PCDetv0.5 install in windows

VsionQing commented 2 years ago

First SharedArray is not supported in windows and Traceback (most recent call last): File "E:\PythonFile\OpenPCDet-master---updated\tools\demo.py", line 112, in main() File "E:\PythonFile\OpenPCDet-master---updated\tools\demo.py", line 98, in main preddicts, = model.forward(data_dict) File "e:\pythonfile\openpcdet-master---updated\pcdet\models\detectors\pv_rcnn.py", line 11, in forward batch_dict = cur_module(batch_dict) File "D:\Anconda\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 1102, in _call_impl return forward_call(*input, *kwargs) File "e:\pythonfile\openpcdet-master---updated\pcdet\models\roi_heads\pvrcnn_head.py", line 149, in forward targets_dict = self.proposal_layer( File "D:\Anconda\envs\pytorch\lib\site-packages\torch\autograd\grad_mode.py", line 28, in decorate_context return func(args, **kwargs) File "e:\pythonfile\openpcdet-master---updated\pcdet\models\roi_heads\roi_head_template.py", line 89, in proposal_layer selected, selected_scores = class_agnostic_nms( File "e:\pythonfile\openpcdet-master---updated\pcdet\models\model_utils\model_nms_utils.py", line 17, in class_agnostic_nms keep_idx, selected_scores = getattr(iou3d_nms_utils, nms_config.NMS_TYPE)( File "e:\pythonfile\openpcdet-master---updated\pcdet\ops\iou3d_nms\iou3d_nms_utils.py", line 98, in nms_gpu num_out = iou3d_nms_cuda.nms_gpu(boxes, keep, thresh) RuntimeError: expected scalar type Int but found Long

The above is my error message（that i have #SharedArray）

VsionQing commented 2 years ago

`assert boxes.shape[1] == 7 order = scores.sort(0, descending=True)[1] if pre_maxsize is not None: order = order[:pre_maxsize]

boxes = boxes[order].contiguous() keep = torch.IntTensor(boxes.size(0))

thresh = int (thresh) num_out = iou3d_nms_cuda.nms_gpu(boxes, keep, thresh)

keep = torch.LongTensor(boxes.size(0))

return order[keep[:num_out].cuda()].contiguous(), None` I replace those code in iou3d_nms_utils.py to solve this problem, but new error appear

C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [32,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [33,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [34,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [35,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [36,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [37,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [38,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [39,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [40,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [41,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [42,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [43,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [44,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [45,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [46,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [47,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [48,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [49,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [50,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [51,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [52,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [53,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [54,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [55,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [56,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [57,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [0,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [1,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [2,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [3,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [4,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [5,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [6,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [7,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [8,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [9,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [10,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [11,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [12,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [13,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [14,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [15,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [16,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [17,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [18,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [19,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [20,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [21,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [22,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [23,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [24,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [25,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [26,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [27,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [28,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [29,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [30,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\cuda\IndexKernel.cu:93: block: [0,0,0], thread: [31,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. Traceback (most recent call last): File "E:\PythonFile\OpenPCDet-master---updated\tools\demo.py", line 112, in main() File "E:\PythonFile\OpenPCDet-master---updated\tools\demo.py", line 98, in main preddicts, = model.forward(data_dict) File "e:\pythonfile\openpcdet-master---updated\pcdet\models\detectors\pv_rcnn.py", line 11, in forward batch_dict = cur_module(batch_dict) File "D:\Anconda\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "e:\pythonfile\openpcdet-master---updated\pcdet\models\roi_heads\pvrcnn_head.py", line 158, in forward pooled_features = self.roi_grid_pool(batch_dict) # (BxN, 6x6x6, C) File "e:\pythonfile\openpcdet-master---updated\pcdet\models\roi_heads\pvrcnn_head.py", line 93, in roi_grid_pool global_roi_grid_points, local_roi_grid_points = self.get_global_grid_points_of_roi( File "e:\pythonfile\openpcdet-master---updated\pcdet\models\roi_heads\pvrcnn_head.py", line 124, in get_global_grid_points_of_roi local_roi_grid_points = self.get_dense_grid_points(rois, batch_size_rcnn, grid_size) # (B, 6x6x6, 3) File "e:\pythonfile\openpcdet-master---updated\pcdet\models\roi_heads\pvrcnn_head.py", line 135, in get_dense_grid_points dense_idx = faked_features.nonzero() # (N, 3) [x_idx, y_idx, z_idx] RuntimeError: CUDA error: device-side assert triggered CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1. PS E:\PythonFile\OpenPCDet-master---updated\tools>

sshaoshuai commented 2 years ago

You could remove the dependency on SharedArray since we do not use shared memory by default.

For the error message, I could not reproduce the error with linux env. (Maybe you could simply change to num_out = iou3d_nms_cuda.nms_gpu(boxes, keep.int(), thresh))

VsionQing commented 2 years ago

I'm glad to receive your reply.And,i had known this solution a few days ago. In this way, i have reproduced the demo，however, i have some problems about train. The problem that bothers me most is how to train my data set to get PTH files, and then show the effect through demo.

Recently, i have some exams, so i have little time to work on it. Look forward to communicating with you next time

------------------ 原始邮件 ------------------ 发件人: "open-mmlab/OpenPCDet" @.>; 发送时间: 2021年12月5日(星期天) 下午5:47 @.>; @.**@.>; 主题: Re: [open-mmlab/OpenPCDet] PCDetv0.5 install in windows (Issue #688)

You could remove the dependency on SharedArray since we do not use shared memory by default.

For the error message, I could not reproduce the error with linux env. (Maybe you could simply change to num_out = iou3d_nms_cuda.nms_gpu(boxes, keep.int(), thresh))

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub, or unsubscribe. Triage notifications on the go with GitHub Mobile for iOS or Android.

Kemo-Huang commented 2 years ago

change the code to this:

keep = torch.IntTensor(boxes.size(0)) num_out = iou3d_nms_cuda.nms_gpu(boxes, keep, thresh) return order[keep[:num_out].long()].contiguous(), None

VsionQing commented 2 years ago

我按照test进行了测试之后，对kitti库进行测试的时候卡在了如下输出Wait 30 seconds for next check (progress: 468.5 / 30 minutes): E:\PythonFile\OpenPCDet-master---updated\output\kitti_model s\pv_rcnn\default\ckpt

使用的指令为 python test.py --cfg_file cfgs/kitti_models/pv_rcnn.yaml --batch_size 1 --eval_all 请问应该如何修改才能得到论文中的测试结果呢？期待您的回信

After I tested according to the test, when I tested the Kitti library, I got stuck in the following output Wait 30 seconds for next check (progress: 468.5 / 30 minutes): E:\PythonFile\OpenPCDet-master---updated\output\kitti model s\pv rcnn\default\ckpt The instructions used are python test. py --cfg file cfgs/kitti models/pv rcnn. yaml --batch size 1 --eval_ all How should I modify it to get the test results in the paper?

Looking forward to your reply

jihanyang commented 2 years ago

use --ckpt to specify a checkpoint rather than --eval all.

VsionQing commented 2 years ago

When I try to train myself with Kitti dataset, the output is as follows

PS E:\PythonFile\OpenPCDet-master---updated\tools> python train.py --cfg_file cfgs/kitti_models/pv_rcnn.yaml --batch_size 1 --workers 1 --epochs 10 --extra_tag 'mydata_1' 2022-02-08 14:05:39,907 INFO **Start logging** 2022-02-08 14:05:39,907 INFO CUDA_VISIBLE_DEVICES=ALL 2022-02-08 14:05:39,908 INFO cfg_file cfgs/kitti_models/pv_rcnn.yaml 2022-02-08 14:05:39,908 INFO batch_size 1 2022-02-08 14:05:39,908 INFO epochs 10 2022-02-08 14:05:39,908 INFO workers 1 2022-02-08 14:05:39,908 INFO extra_tag mydata_1 2022-02-08 14:05:39,908 INFO ckpt None 2022-02-08 14:05:39,908 INFO pretrained_model None 2022-02-08 14:05:39,908 INFO launcher none 2022-02-08 14:05:39,908 INFO tcp_port 18888 2022-02-08 14:05:39,908 INFO sync_bn False 2022-02-08 14:05:39,909 INFO fix_random_seed False 2022-02-08 14:05:39,909 INFO ckpt_save_interval 1 2022-02-08 14:05:39,909 INFO local_rank 0 2022-02-08 14:05:39,909 INFO max_ckpt_save_num 30 2022-02-08 14:05:39,909 INFO merge_all_iters_to_one_epoch False 2022-02-08 14:05:39,909 INFO set_cfgs None 2022-02-08 14:05:39,909 INFO max_waiting_mins 0 2022-02-08 14:05:39,909 INFO start_epoch 0 2022-02-08 14:05:39,909 INFO save_to_file False 2022-02-08 14:05:39,909 INFO cfg.ROOT_DIR: E:\PythonFile\OpenPCDet-master---updated 2022-02-08 14:05:39,909 INFO cfg.LOCAL_RANK: 0 2022-02-08 14:05:39,910 INFO cfg.CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist'] 2022-02-08 14:05:39,910 INFO cfg.DATA_CONFIG = edict() 2022-02-08 14:05:39,910 INFO cfg.DATA_CONFIG.DATASET: KittiDataset 2022-02-08 14:05:39,910 INFO cfg.DATA_CONFIG.DATA_PATH: ../data/kitti 2022-02-08 14:05:39,910 INFO cfg.DATA_CONFIG.POINT_CLOUD_RANGE: [0, -40, -3, 70.4, 40, 1] 2022-02-08 14:05:39,910 INFO cfg.DATA_CONFIG.DATA_SPLIT = edict() 2022-02-08 14:05:39,910 INFO cfg.DATA_CONFIG.DATA_SPLIT.train: training 2022-02-08 14:05:39,910 INFO cfg.DATA_CONFIG.DATA_SPLIT.test: val 2022-02-08 14:05:39,910 INFO cfg.DATA_CONFIG.INFO_PATH = edict() 2022-02-08 14:05:39,910 INFO cfg.DATA_CONFIG.INFO_PATH.train: ['kitti_infos_train.pkl'] 2022-02-08 14:05:39,910 INFO cfg.DATA_CONFIG.INFO_PATH.test: ['kitti_infos_val.pkl'] 2022-02-08 14:05:39,910 INFO cfg.DATA_CONFIG.GET_ITEM_LIST: ['points'] 2022-02-08 14:05:39,910 INFO cfg.DATA_CONFIG.FOV_POINTS_ONLY: True 2022-02-08 14:05:39,911 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR = edict() 2022-02-08 14:05:39,911 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR.DISABLE_AUG_LIST: ['placeholder'] 2022-02-08 14:05:39,911 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR.AUG_CONFIG_LIST: [{'NAME': 'gt_sampling', 'USE_ROAD_PLANE': False, 'DB_INFO_PATH': ['kitti_dbinfos_train.pkl'], 'PREPARE': {'filter_by_min_poin ts': ['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_EXT RA_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': 'r andom_world_scaling', 'WORLD_SCALE_RANGE': [0.95, 1.05]}] 2022-02-08 14:05:39,911 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING = edict() 2022-02-08 14:05:39,911 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.encoding_type: absolute_coordinates_encoding 2022-02-08 14:05:39,911 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.used_feature_list: ['x', 'y', 'z', 'intensity'] 2022-02-08 14:05:39,911 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.src_feature_list: ['x', 'y', 'z', 'intensity'] 2022-02-08 14:05:39,911 INFO cfg.DATA_CONFIG.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}}] 2022-02-08 14:05:39,911 INFO cfg.DATA_CONFIG._BASECONFIG: cfgs/dataset_configs/kitti_dataset.yaml 2022-02-08 14:05:39,911 INFO cfg.MODEL = edict() 2022-02-08 14:05:39,911 INFO cfg.MODEL.NAME: PVRCNN 2022-02-08 14:05:39,912 INFO cfg.MODEL.VFE = edict() 2022-02-08 14:05:39,912 INFO cfg.MODEL.VFE.NAME: MeanVFE 2022-02-08 14:05:39,912 INFO cfg.MODEL.BACKBONE_3D = edict() 2022-02-08 14:05:39,912 INFO cfg.MODEL.BACKBONE_3D.NAME: VoxelBackBone8x 2022-02-08 14:05:39,912 INFO cfg.MODEL.MAP_TO_BEV = edict() 2022-02-08 14:05:39,912 INFO cfg.MODEL.MAP_TO_BEV.NAME: HeightCompression 2022-02-08 14:05:39,912 INFO cfg.MODEL.MAP_TO_BEV.NUM_BEV_FEATURES: 256 2022-02-08 14:05:39,912 INFO cfg.MODEL.BACKBONE_2D = edict() 2022-02-08 14:05:39,912 INFO cfg.MODEL.BACKBONE_2D.NAME: BaseBEVBackbone 2022-02-08 14:05:39,912 INFO cfg.MODEL.BACKBONE_2D.LAYER_NUMS: [5, 5] 2022-02-08 14:05:39,912 INFO cfg.MODEL.BACKBONE_2D.LAYER_STRIDES: [1, 2] 2022-02-08 14:05:39,912 INFO cfg.MODEL.BACKBONE_2D.NUM_FILTERS: [128, 256] 2022-02-08 14:05:39,913 INFO cfg.MODEL.BACKBONE_2D.UPSAMPLE_STRIDES: [1, 2] 2022-02-08 14:05:39,913 INFO cfg.MODEL.BACKBONE_2D.NUM_UPSAMPLE_FILTERS: [256, 256] 2022-02-08 14:05:39,913 INFO cfg.MODEL.DENSE_HEAD = edict() 2022-02-08 14:05:39,913 INFO cfg.MODEL.DENSE_HEAD.NAME: AnchorHeadSingle 2022-02-08 14:05:39,913 INFO cfg.MODEL.DENSE_HEAD.CLASS_AGNOSTIC: False 2022-02-08 14:05:39,913 INFO cfg.MODEL.DENSE_HEAD.USE_DIRECTION_CLASSIFIER: True 2022-02-08 14:05:39,913 INFO cfg.MODEL.DENSE_HEAD.DIR_OFFSET: 0.78539 2022-02-08 14:05:39,913 INFO cfg.MODEL.DENSE_HEAD.DIR_LIMIT_OFFSET: 0.0 2022-02-08 14:05:39,913 INFO cfg.MODEL.DENSE_HEAD.NUM_DIR_BINS: 2 2022-02-08 14:05:39,913 INFO cfg.MODEL.DENSE_HEAD.ANCHOR_GENERATOR_CONFIG: [{'class_name': 'Car', 'anchor_sizes': [[3.9, 1.6, 1.56]], 'anchor_rotations': [0, 1.57], 'anchor_bottom_heights': [-1.78], 'ali gn_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_bo ttom_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_rotation s': [0, 1.57], 'anchor_bottom_heights': [-0.6], 'align_center': False, 'feature_map_stride': 8, 'matched_threshold': 0.5, 'unmatched_threshold': 0.35}] 2022-02-08 14:05:39,913 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG = edict() 2022-02-08 14:05:39,913 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.NAME: AxisAlignedTargetAssigner 2022-02-08 14:05:39,914 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.POS_FRACTION: -1.0 2022-02-08 14:05:39,914 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.SAMPLE_SIZE: 512 2022-02-08 14:05:39,914 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.NORM_BY_NUM_EXAMPLES: False 2022-02-08 14:05:39,914 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.MATCH_HEIGHT: False 2022-02-08 14:05:39,914 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.BOX_CODER: ResidualCoder 2022-02-08 14:05:39,914 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG = edict() 2022-02-08 14:05:39,914 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS = edict() 2022-02-08 14:05:39,914 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.cls_weight: 1.0 2022-02-08 14:05:39,914 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.loc_weight: 2.0 2022-02-08 14:05:39,914 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.dir_weight: 0.2 2022-02-08 14:05:39,914 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.code_weights: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] 2022-02-08 14:05:39,914 INFO cfg.MODEL.PFE = edict() 2022-02-08 14:05:39,914 INFO cfg.MODEL.PFE.NAME: VoxelSetAbstraction 2022-02-08 14:05:39,915 INFO cfg.MODEL.PFE.POINT_SOURCE: raw_points 2022-02-08 14:05:39,915 INFO cfg.MODEL.PFE.NUM_KEYPOINTS: 2048 2022-02-08 14:05:39,915 INFO cfg.MODEL.PFE.NUM_OUTPUT_FEATURES: 128 2022-02-08 14:05:39,915 INFO cfg.MODEL.PFE.SAMPLE_METHOD: FPS 2022-02-08 14:05:39,915 INFO cfg.MODEL.PFE.FEATURES_SOURCE: ['bev', 'x_conv1', 'x_conv2', 'x_conv3', 'x_conv4', 'raw_points'] 2022-02-08 14:05:39,915 INFO cfg.MODEL.PFE.SA_LAYER = edict() 2022-02-08 14:05:39,915 INFO cfg.MODEL.PFE.SA_LAYER.raw_points = edict() 2022-02-08 14:05:39,915 INFO cfg.MODEL.PFE.SA_LAYER.raw_points.MLPS: [[16, 16], [16, 16]] 2022-02-08 14:05:39,915 INFO cfg.MODEL.PFE.SA_LAYER.raw_points.POOL_RADIUS: [0.4, 0.8] 2022-02-08 14:05:39,915 INFO cfg.MODEL.PFE.SA_LAYER.raw_points.NSAMPLE: [16, 16] 2022-02-08 14:05:39,915 INFO cfg.MODEL.PFE.SA_LAYER.x_conv1 = edict() 2022-02-08 14:05:39,915 INFO cfg.MODEL.PFE.SA_LAYER.x_conv1.DOWNSAMPLE_FACTOR: 1 2022-02-08 14:05:39,916 INFO cfg.MODEL.PFE.SA_LAYER.x_conv1.MLPS: [[16, 16], [16, 16]] 2022-02-08 14:05:39,916 INFO cfg.MODEL.PFE.SA_LAYER.x_conv1.POOL_RADIUS: [0.4, 0.8] 2022-02-08 14:05:39,916 INFO cfg.MODEL.PFE.SA_LAYER.x_conv1.NSAMPLE: [16, 16] 2022-02-08 14:05:39,916 INFO cfg.MODEL.PFE.SA_LAYER.x_conv2 = edict() 2022-02-08 14:05:39,916 INFO cfg.MODEL.PFE.SA_LAYER.x_conv2.DOWNSAMPLE_FACTOR: 2 2022-02-08 14:05:39,916 INFO cfg.MODEL.PFE.SA_LAYER.x_conv2.MLPS: [[32, 32], [32, 32]] 2022-02-08 14:05:39,916 INFO cfg.MODEL.PFE.SA_LAYER.x_conv2.POOL_RADIUS: [0.8, 1.2] 2022-02-08 14:05:39,916 INFO cfg.MODEL.PFE.SA_LAYER.x_conv2.NSAMPLE: [16, 32] 2022-02-08 14:05:39,916 INFO cfg.MODEL.PFE.SA_LAYER.x_conv3 = edict() 2022-02-08 14:05:39,916 INFO cfg.MODEL.PFE.SA_LAYER.x_conv3.DOWNSAMPLE_FACTOR: 4 2022-02-08 14:05:39,916 INFO cfg.MODEL.PFE.SA_LAYER.x_conv3.MLPS: [[64, 64], [64, 64]] 2022-02-08 14:05:39,916 INFO cfg.MODEL.PFE.SA_LAYER.x_conv3.POOL_RADIUS: [1.2, 2.4] 2022-02-08 14:05:39,917 INFO cfg.MODEL.PFE.SA_LAYER.x_conv3.NSAMPLE: [16, 32] 2022-02-08 14:05:39,917 INFO cfg.MODEL.PFE.SA_LAYER.x_conv4 = edict() 2022-02-08 14:05:39,917 INFO cfg.MODEL.PFE.SA_LAYER.x_conv4.DOWNSAMPLE_FACTOR: 8 2022-02-08 14:05:39,917 INFO cfg.MODEL.PFE.SA_LAYER.x_conv4.MLPS: [[64, 64], [64, 64]] 2022-02-08 14:05:39,917 INFO cfg.MODEL.PFE.SA_LAYER.x_conv4.POOL_RADIUS: [2.4, 4.8] 2022-02-08 14:05:39,917 INFO cfg.MODEL.PFE.SA_LAYER.x_conv4.NSAMPLE: [16, 32] 2022-02-08 14:05:39,917 INFO cfg.MODEL.POINT_HEAD = edict() 2022-02-08 14:05:39,917 INFO cfg.MODEL.POINT_HEAD.NAME: PointHeadSimple 2022-02-08 14:05:39,917 INFO cfg.MODEL.POINT_HEAD.CLS_FC: [256, 256] 2022-02-08 14:05:39,917 INFO cfg.MODEL.POINT_HEAD.CLASS_AGNOSTIC: True 2022-02-08 14:05:39,917 INFO cfg.MODEL.POINT_HEAD.USE_POINT_FEATURES_BEFORE_FUSION: True 2022-02-08 14:05:39,917 INFO cfg.MODEL.POINT_HEAD.TARGET_CONFIG = edict() 2022-02-08 14:05:39,917 INFO cfg.MODEL.POINT_HEAD.TARGET_CONFIG.GT_EXTRA_WIDTH: [0.2, 0.2, 0.2] 2022-02-08 14:05:39,918 INFO cfg.MODEL.POINT_HEAD.LOSS_CONFIG = edict() 2022-02-08 14:05:39,918 INFO cfg.MODEL.POINT_HEAD.LOSS_CONFIG.LOSS_REG: smooth-l1 2022-02-08 14:05:39,918 INFO cfg.MODEL.POINT_HEAD.LOSS_CONFIG.LOSS_WEIGHTS = edict() 2022-02-08 14:05:39,918 INFO cfg.MODEL.POINT_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.point_cls_weight: 1.0 2022-02-08 14:05:39,918 INFO cfg.MODEL.ROI_HEAD = edict() 2022-02-08 14:05:39,918 INFO cfg.MODEL.ROI_HEAD.NAME: PVRCNNHead 2022-02-08 14:05:39,918 INFO cfg.MODEL.ROI_HEAD.CLASS_AGNOSTIC: True 2022-02-08 14:05:39,918 INFO cfg.MODEL.ROI_HEAD.SHARED_FC: [256, 256] 2022-02-08 14:05:39,918 INFO cfg.MODEL.ROI_HEAD.CLS_FC: [256, 256] 2022-02-08 14:05:39,918 INFO cfg.MODEL.ROI_HEAD.REG_FC: [256, 256] 2022-02-08 14:05:39,918 INFO cfg.MODEL.ROI_HEAD.DP_RATIO: 0.3 2022-02-08 14:05:39,918 INFO cfg.MODEL.ROI_HEAD.NMS_CONFIG = edict() 2022-02-08 14:05:39,919 INFO cfg.MODEL.ROI_HEAD.NMS_CONFIG.TRAIN = edict() 2022-02-08 14:05:39,919 INFO cfg.MODEL.ROI_HEAD.NMS_CONFIG.TRAIN.NMS_TYPE: nms_gpu 2022-02-08 14:05:39,919 INFO cfg.MODEL.ROI_HEAD.NMS_CONFIG.TRAIN.MULTI_CLASSES_NMS: False 2022-02-08 14:05:39,919 INFO cfg.MODEL.ROI_HEAD.NMS_CONFIG.TRAIN.NMS_PRE_MAXSIZE: 9000 2022-02-08 14:05:39,919 INFO cfg.MODEL.ROI_HEAD.NMS_CONFIG.TRAIN.NMS_POST_MAXSIZE: 512 2022-02-08 14:05:39,919 INFO cfg.MODEL.ROI_HEAD.NMS_CONFIG.TRAIN.NMS_THRESH: 0.8 2022-02-08 14:05:39,919 INFO cfg.MODEL.ROI_HEAD.NMS_CONFIG.TEST = edict() 2022-02-08 14:05:39,919 INFO cfg.MODEL.ROI_HEAD.NMS_CONFIG.TEST.NMS_TYPE: nms_gpu 2022-02-08 14:05:39,919 INFO cfg.MODEL.ROI_HEAD.NMS_CONFIG.TEST.MULTI_CLASSES_NMS: False 2022-02-08 14:05:39,919 INFO cfg.MODEL.ROI_HEAD.NMS_CONFIG.TEST.NMS_PRE_MAXSIZE: 1024 2022-02-08 14:05:39,919 INFO cfg.MODEL.ROI_HEAD.NMS_CONFIG.TEST.NMS_POST_MAXSIZE: 100 2022-02-08 14:05:39,919 INFO cfg.MODEL.ROI_HEAD.NMS_CONFIG.TEST.NMS_THRESH: 0.7 2022-02-08 14:05:39,920 INFO cfg.MODEL.ROI_HEAD.ROI_GRID_POOL = edict() 2022-02-08 14:05:39,920 INFO cfg.MODEL.ROI_HEAD.ROI_GRID_POOL.GRID_SIZE: 6 2022-02-08 14:05:39,920 INFO cfg.MODEL.ROI_HEAD.ROI_GRID_POOL.MLPS: [[64, 64], [64, 64]] 2022-02-08 14:05:39,920 INFO cfg.MODEL.ROI_HEAD.ROI_GRID_POOL.POOL_RADIUS: [0.8, 1.6] 2022-02-08 14:05:39,920 INFO cfg.MODEL.ROI_HEAD.ROI_GRID_POOL.NSAMPLE: [16, 16] 2022-02-08 14:05:39,920 INFO cfg.MODEL.ROI_HEAD.ROI_GRID_POOL.POOL_METHOD: max_pool 2022-02-08 14:05:39,920 INFO cfg.MODEL.ROI_HEAD.TARGET_CONFIG = edict() 2022-02-08 14:05:39,920 INFO cfg.MODEL.ROI_HEAD.TARGET_CONFIG.BOX_CODER: ResidualCoder 2022-02-08 14:05:39,920 INFO cfg.MODEL.ROI_HEAD.TARGET_CONFIG.ROI_PER_IMAGE: 128 2022-02-08 14:05:39,920 INFO cfg.MODEL.ROI_HEAD.TARGET_CONFIG.FG_RATIO: 0.5 2022-02-08 14:05:39,920 INFO cfg.MODEL.ROI_HEAD.TARGET_CONFIG.SAMPLE_ROI_BY_EACH_CLASS: True 2022-02-08 14:05:39,920 INFO cfg.MODEL.ROI_HEAD.TARGET_CONFIG.CLS_SCORE_TYPE: roi_iou 2022-02-08 14:05:39,920 INFO cfg.MODEL.ROI_HEAD.TARGET_CONFIG.CLS_FG_THRESH: 0.75 2022-02-08 14:05:39,921 INFO cfg.MODEL.ROI_HEAD.TARGET_CONFIG.CLS_BG_THRESH: 0.25 2022-02-08 14:05:39,921 INFO cfg.MODEL.ROI_HEAD.TARGET_CONFIG.CLS_BG_THRESH_LO: 0.1 2022-02-08 14:05:39,921 INFO cfg.MODEL.ROI_HEAD.TARGET_CONFIG.HARD_BG_RATIO: 0.8 2022-02-08 14:05:39,921 INFO cfg.MODEL.ROI_HEAD.TARGET_CONFIG.REG_FG_THRESH: 0.55 2022-02-08 14:05:39,921 INFO cfg.MODEL.ROI_HEAD.LOSS_CONFIG = edict() 2022-02-08 14:05:39,921 INFO cfg.MODEL.ROI_HEAD.LOSS_CONFIG.CLS_LOSS: BinaryCrossEntropy 2022-02-08 14:05:39,921 INFO cfg.MODEL.ROI_HEAD.LOSS_CONFIG.REG_LOSS: smooth-l1 2022-02-08 14:05:39,921 INFO cfg.MODEL.ROI_HEAD.LOSS_CONFIG.CORNER_LOSS_REGULARIZATION: True 2022-02-08 14:05:39,921 INFO cfg.MODEL.ROI_HEAD.LOSS_CONFIG.LOSS_WEIGHTS = edict() 2022-02-08 14:05:39,921 INFO cfg.MODEL.ROI_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.rcnn_cls_weight: 1.0 2022-02-08 14:05:39,921 INFO cfg.MODEL.ROI_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.rcnn_reg_weight: 1.0 2022-02-08 14:05:39,921 INFO cfg.MODEL.ROI_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.rcnn_corner_weight: 1.0 2022-02-08 14:05:39,921 INFO cfg.MODEL.ROI_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.code_weights: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] 2022-02-08 14:05:39,922 INFO cfg.MODEL.POST_PROCESSING = edict() 2022-02-08 14:05:39,922 INFO cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST: [0.3, 0.5, 0.7] 2022-02-08 14:05:39,922 INFO cfg.MODEL.POST_PROCESSING.SCORE_THRESH: 0.1 2022-02-08 14:05:39,922 INFO cfg.MODEL.POST_PROCESSING.OUTPUT_RAW_SCORE: False 2022-02-08 14:05:39,922 INFO cfg.MODEL.POST_PROCESSING.EVAL_METRIC: kitti 2022-02-08 14:05:39,922 INFO cfg.MODEL.POST_PROCESSING.NMS_CONFIG = edict() 2022-02-08 14:05:39,922 INFO cfg.MODEL.POST_PROCESSING.NMS_CONFIG.MULTI_CLASSES_NMS: False 2022-02-08 14:05:39,922 INFO cfg.MODEL.POST_PROCESSING.NMS_CONFIG.NMS_TYPE: nms_gpu 2022-02-08 14:05:39,922 INFO cfg.MODEL.POST_PROCESSING.NMS_CONFIG.NMS_THRESH: 0.1 2022-02-08 14:05:39,922 INFO cfg.MODEL.POST_PROCESSING.NMS_CONFIG.NMS_PRE_MAXSIZE: 4096 2022-02-08 14:05:39,922 INFO cfg.MODEL.POST_PROCESSING.NMS_CONFIG.NMS_POST_MAXSIZE: 500 2022-02-08 14:05:39,922 INFO cfg.OPTIMIZATION = edict() 2022-02-08 14:05:39,923 INFO cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU: 2 2022-02-08 14:05:39,923 INFO cfg.OPTIMIZATION.NUM_EPOCHS: 80 2022-02-08 14:05:39,923 INFO cfg.OPTIMIZATION.OPTIMIZER: adam_onecycle 2022-02-08 14:05:39,923 INFO cfg.OPTIMIZATION.LR: 0.01 2022-02-08 14:05:39,923 INFO cfg.OPTIMIZATION.WEIGHT_DECAY: 0.01 2022-02-08 14:05:39,923 INFO cfg.OPTIMIZATION.MOMENTUM: 0.9 2022-02-08 14:05:39,923 INFO cfg.OPTIMIZATION.MOMS: [0.95, 0.85] 2022-02-08 14:05:39,923 INFO cfg.OPTIMIZATION.PCT_START: 0.4 2022-02-08 14:05:39,923 INFO cfg.OPTIMIZATION.DIV_FACTOR: 10 2022-02-08 14:05:39,923 INFO cfg.OPTIMIZATION.DECAY_STEP_LIST: [35, 45] 2022-02-08 14:05:39,923 INFO cfg.OPTIMIZATION.LR_DECAY: 0.1 2022-02-08 14:05:39,923 INFO cfg.OPTIMIZATION.LR_CLIP: 1e-07 2022-02-08 14:05:39,923 INFO cfg.OPTIMIZATION.LR_WARMUP: False 2022-02-08 14:05:39,925 INFO cfg.OPTIMIZATION.WARMUP_EPOCH: 1 2022-02-08 14:05:39,925 INFO cfg.OPTIMIZATION.GRAD_NORM_CLIP: 10 2022-02-08 14:05:39,925 INFO cfg.TAG: pv_rcnn 2022-02-08 14:05:39,930 INFO cfg.EXP_GROUP_PATH: kitti_models 'cp' 不是内部或外部命令，也不是可运行的程序或批处理文件。 2022-02-08 14:05:40,036 INFO Database filter by min points Car: 14357 => 13532 2022-02-08 14:05:40,037 INFO Database filter by min points Pedestrian: 2207 => 2168 2022-02-08 14:05:40,037 INFO Database filter by min points Cyclist: 734 => 705 2022-02-08 14:05:40,039 INFO Database filter by difficulty Car: 13532 => 10759 2022-02-08 14:05:40,039 INFO Database filter by difficulty Pedestrian: 2168 => 2075 2022-02-08 14:05:40,039 INFO Database filter by difficulty Cyclist: 705 => 581 2022-02-08 14:05:40,043 INFO Loading KITTI dataset 2022-02-08 14:05:40,122 INFO Total samples for KITTI dataset: 3712 D:\Anconda\envs\pytorch\lib\site-packages\torch\functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ..\aten\s rc\ATen\native\TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] 2022-02-08 14:05:42,453 INFO PVRCNN( (vfe): MeanVFE() (backbone_3d): VoxelBackBone8x( (conv_input): SparseSequential( (0): SubMConv3d(4, 16, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm) (1): BatchNorm1d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (2): ReLU() ) (conv1): SparseSequential( (0): SparseSequential( (0): SubMConv3d(16, 16, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm) (1): BatchNorm1d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (2): ReLU() ) ) (conv2): SparseSequential( (0): SparseSequential( (0): SparseConv3d(16, 32, kernel_size=[3, 3, 3], stride=[2, 2, 2], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm) (1): BatchNorm1d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (2): ReLU() ) (1): SparseSequential( (0): SubMConv3d(32, 32, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm) (1): BatchNorm1d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (2): ReLU() ) (2): SparseSequential( (0): SubMConv3d(32, 32, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm) (1): BatchNorm1d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (2): ReLU() ) ) (conv3): SparseSequential( (0): SparseSequential( (0): SparseConv3d(32, 64, kernel_size=[3, 3, 3], stride=[2, 2, 2], padding=[1, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm) (1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (2): ReLU() ) (1): SparseSequential( (0): SubMConv3d(64, 64, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm) (1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (2): ReLU() ) (2): SparseSequential( (0): SubMConv3d(64, 64, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm) (1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (2): ReLU() ) ) (conv4): SparseSequential( (0): SparseSequential( (0): SparseConv3d(64, 64, kernel_size=[3, 3, 3], stride=[2, 2, 2], padding=[0, 1, 1], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm) (1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (2): ReLU() ) (1): SparseSequential( (0): SubMConv3d(64, 64, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm) (1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (2): ReLU() ) (2): SparseSequential( (0): SubMConv3d(64, 64, kernel_size=[3, 3, 3], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm) (1): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (2): ReLU() ) ) (conv_out): SparseSequential( (0): SparseConv3d(64, 128, kernel_size=[3, 1, 1], stride=[2, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], output_padding=[0, 0, 0], bias=False, algo=ConvAlgo.MaskImplicitGemm) (1): BatchNorm1d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (2): ReLU() ) ) (map_to_bev_module): HeightCompression() (pfe): VoxelSetAbstraction( (SA_layers): ModuleList( (0): StackSAModuleMSG( (groupers): ModuleList( (0): QueryAndGroup() (1): QueryAndGroup() ) (mlps): ModuleList( (0): Sequential( (0): Conv2d(19, 16, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False) (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU() ) (1): Sequential( (0): Conv2d(19, 16, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False) (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU() ) ) ) (1): StackSAModuleMSG( (groupers): ModuleList( (0): QueryAndGroup() (1): QueryAndGroup() ) (mlps): ModuleList( (0): Sequential( (0): Conv2d(35, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU() ) (1): Sequential( (0): Conv2d(35, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU() ) ) ) (2): StackSAModuleMSG( (groupers): ModuleList( (0): QueryAndGroup() (1): QueryAndGroup() ) (mlps): ModuleList( (0): Sequential( (0): Conv2d(67, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU() ) (1): Sequential( (0): Conv2d(67, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU() ) ) ) (3): StackSAModuleMSG( (groupers): ModuleList( (0): QueryAndGroup() (1): QueryAndGroup() ) (mlps): ModuleList( (0): Sequential( (0): Conv2d(67, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU() ) (1): Sequential( (0): Conv2d(67, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU() ) ) ) ) (SA_rawpoints): StackSAModuleMSG( (groupers): ModuleList( (0): QueryAndGroup() (1): QueryAndGroup() ) (mlps): ModuleList( (0): Sequential( (0): Conv2d(4, 16, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False) (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU() ) (1): Sequential( (0): Conv2d(4, 16, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False) (4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU() ) ) ) (vsa_point_feature_fusion): Sequential( (0): Linear(in_features=640, out_features=128, bias=False) (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() ) ) (backbone_2d): BaseBEVBackbone( (blocks): ModuleList( (0): Sequential( (0): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0) (1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), bias=False) (2): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (3): ReLU() (4): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (5): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (6): ReLU() (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (8): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (9): ReLU() (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (11): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (12): ReLU() (13): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (14): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (15): ReLU() (16): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (17): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (18): ReLU() ) (1): Sequential( (0): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0) (1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), bias=False) (2): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (3): ReLU() (4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (5): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (6): ReLU() (7): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (8): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (9): ReLU() (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (11): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (12): ReLU() (13): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (14): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (15): ReLU() (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (17): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (18): ReLU() ) ) (deblocks): ModuleList( (0): Sequential( (0): ConvTranspose2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (2): ReLU() ) (1): Sequential( (0): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2), bias=False) (1): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True) (2): ReLU() ) ) ) (dense_head): AnchorHeadSingle( (cls_loss_func): SigmoidFocalClassificationLoss() (reg_loss_func): WeightedSmoothL1Loss() (dir_loss_func): WeightedCrossEntropyLoss() (conv_cls): Conv2d(512, 18, kernel_size=(1, 1), stride=(1, 1)) (conv_box): Conv2d(512, 42, kernel_size=(1, 1), stride=(1, 1)) (conv_dir_cls): Conv2d(512, 12, kernel_size=(1, 1), stride=(1, 1)) ) (point_head): PointHeadSimple( (cls_loss_func): SigmoidFocalClassificationLoss() (cls_layers): Sequential( (0): Linear(in_features=640, out_features=256, bias=False) (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): Linear(in_features=256, out_features=256, bias=False) (4): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU() (6): Linear(in_features=256, out_features=1, bias=True) ) ) (roi_head): PVRCNNHead( (proposal_target_layer): ProposalTargetLayer() (reg_loss_func): WeightedSmoothL1Loss() (roi_grid_pool_layer): StackSAModuleMSG( (groupers): ModuleList( (0): QueryAndGroup() (1): QueryAndGroup() ) (mlps): ModuleList( (0): Sequential( (0): Conv2d(131, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU() ) (1): Sequential( (0): Conv2d(131, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU() ) ) ) (shared_fc_layer): Sequential( (0): Conv1d(27648, 256, kernel_size=(1,), stride=(1,), bias=False) (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): Dropout(p=0.3, inplace=False) (4): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False) (5): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (6): ReLU() ) (cls_layers): Sequential( (0): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False) (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): Dropout(p=0.3, inplace=False) (4): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False) (5): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (6): ReLU() (7): Conv1d(256, 1, kernel_size=(1,), stride=(1,)) ) (reg_layers): Sequential( (0): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False) (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): Dropout(p=0.3, inplace=False) (4): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False) (5): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (6): ReLU() (7): Conv1d(256, 7, kernel_size=(1,), stride=(1,)) ) ) ) 2022-02-08 14:05:42,458 INFO **Start training kitti_models/pv_rcnn(mydata_1)** epochs: 0%| | 0/10 [00:00<?, ?it/s] train: 0%| | 0/3712 [00:00<?, ?it/s]

The composition format of the data set is as follows:

How should I modify my training

In addition，i have changed pv_rcnn.yaml USE_ROAD_PLANE: False

------------------ 原始邮件 ------------------ 发件人: "open-mmlab/OpenPCDet" @.>; 发送时间: 2022年2月8日(星期二) 中午11:05 @.>; @.**@.>; 主题: Re: [open-mmlab/OpenPCDet] PCDetv0.5 install in windows (Issue #688)

use --ckpt to specify a checkpoint rather than --eval all.

— Reply to this email directly, view it on GitHub, or unsubscribe. Triage notifications on the go with GitHub Mobile for iOS or Android. You are receiving this because you authored the thread.Message ID: @.***>

VsionQing commented 2 years ago

I think it may be the training failure caused by this error message KeyError: Caught KeyError in DataLoader worker process 0. But no solution was found. I hope to get your help In addition, this error message only needs to be configured in yaml file USE_ROAD_PLANE: False
KeyError: 'road_plane' 2022-02-12 15:17:17,801 INFO **Start training kitti_models/pointpillar(default)** epochs: 0%| | 0/10 [00:05<?, ?it/s] Traceback (most recent call last): | 0/3712 [00:00<?, ?it/s] File "E:\PythonFile\OpenPCDet-master---updated\tools\train.py", line 201, in <module> main() File "E:\PythonFile\OpenPCDet-master---updated\tools\train.py", line 153, in main train_model( File "E:\PythonFile\OpenPCDet-master---updated\tools\train_utils\train_utils.py", line 86, in train_model accumulated_iter = train_one_epoch( File "E:\PythonFile\OpenPCDet-master---updated\tools\train_utils\train_utils.py", line 19, in train_one_epoch batch = next(dataloader_iter) File "D:\Anconda\envs\pytorch\lib\site-packages\torch\utils\data\dataloader.py", line 521, in next data = self._next_data() File "D:\Anconda\envs\pytorch\lib\site-packages\torch\utils\data\dataloader.py", line 1203, in _next_data return self._process_data(data) File "D:\Anconda\envs\pytorch\lib\site-packages\torch\utils\data\dataloader.py", line 1229, in _process_data data.reraise() File "D:\Anconda\envs\pytorch\lib\site-packages\torch_utils.py", line 434, in reraise raise exception KeyError: Caught KeyError in DataLoader worker process 0. Original Traceback (most recent call last): File "D:\Anconda\envs\pytorch\lib\site-packages\torch\utils\data_utils\worker.py", line 287, in _worker_loop data = fetcher.fetch(index) File "D:\Anconda\envs\pytorch\lib\site-packages\torch\utils\data_utils\fetch.py", line 49, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "D:\Anconda\envs\pytorch\lib\site-packages\torch\utils\data_utils\fetch.py", line 49, in <listcomp> data = [self.dataset[idx] for idx in possibly_batched_index] File "E:\PythonFile\OpenPCDet-master---updated\tools..\pcdet\datasets\kitti\kitti_dataset.py", line 424, in getitem data_dict = self.prepare_data(data_dict=input_dict) File "E:\PythonFile\OpenPCDet-master---updated\tools..\pcdet\datasets\dataset.py", line 127, in prepare_data data_dict = self.data_augmentor.forward( File "E:\PythonFile\OpenPCDet-master---updated\tools..\pcdet\datasets\augmentor\data_augmentor.py", line 112, in forward data_dict = cur_augmentor(data_dict=data_dict) File "E:\PythonFile\OpenPCDet-master---updated\tools..\pcdet\datasets\augmentor\database_sampler.py", line 245, in call data_dict = self.add_sampled_boxes_to_scene(data_dict, sampled_gt_boxes, total_valid_sampled_dict) File "E:\PythonFile\OpenPCDet-master---updated\tools..\pcdet\datasets\augmentor\database_sampler.py", line 162, in add_sampled_boxes_to_scene sampled_gt_boxes, data_dict['road_plane'], data_dict['calib'] KeyError: 'road_plane'

------------------ 原始邮件 ------------------ 发件人: "open-mmlab/OpenPCDet" @.>; 发送时间: 2022年2月8日(星期二) 中午11:05 @.>; @.**@.>; 主题: Re: [open-mmlab/OpenPCDet] PCDetv0.5 install in windows (Issue #688)

use --ckpt to specify a checkpoint rather than --eval all.

— Reply to this email directly, view it on GitHub, or unsubscribe. Triage notifications on the go with GitHub Mobile for iOS or Android. You are receiving this because you authored the thread.Message ID: @.***>

github-actions[bot] commented 2 years ago

This issue is stale because it has been open for 30 days with no activity.

VsionQing commented 2 years ago

收到谢谢

github-actions[bot] commented 2 years ago

This issue is stale because it has been open for 30 days with no activity.

github-actions[bot] commented 2 years ago

This issue was closed because it has been inactive for 14 days since being marked as stale.

Petros626 commented 1 week ago

###########################
1. Anaconda env mit Python#
###########################
conda create --name openpcdet python=3.8

################################################################################
#2. Deinstallation von alter CUDA Version / Installation von neuer CUDA Version#
################################################################################
https://gist.github.com/kmhofmann/cee7c0053da8cc09d62d74a6a4c1c5e4
https://medium.com/virtual-force-inc/a-step-by-step-guide-to-install-nvidia-drivers-and-cuda-toolkit-855c75efcdb6

##########################################
3. Installation OpenPCDet with Anaconda3:#
##########################################
https://medium.com/@alifyafebriana/setting-up-3d-open-source-openpcdet-with-anaconda-a-step-by-step-guide-66126107215
conda activate openpcdet
PyTorch 2: pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
CUDA 11.8: siehe oben
spconv: pip install spconv-cu118
git clone https://github.com/open-mmlab/OpenPCDet.git
cd OpenPCDet
pip install -r requirements.txt
python setup.py develop
python demo.py --cfg_file /home/rlab10/OpenPCDet/tools/cfgs/kitti_models/pointpillar.yaml --ckpt /home/rlab10/OpenPCDet/pointpillar_7728.pth --data_path /home/rlab10/OpenPCDet/data/kitti/000006.bin

###################################################################
4. Download der KITTI-Daten und Erstellung der Dateninformationen:#
###################################################################
python -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml

the requirements.txt:

numpy
llvmlite
numba
torch>=1.1
tensorboardX
easydict
pyyaml
scikit-image
tqdm
torchvision
SharedArray
opencv-python
pyquaternion
av2==0.2.0
kornia==0.6.5

open-mmlab / OpenPCDet

PCDetv0.5 install in windows #688