[Benchmark] add pointpillars for paddle3D benchmark

.gitmodules

@@ -141,3 +141,7 @@
 	path = frame_benchmark/pytorch/dynamic/PaddleDetection/models/yolov7
 	url = https://github.com/PaddleBenchmark/yolov7.git
 	branch = main
+[submodule "frame_benchmark/pytorch/dynamic/Paddle3D/models/OpenPCDet"]
+	path = frame_benchmark/pytorch/dynamic/Paddle3D/models/OpenPCDet
+	url = git@github.com:PaddleBenchmark/OpenPCDet.git
+	breanch = master

frame_benchmark/docker_images.yaml

@@ -99,3 +99,4 @@ pytorch:
    petrv2: iregistry.baidu-int.com/paddlecloud/base-images:paddlecloud-ubuntu18.04-gcc8.2-cuda11.7-cudnn8.4.1-nccl2.12.12
    VideoSwin: iregistry.baidu-int.com/paddlecloud/base-images:paddlecloud-ubuntu18.04-gcc8.2-cuda11.7-cudnn8.4.1-nccl2.12.12
    vit_adapter: iregistry.baidu-int.com/paddlecloud/base-images:paddlecloud-ubuntu18.04-gcc8.2-cuda11.7-cudnn8.4.1-nccl2.12.12
+   pointpillars: iregistry.baidu-int.com/paddlecloud/base-images:paddlecloud-ubuntu18.04-gcc8.2-cuda11.7-cudnn8.4.1-nccl2.12.12

frame_benchmark/models_path.yaml

@@ -99,3 +99,4 @@ pytorch:
    petrv2: benchmark/frame_benchmark/pytorch/dynamic/Paddle3D/models/PETR
    VideoSwin: benchmark/frame_benchmark/pytorch/dynamic/PaddleVideo/models/Video-Swin-Transformer
    vit_adapter: benchmark/frame_benchmark/pytorch/dynamic/PaddleSeg/models/ViT-Adapter
+   pointpillars: benchmark/frame_benchmark/pytorch/dynamic/Paddle3D/models/OpenPCDet

frame_benchmark/pytorch/dynamic/Paddle3D/scripts/petrv2/benchmark_common/PrepareEnv.sh

@@ -14,7 +14,7 @@ pip install setuptools==59.5.0
 pip install mmcv-full==1.4.0 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.1/index.html
 
 wget https://paddle-wheel.bj.bcebos.com/benchmark/mmdetection-2.24.1.zip
-unzip mmdetection-2.24.1.zip
+unzip -o mmdetection-2.24.1.zip
 rm -rf mmdetection-2.24.1.zip
 mv mmdetection-2.24.1 mmdetection
 cd mmdetection

frame_benchmark/pytorch/dynamic/Paddle3D/scripts/pointpillars/N1C1/pointpillars_bs2_fp16_DP.sh

@@ -0,0 +1,10 @@
+model_item="pointpillars"
+bs_item=2
+fp_item=fp16
+run_mode=DP
+device_num=N1C1
+max_iter=400
+num_workers=8
+
+bash prepare.sh;
+bash run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num} ${max_iter} ${num_workers} 2>&1;

frame_benchmark/pytorch/dynamic/Paddle3D/scripts/pointpillars/N1C1/pointpillars_bs2_fp32_DP.sh

@@ -0,0 +1,10 @@
+model_item="pointpillars"
+bs_item=2
+fp_item=fp32
+run_mode=DP
+device_num=N1C1
+max_iter=400
+num_workers=8
+
+bash prepare.sh;
+bash run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num} ${max_iter} ${num_workers} 2>&1;

frame_benchmark/pytorch/dynamic/Paddle3D/scripts/pointpillars/N1C4/pointpillars_bs2_fp16_DP.sh

@@ -0,0 +1,10 @@
+model_item="pointpillars"
+bs_item=2
+fp_item=fp16
+run_mode=DP
+device_num=N1C4
+max_iter=400
+num_workers=8
+
+bash prepare.sh;
+bash run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num} ${max_iter} ${num_workers} 2>&1;

frame_benchmark/pytorch/dynamic/Paddle3D/scripts/pointpillars/N1C4/pointpillars_bs2_fp32_DP.sh

@@ -0,0 +1,10 @@
+model_item="pointpillars"
+bs_item=2
+fp_item=fp32
+run_mode=DP
+device_num=N1C4
+max_iter=400
+num_workers=8
+
+bash prepare.sh;
+bash run_benchmark.sh ${model_item} ${bs_item} ${fp_item} ${run_mode} ${device_num} ${max_iter} ${num_workers} 2>&1;

frame_benchmark/pytorch/dynamic/Paddle3D/scripts/pointpillars/benchmark_common/analysis_log.py

@@ -0,0 +1,70 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# encoding=utf-8 vi:ts=4:sw=4:expandtab:ft=python
+
+import re
+import sys
+import json
+import os
+
+def analyze(model_name, batch_size, log_file, res_log_file, device_num):
+    gpu_ids_pat = re.compile(r"GPU (.*):")
+    time_pat = re.compile(r"Batch time: (.*)\(.*\)")
+
+    logs = open(log_file).readlines()
+    logs = ";".join(logs)
+    gpu_ids_res = gpu_ids_pat.findall(logs)
+    time_res = time_pat.findall(logs)
+    print(time_res, "***********************")
+
+    print("---device_num:-", device_num)
+    index_c = device_num.index('C')
+    print("---index_c:-", index_c)
+    gpu_num = int(device_num[index_c + 1:len(device_num)])
+    print("-----gpu_num:", gpu_num)
+
+    fail_flag = 0
+    run_mode = ""
+    fp_item = "fp32"
+    ips = 0
+
+    run_mode = "DP"
+    skip_num = 10
+    total_time = 0
+    for i in range(skip_num, len(time_res)):
+        total_time += float(time_res[i])
+    avg_time = total_time / (len(time_res) - skip_num)
+    ips = float(batch_size) * round(1 / avg_time, 3)
+
+    info = {    "model_branch": os.getenv('model_branch'),
+                "model_commit": os.getenv('model_commit'),
+                "model_name": model_name,
+                "batch_size": batch_size,
+                "fp_item": fp_item,
+                "run_mode": run_mode,
+                "convergence_value": 0,
+                "convergence_key": "",
+                "ips": ips * int(gpu_num),
+                "speed_unit":"images/s",
+                "device_num": device_num,
+                "model_run_time": os.getenv('model_run_time'),
+                "frame_commit": "",
+                "frame_version": os.getenv('frame_version'),
+        }
+    print(info)
+    json_info = json.dumps(info)
+    with open(res_log_file, "w") as of:
+        of.write(json_info)
+
+if __name__ == "__main__":
+    if len(sys.argv) != 6:
+        print("Usage:" + sys.argv[0] + " model_name path/to/log/file path/to/res/log/file")
+        sys.exit()
+
+    model_name = sys.argv[1]
+    batch_size = sys.argv[2]
+    log_file = sys.argv[3]
+    res_log_file = sys.argv[4]
+    device_num = sys.argv[5]
+
+    analyze(model_name, batch_size, log_file, res_log_file, device_num) 

frame_benchmark/pytorch/dynamic/Paddle3D/scripts/pointpillars/benchmark_common/kitti_dataset.yaml

@@ -0,0 +1,70 @@
+DATASET: 'KittiDataset'
+DATA_PATH: '../data/KITTI_800'
+
+POINT_CLOUD_RANGE: [0, -40, -3, 70.4, 40, 1]
+
+DATA_SPLIT: {
+    'train': train,
+    'test': val
+}
+
+INFO_PATH: {
+    'train': [kitti_infos_train.pkl],
+    'test': [kitti_infos_val.pkl],
+}
+
+GET_ITEM_LIST: ["points"]
+FOV_POINTS_ONLY: True
+
+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:20','Pedestrian:15', 'Cyclist:15']
+          NUM_POINT_FEATURES: 4
+          DATABASE_WITH_FAKELIDAR: False
+          REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0]
+          LIMIT_WHOLE_SCENE: True
+
+        - 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]
+
+
+POINT_FEATURE_ENCODING: {
+    encoding_type: absolute_coordinates_encoding,
+    used_feature_list: ['x', 'y', 'z', 'intensity'],
+    src_feature_list: ['x', 'y', 'z', 'intensity'],
+}
+
+
+DATA_PROCESSOR:
+    - NAME: mask_points_and_boxes_outside_range
+      REMOVE_OUTSIDE_BOXES: True
+
+    - NAME: shuffle_points
+      SHUFFLE_ENABLED: {
+        'train': True,
+        'test': False
+      }
+
+    - NAME: transform_points_to_voxels
+      VOXEL_SIZE: [0.05, 0.05, 0.1]
+      MAX_POINTS_PER_VOXEL: 5
+      MAX_NUMBER_OF_VOXELS: {
+        'train': 16000,
+        'test': 40000
+      }

frame_benchmark/pytorch/dynamic/Paddle3D/scripts/pointpillars/benchmark_common/pointpillars.yaml

@@ -0,0 +1,144 @@
+CLASS_NAMES: ['Car']
+
+DATA_CONFIG: 
+    _BASE_CONFIG_: kitti_dataset.yaml
+    POINT_CLOUD_RANGE: [0, -39.68, -3, 69.12, 39.68, 1]
+    DATA_PROCESSOR:
+        - NAME: mask_points_and_boxes_outside_range
+          REMOVE_OUTSIDE_BOXES: True
+
+        - NAME: shuffle_points
+          SHUFFLE_ENABLED: {
+            'train': True,
+            'test': False
+          }
+
+        - NAME: transform_points_to_voxels
+          VOXEL_SIZE: [0.16, 0.16, 4]
+          MAX_POINTS_PER_VOXEL: 32
+          MAX_NUMBER_OF_VOXELS: {
+            'train': 16000,
+            'test': 40000
+          }
+    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: PointPillar
+
+    VFE:
+        NAME: PillarVFE
+        WITH_DISTANCE: False
+        USE_ABSLOTE_XYZ: True
+        USE_NORM: True
+        NUM_FILTERS: [64]
+
+    MAP_TO_BEV:
+        NAME: PointPillarScatter
+        NUM_BEV_FEATURES: 64
+
+    BACKBONE_2D:
+        NAME: BaseBEVBackbone
+        LAYER_NUMS: [3, 5, 5]
+        LAYER_STRIDES: [2, 2, 2]
+        NUM_FILTERS: [64, 128, 256]
+        UPSAMPLE_STRIDES: [1, 2, 4]
+        NUM_UPSAMPLE_FILTERS: [128, 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': 2,
+                '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]
+            }
+
+    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: 2
+    NUM_EPOCHS: 1
+
+    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

frame_benchmark/pytorch/dynamic/Paddle3D/scripts/pointpillars/benchmark_common/prepare.sh

@@ -0,0 +1,37 @@
+#!/usr/bin/env bash
+
+# install env
+echo "*******prepare benchmark start ***********"
+pip install -U pip
+echo `pip --version`
+
+
+cd ..
+unset https_proxy && unset http_proxy
+if [ ! -f "torch_dev_whls.tar" ];then
+  wget ${FLAG_TORCH_WHL_URL}
+fi
+export https_proxy=${HTTP_PRO} && export http_proxy=${HTTPS_PRO}
+tar -xf torch_dev_whls.tar
+for whl_file in torch_dev_whls/*
+do
+  pip install ${whl_file}
+done
+pip install kornia
+unset https_proxy && unset http_proxy
+pip install spconv-cu117	
+python setup.py develop
+
+
+# 由于kitti数据集太大，为避免每次下载过于耗时，请将kitti数据集下载后，软链到/data/Dataset/kitti
+mkdir -p /data/Dataset
+if [ ! -d "/data/Dataset/KITTI_800" ]; then
+    cd /data/Dataset
+    wget https://paddle3d.bj.bcebos.com/TIPC/dataset/KITTI_800.tar --no-check-certificate
+    tar -xf KITTI_800.tar
+    cd -
+fi
+rm -rf data/KITTI_800
+ln -s /data/Dataset/KITTI_800 data/KITTI_800
+cd -
+echo "*******prepare benchmark end***********"