8 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [2.4, 4.8] + NSAMPLE: [16, 32] + + POINT_HEAD: + NAME: PointHeadSimple + CLS_FC: [256, 256] + CLASS_AGNOSTIC: True + USE_POINT_FEATURES_BEFORE_FUSION: True + TARGET_CONFIG: + GT_EXTRA_WIDTH: [0.2, 0.2, 0.2] + LOSS_CONFIG: + LOSS_REG: smooth-l1 + LOSS_WEIGHTS: { + 'point_cls_weight': 1.0, + } + + ROI_HEAD: + NAME: PVRCNNHead + CLASS_AGNOSTIC: True + + SHARED_FC: [256, 256] + CLS_FC: [256, 256] + REG_FC: [256, 256] + DP_RATIO: 0.3 + + 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_GRID_POOL: + GRID_SIZE: 6 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [0.8, 1.6] + NSAMPLE: [16, 16] + POOL_METHOD: max_pool + + 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: 0.55 + + 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: + BATCH_SIZE_PER_GPU: 2 + NUM_EPOCHS: 5 + + 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 diff --git a/tools/cfgs/kitti_models/pv_rcnn_frozen_car_class_only.yaml b/tools/cfgs/kitti_models/pv_rcnn_frozen_car_class_only.yaml new file mode 100644 index 000000000..25bdd327b --- /dev/null +++ b/tools/cfgs/kitti_models/pv_rcnn_frozen_car_class_only.yaml @@ -0,0 +1,235 @@ +PRE_TRAINED: + MODEL_PATH: ../output/cfgs/kitti_models/pv_rcnn_car_class_only/2023-09-21_08-45-44/ckpt/checkpoint_epoch_75.pth + LEARNABLE_LAYER: ['roi_head.reg_layers', 'roi_head.cls_layers'] + + +CLASS_NAMES: ['Car'] + +DATA_CONFIG: + _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml + DATA_AUGMENTOR: + DISABLE_AUG_LIST: ['placeholder'] + AUG_CONFIG_LIST: + - NAME: gt_sampling + USE_ROAD_PLANE: True + DB_INFO_PATH: + - kitti_dbinfos_train.pkl + PREPARE: { + filter_by_min_points: ['Car:5'], + filter_by_difficulty: [-1], + } + + SAMPLE_GROUPS: ['Car:15'] + NUM_POINT_FEATURES: 4 + DATABASE_WITH_FAKELIDAR: False + REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0] + LIMIT_WHOLE_SCENE: False + + - NAME: random_world_flip + ALONG_AXIS_LIST: ['x'] + + - NAME: random_world_rotation + WORLD_ROT_ANGLE: [-0.78539816, 0.78539816] + + - NAME: random_world_scaling + WORLD_SCALE_RANGE: [0.95, 1.05] + +MODEL: + NAME: PVRCNN + FROZEN: True + + VFE: + NAME: MeanVFE + + BACKBONE_3D: + NAME: VoxelBackBone8x + + 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: AnchorHeadSingle + CLASS_AGNOSTIC: False + + USE_DIRECTION_CLASSIFIER: True + DIR_OFFSET: 0.78539 + DIR_LIMIT_OFFSET: 0.0 + NUM_DIR_BINS: 2 + + ANCHOR_GENERATOR_CONFIG: [ + { + 'class_name': 'Car', + 'anchor_sizes': [[3.9, 1.6, 1.56]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [-1.78], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.6, + 'unmatched_threshold': 0.45 + }, + ] + + TARGET_ASSIGNER_CONFIG: + NAME: AxisAlignedTargetAssigner + POS_FRACTION: -1.0 + SAMPLE_SIZE: 512 + NORM_BY_NUM_EXAMPLES: False + MATCH_HEIGHT: False + BOX_CODER: ResidualCoder + + LOSS_CONFIG: + LOSS_WEIGHTS: { + 'cls_weight': 1.0, + 'loc_weight': 2.0, + 'dir_weight': 0.2, + 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + + PFE: + NAME: VoxelSetAbstraction + POINT_SOURCE: raw_points + NUM_KEYPOINTS: 2048 + NUM_OUTPUT_FEATURES: 128 + SAMPLE_METHOD: FPS + + FEATURES_SOURCE: ['bev', 'x_conv1', 'x_conv2', 'x_conv3', 'x_conv4', 'raw_points'] + SA_LAYER: + raw_points: + MLPS: [[16, 16], [16, 16]] + POOL_RADIUS: [0.4, 0.8] + NSAMPLE: [16, 16] + x_conv1: + DOWNSAMPLE_FACTOR: 1 + MLPS: [[16, 16], [16, 16]] + POOL_RADIUS: [0.4, 0.8] + NSAMPLE: [16, 16] + x_conv2: + DOWNSAMPLE_FACTOR: 2 + MLPS: [[32, 32], [32, 32]] + POOL_RADIUS: [0.8, 1.2] + NSAMPLE: [16, 32] + x_conv3: + DOWNSAMPLE_FACTOR: 4 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [1.2, 2.4] + NSAMPLE: [16, 32] + x_conv4: + DOWNSAMPLE_FACTOR: 8 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [2.4, 4.8] + NSAMPLE: [16, 32] + + POINT_HEAD: + NAME: PointHeadSimple + CLS_FC: [256, 256] + CLASS_AGNOSTIC: True + USE_POINT_FEATURES_BEFORE_FUSION: True + TARGET_CONFIG: + GT_EXTRA_WIDTH: [0.2, 0.2, 0.2] + LOSS_CONFIG: + LOSS_REG: smooth-l1 + LOSS_WEIGHTS: { + 'point_cls_weight': 1.0, + } + + ROI_HEAD: + NAME: PVRCNNHead + CLASS_AGNOSTIC: True + + SHARED_FC: [256, 256] + CLS_FC: [256, 256] + REG_FC: [256, 256] + DP_RATIO: 0.3 + + 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_GRID_POOL: + GRID_SIZE: 6 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [0.8, 1.6] + NSAMPLE: [16, 16] + POOL_METHOD: max_pool + + 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: 0.55 + + 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: + BATCH_SIZE_PER_GPU: 2 + NUM_EPOCHS: 2 + + 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 diff --git a/tools/cfgs/kitti_models/pv_rcnn_frozen_relation.yaml b/tools/cfgs/kitti_models/pv_rcnn_frozen_relation.yaml new file mode 100644 index 000000000..71f1d29cc --- /dev/null +++ b/tools/cfgs/kitti_models/pv_rcnn_frozen_relation.yaml @@ -0,0 +1,299 @@ +PRE_TRAINED: + MODEL_PATH: ../output/cfgs/kitti_models/pv_rcnn/2023-09-04_09-52-39/ckpt/checkpoint_epoch_78.pth + LEARNABLE_LAYER: ['roi_head.reg_layers', 'roi_head.cls_layers', 'object_relation'] + + +CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist'] + +DATA_CONFIG: + _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml + DATA_AUGMENTOR: + DISABLE_AUG_LIST: ['placeholder'] + AUG_CONFIG_LIST: + - NAME: gt_sampling + USE_ROAD_PLANE: True + DB_INFO_PATH: + - kitti_dbinfos_train.pkl + PREPARE: { + filter_by_min_points: ['Car:5', 'Pedestrian:5', 'Cyclist:5'], + filter_by_difficulty: [-1], + } + + SAMPLE_GROUPS: ['Car:15','Pedestrian:10', 'Cyclist:10'] + NUM_POINT_FEATURES: 4 + DATABASE_WITH_FAKELIDAR: False + REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0] + LIMIT_WHOLE_SCENE: False + + - NAME: random_world_flip + ALONG_AXIS_LIST: ['x'] + + - NAME: random_world_rotation + WORLD_ROT_ANGLE: [-0.78539816, 0.78539816] + + - NAME: random_world_scaling + WORLD_SCALE_RANGE: [0.95, 1.05] + +MODEL: + NAME: PVRCNNRelation + FROZEN: True + + VFE: + NAME: MeanVFE + + BACKBONE_3D: + NAME: VoxelBackBone8x + + 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: AnchorHeadSingle + CLASS_AGNOSTIC: False + + USE_DIRECTION_CLASSIFIER: True + DIR_OFFSET: 0.78539 + DIR_LIMIT_OFFSET: 0.0 + NUM_DIR_BINS: 2 + + ANCHOR_GENERATOR_CONFIG: [ + { + 'class_name': 'Car', + 'anchor_sizes': [[3.9, 1.6, 1.56]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [-1.78], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.6, + 'unmatched_threshold': 0.45 + }, + { + 'class_name': 'Pedestrian', + 'anchor_sizes': [[0.8, 0.6, 1.73]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [-0.6], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.5, + 'unmatched_threshold': 0.35 + }, + { + 'class_name': 'Cyclist', + 'anchor_sizes': [[1.76, 0.6, 1.73]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [-0.6], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.5, + 'unmatched_threshold': 0.35 + } + ] + + TARGET_ASSIGNER_CONFIG: + NAME: AxisAlignedTargetAssigner + POS_FRACTION: -1.0 + SAMPLE_SIZE: 512 + NORM_BY_NUM_EXAMPLES: False + MATCH_HEIGHT: False + BOX_CODER: ResidualCoder + + LOSS_CONFIG: + LOSS_WEIGHTS: { + 'cls_weight': 1.0, + 'loc_weight': 2.0, + 'dir_weight': 0.2, + 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + + PFE: + NAME: VoxelSetAbstraction + POINT_SOURCE: raw_points + NUM_KEYPOINTS: 2048 + NUM_OUTPUT_FEATURES: 128 + SAMPLE_METHOD: FPS + + FEATURES_SOURCE: ['bev', 'x_conv1', 'x_conv2', 'x_conv3', 'x_conv4', 'raw_points'] + SA_LAYER: + raw_points: + MLPS: [[16, 16], [16, 16]] + POOL_RADIUS: [0.4, 0.8] + NSAMPLE: [16, 16] + x_conv1: + DOWNSAMPLE_FACTOR: 1 + MLPS: [[16, 16], [16, 16]] + POOL_RADIUS: [0.4, 0.8] + NSAMPLE: [16, 16] + x_conv2: + DOWNSAMPLE_FACTOR: 2 + MLPS: [[32, 32], [32, 32]] + POOL_RADIUS: [0.8, 1.2] + NSAMPLE: [16, 32] + x_conv3: + DOWNSAMPLE_FACTOR: 4 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [1.2, 2.4] + NSAMPLE: [16, 32] + x_conv4: + DOWNSAMPLE_FACTOR: 8 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [2.4, 4.8] + NSAMPLE: [16, 32] + + POINT_HEAD: + NAME: PointHeadSimple + CLS_FC: [256, 256] + CLASS_AGNOSTIC: True + USE_POINT_FEATURES_BEFORE_FUSION: True + TARGET_CONFIG: + GT_EXTRA_WIDTH: [0.2, 0.2, 0.2] + LOSS_CONFIG: + LOSS_REG: smooth-l1 + LOSS_WEIGHTS: { + 'point_cls_weight': 1.0, + } + + ROI_HEAD: + NAME: PVRCNNHeadRelation + CLASS_AGNOSTIC: True + + SHARED_FC: [256, 256] + CLS_FC: [256, 256] + REG_FC: [256, 256] + DP_RATIO: 0.3 + + NMS_CONFIG: + TRAIN: + NMS_TYPE: nms_gpu + MULTI_CLASSES_NMS: False + # the proposals are soreted according to scores and then we take the NMS_PRE_MAXSIZE first + NMS_PRE_MAXSIZE: 9000 + # proposals with more than NMS_THRESH overlap with others will be removed + NMS_THRESH: 0.8 + # the proposals after NMS will be limited to NMS_POST_MAXSIZE + NMS_POST_MAXSIZE: 512 + # values are different for testing + TEST: + NMS_TYPE: nms_gpu + MULTI_CLASSES_NMS: False + NMS_PRE_MAXSIZE: 1024 + NMS_POST_MAXSIZE: 100 + NMS_THRESH: 0.7 + + ROI_GRID_POOL: + GRID_SIZE: 6 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [0.8, 1.6] + NSAMPLE: [16, 16] + POOL_METHOD: max_pool + + TARGET_CONFIG: + BOX_CODER: ResidualCoder + # number of proposal after the sampling + ROI_PER_IMAGE: 128 + # max number of foreground proposal + FG_RATIO: 0.5 + + SAMPLE_ROI_BY_EACH_CLASS: True + CLS_SCORE_TYPE: roi_iou + + # thresholds defining the foregrouns, easy background and hard background + CLS_FG_THRESH: 0.75 + CLS_BG_THRESH: 0.25 + CLS_BG_THRESH_LO: 0.1 + # from the background proposals, HARD_BG_RATIO are ones with very small iou with gt (1-HARD_BG_RATIO are with large iou with gt -> still background) + HARD_BG_RATIO: 0.8 + + REG_FG_THRESH: 0.55 + + 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] + } + + OBJECT_RELATION: + NAME: GNN + # NAME: radius_graph, + # RADIUS: 6, + # CONNECT_ONLY_SAME_CLASS: False + # } + GRAPH: { + CONV: { + NAME: EdgeConv, + EDGE_EMBEDDING: True, + # HEADS: 4, + SKIP_CONNECTION: False, + }, + DYNAMIC: False, + NAME: knn_graph, + K: 16, + CONNECT_ONLY_SAME_CLASS: False, + SPACE: R3, + # EDGE_EMBEDDING: True, + } + LAYERS: [256, 256, 256, 256] + SKIP_CONNECTION: True + # IN_BETWEEN_MLP: [256, 256, 256, 256] + IN_BETWEEN_MLP: False + # GLOBAL_INFORMATION: { + # MLP_LAYERS: [256, 256], + # CONCATENATED: False, + # } + GLOBAL_INFORMATION: False + DP_RATIO: 0.3 + + + POST_PROCESSING: + # IoU Threshold + RECALL_THRESH_LIST: [0.3, 0.5, 0.7] + # Car, Pedestrian, Cyclist + SCORE_THRESH: 0.1 + # SCORE_THRESH: 0.0 + OUTPUT_RAW_SCORE: False + + EVAL_METRIC: kitti + + NMS_CONFIG: + MULTI_CLASSES_NMS: False + NMS_TYPE: nms_gpu + NMS_THRESH: 0.1 + # NMS_THRESH: 1.0 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 + + +OPTIMIZATION: + BATCH_SIZE_PER_GPU: 2 + NUM_EPOCHS: 5 + + 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 diff --git a/tools/cfgs/kitti_models/pv_rcnn_frozen_relation_car_class_only.yaml b/tools/cfgs/kitti_models/pv_rcnn_frozen_relation_car_class_only.yaml new file mode 100644 index 000000000..519381b25 --- /dev/null +++ b/tools/cfgs/kitti_models/pv_rcnn_frozen_relation_car_class_only.yaml @@ -0,0 +1,279 @@ +PRE_TRAINED: + MODEL_PATH: ../output/cfgs/kitti_models/pv_rcnn_car_class_only/2023-09-21_08-45-44/ckpt/checkpoint_epoch_75.pth + LEARNABLE_LAYER: ['roi_head.reg_layers', 'roi_head.cls_layers', 'object_relation'] + + +CLASS_NAMES: ['Car'] + +DATA_CONFIG: + _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml + DATA_AUGMENTOR: + DISABLE_AUG_LIST: ['placeholder'] + AUG_CONFIG_LIST: + - NAME: gt_sampling + USE_ROAD_PLANE: True + DB_INFO_PATH: + - kitti_dbinfos_train.pkl + PREPARE: { + filter_by_min_points: ['Car:5'], + filter_by_difficulty: [-1], + } + + SAMPLE_GROUPS: ['Car:15'] + NUM_POINT_FEATURES: 4 + DATABASE_WITH_FAKELIDAR: False + REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0] + LIMIT_WHOLE_SCENE: False + + - NAME: random_world_flip + ALONG_AXIS_LIST: ['x'] + + - NAME: random_world_rotation + WORLD_ROT_ANGLE: [-0.78539816, 0.78539816] + + - NAME: random_world_scaling + WORLD_SCALE_RANGE: [0.95, 1.05] + +MODEL: + NAME: PVRCNNRelation + FROZEN: True + + VFE: + NAME: MeanVFE + + BACKBONE_3D: + NAME: VoxelBackBone8x + + 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: AnchorHeadSingle + CLASS_AGNOSTIC: False + + USE_DIRECTION_CLASSIFIER: True + DIR_OFFSET: 0.78539 + DIR_LIMIT_OFFSET: 0.0 + NUM_DIR_BINS: 2 + + ANCHOR_GENERATOR_CONFIG: [ + { + 'class_name': 'Car', + 'anchor_sizes': [[3.9, 1.6, 1.56]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [-1.78], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.6, + 'unmatched_threshold': 0.45 + }, + ] + + TARGET_ASSIGNER_CONFIG: + NAME: AxisAlignedTargetAssigner + POS_FRACTION: -1.0 + SAMPLE_SIZE: 512 + NORM_BY_NUM_EXAMPLES: False + MATCH_HEIGHT: False + BOX_CODER: ResidualCoder + + LOSS_CONFIG: + LOSS_WEIGHTS: { + 'cls_weight': 1.0, + 'loc_weight': 2.0, + 'dir_weight': 0.2, + 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + + PFE: + NAME: VoxelSetAbstraction + POINT_SOURCE: raw_points + NUM_KEYPOINTS: 2048 + NUM_OUTPUT_FEATURES: 128 + SAMPLE_METHOD: FPS + + FEATURES_SOURCE: ['bev', 'x_conv1', 'x_conv2', 'x_conv3', 'x_conv4', 'raw_points'] + SA_LAYER: + raw_points: + MLPS: [[16, 16], [16, 16]] + POOL_RADIUS: [0.4, 0.8] + NSAMPLE: [16, 16] + x_conv1: + DOWNSAMPLE_FACTOR: 1 + MLPS: [[16, 16], [16, 16]] + POOL_RADIUS: [0.4, 0.8] + NSAMPLE: [16, 16] + x_conv2: + DOWNSAMPLE_FACTOR: 2 + MLPS: [[32, 32], [32, 32]] + POOL_RADIUS: [0.8, 1.2] + NSAMPLE: [16, 32] + x_conv3: + DOWNSAMPLE_FACTOR: 4 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [1.2, 2.4] + NSAMPLE: [16, 32] + x_conv4: + DOWNSAMPLE_FACTOR: 8 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [2.4, 4.8] + NSAMPLE: [16, 32] + + POINT_HEAD: + NAME: PointHeadSimple + CLS_FC: [256, 256] + CLASS_AGNOSTIC: True + USE_POINT_FEATURES_BEFORE_FUSION: True + TARGET_CONFIG: + GT_EXTRA_WIDTH: [0.2, 0.2, 0.2] + LOSS_CONFIG: + LOSS_REG: smooth-l1 + LOSS_WEIGHTS: { + 'point_cls_weight': 1.0, + } + + ROI_HEAD: + NAME: PVRCNNHeadRelation + CLASS_AGNOSTIC: True + + SHARED_FC: [256, 256] + CLS_FC: [256, 256] + REG_FC: [256, 256] + DP_RATIO: 0.3 + + NMS_CONFIG: + TRAIN: + NMS_TYPE: nms_gpu + MULTI_CLASSES_NMS: False + # the proposals are soreted according to scores and then we take the NMS_PRE_MAXSIZE first + NMS_PRE_MAXSIZE: 9000 + # proposals with more than NMS_THRESH overlap with others will be removed + NMS_THRESH: 0.8 + # the proposals after NMS will be limited to NMS_POST_MAXSIZE + NMS_POST_MAXSIZE: 512 + # values are different for testing + TEST: + NMS_TYPE: nms_gpu + MULTI_CLASSES_NMS: False + NMS_PRE_MAXSIZE: 1024 + NMS_POST_MAXSIZE: 100 + NMS_THRESH: 0.7 + + ROI_GRID_POOL: + GRID_SIZE: 6 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [0.8, 1.6] + NSAMPLE: [16, 16] + POOL_METHOD: max_pool + + TARGET_CONFIG: + BOX_CODER: ResidualCoder + # number of proposal after the sampling + ROI_PER_IMAGE: 128 + # max number of foreground proposal + FG_RATIO: 0.5 + + SAMPLE_ROI_BY_EACH_CLASS: True + CLS_SCORE_TYPE: roi_iou + + # thresholds defining the foregrouns, easy background and hard background + CLS_FG_THRESH: 0.75 + CLS_BG_THRESH: 0.25 + CLS_BG_THRESH_LO: 0.1 + # from the background proposals, HARD_BG_RATIO are ones with very small iou with gt (1-HARD_BG_RATIO are with large iou with gt -> still background) + HARD_BG_RATIO: 0.8 + + REG_FG_THRESH: 0.55 + + 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] + } + + OBJECT_RELATION: + NAME: GNN + # NAME: radius_graph, + # RADIUS: 6, + # CONNECT_ONLY_SAME_CLASS: False + # } + GRAPH: { + CONV: { + NAME: EdgeConv, + EDGE_EMBEDDING: True, + # HEADS: 4, + SKIP_CONNECTION: False, + }, + DYNAMIC: False, + NAME: knn_graph, + K: 16, + CONNECT_ONLY_SAME_CLASS: False, + SPACE: R3, + # EDGE_EMBEDDING: True, + } + LAYERS: [256, 256, 256, 256] + SKIP_CONNECTION: True + # IN_BETWEEN_MLP: [256, 256, 256, 256] + IN_BETWEEN_MLP: False + # GLOBAL_INFORMATION: { + # MLP_LAYERS: [256, 256], + # CONCATENATED: False, + # } + GLOBAL_INFORMATION: False + DP_RATIO: 0.3 + + + POST_PROCESSING: + # IoU Threshold + RECALL_THRESH_LIST: [0.3, 0.5, 0.7] + # Car, Pedestrian, Cyclist + SCORE_THRESH: 0.1 + # SCORE_THRESH: 0.0 + OUTPUT_RAW_SCORE: False + + EVAL_METRIC: kitti + + NMS_CONFIG: + MULTI_CLASSES_NMS: False + NMS_TYPE: nms_gpu + NMS_THRESH: 0.1 + # NMS_THRESH: 1.0 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 + + +OPTIMIZATION: + BATCH_SIZE_PER_GPU: 2 + NUM_EPOCHS: 5 + + 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 diff --git a/tools/scripts/experiments.py b/tools/scripts/experiments.py new file mode 100644 index 000000000..ba824e94e --- /dev/null +++ b/tools/scripts/experiments.py @@ -0,0 +1,63 @@ +import os +import glob +import subprocess +import time + +# Initialize paths and names +experiment_name = 'relation_backbone' + +BASE_PATH = '/root/OpenPCDet/tools' +base_path = "./cfgs/kitti_models" +experiments_input_path = os.path.join(base_path, "experiments", experiment_name) + +# Initialize output paths +kitti_output_path = "../output/cfgs/kitti_models" +experiment_output_path = os.path.join(kitti_output_path, "experiments", experiment_name) + +# Read all model config paths from the directory +yaml_files = glob.glob(os.path.join(experiments_input_path, "*.yaml")) +experiments = [os.path.splitext(os.path.basename(f))[0] for f in yaml_files] +# sort the experiments +experiments = sorted(experiments, key=lambda x: int(x.split('_')[0])) + +# print the experiments +print(f"Running experiments at {experiments_input_path}: {experiments}") + +# create tensorboard directory +os.makedirs(os.path.join(experiment_output_path, 'tensorboard'), exist_ok=True) +cmd = f'(cd {experiment_output_path}; tensorboard dev upload --logdir tensorboard --name "{experiment_name}")' +tensorboard_process = subprocess.Popen(cmd, shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + +# Run all experiments +for e in experiments: + success = False + while not success: + try: + config_path = os.path.join(experiments_input_path, f"{e}.yaml") + cmd = ["python", "train.py", "--cfg", config_path, "--extra_tag", f"{e}"] + subprocess.run(cmd, check=True, cwd=BASE_PATH) + + source_path = os.path.join(experiment_output_path, f"{e}/{e}/tensorboard") + tensor_file = glob.glob(os.path.join(source_path, "*")) + dest_path = os.path.join(experiment_output_path, 'tensorboard', f"{e}") + os.makedirs(dest_path, exist_ok=True) + + # Get the base name of the source file + file_name = os.path.basename(tensor_file[0]) + + # Create the full destination path including the file name + full_dest_path = os.path.join(dest_path, file_name) + + # Perform the copy + os.system(f"cp -r {tensor_file[0]} {full_dest_path}") + print(f"Finished experiment {e}") + success = True + except Exception as ex: + print(f"Error while running experiment {e}: {ex}") + time.sleep(60) + print(f"Retrying experiment {e}") + +# Kill tensorboard process +# wait for 10 minutes to make sure tensorboard is done +time.sleep(600) +tensorboard_process.terminate() From 01f18aacdeb733a132136f7fc9c2c003d705f0dd Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Wed, 18 Oct 2023 07:44:07 +0000 Subject: [PATCH 76/93] Update code for pretrained model --- pcdet/models/detectors/pv_rcnn.py | 2 +- pcdet/models/detectors/pv_rcnn_relation.py | 2 +- .../models/roi_heads/pvrcnn_head_relation.py | 24 +++++++++++++++---- tools/train.py | 13 ++++++++-- 4 files changed, 32 insertions(+), 9 deletions(-) diff --git a/pcdet/models/detectors/pv_rcnn.py b/pcdet/models/detectors/pv_rcnn.py index 45ea30506..8a1f87c98 100644 --- a/pcdet/models/detectors/pv_rcnn.py +++ b/pcdet/models/detectors/pv_rcnn.py @@ -5,7 +5,7 @@ class PVRCNN(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() - self.frozen = True if "FROZEN" in model_cfg.keys() else model_cfg.FROZEN + self.frozen = model_cfg.FROZEN if "FROZEN" in model_cfg.keys() else False def forward(self, batch_dict): for cur_module in self.module_list: diff --git a/pcdet/models/detectors/pv_rcnn_relation.py b/pcdet/models/detectors/pv_rcnn_relation.py index 6d9e32085..6ac3cc804 100644 --- a/pcdet/models/detectors/pv_rcnn_relation.py +++ b/pcdet/models/detectors/pv_rcnn_relation.py @@ -9,7 +9,7 @@ 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() self.object_relation = build_object_relation_module(model_cfg.OBJECT_RELATION) - self.frozen = True if "FROZEN" in model_cfg.keys() else model_cfg.FROZEN + self.frozen = model_cfg.FROZEN if "FROZEN" in model_cfg.keys() else False def forward(self, batch_dict): # MeanVFE: Voxelisation diff --git a/pcdet/models/roi_heads/pvrcnn_head_relation.py b/pcdet/models/roi_heads/pvrcnn_head_relation.py index 7b499b37b..d341c07a9 100644 --- a/pcdet/models/roi_heads/pvrcnn_head_relation.py +++ b/pcdet/models/roi_heads/pvrcnn_head_relation.py @@ -32,6 +32,7 @@ def __init__(self, input_channels, model_cfg, num_class=1, object_relation_confi self.shared_fc_layer = nn.Sequential(*shared_fc_list) if object_relation_config.NAME == 'GNN': + self.skip_head = False if object_relation_config.GLOBAL_INFORMATION: initial_input_dim = object_relation_config.GLOBAL_INFORMATION.MLP_LAYERS[-1] if not object_relation_config.GLOBAL_INFORMATION.CONCATENATED: @@ -42,10 +43,19 @@ def __init__(self, input_channels, model_cfg, num_class=1, object_relation_confi if object_relation_config.SKIP_CONNECTION: self.head_input_channels = initial_input_dim + sum(object_relation_config.LAYERS) else: - self.head_input_channels = object_relation_config.LAYERS[-1] + if len(object_relation_config.LAYERS) == 0: + self.head_input_channels = self.model_cfg.SHARED_FC[-1] + else: + self.head_input_channels = object_relation_config.LAYERS[-1] elif object_relation_config.NAME == 'CGNLNet': # TODO: udpate this self.head_input_channels = self.model_cfg.SHARED_FC[-1] + 256 + 256 + elif object_relation_config.NAME == 'GNN_BADET': + self.head_input_channels = 256 + self.skip_head = True + elif object_relation_config.NAME == 'GNN_NEW': + self.head_input_channels = 256 + self.skip_head = False else: raise NotImplementedError @@ -199,10 +209,14 @@ def forward(self, batch_dict): return batch_dict def final_predictions(self, batch_dict): - shared_features = batch_dict['related_features'] - shared_features = shared_features.view(-1, self.head_input_channels, 1) - rcnn_cls = self.cls_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1) # (B, 1 or 2) - rcnn_reg = self.reg_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1) # (B, C) + if self.skip_head: + rcnn_cls = batch_dict['rcnn_cls'] + rcnn_reg = batch_dict['rcnn_reg'] + else: + shared_features = batch_dict['related_features'] + shared_features = shared_features.view(-1, self.head_input_channels, 1) + rcnn_cls = self.cls_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1) # (B, 1 or 2) + rcnn_reg = self.reg_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1) # (B, C) if not self.training: batch_cls_preds, batch_box_preds = self.generate_predicted_boxes( diff --git a/tools/train.py b/tools/train.py index 8f50e25a7..47b5dbc33 100644 --- a/tools/train.py +++ b/tools/train.py @@ -11,7 +11,7 @@ import torch.nn as nn from tensorboardX import SummaryWriter -from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file +from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file, second_cfg_from_yaml_file from pcdet.datasets import build_dataloader from pcdet.models import build_network, model_fn_decorator from pcdet.utils import common_utils @@ -50,7 +50,6 @@ def parse_config(): parser.add_argument('--ckpt_save_time_interval', type=int, default=300, help='in terms of seconds') parser.add_argument('--wo_gpu_stat', action='store_true', help='') parser.add_argument('--use_amp', action='store_true', help='use mix precision training') - args = parser.parse_args() @@ -129,6 +128,7 @@ def main(): ) model = build_network(model_cfg=cfg.MODEL, num_class=len(cfg.CLASS_NAMES), dataset=train_set) + if args.sync_bn: model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model) model.cuda() @@ -140,6 +140,15 @@ def main(): last_epoch = -1 if args.pretrained_model is not None: model.load_params_from_file(filename=args.pretrained_model, to_cpu=dist_train, logger=logger) + + if 'PRE_TRAINED' in cfg: + model.load_params_from_file(filename=cfg.PRE_TRAINED.MODEL_PATH, to_cpu=dist_train, logger=logger, learnable_layer=cfg.PRE_TRAINED.LEARNABLE_LAYER) + # freeze all other layers + for name, param in model.named_parameters(): + if any([(l in name) for l in cfg.PRE_TRAINED.LEARNABLE_LAYER]): + print('learnable layer: ', name) + else: + param.requires_grad = False if args.ckpt is not None: it, start_epoch = model.load_params_with_optimizer(args.ckpt, to_cpu=dist_train, optimizer=optimizer, logger=logger) From 362113f0c6ecb98fb53efedc430de66a260ca1e7 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Wed, 25 Oct 2023 14:09:56 +0000 Subject: [PATCH 77/93] Update ReadMe --- README.md | 2 +- resources/gnn_architecture.png | Bin 0 -> 229298 bytes 2 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 resources/gnn_architecture.png diff --git a/README.md b/README.md index c68083467..685aa4bd9 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,7 @@ If we look at the full scene, it becomes clear that context can help us detect t To model object relation, this project introduces a GNN. The nodes in the graph are the detected objects; their features are obtained by the second stage of the model (refined features). The edges of the graph are obtained via a radius graph or knn (distance can be measured in R3 or in feature space). Global information, how the objects relate to each other, are infused into the edges. After the GNN is applied the model obtains a feature vector for each object that contains information about all the other objects. - + ## Results diff --git a/resources/gnn_architecture.png b/resources/gnn_architecture.png new file mode 100644 index 0000000000000000000000000000000000000000..fd9699595ca858ee35c3da053cfc9cfc637c2cdd GIT binary patch literal 229298 zcmeFZcT`l%_CJV#2nvXzAh{)qB*{4_l9PhuB(Wvu*aQiJN|c;Y5KwYPLW6>Wq;7I( zpc^DKInzyM4)@*n?wfgEyx%`&t@*84kF~nHICbh&)!w^y`0T3l=83uj$yMsBI5;>Y zN{Wv)ad57L;^5$y5)uGc?q5j&?JA7^`(H9cQG=F(Q%|QK=HZiT8S@JSr zAFjZAqD6^-pinv*8lv|k8E1|grLs;hbZO@e&Q{OL@>$3zHZRGT--VIg+T6K|ypCh? zBU$m_IGlgS{h;M#pB!#N7JI({F&C)^wPP>r6)j*J7H^PmRw zG>ADyc>avDKLct=BGbrgk|KjJ9yKD4%=ITSXh^zH)$W~+}O!dez z-fxfJ%Oop%`%1`CS=9b^U=o}5_r#6{o^CbsqFqz)wfg)1kvbWfDY(3s&AS<&x(a+3 zc%6CGm7MXJ6jMZ%Xg{8mrhbz|pvvpnSHJpX@}zVU*h${#9wg6G68BI}q;UO`FWN-5 zu6}=D?^J!>|SMZ#m$xi0-F1G(i^rsSOVZq173asa#_Q1c&z+6l=jal?1)X`s zYSYs_%+IM)g{@ayF$Dj`co 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zkHoZJdkX$k3K9&8 z-wzBb=fs%P20(oHhp>c}g+<%2p|GG$Ki}w8UJ}4i;f*W3V4EolTDwvVdfgtXf1mO) znCl{6_;=iT`Ut=-Ta-oi26-aQRWJ%9Xw>~3XNIw8JrTc@$1-RHD3S0n4gZ(c*NU~T z*GL597+}oT*!~YqtGoaXkl%nO;S;)}o+HF0Xwo;GlMFHyAYk-M(uUd9A0`sM3}5M? zjo=&q7$D>j_>#^k5O^lOTI}chlnzb_3r<_Nb?9G^{qCy(pRmW4?c_s{7A!;S-G@dmmF zYsTOg;BoS#=bHsENtp56c7kuRw*B>izF6uHFri1zZ Date: Wed, 15 Nov 2023 08:44:40 +0000 Subject: [PATCH 78/93] Add model configs --- .../kitti_models/PartA2_car_class_only.yaml | 171 +++++++++ .../pv_rcnn_BADet_car_class_only.yaml | 254 ++++++++++++++ .../kitti_models/pv_rcnn_car_class_only.yaml | 229 ++++++++++++ ...rcnn_plusplus_relation_car_class_only.yaml | 332 ++++++++++++++++++ ...eproduced_by_community_car_class_only.yaml | 301 ++++++++++++++++ tools/cfgs/waymo_models/pv_rcnn_relation.yaml | 250 +++++++++++++ 6 files changed, 1537 insertions(+) create mode 100644 tools/cfgs/kitti_models/PartA2_car_class_only.yaml create mode 100644 tools/cfgs/kitti_models/pv_rcnn_BADet_car_class_only.yaml create mode 100644 tools/cfgs/kitti_models/pv_rcnn_car_class_only.yaml create mode 100644 tools/cfgs/kitti_models/pv_rcnn_plusplus_relation_car_class_only.yaml create mode 100644 tools/cfgs/kitti_models/pv_rcnn_plusplus_reproduced_by_community_car_class_only.yaml create mode 100644 tools/cfgs/waymo_models/pv_rcnn_relation.yaml diff --git a/tools/cfgs/kitti_models/PartA2_car_class_only.yaml b/tools/cfgs/kitti_models/PartA2_car_class_only.yaml new file mode 100644 index 000000000..d9d19f749 --- /dev/null +++ b/tools/cfgs/kitti_models/PartA2_car_class_only.yaml @@ -0,0 +1,171 @@ +CLASS_NAMES: ['Car'] + +DATA_CONFIG: + _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml + + +MODEL: + NAME: PartA2Net + + VFE: + NAME: MeanVFE + + BACKBONE_3D: + NAME: UNetV2 + + 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: AnchorHeadSingle + CLASS_AGNOSTIC: False + + USE_DIRECTION_CLASSIFIER: True + DIR_OFFSET: 0.78539 + DIR_LIMIT_OFFSET: 0.0 + NUM_DIR_BINS: 2 + + ANCHOR_GENERATOR_CONFIG: [ + { + 'class_name': 'Car', + 'anchor_sizes': [[3.9, 1.6, 1.56]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [-1.78], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.6, + 'unmatched_threshold': 0.45 + } + ] + + TARGET_ASSIGNER_CONFIG: + NAME: AxisAlignedTargetAssigner + POS_FRACTION: -1.0 + SAMPLE_SIZE: 512 + NORM_BY_NUM_EXAMPLES: False + MATCH_HEIGHT: False + BOX_CODER: ResidualCoder + + LOSS_CONFIG: + LOSS_WEIGHTS: { + 'cls_weight': 1.0, + 'loc_weight': 2.0, + 'dir_weight': 0.2, + 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + + POINT_HEAD: + NAME: PointIntraPartOffsetHead + CLS_FC: [] + PART_FC: [] + CLASS_AGNOSTIC: True + TARGET_CONFIG: + GT_EXTRA_WIDTH: [0.2, 0.2, 0.2] + LOSS_CONFIG: + LOSS_REG: smooth-l1 + LOSS_WEIGHTS: { + 'point_cls_weight': 1.0, + 'point_part_weight': 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 + + SEG_MASK_SCORE_THRESH: 0.3 + + 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: + BATCH_SIZE_PER_GPU: 4 + NUM_EPOCHS: 80 + + 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 diff --git a/tools/cfgs/kitti_models/pv_rcnn_BADet_car_class_only.yaml b/tools/cfgs/kitti_models/pv_rcnn_BADet_car_class_only.yaml new file mode 100644 index 000000000..2f060eb9a --- /dev/null +++ b/tools/cfgs/kitti_models/pv_rcnn_BADet_car_class_only.yaml @@ -0,0 +1,254 @@ +CLASS_NAMES: ['Car'] + +DATA_CONFIG: + _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml + DATA_AUGMENTOR: + DISABLE_AUG_LIST: ['placeholder'] + AUG_CONFIG_LIST: + - NAME: gt_sampling + USE_ROAD_PLANE: True + DB_INFO_PATH: + - kitti_dbinfos_train.pkl + PREPARE: { + filter_by_min_points: ['Car:5'], + filter_by_difficulty: [-1], + } + + SAMPLE_GROUPS: ['Car:15'] + NUM_POINT_FEATURES: 4 + DATABASE_WITH_FAKELIDAR: False + REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0] + LIMIT_WHOLE_SCENE: False + + - NAME: random_world_flip + ALONG_AXIS_LIST: ['x'] + + - NAME: random_world_rotation + WORLD_ROT_ANGLE: [-0.78539816, 0.78539816] + + - NAME: random_world_scaling + WORLD_SCALE_RANGE: [0.95, 1.05] + +MODEL: + FROZEN: False + NAME: PVRCNNRelation + + VFE: + NAME: MeanVFE + + BACKBONE_3D: + NAME: VoxelBackBone8x + + 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: AnchorHeadSingle + CLASS_AGNOSTIC: False + + USE_DIRECTION_CLASSIFIER: True + DIR_OFFSET: 0.78539 + DIR_LIMIT_OFFSET: 0.0 + NUM_DIR_BINS: 2 + + ANCHOR_GENERATOR_CONFIG: [ + { + 'class_name': 'Car', + 'anchor_sizes': [[3.9, 1.6, 1.56]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [-1.78], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.6, + 'unmatched_threshold': 0.45 + }, + ] + + TARGET_ASSIGNER_CONFIG: + NAME: AxisAlignedTargetAssigner + POS_FRACTION: -1.0 + SAMPLE_SIZE: 512 + NORM_BY_NUM_EXAMPLES: False + MATCH_HEIGHT: False + BOX_CODER: ResidualCoder + + LOSS_CONFIG: + LOSS_WEIGHTS: { + 'cls_weight': 1.0, + 'loc_weight': 2.0, + 'dir_weight': 0.2, + 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + + PFE: + NAME: VoxelSetAbstraction + POINT_SOURCE: raw_points + NUM_KEYPOINTS: 2048 + NUM_OUTPUT_FEATURES: 128 + SAMPLE_METHOD: FPS + + FEATURES_SOURCE: ['bev', 'x_conv1', 'x_conv2', 'x_conv3', 'x_conv4', 'raw_points'] + SA_LAYER: + raw_points: + MLPS: [[16, 16], [16, 16]] + POOL_RADIUS: [0.4, 0.8] + NSAMPLE: [16, 16] + x_conv1: + DOWNSAMPLE_FACTOR: 1 + MLPS: [[16, 16], [16, 16]] + POOL_RADIUS: [0.4, 0.8] + NSAMPLE: [16, 16] + x_conv2: + DOWNSAMPLE_FACTOR: 2 + MLPS: [[32, 32], [32, 32]] + POOL_RADIUS: [0.8, 1.2] + NSAMPLE: [16, 32] + x_conv3: + DOWNSAMPLE_FACTOR: 4 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [1.2, 2.4] + NSAMPLE: [16, 32] + x_conv4: + DOWNSAMPLE_FACTOR: 8 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [2.4, 4.8] + NSAMPLE: [16, 32] + + POINT_HEAD: + NAME: PointHeadSimple + CLS_FC: [256, 256] + CLASS_AGNOSTIC: True + USE_POINT_FEATURES_BEFORE_FUSION: True + TARGET_CONFIG: + GT_EXTRA_WIDTH: [0.2, 0.2, 0.2] + LOSS_CONFIG: + LOSS_REG: smooth-l1 + LOSS_WEIGHTS: { + 'point_cls_weight': 1.0, + } + + ROI_HEAD: + NAME: PVRCNNHeadRelation + CLASS_AGNOSTIC: True + + SHARED_FC: [256, 256] + CLS_FC: [256, 256] + REG_FC: [256, 256] + DP_RATIO: 0.3 + + NMS_CONFIG: + TRAIN: + NMS_TYPE: nms_gpu + MULTI_CLASSES_NMS: False + # the proposals are soreted according to scores and then we take the NMS_PRE_MAXSIZE first + NMS_PRE_MAXSIZE: 9000 + # proposals with more than NMS_THRESH overlap with others will be removed + NMS_THRESH: 0.8 + # the proposals after NMS will be limited to NMS_POST_MAXSIZE + NMS_POST_MAXSIZE: 512 + # values are different for testing + TEST: + NMS_TYPE: nms_gpu + MULTI_CLASSES_NMS: False + NMS_PRE_MAXSIZE: 1024 + NMS_POST_MAXSIZE: 100 + NMS_THRESH: 0.7 + + ROI_GRID_POOL: + GRID_SIZE: 6 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [0.8, 1.6] + NSAMPLE: [16, 16] + POOL_METHOD: max_pool + + TARGET_CONFIG: + BOX_CODER: ResidualCoder + # number of proposal after the sampling + ROI_PER_IMAGE: 128 + # max number of foreground proposal + FG_RATIO: 0.5 + + SAMPLE_ROI_BY_EACH_CLASS: True + CLS_SCORE_TYPE: roi_iou + + # thresholds defining the foregrouns, easy background and hard background + CLS_FG_THRESH: 0.75 + CLS_BG_THRESH: 0.25 + CLS_BG_THRESH_LO: 0.1 + # from the background proposals, HARD_BG_RATIO are ones with very small iou with gt (1-HARD_BG_RATIO are with large iou with gt -> still background) + HARD_BG_RATIO: 0.8 + + REG_FG_THRESH: 0.55 + + 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] + } + + OBJECT_RELATION: + NAME: GNN_BADET + STATE_DIM: 256 # 120 in the paper + ITERATIONS: 3 + GRAPH: { + NAME: radius_graph, + RADIUS: 0.1, + CONNECT_ONLY_SAME_CLASS: False + } + + + + POST_PROCESSING: + # IoU Threshold + RECALL_THRESH_LIST: [0.3, 0.5, 0.7] + # Car, Pedestrian, Cyclist + SCORE_THRESH: 0.1 + # SCORE_THRESH: 0.0 + OUTPUT_RAW_SCORE: False + + EVAL_METRIC: kitti + + NMS_CONFIG: + MULTI_CLASSES_NMS: False + NMS_TYPE: nms_gpu + NMS_THRESH: 0.1 + # NMS_THRESH: 1.0 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 + + +OPTIMIZATION: + BATCH_SIZE_PER_GPU: 5 + NUM_EPOCHS: 80 + + 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 diff --git a/tools/cfgs/kitti_models/pv_rcnn_car_class_only.yaml b/tools/cfgs/kitti_models/pv_rcnn_car_class_only.yaml new file mode 100644 index 000000000..3c078f1e0 --- /dev/null +++ b/tools/cfgs/kitti_models/pv_rcnn_car_class_only.yaml @@ -0,0 +1,229 @@ +CLASS_NAMES: ['Car'] + +DATA_CONFIG: + _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml + DATA_AUGMENTOR: + DISABLE_AUG_LIST: ['placeholder'] + AUG_CONFIG_LIST: + - NAME: gt_sampling + USE_ROAD_PLANE: True + DB_INFO_PATH: + - kitti_dbinfos_train.pkl + PREPARE: { + filter_by_min_points: ['Car:5'], + filter_by_difficulty: [-1], + } + + SAMPLE_GROUPS: ['Car:15'] + NUM_POINT_FEATURES: 4 + DATABASE_WITH_FAKELIDAR: False + REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0] + LIMIT_WHOLE_SCENE: False + + - NAME: random_world_flip + ALONG_AXIS_LIST: ['x'] + + - NAME: random_world_rotation + WORLD_ROT_ANGLE: [-0.78539816, 0.78539816] + + - NAME: random_world_scaling + WORLD_SCALE_RANGE: [0.95, 1.05] + +MODEL: + NAME: PVRCNN + + VFE: + NAME: MeanVFE + + BACKBONE_3D: + NAME: VoxelBackBone8x + + 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: AnchorHeadSingle + CLASS_AGNOSTIC: False + + USE_DIRECTION_CLASSIFIER: True + DIR_OFFSET: 0.78539 + DIR_LIMIT_OFFSET: 0.0 + NUM_DIR_BINS: 2 + + ANCHOR_GENERATOR_CONFIG: [ + { + 'class_name': 'Car', + 'anchor_sizes': [[3.9, 1.6, 1.56]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [-1.78], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.6, + 'unmatched_threshold': 0.45 + }, + ] + + TARGET_ASSIGNER_CONFIG: + NAME: AxisAlignedTargetAssigner + POS_FRACTION: -1.0 + SAMPLE_SIZE: 512 + NORM_BY_NUM_EXAMPLES: False + MATCH_HEIGHT: False + BOX_CODER: ResidualCoder + + LOSS_CONFIG: + LOSS_WEIGHTS: { + 'cls_weight': 1.0, + 'loc_weight': 2.0, + 'dir_weight': 0.2, + 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + + PFE: + NAME: VoxelSetAbstraction + POINT_SOURCE: raw_points + NUM_KEYPOINTS: 2048 + NUM_OUTPUT_FEATURES: 128 + SAMPLE_METHOD: FPS + + FEATURES_SOURCE: ['bev', 'x_conv1', 'x_conv2', 'x_conv3', 'x_conv4', 'raw_points'] + SA_LAYER: + raw_points: + MLPS: [[16, 16], [16, 16]] + POOL_RADIUS: [0.4, 0.8] + NSAMPLE: [16, 16] + x_conv1: + DOWNSAMPLE_FACTOR: 1 + MLPS: [[16, 16], [16, 16]] + POOL_RADIUS: [0.4, 0.8] + NSAMPLE: [16, 16] + x_conv2: + DOWNSAMPLE_FACTOR: 2 + MLPS: [[32, 32], [32, 32]] + POOL_RADIUS: [0.8, 1.2] + NSAMPLE: [16, 32] + x_conv3: + DOWNSAMPLE_FACTOR: 4 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [1.2, 2.4] + NSAMPLE: [16, 32] + x_conv4: + DOWNSAMPLE_FACTOR: 8 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [2.4, 4.8] + NSAMPLE: [16, 32] + + POINT_HEAD: + NAME: PointHeadSimple + CLS_FC: [256, 256] + CLASS_AGNOSTIC: True + USE_POINT_FEATURES_BEFORE_FUSION: True + TARGET_CONFIG: + GT_EXTRA_WIDTH: [0.2, 0.2, 0.2] + LOSS_CONFIG: + LOSS_REG: smooth-l1 + LOSS_WEIGHTS: { + 'point_cls_weight': 1.0, + } + + ROI_HEAD: + NAME: PVRCNNHead + CLASS_AGNOSTIC: True + + SHARED_FC: [256, 256] + CLS_FC: [256, 256] + REG_FC: [256, 256] + DP_RATIO: 0.3 + + 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_GRID_POOL: + GRID_SIZE: 6 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [0.8, 1.6] + NSAMPLE: [16, 16] + POOL_METHOD: max_pool + + 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: 0.55 + + 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: + BATCH_SIZE_PER_GPU: 2 + NUM_EPOCHS: 80 + + 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 diff --git a/tools/cfgs/kitti_models/pv_rcnn_plusplus_relation_car_class_only.yaml b/tools/cfgs/kitti_models/pv_rcnn_plusplus_relation_car_class_only.yaml new file mode 100644 index 000000000..f90ec7609 --- /dev/null +++ b/tools/cfgs/kitti_models/pv_rcnn_plusplus_relation_car_class_only.yaml @@ -0,0 +1,332 @@ +CLASS_NAMES: ['Car'] + +DATA_CONFIG: + _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml + DATA_AUGMENTOR: + DISABLE_AUG_LIST: ['placeholder'] + AUG_CONFIG_LIST: + - NAME: gt_sampling + USE_ROAD_PLANE: True + DB_INFO_PATH: + - kitti_dbinfos_train.pkl + PREPARE: { + filter_by_min_points: ['Car:5'], + filter_by_difficulty: [-1], + } + + SAMPLE_GROUPS: ['Car:15'] + NUM_POINT_FEATURES: 4 + DATABASE_WITH_FAKELIDAR: False + REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0] + LIMIT_WHOLE_SCENE: False + - NAME: random_world_flip + ALONG_AXIS_LIST: ['x'] + + - NAME: random_world_rotation + WORLD_ROT_ANGLE: [-0.78539816, 0.78539816] + + - NAME: random_world_scaling + WORLD_SCALE_RANGE: [0.95, 1.05] + +MODEL: + NAME: PVRCNNPlusPlusRelation + + VFE: + NAME: MeanVFE + + BACKBONE_3D: + NAME: VoxelBackBone8x + + 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: CenterHead + CLASS_AGNOSTIC: False + + CLASS_NAMES_EACH_HEAD: [ + [ 'Car'] + ] + + SHARED_CONV_CHANNEL: 64 + USE_BIAS_BEFORE_NORM: True + NUM_HM_CONV: 2 + SEPARATE_HEAD_CFG: + HEAD_ORDER: [ 'center', 'center_z', 'dim', 'rot' ] + HEAD_DICT: { + 'center': { 'out_channels': 2, 'num_conv': 2 }, + 'center_z': { 'out_channels': 1, 'num_conv': 2 }, + 'dim': { 'out_channels': 3, 'num_conv': 2 }, + 'rot': { 'out_channels': 2, 'num_conv': 2 }, + } + + TARGET_ASSIGNER_CONFIG: + FEATURE_MAP_STRIDE: 8 + NUM_MAX_OBJS: 500 + GAUSSIAN_OVERLAP: 0.1 + MIN_RADIUS: 2 + + LOSS_CONFIG: + LOSS_WEIGHTS: { + 'cls_weight': 1.0, + 'loc_weight': 2.0, + 'code_weights': [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] + } + + POST_PROCESSING: + SCORE_THRESH: 0.01 + POST_CENTER_LIMIT_RANGE: [ -75.2, -75.2, -2, 75.2, 75.2, 4 ] + MAX_OBJ_PER_SAMPLE: 500 + NMS_CONFIG: + NMS_TYPE: nms_gpu + NMS_THRESH: 0.7 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 + + PFE: + NAME: VoxelSetAbstraction + POINT_SOURCE: raw_points + NUM_KEYPOINTS: 4096 + NUM_OUTPUT_FEATURES: 90 + SAMPLE_METHOD: SPC + SPC_SAMPLING: + NUM_SECTORS: 6 + SAMPLE_RADIUS_WITH_ROI: 1.6 + + FEATURES_SOURCE: ['bev', 'x_conv3', 'x_conv4', 'raw_points'] + SA_LAYER: + raw_points: + NAME: VectorPoolAggregationModuleMSG + NUM_GROUPS: 2 + LOCAL_AGGREGATION_TYPE: local_interpolation + NUM_REDUCED_CHANNELS: 1 + NUM_CHANNELS_OF_LOCAL_AGGREGATION: 32 + MSG_POST_MLPS: [ 32 ] + FILTER_NEIGHBOR_WITH_ROI: True + RADIUS_OF_NEIGHBOR_WITH_ROI: 2.4 + + GROUP_CFG_0: + NUM_LOCAL_VOXEL: [ 2, 2, 2 ] + MAX_NEIGHBOR_DISTANCE: 0.2 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [ 32, 32 ] + GROUP_CFG_1: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 0.4 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [ 32, 32 ] + + x_conv3: + DOWNSAMPLE_FACTOR: 4 + INPUT_CHANNELS: 64 + + NAME: VectorPoolAggregationModuleMSG + NUM_GROUPS: 2 + LOCAL_AGGREGATION_TYPE: local_interpolation + NUM_REDUCED_CHANNELS: 32 + NUM_CHANNELS_OF_LOCAL_AGGREGATION: 32 + MSG_POST_MLPS: [128] + FILTER_NEIGHBOR_WITH_ROI: True + RADIUS_OF_NEIGHBOR_WITH_ROI: 4.0 + + GROUP_CFG_0: + NUM_LOCAL_VOXEL: [3, 3, 3] + MAX_NEIGHBOR_DISTANCE: 1.2 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [64, 64] + GROUP_CFG_1: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 2.4 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [ 64, 64 ] + + x_conv4: + DOWNSAMPLE_FACTOR: 8 + INPUT_CHANNELS: 64 + + NAME: VectorPoolAggregationModuleMSG + NUM_GROUPS: 2 + LOCAL_AGGREGATION_TYPE: local_interpolation + NUM_REDUCED_CHANNELS: 32 + NUM_CHANNELS_OF_LOCAL_AGGREGATION: 32 + MSG_POST_MLPS: [ 128 ] + FILTER_NEIGHBOR_WITH_ROI: True + RADIUS_OF_NEIGHBOR_WITH_ROI: 6.4 + + GROUP_CFG_0: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 2.4 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [ 64, 64 ] + GROUP_CFG_1: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 4.8 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [ 64, 64 ] + + + POINT_HEAD: + NAME: PointHeadSimple + CLS_FC: [256, 256] + CLASS_AGNOSTIC: True + USE_POINT_FEATURES_BEFORE_FUSION: True + TARGET_CONFIG: + GT_EXTRA_WIDTH: [0.2, 0.2, 0.2] + LOSS_CONFIG: + LOSS_REG: smooth-l1 + LOSS_WEIGHTS: { + 'point_cls_weight': 1.0, + } + + ROI_HEAD: + NAME: PVRCNNHeadRelation + CLASS_AGNOSTIC: True + + SHARED_FC: [256, 256] + CLS_FC: [256, 256] + REG_FC: [256, 256] + DP_RATIO: 0.3 + + 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 + SCORE_THRESH: 0.1 + +# NMS_PRE_MAXSIZE: 4096 +# NMS_POST_MAXSIZE: 500 +# NMS_THRESH: 0.85 + + + ROI_GRID_POOL: + GRID_SIZE: 6 + + NAME: VectorPoolAggregationModuleMSG + NUM_GROUPS: 2 + LOCAL_AGGREGATION_TYPE: voxel_random_choice + NUM_REDUCED_CHANNELS: 30 + NUM_CHANNELS_OF_LOCAL_AGGREGATION: 32 + MSG_POST_MLPS: [ 128 ] + + GROUP_CFG_0: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 0.8 + NEIGHBOR_NSAMPLE: 32 + POST_MLPS: [ 64, 64 ] + GROUP_CFG_1: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 1.6 + NEIGHBOR_NSAMPLE: 32 + POST_MLPS: [ 64, 64 ] + + 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: 0.55 + + 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] + } + + OBJECT_RELATION: + NAME: GNN + # NAME: radius_graph, + # RADIUS: 6, + # CONNECT_ONLY_SAME_CLASS: False + # } + GRAPH: { + CONV: { + NAME: EdgeConv, + EDGE_EMBEDDING: True, + SKIP_CONNECTION: False + # HEADS: 4, + }, + DYNAMIC: False, + NAME: knn_graph, + K: 16, + CONNECT_ONLY_SAME_CLASS: False, + SPACE: R3, + # EDGE_EMBEDDING: True, + } + LAYERS: [256, 256, 256, 256] + SKIP_CONNECTION: True + # IN_BETWEEN_MLP: [256, 256, 256, 256] + IN_BETWEEN_MLP: False + # GLOBAL_INFORMATION: { + # MLP_LAYERS: [256, 256], + # CONCATENATED: False, + # } + GLOBAL_INFORMATION: False + DP_RATIO: 0.3 + + 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: 0.7 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 + + +OPTIMIZATION: + BATCH_SIZE_PER_GPU: 2 + NUM_EPOCHS: 80 + + OPTIMIZER: adam_onecycle + LR: 0.01 + WEIGHT_DECAY: 0.001 + 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 \ No newline at end of file diff --git a/tools/cfgs/kitti_models/pv_rcnn_plusplus_reproduced_by_community_car_class_only.yaml b/tools/cfgs/kitti_models/pv_rcnn_plusplus_reproduced_by_community_car_class_only.yaml new file mode 100644 index 000000000..e1dddf3ee --- /dev/null +++ b/tools/cfgs/kitti_models/pv_rcnn_plusplus_reproduced_by_community_car_class_only.yaml @@ -0,0 +1,301 @@ +CLASS_NAMES: ['Car'] + +DATA_CONFIG: + _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml + DATA_AUGMENTOR: + DISABLE_AUG_LIST: ['placeholder'] + AUG_CONFIG_LIST: + - NAME: gt_sampling + USE_ROAD_PLANE: True + DB_INFO_PATH: + - kitti_dbinfos_train.pkl + PREPARE: { + filter_by_min_points: ['Car:5'], + filter_by_difficulty: [-1], + } + + SAMPLE_GROUPS: ['Car:15'] + NUM_POINT_FEATURES: 4 + DATABASE_WITH_FAKELIDAR: False + REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0] + LIMIT_WHOLE_SCENE: False + - NAME: random_world_flip + ALONG_AXIS_LIST: ['x'] + + - NAME: random_world_rotation + WORLD_ROT_ANGLE: [-0.78539816, 0.78539816] + + - NAME: random_world_scaling + WORLD_SCALE_RANGE: [0.95, 1.05] + +MODEL: + NAME: PVRCNNPlusPlus + + VFE: + NAME: MeanVFE + + BACKBONE_3D: + NAME: VoxelBackBone8x + + 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: CenterHead + CLASS_AGNOSTIC: False + + CLASS_NAMES_EACH_HEAD: [ + [ 'Car'] + ] + + SHARED_CONV_CHANNEL: 64 + USE_BIAS_BEFORE_NORM: True + NUM_HM_CONV: 2 + SEPARATE_HEAD_CFG: + HEAD_ORDER: [ 'center', 'center_z', 'dim', 'rot' ] + HEAD_DICT: { + 'center': { 'out_channels': 2, 'num_conv': 2 }, + 'center_z': { 'out_channels': 1, 'num_conv': 2 }, + 'dim': { 'out_channels': 3, 'num_conv': 2 }, + 'rot': { 'out_channels': 2, 'num_conv': 2 }, + } + + TARGET_ASSIGNER_CONFIG: + FEATURE_MAP_STRIDE: 8 + NUM_MAX_OBJS: 500 + GAUSSIAN_OVERLAP: 0.1 + MIN_RADIUS: 2 + + LOSS_CONFIG: + LOSS_WEIGHTS: { + 'cls_weight': 1.0, + 'loc_weight': 2.0, + 'code_weights': [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] + } + + POST_PROCESSING: + SCORE_THRESH: 0.01 + POST_CENTER_LIMIT_RANGE: [ -75.2, -75.2, -2, 75.2, 75.2, 4 ] + MAX_OBJ_PER_SAMPLE: 500 + NMS_CONFIG: + NMS_TYPE: nms_gpu + NMS_THRESH: 0.7 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 + + PFE: + NAME: VoxelSetAbstraction + POINT_SOURCE: raw_points + NUM_KEYPOINTS: 4096 + NUM_OUTPUT_FEATURES: 90 + SAMPLE_METHOD: SPC + SPC_SAMPLING: + NUM_SECTORS: 6 + SAMPLE_RADIUS_WITH_ROI: 1.6 + + FEATURES_SOURCE: ['bev', 'x_conv3', 'x_conv4', 'raw_points'] + SA_LAYER: + raw_points: + NAME: VectorPoolAggregationModuleMSG + NUM_GROUPS: 2 + LOCAL_AGGREGATION_TYPE: local_interpolation + NUM_REDUCED_CHANNELS: 1 + NUM_CHANNELS_OF_LOCAL_AGGREGATION: 32 + MSG_POST_MLPS: [ 32 ] + FILTER_NEIGHBOR_WITH_ROI: True + RADIUS_OF_NEIGHBOR_WITH_ROI: 2.4 + + GROUP_CFG_0: + NUM_LOCAL_VOXEL: [ 2, 2, 2 ] + MAX_NEIGHBOR_DISTANCE: 0.2 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [ 32, 32 ] + GROUP_CFG_1: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 0.4 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [ 32, 32 ] + + x_conv3: + DOWNSAMPLE_FACTOR: 4 + INPUT_CHANNELS: 64 + + NAME: VectorPoolAggregationModuleMSG + NUM_GROUPS: 2 + LOCAL_AGGREGATION_TYPE: local_interpolation + NUM_REDUCED_CHANNELS: 32 + NUM_CHANNELS_OF_LOCAL_AGGREGATION: 32 + MSG_POST_MLPS: [128] + FILTER_NEIGHBOR_WITH_ROI: True + RADIUS_OF_NEIGHBOR_WITH_ROI: 4.0 + + GROUP_CFG_0: + NUM_LOCAL_VOXEL: [3, 3, 3] + MAX_NEIGHBOR_DISTANCE: 1.2 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [64, 64] + GROUP_CFG_1: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 2.4 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [ 64, 64 ] + + x_conv4: + DOWNSAMPLE_FACTOR: 8 + INPUT_CHANNELS: 64 + + NAME: VectorPoolAggregationModuleMSG + NUM_GROUPS: 2 + LOCAL_AGGREGATION_TYPE: local_interpolation + NUM_REDUCED_CHANNELS: 32 + NUM_CHANNELS_OF_LOCAL_AGGREGATION: 32 + MSG_POST_MLPS: [ 128 ] + FILTER_NEIGHBOR_WITH_ROI: True + RADIUS_OF_NEIGHBOR_WITH_ROI: 6.4 + + GROUP_CFG_0: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 2.4 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [ 64, 64 ] + GROUP_CFG_1: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 4.8 + NEIGHBOR_NSAMPLE: -1 + POST_MLPS: [ 64, 64 ] + + + POINT_HEAD: + NAME: PointHeadSimple + CLS_FC: [256, 256] + CLASS_AGNOSTIC: True + USE_POINT_FEATURES_BEFORE_FUSION: True + TARGET_CONFIG: + GT_EXTRA_WIDTH: [0.2, 0.2, 0.2] + LOSS_CONFIG: + LOSS_REG: smooth-l1 + LOSS_WEIGHTS: { + 'point_cls_weight': 1.0, + } + + ROI_HEAD: + NAME: PVRCNNHead + CLASS_AGNOSTIC: True + + SHARED_FC: [256, 256] + CLS_FC: [256, 256] + REG_FC: [256, 256] + DP_RATIO: 0.3 + + 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 + SCORE_THRESH: 0.1 + +# NMS_PRE_MAXSIZE: 4096 +# NMS_POST_MAXSIZE: 500 +# NMS_THRESH: 0.85 + + + ROI_GRID_POOL: + GRID_SIZE: 6 + + NAME: VectorPoolAggregationModuleMSG + NUM_GROUPS: 2 + LOCAL_AGGREGATION_TYPE: voxel_random_choice + NUM_REDUCED_CHANNELS: 30 + NUM_CHANNELS_OF_LOCAL_AGGREGATION: 32 + MSG_POST_MLPS: [ 128 ] + + GROUP_CFG_0: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 0.8 + NEIGHBOR_NSAMPLE: 32 + POST_MLPS: [ 64, 64 ] + GROUP_CFG_1: + NUM_LOCAL_VOXEL: [ 3, 3, 3 ] + MAX_NEIGHBOR_DISTANCE: 1.6 + NEIGHBOR_NSAMPLE: 32 + POST_MLPS: [ 64, 64 ] + + 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: 0.55 + + 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: 0.7 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 + + +OPTIMIZATION: + BATCH_SIZE_PER_GPU: 2 + NUM_EPOCHS: 80 + + OPTIMIZER: adam_onecycle + LR: 0.01 + WEIGHT_DECAY: 0.001 + 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 \ No newline at end of file diff --git a/tools/cfgs/waymo_models/pv_rcnn_relation.yaml b/tools/cfgs/waymo_models/pv_rcnn_relation.yaml new file mode 100644 index 000000000..babc9907d --- /dev/null +++ b/tools/cfgs/waymo_models/pv_rcnn_relation.yaml @@ -0,0 +1,250 @@ +CLASS_NAMES: ['Vehicle', 'Pedestrian', 'Cyclist'] + +DATA_CONFIG: + _BASE_CONFIG_: cfgs/dataset_configs/waymo_dataset.yaml + + +MODEL: + NAME: PVRCNNRelation + + VFE: + NAME: MeanVFE + + BACKBONE_3D: + NAME: VoxelBackBone8x + + 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: AnchorHeadSingle + CLASS_AGNOSTIC: False + + USE_DIRECTION_CLASSIFIER: True + DIR_OFFSET: 0.78539 + DIR_LIMIT_OFFSET: 0.0 + NUM_DIR_BINS: 2 + + ANCHOR_GENERATOR_CONFIG: [ + { + 'class_name': 'Vehicle', + 'anchor_sizes': [[4.7, 2.1, 1.7]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [0], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.55, + 'unmatched_threshold': 0.4 + }, + { + 'class_name': 'Pedestrian', + 'anchor_sizes': [[0.91, 0.86, 1.73]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [0], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.5, + 'unmatched_threshold': 0.35 + }, + { + 'class_name': 'Cyclist', + 'anchor_sizes': [[1.78, 0.84, 1.78]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [0], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.5, + 'unmatched_threshold': 0.35 + } + ] + + TARGET_ASSIGNER_CONFIG: + NAME: AxisAlignedTargetAssigner + POS_FRACTION: -1.0 + SAMPLE_SIZE: 512 + NORM_BY_NUM_EXAMPLES: False + MATCH_HEIGHT: False + BOX_CODER: ResidualCoder + + LOSS_CONFIG: + LOSS_WEIGHTS: { + 'cls_weight': 1.0, + 'loc_weight': 2.0, + 'dir_weight': 0.2, + 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + + PFE: + NAME: VoxelSetAbstraction + POINT_SOURCE: raw_points + NUM_KEYPOINTS: 4096 + NUM_OUTPUT_FEATURES: 128 + SAMPLE_METHOD: FPS + + FEATURES_SOURCE: ['bev', 'x_conv3', 'x_conv4', 'raw_points'] + SA_LAYER: + raw_points: + MLPS: [[16, 16], [16, 16]] + POOL_RADIUS: [0.4, 0.8] + NSAMPLE: [16, 16] + x_conv1: + DOWNSAMPLE_FACTOR: 1 + MLPS: [[16, 16], [16, 16]] + POOL_RADIUS: [0.4, 0.8] + NSAMPLE: [16, 16] + x_conv2: + DOWNSAMPLE_FACTOR: 2 + MLPS: [[32, 32], [32, 32]] + POOL_RADIUS: [0.8, 1.2] + NSAMPLE: [16, 32] + x_conv3: + DOWNSAMPLE_FACTOR: 4 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [1.2, 2.4] + NSAMPLE: [16, 32] + x_conv4: + DOWNSAMPLE_FACTOR: 8 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [2.4, 4.8] + NSAMPLE: [16, 32] + + POINT_HEAD: + NAME: PointHeadSimple + CLS_FC: [256, 256] + CLASS_AGNOSTIC: True + USE_POINT_FEATURES_BEFORE_FUSION: True + TARGET_CONFIG: + GT_EXTRA_WIDTH: [0.2, 0.2, 0.2] + LOSS_CONFIG: + LOSS_REG: smooth-l1 + LOSS_WEIGHTS: { + 'point_cls_weight': 1.0, + } + + ROI_HEAD: + NAME: PVRCNNHeadRelation + CLASS_AGNOSTIC: True + + SHARED_FC: [256, 256] + CLS_FC: [256, 256] + REG_FC: [256, 256] + DP_RATIO: 0.3 + + 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 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 300 + NMS_THRESH: 0.85 + + + ROI_GRID_POOL: + GRID_SIZE: 6 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [0.8, 1.6] + NSAMPLE: [16, 16] + POOL_METHOD: max_pool + + 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: 0.55 + + 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] + } + + OBJECT_RELATION: + NAME: GNN + GRAPH: { + CONV: { + NAME: EdgeConv, + EDGE_EMBEDDING: True, + SKIP_CONNECTION: False + }, + DYNAMIC: False, + NAME: knn_graph, + K: 16, + CONNECT_ONLY_SAME_CLASS: False, + SPACE: R3, + } + LAYERS: [256, 256, 256, 256] + SKIP_CONNECTION: True + IN_BETWEEN_MLP: False + GLOBAL_INFORMATION: False + DP_RATIO: 0.3 + + POST_PROCESSING: + RECALL_THRESH_LIST: [0.3, 0.5, 0.7] + SCORE_THRESH: 0.1 + OUTPUT_RAW_SCORE: False + + EVAL_METRIC: waymo + + NMS_CONFIG: + MULTI_CLASSES_NMS: False + NMS_TYPE: nms_gpu + # NMS_THRESH: 1.0 + # NMS_THRESH: 0.7 + NMS_THRESH: 0.1 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 + + +OPTIMIZATION: + BATCH_SIZE_PER_GPU: 2 + NUM_EPOCHS: 30 + + OPTIMIZER: adam_onecycle + LR: 0.01 + WEIGHT_DECAY: 0.001 + 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 \ No newline at end of file From 7766ba7dd6a5401cd103129733985ceb7d7392f9 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Wed, 15 Nov 2023 09:34:18 +0000 Subject: [PATCH 79/93] Update readme --- README.md | 59 ++++++++++++++++++++++++++++++++++++++----------------- 1 file changed, 41 insertions(+), 18 deletions(-) diff --git a/README.md b/README.md index 685aa4bd9..114ac45f8 100644 --- a/README.md +++ b/README.md @@ -1,32 +1,55 @@ -# Object Relation for 3D Object Detection +# Object Relations for 3D Object Detection -**Can you spot the vehicle in this point-cloud and can you precisely draw a bounding box around it?** -

- -
+This work focuses on exploring the impact of modeling object relation in two-stage object detection pipelines, aiming to enhance their detection performance. It extends OpenPCDet with a module that models object relations which can be integrated into existing object detectors. To get this project running please check [OpenPCDet](https://github.com/open-mmlab/OpenPCDet). + -In this example it seems difficult to detect the object. However, that's exactly what we expecet of current SoTA object detectors, given their limited receptive field. This project hypothesises that the receptive field of different architectures does not capture enough context to deal with occlusions and sparsity of 3D data. +## Project Structure -
- -
+This project extends [OpenPCDet](https://github.com/open-mmlab/OpenPCDet) with a module that relates object proposals with each other. Different implementation of this model can be found in **pcdet/models/object_relation**. -If we look at the full scene, it becomes clear that context can help us detect the vehicle correctly. However, simply increasing the receptive field would not give enough context while still being computationally feasible. Therefore, this project, comparable to previous work, proposes to model context efficiently with object relation. +## Motivation -
- -
Object Relation as an efficient Way to model Context
-
+There are four motivations for modeling object relations in the refinement stage of two-stage object detection pipelines. -## Architecture +- **Detecting Occlusions:** If information between an occluded and an occluder object is shared, the occluded object can be informed about its occlusion status. This can help the model learn the difference between sparse proposals that are heavily occluded and noise in the data. -To model object relation, this project introduces a GNN. The nodes in the graph are the detected objects; their features are obtained by the second stage of the model (refined features). The edges of the graph are obtained via a radius graph or knn (distance can be measured in R3 or in feature space). Global information, how the objects relate to each other, are infused into the edges. After the GNN is applied the model obtains a feature vector for each object that contains information about all the other objects. +- **Exploiting Patterns:** Traffic scenes often follow specific patterns that can be exploited by the object detector. + +- **Increase of Receptive Fields:** Current object detectors fail to incorporate enough context in the refinement stage because their receptive fields are too small. Object relations can be seen as an efficient mechanism to increase the receptive field. + +- **Proposal Consensus:** Proposals often form clusters around potential objects. Each proposal might have a different view of the object. Sharing information between these proposals leads to a consensus prediction. + + +| ![Image 1](resources/occlusion.png) | ![Image 2](resources/pattern.png) | +|:--------------------------:|:--------------------------:| +| *Detecting Occlusions* | *Exploiting Patterns* | +| ![Image 3](resources/radius.png) | ![Image 4](resources/proposal_consensus.png) | +|:--------------------------:|:--------------------------:| +| *Increase of Receptive Fields* | *Proposals Consensus* | + +# PV-RCNN-Relation + +PV-RCNN-Relation is a implementation of a object relations module applied to the PVRCNN baseline. It beats its baseline on all difficulties for the vehicle class. - ## Results - +| Model | Easy R11 / R40 | Moderate R11 / R40 | Hard R11 / R40 | All R11 / R40 | +|-------------------|----------------|--------------------|----------------|---------------| +| PVRCNN | 89.39 / 92.02 | 83.63 / 84.80 | 78.86 / 82.58 | 83.91 / 86.45 | +| PVRCNN-Relation | 89.59 / 92.53 | 84.56 / 85.22 | 79.04 / 82.99 | 84.35 / 86.90 | +| *Improvement* | **+0.20 / +0.51** | **+0.93 / +0.42** | **+0.18 / +0.41** | **+0.44 / +0.45** | + +*Comparison of PVRCNN and PVRCNN-Relation on KITTI validation set. Trained and evaluated only on the car class. All models were trained for 80 epochs and the best-performing epoch per model and metric was chosen. **Both models were trained three times** and the average is reported. The* Improvement *row represents the difference in mAP between the two models. See table for full results.* + + +| | | +|:-------------------------:|:-------------------------:| +| ![Image 1](resources/side.png) | ![Image 2](resources/relation_side.png) | +| | | + +Qualitative results for PV-RCNN baseline and PV-RCNN-Relation on Waymo data. 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insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 114ac45f8..98c5d4e82 100644 --- a/README.md +++ b/README.md @@ -21,11 +21,11 @@ There are four motivations for modeling object relations in the refinement stage | ![Image 1](resources/occlusion.png) | ![Image 2](resources/pattern.png) | -|:--------------------------:|:--------------------------:| +|:-:|:-:| | *Detecting Occlusions* | *Exploiting Patterns* | -| ![Image 3](resources/radius.png) | ![Image 4](resources/proposal_consensus.png) | -|:--------------------------:|:--------------------------:| +| ![Image 3](resources/radius.png) | ![Image 4](resources/proposal_consensus.png) | *Increase of Receptive Fields* | *Proposals Consensus* | +|| # PV-RCNN-Relation From 49d46ffd82219bd31b2a609011af5b6bca57f6be Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Wed, 15 Nov 2023 09:41:24 +0000 Subject: [PATCH 82/93] Update readme --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 98c5d4e82..25347d9d9 100644 --- a/README.md +++ b/README.md @@ -25,7 +25,7 @@ There are four motivations for modeling object relations in the refinement stage | *Detecting Occlusions* | *Exploiting Patterns* | | ![Image 3](resources/radius.png) | ![Image 4](resources/proposal_consensus.png) | *Increase of Receptive Fields* | *Proposals Consensus* | -|| + # PV-RCNN-Relation @@ -36,8 +36,8 @@ PV-RCNN-Relation is a implementation of a object relations module applied to the | Model | Easy R11 / R40 | Moderate R11 / R40 | Hard R11 / R40 | All R11 / R40 | |-------------------|----------------|--------------------|----------------|---------------| -| PVRCNN | 89.39 / 92.02 | 83.63 / 84.80 | 78.86 / 82.58 | 83.91 / 86.45 | -| PVRCNN-Relation | 89.59 / 92.53 | 84.56 / 85.22 | 79.04 / 82.99 | 84.35 / 86.90 | +| PV-RCNN | 89.39 / 92.02 | 83.63 / 84.80 | 78.86 / 82.58 | 83.91 / 86.45 | +| PV-RCNN-Relation | 89.59 / 92.53 | 84.56 / 85.22 | 79.04 / 82.99 | 84.35 / 86.90 | | *Improvement* | **+0.20 / +0.51** | **+0.93 / +0.42** | **+0.18 / +0.41** | **+0.44 / +0.45** | *Comparison of PVRCNN and PVRCNN-Relation on KITTI validation set. Trained and evaluated only on the car class. All models were trained for 80 epochs and the best-performing epoch per model and metric was chosen. **Both models were trained three times** and the average is reported. The* Improvement *row represents the difference in mAP between the two models. See table for full results.* From 9c6bbb74dc37c9e35e288205421cee9b9b608783 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Wed, 15 Nov 2023 09:42:43 +0000 Subject: [PATCH 83/93] Update readme --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 25347d9d9..907d259e4 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ This work focuses on exploring the impact of modeling object relation in two-sta ## Project Structure -This project extends [OpenPCDet](https://github.com/open-mmlab/OpenPCDet) with a module that relates object proposals with each other. Different implementation of this model can be found in **pcdet/models/object_relation**. +This project extends [OpenPCDet](https://github.com/open-mmlab/OpenPCDet) with a module that relates object proposals with each other. Different implementations of this model can be found in **pcdet/models/object_relation**. ## Motivation @@ -29,7 +29,7 @@ There are four motivations for modeling object relations in the refinement stage # PV-RCNN-Relation -PV-RCNN-Relation is a implementation of a object relations module applied to the PVRCNN baseline. It beats its baseline on all difficulties for the vehicle class. +PV-RCNN-Relation is an implementation of an object relations module applied to the PVRCNN baseline. It beats its baseline on all difficulties for the vehicle class. ## Results @@ -48,7 +48,7 @@ PV-RCNN-Relation is a implementation of a object relations module applied to the | ![Image 1](resources/side.png) | ![Image 2](resources/relation_side.png) | | | | -Qualitative results for PV-RCNN baseline and PV-RCNN-Relation on Waymo data. Predictions are shown in green, labels in blue and edges that connect proposals to share information in red. +Qualitative results for PV-RCNN baseline and PV-RCNN-Relation on Waymo data. Predictions are shown in green, labels in blue, and edges that connect proposals to share information in red. 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z`T9Q>SDv};3LH?_l6QAjnV!dv9Su+79|=i_|GPIg_V8QVbo6eaDSz2Y!imuRmk@tX8ww`y4a=LOSL!sQiyTyvznzgH|BMZ>CRR zCs1W0RT)oMftB|jO Date: Mon, 11 Sep 2023 16:53:26 +0200 Subject: [PATCH 53/93] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 2de3a137a..203a1643c 100644 --- a/README.md +++ b/README.md @@ -17,6 +17,7 @@ To model object relation, this project introduces a GNN. The nodes in the graph ## Results + From e874822f58d7bc589a26f31427908dc64cf4a7e5 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Tue, 12 Sep 2023 13:22:04 +0000 Subject: [PATCH 54/93] Add CenterPoint second stage --- .vscode/launch.json | 10 + pcdet/models/detectors/__init__.py | 2 + .../models/detectors/centerpoint_twostage.py | 55 ++++++ pcdet/models/roi_heads/roi_head.py | 171 +++++++++--------- .../kitti_models/centerpoint_twostage.yaml | 34 ++-- 5 files changed, 176 insertions(+), 96 deletions(-) create mode 100644 pcdet/models/detectors/centerpoint_twostage.py diff --git a/.vscode/launch.json b/.vscode/launch.json index 874e8b484..93115c160 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -77,6 +77,16 @@ "program": "${file}", "console": "integratedTerminal", "justMyCode": true, + "args": ["--cfg_file", "./cfgs/kitti_models/centerpoint.yaml", "--extra_tag", "debug"], + "cwd": "/root/OpenPCDet/tools/", + }, + { + "name": "Python: Train KITTI CenterPointTwoStage", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": true, "args": ["--cfg_file", "./cfgs/kitti_models/centerpoint_twostage.yaml", "--extra_tag", "debug"], "cwd": "/root/OpenPCDet/tools/", }, diff --git a/pcdet/models/detectors/__init__.py b/pcdet/models/detectors/__init__.py index b8cacad47..6bcedb61b 100644 --- a/pcdet/models/detectors/__init__.py +++ b/pcdet/models/detectors/__init__.py @@ -17,6 +17,7 @@ from .bevfusion import BevFusion from .pv_rcnn_relation import PVRCNNRelation from .pv_rcnn_plusplus_relation import PVRCNNPlusPlusRelation +from .centerpoint_twostage import CenterPointTwoStage __all__ = { 'Detector3DTemplate': Detector3DTemplate, @@ -39,6 +40,7 @@ 'BevFusion': BevFusion, 'PVRCNNRelation': PVRCNNRelation, 'PVRCNNPlusPlusRelation': PVRCNNPlusPlusRelation, + 'CenterPointTwoStage': CenterPointTwoStage, } diff --git a/pcdet/models/detectors/centerpoint_twostage.py b/pcdet/models/detectors/centerpoint_twostage.py new file mode 100644 index 000000000..fee22acb9 --- /dev/null +++ b/pcdet/models/detectors/centerpoint_twostage.py @@ -0,0 +1,55 @@ +from .detector3d_template import Detector3DTemplate + + +class CenterPointTwoStage(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): + batch_dict = self.vfe(batch_dict) + batch_dict = self.backbone_3d(batch_dict) + batch_dict = self.map_to_bev_module(batch_dict) + batch_dict = self.backbone_2d(batch_dict) + batch_dict = self.dense_head(batch_dict) + batch_dict = self.roi_head(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_rpn, tb_dict = self.dense_head.get_loss() + tb_dict = { + 'loss_rpn': loss_rpn.item(), + **tb_dict + } + + loss_rcnn, tb_dict = self.roi_head.get_loss(tb_dict) + + loss = loss_rpn + loss_rcnn + return loss, tb_dict, disp_dict + + def post_processing(self, batch_dict): + post_process_cfg = self.model_cfg.POST_PROCESSING + batch_size = batch_dict['batch_size'] + final_pred_dict = batch_dict['final_box_dicts'] + recall_dict = {} + for index in range(batch_size): + pred_boxes = final_pred_dict[index]['pred_boxes'] + + recall_dict = self.generate_recall_record( + box_preds=pred_boxes, + recall_dict=recall_dict, batch_index=index, data_dict=batch_dict, + thresh_list=post_process_cfg.RECALL_THRESH_LIST + ) + + return final_pred_dict, recall_dict diff --git a/pcdet/models/roi_heads/roi_head.py b/pcdet/models/roi_heads/roi_head.py index b626e5513..cd583798f 100644 --- a/pcdet/models/roi_heads/roi_head.py +++ b/pcdet/models/roi_heads/roi_head.py @@ -5,32 +5,84 @@ from .roi_head_template import RoIHeadTemplate class RoIHead(RoIHeadTemplate): - def __init__(self, model_cfg): - super().__init__(model_cfg=model_cfg) - self.bev_feature_extractor = BEVFeatureExtractor(pc_start, voxel_size, out_stride) + def __init__(self, model_cfg, voxel_size, point_cloud_range, num_class=1, **kwargs): + # backbone_channels, point_cloud_range + super().__init__(num_class=num_class, model_cfg=model_cfg) + + out_stride = model_cfg.FEATURE_MAP_STRIDE + pc_range_start = point_cloud_range[:2] + self.add_box_param = model_cfg.ADD_BOX_PARAM + self.num_point = model_cfg.NUM_POINTS + self.bev_feature_extractor = BEVFeatureExtractor(pc_range_start, voxel_size, out_stride, self.num_point) + + fc_input_dim = (model_cfg.BEV_FEATURE_DIM * self.num_point) + (self.box_coder.code_size + 1 if self.add_box_param else 0) + shared_fc_list = [] + for k in range(0, self.model_cfg.SHARED_FC.__len__()): + shared_fc_list.extend([ + nn.Conv1d(fc_input_dim if k==0 else self.model_cfg.SHARED_FC[k-1], self.model_cfg.SHARED_FC[k], kernel_size=1, bias=False), + nn.BatchNorm1d(self.model_cfg.SHARED_FC[k]), + nn.ReLU() + ]) + pre_channel = self.model_cfg.SHARED_FC[k] + + if k != self.model_cfg.SHARED_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0: + shared_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO)) + + self.shared_fc_layer = nn.Sequential(*shared_fc_list) + + self.cls_layers = self.make_fc_layers( + input_channels=pre_channel, output_channels=self.num_class, fc_list=self.model_cfg.CLS_FC + ) + self.reg_layers = self.make_fc_layers( + input_channels=pre_channel, + output_channels=self.box_coder.code_size, + fc_list=self.model_cfg.REG_FC + ) + self.init_weights(weight_init='xavier') + + def init_weights(self, weight_init='xavier'): + if weight_init == 'kaiming': + init_func = nn.init.kaiming_normal_ + elif weight_init == 'xavier': + init_func = nn.init.xavier_normal_ + elif weight_init == 'normal': + init_func = nn.init.normal_ + else: + raise NotImplementedError + + for m in self.modules(): + if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d): + if weight_init == 'normal': + init_func(m.weight, mean=0, std=0.001) + else: + init_func(m.weight) + if m.bias is not None: + nn.init.constant_(m.bias, 0) def forward(self, batch_dict): + if self.training: + targets_dict = batch_dict.get('roi_targets_dict', None) + if targets_dict is None: + targets_dict = self.assign_targets(batch_dict) + batch_dict['rois'] = targets_dict['rois'] + batch_dict['roi_labels'] = targets_dict['roi_labels'] + batch_dict['roi_scores'] = targets_dict['roi_scores'] - features = self.bev_feature_extractor(batch_dict, batch_centers, num_point) - - self.reorder_first_stage_pred_and_feature(first_pred=one_stage_pred, example=example, features=features) - + B,N,_ = batch_dict['rois'].shape + batch_centers = self.get_box_center(batch_dict['rois']) + bev_features = self.bev_feature_extractor(batch_dict['spatial_features_2d'], batch_centers) + first_stage_pred = batch_dict['rois'] - batch_dict['batch_size'] = len(batch_dict['rois']) - if training: - targets_dict = self.assign_targets(batch_dict) - batch_dict['rois'] = targets_dict['rois'] - batch_dict['roi_labels'] = targets_dict['roi_labels'] - batch_dict['roi_features'] = targets_dict['roi_features'] - batch_dict['roi_scores'] = targets_dict['roi_scores'] + # target_dict = self.reorder_first_stage_pred_and_feature(batch_dict, first_stage_pred, bev_features) # RoI aware pooling + # if self.add_box_param: if self.add_box_param: - batch_dict['roi_features'] = torch.cat([batch_dict['roi_features'], batch_dict['rois'], batch_dict['roi_scores'].unsqueeze(-1)], dim=-1) - - pooled_features = batch_dict['roi_features'].reshape(-1, 1, - batch_dict['roi_features'].shape[-1]).contiguous() # (BxN, 1, C) + pooled_features = torch.cat([bev_features, batch_dict['rois'], batch_dict['roi_scores'].unsqueeze(-1)], dim=-1) + else: + pooled_features = bev_features + pooled_features = pooled_features.reshape(B*N,1,-1).contiguous() batch_size_rcnn = pooled_features.shape[0] pooled_features = pooled_features.permute(0, 2, 1).contiguous() # (BxN, C, 1) @@ -38,7 +90,7 @@ def forward(self, batch_dict): rcnn_cls = self.cls_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1) # (B, 1 or 2) rcnn_reg = self.reg_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1) # (B, C) - if not training: + if not self.training: batch_cls_preds, batch_box_preds = self.generate_predicted_boxes( batch_size=batch_dict['batch_size'], rois=batch_dict['rois'], cls_preds=rcnn_cls, box_preds=rcnn_reg ) @@ -52,63 +104,20 @@ def forward(self, batch_dict): self.forward_ret_dict = targets_dict return batch_dict - - def reorder_first_stage_pred_and_feature(self, first_pred, example, features): - batch_size = len(first_pred) - box_length = first_pred[0]['box3d_lidar'].shape[1] - feature_vector_length = sum([feat[0].shape[-1] for feat in features]) - - rois = first_pred[0]['box3d_lidar'].new_zeros((batch_size, - self.NMS_POST_MAXSIZE, box_length - )) - roi_scores = first_pred[0]['scores'].new_zeros((batch_size, - self.NMS_POST_MAXSIZE - )) - roi_labels = first_pred[0]['label_preds'].new_zeros((batch_size, - self.NMS_POST_MAXSIZE), dtype=torch.long - ) - roi_features = features[0][0].new_zeros((batch_size, - self.NMS_POST_MAXSIZE, feature_vector_length - )) - - for i in range(batch_size): - num_obj = features[0][i].shape[0] - # basically move rotation to position 6, so now the box is 7 + C . C is 2 for nuscenes to - # include velocity target - - box_preds = first_pred[i]['box3d_lidar'] - - if self.roi_head.code_size == 9: - # x, y, z, w, l, h, rotation_y, velocity_x, velocity_y - box_preds = box_preds[:, [0, 1, 2, 3, 4, 5, 8, 6, 7]] - - rois[i, :num_obj] = box_preds - roi_labels[i, :num_obj] = first_pred[i]['label_preds'] + 1 - roi_scores[i, :num_obj] = first_pred[i]['scores'] - roi_features[i, :num_obj] = torch.cat([feat[i] for feat in features], dim=-1) - - example['rois'] = rois # used in pooled features - example['roi_labels'] = roi_labels - example['roi_scores'] = roi_scores # used in pooled features - example['roi_features'] = roi_features # used in pooled features - - example['has_class_labels']= True - - return example def get_box_center(self, boxes): # box [List] - centers = [] + centers = [] for box in boxes: - if self.num_point == 1 or len(box['box3d_lidar']) == 0: - centers.append(box['box3d_lidar'][:, :3]) + if self.num_point == 1: + centers.append(box[:, :3]) elif self.num_point == 5: - center2d = box['box3d_lidar'][:, :2] - height = box['box3d_lidar'][:, 2:3] - dim2d = box['box3d_lidar'][:, 3:5] - rotation_y = box['box3d_lidar'][:, -1] + center2d = box[:, :2] + height = box[:, 2:3] + dim2d = box[:, 3:5] + rotation_y = box[:, -1] corners = center_to_corner_box2d(center2d, dim2d, rotation_y) @@ -117,8 +126,7 @@ def get_box_center(self, boxes): left_middle = torch.cat([(corners[:, 0] + corners[:, 3])/2, height], dim=-1) right_middle = torch.cat([(corners[:, 1] + corners[:, 2])/2, height], dim=-1) - points = torch.cat([box['box3d_lidar'][:, :3], front_middle, back_middle, left_middle, \ - right_middle], dim=0) + points = torch.cat([box[:,:3], front_middle, back_middle, left_middle, right_middle], dim=0) centers.append(points) else: @@ -130,12 +138,12 @@ def get_box_center(self, boxes): class BEVFeatureExtractor(nn.Module): - def __init__(self, pc_start, - voxel_size, out_stride): + def __init__(self, pc_start, voxel_size, out_stride, num_point): super().__init__() self.pc_start = pc_start self.voxel_size = voxel_size self.out_stride = out_stride + self.num_point = num_point def absl_to_relative(self, absolute): a1 = (absolute[..., 0] - self.pc_start[0]) / self.voxel_size[0] / self.out_stride @@ -143,21 +151,22 @@ def absl_to_relative(self, absolute): return a1, a2 - def forward(self, example, batch_centers, num_point): - batch_size = len(example['bev_feature']) - ret_maps = [] + def forward(self, bev_features, batch_centers): + + batch_size = len(bev_features) + ret_maps = [] for batch_idx in range(batch_size): xs, ys = self.absl_to_relative(batch_centers[batch_idx]) # N x C - feature_map = bilinear_interpolate_torch(example['bev_feature'][batch_idx], + feature_map = bilinear_interpolate_torch(bev_features[batch_idx], xs, ys) - if num_point > 1: - section_size = len(feature_map) // num_point - feature_map = torch.cat([feature_map[i*section_size: (i+1)*section_size] for i in range(num_point)], dim=1) + if self.num_point > 1: + section_size = len(feature_map) // self.num_point + feature_map = torch.cat([feature_map[i*section_size: (i+1)*section_size] for i in range(self.num_point)], dim=1) ret_maps.append(feature_map) - return ret_maps \ No newline at end of file + return torch.stack(ret_maps) \ No newline at end of file diff --git a/tools/cfgs/kitti_models/centerpoint_twostage.yaml b/tools/cfgs/kitti_models/centerpoint_twostage.yaml index 763d05d87..6ac0d2e04 100644 --- a/tools/cfgs/kitti_models/centerpoint_twostage.yaml +++ b/tools/cfgs/kitti_models/centerpoint_twostage.yaml @@ -6,7 +6,7 @@ DATA_CONFIG: MODEL: - NAME: CenterPoint + NAME: CenterPointTwoStage VFE: NAME: MeanVFE @@ -30,7 +30,6 @@ MODEL: DENSE_HEAD: NAME: CenterHead CLASS_AGNOSTIC: False - USE_DIRECTION_CLASSIFIER: False ############################## SHARED_CONV_CHANNEL: 64 @@ -66,19 +65,24 @@ MODEL: 'loc_weight': 2.0, 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] } - # POST_PROCESSING: - # SCORE_THRESH: 0.1 - # POST_CENTER_LIMIT_RANGE: [-75.2, -75.2, -2, 75.2, 75.2, 4] - # MAX_OBJ_PER_SAMPLE: 500 - # NMS_CONFIG: - # NMS_TYPE: nms_gpu - # NMS_THRESH: 0.01 - # NMS_PRE_MAXSIZE: 4096 - # NMS_POST_MAXSIZE: 500 + POST_PROCESSING: + SCORE_THRESH: 0.1 + POST_CENTER_LIMIT_RANGE: [-75.2, -75.2, -2, 75.2, 75.2, 4] + MAX_OBJ_PER_SAMPLE: 500 + NMS_CONFIG: + NMS_TYPE: nms_gpu + NMS_THRESH: 0.01 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 ROI_HEAD: NAME: ROIHead - NUM_POINT_FEATURES: 1920 # -> 128*3*5 + #### Not sure about this #### + BEV_FEATURE_DIM: 352 + FEATURE_MAP_STRIDE: 4 + NUM_POINTS: 5 # anchor points for pooling either 1 or 5 + ADD_BOX_PARAM: True + ############################# CLASS_AGNOSTIC: True SHARED_FC: [256, 256] CLS_FC: [256, 256] @@ -86,7 +90,7 @@ MODEL: DP_RATIO: 0.3 TARGET_CONFIG: - # BOX_CODER: ResidualCoder + BOX_CODER: ResidualCoder ROI_PER_IMAGE: 128 FG_RATIO: 0.5 @@ -110,7 +114,7 @@ MODEL: 'rcnn_corner_weight': 1.0, 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] } - + POST_PROCESSING: @@ -129,7 +133,7 @@ MODEL: OPTIMIZATION: - BATCH_SIZE_PER_GPU: 8 + BATCH_SIZE_PER_GPU: 2 NUM_EPOCHS: 80 OPTIMIZER: adam_onecycle From 574ddb2108c1c4a5d3e56921a89b1d257dd80d6a Mon Sep 17 00:00:00 2001 From: MarcBrede <34718040+MarcBrede@users.noreply.github.com> Date: Tue, 12 Sep 2023 15:31:49 +0200 Subject: [PATCH 55/93] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 203a1643c..94525fbe0 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ ## Architecture -To model object relation, this project introduces a GNN. The nodes in the graph are the detected objects; their features are obtained by the second stage of the model (refined features). The edges of the graph are obtained via a radius graph or knn (distance can be measured in R3 or in feature space). Global information, how the objects relate to each other, are infused into the edges. After the GNN, is applied the model obtains a feature vector for each object that contains information about all the other objects. +To model object relation, this project introduces a GNN. The nodes in the graph are the detected objects; their features are obtained by the second stage of the model (refined features). The edges of the graph are obtained via a radius graph or knn (distance can be measured in R3 or in feature space). Global information, how the objects relate to each other, are infused into the edges. After the GNN is applied the model obtains a feature vector for each object that contains information about all the other objects. From 23be0077c09a95758138fefea1646244db51a70e Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Wed, 13 Sep 2023 07:04:38 +0000 Subject: [PATCH 56/93] Update pv_rcnn_plusplus_relation config --- tools/cfgs/kitti_models/pv_rcnn_plusplus_relation.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/cfgs/kitti_models/pv_rcnn_plusplus_relation.yaml b/tools/cfgs/kitti_models/pv_rcnn_plusplus_relation.yaml index c85727cd0..15aeeab5d 100644 --- a/tools/cfgs/kitti_models/pv_rcnn_plusplus_relation.yaml +++ b/tools/cfgs/kitti_models/pv_rcnn_plusplus_relation.yaml @@ -308,7 +308,7 @@ MODEL: OPTIMIZATION: BATCH_SIZE_PER_GPU: 2 - NUM_EPOCHS: 140 + NUM_EPOCHS: 80 OPTIMIZER: adam_onecycle LR: 0.01 From 82253e48504467284d08415f7ccb9655676ed1fd Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Wed, 13 Sep 2023 09:57:41 +0000 Subject: [PATCH 57/93] Fix bug in pvrcnn_head_relation --- pcdet/models/roi_heads/pvrcnn_head_relation.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/pcdet/models/roi_heads/pvrcnn_head_relation.py b/pcdet/models/roi_heads/pvrcnn_head_relation.py index ccd6d568d..372f1b5e3 100644 --- a/pcdet/models/roi_heads/pvrcnn_head_relation.py +++ b/pcdet/models/roi_heads/pvrcnn_head_relation.py @@ -145,9 +145,6 @@ def forward(self, batch_dict): :param input_data: input dict :return: """ - # TODO fix that num_proposals might night be nms_post_max_size - # this might fail if we have less than nms_post_max_size proposals - num_proposals = self.model_cfg.TARGET_CONFIG.ROI_PER_IMAGE if self.training else self.model_cfg.NMS_CONFIG.TEST.NMS_POST_MAXSIZE targets_dict = self.proposal_layer( batch_dict, nms_config=self.model_cfg.NMS_CONFIG['TRAIN' if self.training else 'TEST'] @@ -159,17 +156,17 @@ def forward(self, batch_dict): batch_dict['rois'] = targets_dict['rois'] batch_dict['roi_labels'] = targets_dict['roi_labels'] + _, N, _ = batch_dict['rois'].shape # RoI aware pooling pooled_features = self.roi_grid_pool(batch_dict) # (BxN, 6x6x6, C) - _, num_grid_points, C = pooled_features.shape grid_size = self.model_cfg.ROI_GRID_POOL.GRID_SIZE batch_size_rcnn = pooled_features.shape[0] pooled_features = pooled_features.permute(0, 2, 1).\ contiguous().view(batch_size_rcnn, -1, grid_size, grid_size, grid_size) # (BxN, C, 6, 6, 6) - shared_features = self.shared_fc_layer(pooled_features.view(batch_size_rcnn, -1, 1)) - batch_dict['pooled_features'] = shared_features.view(-1, num_proposals, self.model_cfg.SHARED_FC[-1]) + pooled_features = self.shared_fc_layer(pooled_features.view(batch_size_rcnn, -1, 1)) + batch_dict['pooled_features'] = pooled_features.view(-1, N, self.model_cfg.SHARED_FC[-1]) self.forward_ret_dict = targets_dict From 0417ab8c574c6fd7d4625b2d14057d18deaaffa1 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Wed, 13 Sep 2023 09:58:00 +0000 Subject: [PATCH 58/93] Update eval script --- tools/scripts/eval_with_tensorboard.sh | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/tools/scripts/eval_with_tensorboard.sh b/tools/scripts/eval_with_tensorboard.sh index 7d459010b..268495f16 100755 --- a/tools/scripts/eval_with_tensorboard.sh +++ b/tools/scripts/eval_with_tensorboard.sh @@ -1,6 +1,9 @@ #!/bin/bash -date='2023-08-27_11-56-11' -architecture='pv_rcnn_relation' +date='2023-09-11_09-37-34' +# architecture='pv_rcnn_relation' +# architecture='centerpoint' +# architecture='pv_rcnn_plusplus_reproduced_by_community' +architecture='pv_rcnn_plusplus_relation' # tmux kill-server From 08ea01abdcc5e081277a0abbe5da42fd9683e69c Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Wed, 13 Sep 2023 12:06:13 +0000 Subject: [PATCH 59/93] Add centerpoint kitti configs --- tools/cfgs/kitti_models/centerpoint.yaml | 106 +++++++++++++++++++++++ 1 file changed, 106 insertions(+) create mode 100644 tools/cfgs/kitti_models/centerpoint.yaml diff --git a/tools/cfgs/kitti_models/centerpoint.yaml b/tools/cfgs/kitti_models/centerpoint.yaml new file mode 100644 index 000000000..415d0d28a --- /dev/null +++ b/tools/cfgs/kitti_models/centerpoint.yaml @@ -0,0 +1,106 @@ +CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist'] + +DATA_CONFIG: + _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml + + +MODEL: + NAME: CenterPoint + + VFE: + NAME: MeanVFE + + BACKBONE_3D: + NAME: VoxelResBackBone8x + + MAP_TO_BEV: + NAME: HeightCompression + NUM_BEV_FEATURES: 256 + + BACKBONE_2D: + NAME: BaseBEVBackbone + + LAYER_NUMS: [5] + LAYER_STRIDES: [1] + NUM_FILTERS: [128] + UPSAMPLE_STRIDES: [2] + NUM_UPSAMPLE_FILTERS: [256] + + DENSE_HEAD: + NAME: CenterHead + CLASS_AGNOSTIC: False + + USE_DIRECTION_CLASSIFIER: False + SHARED_CONV_CHANNEL: 64 + USE_BIAS_BEFORE_NORM: True + NUM_HM_CONV: 2 + SEPARATE_HEAD_CFG: + HEAD_ORDER: ['center', 'center_z', 'dim', 'rot'] + HEAD_DICT: { + 'center': {'out_channels': 2, 'num_conv': 2}, + 'center_z': {'out_channels': 1, 'num_conv': 2}, + 'dim': {'out_channels': 3, 'num_conv': 2}, + 'rot': {'out_channels': 2, 'num_conv': 2}, + } + + CLASS_NAMES_EACH_HEAD: [ + ['Car', 'Pedestrian', 'Cyclist'] + ] + + TARGET_ASSIGNER_CONFIG: + FEATURE_MAP_STRIDE: 4 + NUM_MAX_OBJS: 500 + GAUSSIAN_OVERLAP: 0.1 + MIN_RADIUS: 2 + + LOSS_CONFIG: + LOSS_WEIGHTS: { + 'cls_weight': 1.0, + 'loc_weight': 2.0, + 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + POST_PROCESSING: + SCORE_THRESH: 0.1 + POST_CENTER_LIMIT_RANGE: [-75.2, -75.2, -2, 75.2, 75.2, 4] + MAX_OBJ_PER_SAMPLE: 500 + NMS_CONFIG: + NMS_TYPE: nms_gpu + NMS_THRESH: 0.01 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 + + 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: 0.01 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 + + +OPTIMIZATION: + BATCH_SIZE_PER_GPU: 8 + NUM_EPOCHS: 80 + + 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 \ No newline at end of file From efb2f0baf6e50e082d059c2a5bf70a49b7865986 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Wed, 13 Sep 2023 12:10:06 +0000 Subject: [PATCH 60/93] Remove not needed field in centerpoint.yaml --- tools/cfgs/kitti_models/centerpoint.yaml | 1 - 1 file changed, 1 deletion(-) diff --git a/tools/cfgs/kitti_models/centerpoint.yaml b/tools/cfgs/kitti_models/centerpoint.yaml index 415d0d28a..7a725350d 100644 --- a/tools/cfgs/kitti_models/centerpoint.yaml +++ b/tools/cfgs/kitti_models/centerpoint.yaml @@ -30,7 +30,6 @@ MODEL: NAME: CenterHead CLASS_AGNOSTIC: False - USE_DIRECTION_CLASSIFIER: False SHARED_CONV_CHANNEL: 64 USE_BIAS_BEFORE_NORM: True NUM_HM_CONV: 2 From b3864a773fdd0614a0488505acb614ee437ad882 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Thu, 14 Sep 2023 13:10:44 +0000 Subject: [PATCH 61/93] Add CGNL method --- pcdet/models/detectors/pv_rcnn_relation.py | 5 +- pcdet/models/object_relation/__init__.py | 13 + pcdet/models/object_relation/cgnl.py | 90 ++++++ pcdet/models/object_relation/fc.py | 75 +++++ pcdet/models/object_relation/gnn.py | 15 +- pcdet/models/object_relation/utils.py | 15 + .../models/roi_heads/pvrcnn_head_relation.py | 20 +- .../kitti_models/pv_rcnn_relation_fc.yaml | 271 ++++++++++++++++++ 8 files changed, 481 insertions(+), 23 deletions(-) create mode 100644 pcdet/models/object_relation/cgnl.py create mode 100644 pcdet/models/object_relation/fc.py create mode 100644 pcdet/models/object_relation/utils.py create mode 100644 tools/cfgs/kitti_models/pv_rcnn_relation_fc.yaml diff --git a/pcdet/models/detectors/pv_rcnn_relation.py b/pcdet/models/detectors/pv_rcnn_relation.py index 5196e3130..d3f5cbcd2 100644 --- a/pcdet/models/detectors/pv_rcnn_relation.py +++ b/pcdet/models/detectors/pv_rcnn_relation.py @@ -1,13 +1,14 @@ from .detector3d_template import Detector3DTemplate from pcdet.models.object_relation.gnn import GNN +from ..object_relation import build_object_relation_module class PVRCNNRelation(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() - self.gnn = GNN(model_cfg.OBJECT_RELATION) + self.object_relation = build_object_relation_module(model_cfg.OBJECT_RELATION) def forward(self, batch_dict): # MeanVFE: Voxelisation @@ -27,7 +28,7 @@ def forward(self, batch_dict): # PVRCNNHead: Proposal refinement batch_dict = self.roi_head(batch_dict) # GNN: Object relation - batch_dict = self.gnn(batch_dict) + batch_dict = self.object_relation(batch_dict) batch_dict = self.roi_head.final_predictions(batch_dict) diff --git a/pcdet/models/object_relation/__init__.py b/pcdet/models/object_relation/__init__.py index e69de29bb..c5ceea15f 100644 --- a/pcdet/models/object_relation/__init__.py +++ b/pcdet/models/object_relation/__init__.py @@ -0,0 +1,13 @@ +from .gnn import GNN +from .fc import CGNLNet + +__all__ = { + 'GNN': GNN, + 'CGNLNet': CGNLNet +} + +def build_object_relation_module(model_cfg): + model = __all__[model_cfg.NAME]( + model_cfg + ) + return model \ No newline at end of file diff --git a/pcdet/models/object_relation/cgnl.py b/pcdet/models/object_relation/cgnl.py new file mode 100644 index 000000000..4fdaf19ff --- /dev/null +++ b/pcdet/models/object_relation/cgnl.py @@ -0,0 +1,90 @@ +import torch +import torch.nn as nn + +""" +Code taken from here: https://github.com/NUAAXQ/MLCVNet/blob/master/models/CGNL.py +""" + +class SpatialCGNL(nn.Module): + """Spatial CGNL block with dot production kernel for image classfication. + """ + def __init__(self, inplanes, planes, use_scale=False, groups=None): + self.use_scale = use_scale + self.groups = groups + + super(SpatialCGNL, self).__init__() + # conv theta + self.t = nn.Conv2d(inplanes, planes, kernel_size=1, stride=1, bias=False) + # conv phi + self.p = nn.Conv2d(inplanes, planes, kernel_size=1, stride=1, bias=False) + # conv g + self.g = nn.Conv2d(inplanes, planes, kernel_size=1, stride=1, bias=False) + # conv z + self.z = nn.Conv2d(planes, inplanes, kernel_size=1, stride=1, + groups=self.groups, bias=False) + self.gn = nn.GroupNorm(num_groups=self.groups, num_channels=inplanes) + + if self.use_scale: + print("=> WARN: SpatialCGNL block uses 'SCALE'", \ + 'yellow') + if self.groups: + print("=> WARN: SpatialCGNL block uses '{}' groups".format(self.groups), \ + 'yellow') + + def kernel(self, t, p, g, b, c, h, w): + """The linear kernel (dot production). + + Args: + t: output of conv theata + p: output of conv phi + g: output of conv g + b: batch size + c: channels number + h: height of featuremaps + w: width of featuremaps + """ + t = t.view(b, 1, c * h * w) + p = p.view(b, 1, c * h * w) + g = g.view(b, c * h * w, 1) + + att = torch.bmm(p, g) + + if self.use_scale: + att = att.div((c*h*w)**0.5) + + x = torch.bmm(att, t) + x = x.view(b, c, h, w) + + return x + + def forward(self, x): + residual = x + + t = self.t(x) + p = self.p(x) + g = self.g(x) + + b, c, h, w = t.size() + + if self.groups and self.groups > 1: + _c = int(c / self.groups) + + ts = torch.split(t, split_size_or_sections=_c, dim=1) + ps = torch.split(p, split_size_or_sections=_c, dim=1) + gs = torch.split(g, split_size_or_sections=_c, dim=1) + + _t_sequences = [] + for i in range(self.groups): + _x = self.kernel(ts[i], ps[i], gs[i], + b, _c, h, w) + _t_sequences.append(_x) + + x = torch.cat(_t_sequences, dim=1) + else: + x = self.kernel(t, p, g, + b, c, h, w) + + x = self.z(x) + x = self.gn(x) + residual + + return x \ No newline at end of file diff --git a/pcdet/models/object_relation/fc.py b/pcdet/models/object_relation/fc.py new file mode 100644 index 000000000..deb11288c --- /dev/null +++ b/pcdet/models/object_relation/fc.py @@ -0,0 +1,75 @@ +import torch.nn as nn +import torch +import torch.nn.functional as F +import math + +from .cgnl import SpatialCGNL +from .utils import build_mlp + +class CGNLNet(nn.Module): + def __init__(self, object_relation_cfg, input_dim=256): + super(CGNLNet, self).__init__() + self.global_information = object_relation_cfg.GLOBAL_INFORMATION if 'GLOBAL_INFORMATION' in object_relation_cfg else None + self.drop_out = object_relation_cfg.DP_RATIO + self.skip_connection = False if 'SKIP_CONNECTION' not in object_relation_cfg else object_relation_cfg.SKIP_CONNECTION + + self.cgnl_input_dim = input_dim + if self.global_information: + self.cgnl_input_dim += object_relation_cfg.GLOBAL_INFORMATION.MLP_LAYERS[-1] if not self.global_information.CONCATENATED else object_relation_cfg.GLOBAL_INFORMATION.MLP_LAYERS[-1] + 8 + # groups = 8 from the CGNL paper + self.cgnl_1 = SpatialCGNL(self.cgnl_input_dim, int(self.cgnl_input_dim / 2), use_scale=False, groups=8) + self.cgnl_2 = SpatialCGNL(self.cgnl_input_dim, int(self.cgnl_input_dim / 2), use_scale=False, groups=8) + self.conv = torch.nn.Conv1d(self.cgnl_input_dim,self.cgnl_input_dim,1) + self.bn = torch.nn.BatchNorm1d(self.cgnl_input_dim) + + if self.global_information: + global_mlp_input_dim = input_dim + 8 if self.global_information.CONCATENATED else 8 + self.global_info_mlp = build_mlp(global_mlp_input_dim, self.global_information.MLP_LAYERS, activation='ReLU', bn=True, drop_out=self.drop_out) + + self.init_weights() + + + def forward(self, batch_dict): + (B, N, C) = batch_dict['pooled_features'].shape + assert math.sqrt(N) == int(math.sqrt(N)), "N must be a square number" + pooled_features = batch_dict['pooled_features'] + initial_pooled_features = pooled_features + proposal_boxes = batch_dict['rois'] + proposal_labels = batch_dict['roi_labels'] + + if self.global_information: + global_information = torch.cat((proposal_boxes, proposal_labels.unsqueeze(-1)), dim=-1).view(B*N, -1) + embedded_global_information = self.global_info_mlp(global_information) + pooled_features = torch.cat([pooled_features, embedded_global_information.view(B,N,-1)], dim=-1) + C = pooled_features.shape[-1] + + # permute to form image plane + pooled_features = pooled_features.permute((0,2,1)).contiguous() + pooled_features_plane = pooled_features.view(B, C, int(math.sqrt(N)), int(math.sqrt(N))) + pooled_features_plane = self.cgnl_1(pooled_features_plane) + pooled_features_plane = self.cgnl_2(pooled_features_plane) + + related_features = pooled_features_plane.view(B, C, N) + related_features = F.relu(self.bn(self.conv(related_features))) + # permute back to (B,N,C) + related_features = related_features.permute((0,2,1)).contiguous().view(B,N,C) + + if self.skip_connection: + related_features = torch.cat([related_features, initial_pooled_features], dim=-1) + + batch_dict['related_features'] = related_features + return batch_dict + + def init_weights(self, weight_init='xavier'): + if weight_init == 'xavier': + init_func = nn.init.xavier_uniform_ + if self.global_information: + for m in self.global_info_mlp: + if isinstance(m, nn.Linear): + init_func(m.weight) + + def get_output_dim(self): + return self.input_dim + + + diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py index 7c999cc23..da391b852 100644 --- a/pcdet/models/object_relation/gnn.py +++ b/pcdet/models/object_relation/gnn.py @@ -2,6 +2,7 @@ import torch.nn as nn import torch_geometric as tg import torch.nn.functional as F +from .utils import build_mlp # import torch_scatter # custom implementation for EdgeConv @@ -183,20 +184,6 @@ def get_edges(self, edge_generating_tensor, proposal_labels, shape): edge_index = f(edge_generating_tensor, a, batch=batch_vector, loop=False) return edge_index -def build_mlp(input_dim, hidden_dims, activation='ReLU', bn=False, drop_out=None): - mlp_list = [] - for i in range(len(hidden_dims)): - if i == 0: - mlp_list.append(nn.Linear(input_dim, hidden_dims[i])) - else: - mlp_list.append(nn.Linear(hidden_dims[i-1], hidden_dims[i])) - if bn: - mlp_list.append(nn.BatchNorm1d(hidden_dims[i])) - mlp_list.append(getattr(nn, activation)()) - if drop_out: - mlp_list.append(nn.Dropout(drop_out)) - return nn.Sequential(*mlp_list) - if __name__ == '__main__': from easydict import EasyDict as edict diff --git a/pcdet/models/object_relation/utils.py b/pcdet/models/object_relation/utils.py new file mode 100644 index 000000000..796b3b532 --- /dev/null +++ b/pcdet/models/object_relation/utils.py @@ -0,0 +1,15 @@ +import torch.nn as nn + +def build_mlp(input_dim, hidden_dims, activation='ReLU', bn=False, drop_out=None): + mlp_list = [] + for i in range(len(hidden_dims)): + if i == 0: + mlp_list.append(nn.Linear(input_dim, hidden_dims[i])) + else: + mlp_list.append(nn.Linear(hidden_dims[i-1], hidden_dims[i])) + if bn: + mlp_list.append(nn.BatchNorm1d(hidden_dims[i])) + mlp_list.append(getattr(nn, activation)()) + if drop_out: + mlp_list.append(nn.Dropout(drop_out)) + return nn.Sequential(*mlp_list) diff --git a/pcdet/models/roi_heads/pvrcnn_head_relation.py b/pcdet/models/roi_heads/pvrcnn_head_relation.py index 372f1b5e3..58993fbe9 100644 --- a/pcdet/models/roi_heads/pvrcnn_head_relation.py +++ b/pcdet/models/roi_heads/pvrcnn_head_relation.py @@ -31,14 +31,20 @@ def __init__(self, input_channels, model_cfg, num_class=1, object_relation_confi self.shared_fc_layer = nn.Sequential(*shared_fc_list) - if 'GLOBAL_INFORMATION' in object_relation_config: - initial_input_dim = object_relation_config.GLOBAL_INFORMATION.MLP_LAYERS[-1] - if not object_relation_config.GLOBAL_INFORMATION.CONCATENATED: - initial_input_dim += self.model_cfg.SHARED_FC[-1] + if object_relation_config.NAME == 'GNN': + if 'GLOBAL_INFORMATION' in object_relation_config: + initial_input_dim = object_relation_config.GLOBAL_INFORMATION.MLP_LAYERS[-1] + if not object_relation_config.GLOBAL_INFORMATION.CONCATENATED: + initial_input_dim += self.model_cfg.SHARED_FC[-1] + else: + initial_input_dim = self.model_cfg.SHARED_FC[-1] + + self.head_input_channels = initial_input_dim + sum(object_relation_config.LAYERS) + elif object_relation_config.NAME == 'CGNLNet': + # TODO: udpate this + self.head_input_channels = self.model_cfg.SHARED_FC[-1] + 256 + 256 else: - initial_input_dim = self.model_cfg.SHARED_FC[-1] - - self.head_input_channels = initial_input_dim + sum(object_relation_config.LAYERS) + raise NotImplementedError self.cls_layers = self.make_fc_layers( input_channels=self.head_input_channels, output_channels=self.num_class, fc_list=self.model_cfg.CLS_FC diff --git a/tools/cfgs/kitti_models/pv_rcnn_relation_fc.yaml b/tools/cfgs/kitti_models/pv_rcnn_relation_fc.yaml new file mode 100644 index 000000000..055c5b9b6 --- /dev/null +++ b/tools/cfgs/kitti_models/pv_rcnn_relation_fc.yaml @@ -0,0 +1,271 @@ +CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist'] + +DATA_CONFIG: + _BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml + DATA_AUGMENTOR: + DISABLE_AUG_LIST: ['placeholder'] + AUG_CONFIG_LIST: + - NAME: gt_sampling + USE_ROAD_PLANE: True + DB_INFO_PATH: + - kitti_dbinfos_train.pkl + PREPARE: { + filter_by_min_points: ['Car:5', 'Pedestrian:5', 'Cyclist:5'], + filter_by_difficulty: [-1], + } + + SAMPLE_GROUPS: ['Car:15','Pedestrian:10', 'Cyclist:10'] + NUM_POINT_FEATURES: 4 + DATABASE_WITH_FAKELIDAR: False + REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0] + LIMIT_WHOLE_SCENE: False + + - NAME: random_world_flip + ALONG_AXIS_LIST: ['x'] + + - NAME: random_world_rotation + WORLD_ROT_ANGLE: [-0.78539816, 0.78539816] + + - NAME: random_world_scaling + WORLD_SCALE_RANGE: [0.95, 1.05] + +MODEL: + NAME: PVRCNNRelation + + VFE: + NAME: MeanVFE + + BACKBONE_3D: + NAME: VoxelBackBone8x + + 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: AnchorHeadSingle + CLASS_AGNOSTIC: False + + USE_DIRECTION_CLASSIFIER: True + DIR_OFFSET: 0.78539 + DIR_LIMIT_OFFSET: 0.0 + NUM_DIR_BINS: 2 + + ANCHOR_GENERATOR_CONFIG: [ + { + 'class_name': 'Car', + 'anchor_sizes': [[3.9, 1.6, 1.56]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [-1.78], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.6, + 'unmatched_threshold': 0.45 + }, + { + 'class_name': 'Pedestrian', + 'anchor_sizes': [[0.8, 0.6, 1.73]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [-0.6], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.5, + 'unmatched_threshold': 0.35 + }, + { + 'class_name': 'Cyclist', + 'anchor_sizes': [[1.76, 0.6, 1.73]], + 'anchor_rotations': [0, 1.57], + 'anchor_bottom_heights': [-0.6], + 'align_center': False, + 'feature_map_stride': 8, + 'matched_threshold': 0.5, + 'unmatched_threshold': 0.35 + } + ] + + TARGET_ASSIGNER_CONFIG: + NAME: AxisAlignedTargetAssigner + POS_FRACTION: -1.0 + SAMPLE_SIZE: 512 + NORM_BY_NUM_EXAMPLES: False + MATCH_HEIGHT: False + BOX_CODER: ResidualCoder + + LOSS_CONFIG: + LOSS_WEIGHTS: { + 'cls_weight': 1.0, + 'loc_weight': 2.0, + 'dir_weight': 0.2, + 'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + } + + PFE: + NAME: VoxelSetAbstraction + POINT_SOURCE: raw_points + NUM_KEYPOINTS: 2048 + NUM_OUTPUT_FEATURES: 128 + SAMPLE_METHOD: FPS + + FEATURES_SOURCE: ['bev', 'x_conv1', 'x_conv2', 'x_conv3', 'x_conv4', 'raw_points'] + SA_LAYER: + raw_points: + MLPS: [[16, 16], [16, 16]] + POOL_RADIUS: [0.4, 0.8] + NSAMPLE: [16, 16] + x_conv1: + DOWNSAMPLE_FACTOR: 1 + MLPS: [[16, 16], [16, 16]] + POOL_RADIUS: [0.4, 0.8] + NSAMPLE: [16, 16] + x_conv2: + DOWNSAMPLE_FACTOR: 2 + MLPS: [[32, 32], [32, 32]] + POOL_RADIUS: [0.8, 1.2] + NSAMPLE: [16, 32] + x_conv3: + DOWNSAMPLE_FACTOR: 4 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [1.2, 2.4] + NSAMPLE: [16, 32] + x_conv4: + DOWNSAMPLE_FACTOR: 8 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [2.4, 4.8] + NSAMPLE: [16, 32] + + POINT_HEAD: + NAME: PointHeadSimple + CLS_FC: [256, 256] + CLASS_AGNOSTIC: True + USE_POINT_FEATURES_BEFORE_FUSION: True + TARGET_CONFIG: + GT_EXTRA_WIDTH: [0.2, 0.2, 0.2] + LOSS_CONFIG: + LOSS_REG: smooth-l1 + LOSS_WEIGHTS: { + 'point_cls_weight': 1.0, + } + + ROI_HEAD: + NAME: PVRCNNHeadRelation + CLASS_AGNOSTIC: True + + SHARED_FC: [256, 256] + CLS_FC: [256, 256] + REG_FC: [256, 256] + DP_RATIO: 0.3 + + NMS_CONFIG: + TRAIN: + NMS_TYPE: nms_gpu + MULTI_CLASSES_NMS: False + # the proposals are soreted according to scores and then we take the NMS_PRE_MAXSIZE first + NMS_PRE_MAXSIZE: 9000 + # proposals with more than NMS_THRESH overlap with others will be removed + NMS_THRESH: 0.8 + # the proposals after NMS will be limited to NMS_POST_MAXSIZE + NMS_POST_MAXSIZE: 512 + # values are different for testing + TEST: + NMS_TYPE: nms_gpu + MULTI_CLASSES_NMS: False + NMS_PRE_MAXSIZE: 1024 + NMS_POST_MAXSIZE: 100 + NMS_THRESH: 0.7 + + ROI_GRID_POOL: + GRID_SIZE: 6 + MLPS: [[64, 64], [64, 64]] + POOL_RADIUS: [0.8, 1.6] + NSAMPLE: [16, 16] + POOL_METHOD: max_pool + + TARGET_CONFIG: + BOX_CODER: ResidualCoder + # number of proposal after the sampling + ROI_PER_IMAGE: 121 # from 128 + # max number of foreground proposal + FG_RATIO: 0.5 + + SAMPLE_ROI_BY_EACH_CLASS: True + CLS_SCORE_TYPE: roi_iou + + # thresholds defining the foregrouns, easy background and hard background + CLS_FG_THRESH: 0.75 + CLS_BG_THRESH: 0.25 + CLS_BG_THRESH_LO: 0.1 + # from the background proposals, HARD_BG_RATIO are ones with very small iou with gt (1-HARD_BG_RATIO are with large iou with gt -> still background) + HARD_BG_RATIO: 0.8 + + REG_FG_THRESH: 0.55 + + 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] + } + + OBJECT_RELATION: + NAME: CGNLNet + GLOBAL_INFORMATION: { + MLP_LAYERS: [256, 256], + CONCATENATED: False, + } + SKIP_CONNECTION: True + DP_RATIO: 0.3 + + + POST_PROCESSING: + # IoU Threshold + RECALL_THRESH_LIST: [0.3, 0.5, 0.7] + # Car, Pedestrian, Cyclist + SCORE_THRESH: 0.1 + # SCORE_THRESH: 0.0 + OUTPUT_RAW_SCORE: False + + EVAL_METRIC: kitti + + NMS_CONFIG: + MULTI_CLASSES_NMS: False + NMS_TYPE: nms_gpu + NMS_THRESH: 0.1 + # NMS_THRESH: 1.0 + NMS_PRE_MAXSIZE: 4096 + NMS_POST_MAXSIZE: 500 + + +OPTIMIZATION: + BATCH_SIZE_PER_GPU: 2 + NUM_EPOCHS: 80 + + 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 From 1c7992fe14e458a3ec8be7480b6997a7db77173b Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Thu, 14 Sep 2023 13:35:24 +0000 Subject: [PATCH 62/93] Update for CGNL --- pcdet/models/detectors/detector3d_template.py | 2 +- tools/scripts/eval_with_tensorboard.sh | 13 +++++++------ 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/pcdet/models/detectors/detector3d_template.py b/pcdet/models/detectors/detector3d_template.py index 672a2c963..9cea18b19 100644 --- a/pcdet/models/detectors/detector3d_template.py +++ b/pcdet/models/detectors/detector3d_template.py @@ -275,7 +275,7 @@ def post_processing(self, batch_dict): final_labels = label_preds[selected] final_boxes = box_preds[selected] - if "OBJECT_RELATION" in self.model_cfg: + if "OBJECT_RELATION" in self.model_cfg and self.model_cfg.OBJECT_RELATION.NAME == "GNN": edges = batch_dict['gnn_edges'] from_node, to_node = edges edges_mask = torch.isin(from_node, selected) & torch.isin(to_node, selected) diff --git a/tools/scripts/eval_with_tensorboard.sh b/tools/scripts/eval_with_tensorboard.sh index 268495f16..566fa9398 100755 --- a/tools/scripts/eval_with_tensorboard.sh +++ b/tools/scripts/eval_with_tensorboard.sh @@ -1,18 +1,19 @@ #!/bin/bash -date='2023-09-11_09-37-34' +date='2023-09-14_13-12-43' # architecture='pv_rcnn_relation' # architecture='centerpoint' # architecture='pv_rcnn_plusplus_reproduced_by_community' -architecture='pv_rcnn_plusplus_relation' +# architecture='pv_rcnn_plusplus_relation' +architecture='pv_rcnn_relation_fc' # tmux kill-server # Create the first tmux session and run a command -tmux new-session -d -s Session1 -tmux send-keys -t Session1 "(cd tools/; python test.py --cfg ./cfgs/kitti_models/$architecture.yaml --eval_all --extra_tag $date)" C-m +tmux new-session -d -s Session3 +tmux send-keys -t Session3 "(cd tools/; python test.py --cfg ./cfgs/kitti_models/$architecture.yaml --eval_all --extra_tag $date --max_waiting_mins 1440)" C-m sleep 300 # Create the second tmux session and run another command -tmux new-session -d -s Session2 -tmux send-keys -t Session2 "(cd output/cfgs/kitti_models/$architecture/$date/eval/eval_all_default/default; tensorboard dev upload --logdir tensorboard_val --name "${architecture//-/ }_KITTI_Evaluation_$date")" C-m +tmux new-session -d -s Session4 +tmux send-keys -t Session4 "(cd output/cfgs/kitti_models/$architecture/$date/eval/eval_all_default/default; tensorboard dev upload --logdir tensorboard_val --name "${architecture//-/ }_KITTI_Evaluation_$date")" C-m From dae33c1724261844725329a4827c3eb9f943c512 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Thu, 14 Sep 2023 13:36:29 +0000 Subject: [PATCH 63/93] Update run configs --- .vscode/launch.json | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/.vscode/launch.json b/.vscode/launch.json index 93115c160..ec8d9ab36 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -127,6 +127,33 @@ "args": ["--cfg_file", "./cfgs/kitti_models/pv_rcnn_plusplus.yaml", "--batch_size", "8", "--extra_tag", "2023-07-31_15-59-33", "--eval_all"], "cwd": "/root/OpenPCDet/tools/" }, + { + "name": "Python: Evaluate KITTI PV_RCNN++-Relatoin ALL", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "args": ["--cfg_file", "./cfgs/kitti_models/pv_rcnn_plusplus_relation.yaml", "--extra_tag", "2023-09-11_09-37-34", "--eval_all"], + "cwd": "/root/OpenPCDet/tools/" + }, + { + "name": "Python: Evaluate KITTI CenterPointTwoStage ALL", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "args": ["--cfg_file", "./cfgs/kitti_models/centerpoint_twostage.yaml", "--extra_tag", "2023-09-12_13-29-56", "--eval_all"], + "cwd": "/root/OpenPCDet/tools/" + }, + { + "name": "Python: Evaluate KITTI Object Relation FC ALL", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "args": ["--cfg_file", "./cfgs/kitti_models/pv_rcnn_relation_fc.yaml", "--extra_tag", "2023-09-14_13-12-43", "--eval_all"], + "cwd": "/root/OpenPCDet/tools/" + }, { "name": "Python: Train PV-RCNN++ with ckpt", "type": "python", From 0b1e1c58ae09c59f1b77a528daaee3934e641677 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Thu, 14 Sep 2023 13:43:09 +0000 Subject: [PATCH 64/93] Fix bugs in detector template --- pcdet/models/detectors/detector3d_template.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/pcdet/models/detectors/detector3d_template.py b/pcdet/models/detectors/detector3d_template.py index 9cea18b19..cc2aaaa5c 100644 --- a/pcdet/models/detectors/detector3d_template.py +++ b/pcdet/models/detectors/detector3d_template.py @@ -275,7 +275,8 @@ def post_processing(self, batch_dict): final_labels = label_preds[selected] final_boxes = box_preds[selected] - if "OBJECT_RELATION" in self.model_cfg and self.model_cfg.OBJECT_RELATION.NAME == "GNN": + include_edges = "OBJECT_RELATION" in self.model_cfg and self.model_cfg.OBJECT_RELATION.NAME == "GNN" + if include_edges: edges = batch_dict['gnn_edges'] from_node, to_node = edges edges_mask = torch.isin(from_node, selected) & torch.isin(to_node, selected) @@ -292,8 +293,8 @@ def post_processing(self, batch_dict): 'pred_boxes': final_boxes, 'pred_scores': final_scores, 'pred_labels': final_labels, - 'gnn_edges_final': final_edges if "OBJECT_RELATION" in self.model_cfg else None, - 'edge_to_pred': edge_to_pred if "OBJECT_RELATION" in self.model_cfg else None, + 'gnn_edges_final': final_edges if include_edges else None, + 'edge_to_pred': edge_to_pred if include_edges else None, } pred_dicts.append(record_dict) From 484ff5e9ac809c8ed1b4d0efa5cc2086d24b6019 Mon Sep 17 00:00:00 2001 From: Marc Brede Date: Fri, 15 Sep 2023 08:19:34 +0000 Subject: [PATCH 65/93] Update GNN with option to either skip or not --- pcdet/models/object_relation/gnn.py | 6 +++++- pcdet/models/roi_heads/pvrcnn_head_relation.py | 5 ++++- tools/cfgs/kitti_models/pv_rcnn_relation.yaml | 5 +++-- 3 files changed, 12 insertions(+), 4 deletions(-) diff --git a/pcdet/models/object_relation/gnn.py b/pcdet/models/object_relation/gnn.py index da391b852..8409b0919 100644 --- a/pcdet/models/object_relation/gnn.py +++ b/pcdet/models/object_relation/gnn.py @@ -51,6 +51,7 @@ def __init__(self, object_relation_cfg, number_classes=3, pooled_feature_dim=256 self.global_information = object_relation_cfg.GLOBAL_INFORMATION if 'GLOBAL_INFORMATION' in object_relation_cfg else None self.number_classes = number_classes self.drop_out = object_relation_cfg.DP_RATIO + self.skip_connection = object_relation_cfg.SKIP_CONNECTION self.pooled_feature_dim = pooled_feature_dim if self.global_information: @@ -158,7 +159,10 @@ def forward(self, batch_dict): from_node, to_node = edge_index edge_attr = proposal_boxes[from_node] - proposal_boxes[to_node] - batch_dict['related_features'] = torch.cat(gnn_features, dim=-1) + if self.skip_connection: + batch_dict['related_features'] = torch.cat(gnn_features, dim=-1) + else: + batch_dict['related_features'] = x return batch_dict diff --git a/pcdet/models/roi_heads/pvrcnn_head_relation.py b/pcdet/models/roi_heads/pvrcnn_head_relation.py index 58993fbe9..4fefcbc6b 100644 --- a/pcdet/models/roi_heads/pvrcnn_head_relation.py +++ b/pcdet/models/roi_heads/pvrcnn_head_relation.py @@ -39,7 +39,10 @@ def __init__(self, input_channels, model_cfg, num_class=1, object_relation_confi else: initial_input_dim = self.model_cfg.SHARED_FC[-1] - self.head_input_channels = initial_input_dim + sum(object_relation_config.LAYERS) + if object_relation_config.SKIP_CONNECTION: + self.head_input_channels = initial_input_dim + sum(object_relation_config.LAYERS) + else: + self.head_input_channels = object_relation_config.LAYERS[-1] elif object_relation_config.NAME == 'CGNLNet': # TODO: udpate this self.head_input_channels = self.model_cfg.SHARED_FC[-1] + 256 + 256 diff --git a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml index a4220445d..efb1b0bc5 100644 --- a/tools/cfgs/kitti_models/pv_rcnn_relation.yaml +++ b/tools/cfgs/kitti_models/pv_rcnn_relation.yaml @@ -229,17 +229,18 @@ MODEL: GRAPH: { CONV: { NAME: EdgeConv, - EDGE_EMBEDDING: False, + EDGE_EMBEDDING: True, # HEADS: 4, }, 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