fightingshao commented 1 month ago

安装官网教程测试图像分类没有任何问题，自己测试目标识别出现问题，执行代码如下：

加载dockers： docker run --name paddlex -v /model_pipeline_sll/paddlex/:/paddle --shm-size=8G --network=host --gpus all -it registry.baidubce.com/paddlex/paddlex:3.0.0b0-gpu-cuda11.8-cudnn8.9-trt8.5 /bin/bash

数据校验： wget https://paddle-model-ecology.bj.bcebos.com/paddlex/data/det_coco_examples.tar -P ./dataset tar -xf ./dataset/det_coco_examples.tar -C ./dataset/ python main.py -c paddlex/configs/object_detection/PP-YOLOE_plus-S.yaml \ -o Global.mode=check_dataset \ -o Global.dataset_dir=./dataset/det_coco_examples

训练： python main.py -c paddlex/configs/object_detection/PP-YOLOE_plus-S.yaml \ -o Global.mode=train \ -o Global.dataset_dir=./dataset/det_coco_examples -o Global.device=gpu:0 \ -o Train.epochs_iters=10

报错信息如下： λ DESKTOP-2583UDJ ~/PaddleX python main.py -c paddlex/configs/object_detection/PP-YOLOE_plus-S.yaml \
```
-o Global.mode=train \
-o Global.dataset_dir=./dataset/det_coco_examples  -o Global.device=gpu:0 \
```
-o Train.epochs_iters=10 grep: warning: GREP_OPTIONS is deprecated; please use an alias or script ['/usr/bin/python', 'tools/train.py', '--eval', '--config', '/root/.paddlex/tmpvk3p9s58/detmodel_PP-YOLOE_plus-S.yml', '--use_vdl', 'True', '--vdl_log_dir', '/root/PaddleX/output']

Log path: /root/PaddleX/output/train.log

grep: warning: GREP_OPTIONS is deprecated; please use an alias or script ======================= Modified FLAGS detected ======================= FLAGS(name='FLAGS_curand_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia/curand/lib', default_value='') FLAGS(name='FLAGS_cublas_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia/cublas/lib', default_value='') FLAGS(name='FLAGS_cusolver_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia/cusolver/lib', default_value='') FLAGS(name='FLAGS_cupti_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia/cuda_cupti/lib', default_value='') FLAGS(name='FLAGS_cusparse_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia/cusparse/lib', default_value='') FLAGS(name='FLAGS_nccl_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia/nccl/lib', default_value='') FLAGS(name='FLAGS_nvidia_package_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia', default_value='') FLAGS(name='FLAGS_cudnn_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia/cudnn/lib', default_value='')

loading annotations into memory... Done (t=0.00s) creating index... index created! [07/12 09:09:43] ppdet.data.source.coco INFO: Load [701 samples valid, 0 samples invalid] in file /root/PaddleX/dataset/det_coco_examples/annotations/instance_train.json. W0712 09:09:43.091182 568 gpu_resources.cc:119] Please NOTE: device: 0, GPU Compute Capability: 8.9, Driver API Version: 12.2, Runtime API Version: 11.8 W0712 09:09:43.092409 568 gpu_resources.cc:164] device: 0, cuDNN Version: 8.9. [07/12 09:09:45] ppdet.utils.download INFO: Downloading ppyoloe_crn_s_obj365_pretrained.pdparams from https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_s_obj365_pretrained.pdparams 100%|██████████| 38442/38442 [00:01<00:00, 24543.91KB/s] [07/12 09:09:47] ppdet.utils.checkpoint INFO: The shape [365] in pretrained weight yolo_head.pred_cls.0.bias is unmatched with the shape [4] in model yolo_head.pred_cls.0.bias. And the weight yolo_head.pred_cls.0.bias will not be loaded [07/12 09:09:47] ppdet.utils.checkpoint INFO: The shape [365, 384, 3, 3] in pretrained weight yolo_head.pred_cls.0.weight is unmatched with the shape [4, 384, 3, 3] in model yolo_head.pred_cls.0.weight. And the weight yolo_head.pred_cls.0.weight will not be loaded [07/12 09:09:47] ppdet.utils.checkpoint INFO: The shape [365] in pretrained weight yolo_head.pred_cls.1.bias is unmatched with the shape [4] in model yolo_head.pred_cls.1.bias. And the weight yolo_head.pred_cls.1.bias will not be loaded [07/12 09:09:47] ppdet.utils.checkpoint INFO: The shape [365, 192, 3, 3] in pretrained weight yolo_head.pred_cls.1.weight is unmatched with the shape [4, 192, 3, 3] in model yolo_head.pred_cls.1.weight. And the weight yolo_head.pred_cls.1.weight will not be loaded [07/12 09:09:47] ppdet.utils.checkpoint INFO: The shape [365] in pretrained weight yolo_head.pred_cls.2.bias is unmatched with the shape [4] in model yolo_head.pred_cls.2.bias. And the weight yolo_head.pred_cls.2.bias will not be loaded [07/12 09:09:47] ppdet.utils.checkpoint INFO: The shape [365, 96, 3, 3] in pretrained weight yolo_head.pred_cls.2.weight is unmatched with the shape [4, 96, 3, 3] in model yolo_head.pred_cls.2.weight. And the weight yolo_head.pred_cls.2.weight will not be loaded [07/12 09:09:47] ppdet.utils.checkpoint INFO: Finish loading model weights: /root/.cache/paddle/weights/ppyoloe_crn_s_obj365_pretrained.pdparams [07/12 09:09:48] ppdet.engine INFO: Epoch: [0] [ 0/87] learning_rate: 0.000000 loss: 4.742952 loss_cls: 3.988727 loss_iou: 0.137742 loss_dfl: 0.819739 loss_l1: 0.304583 eta: 0:22:27 batch_cost: 1.5483 data_cost: 0.0025 ips: 5.1670 images/s max_mem_reserved: 4456 MB max_mem_allocated: 3606 MB [07/12 09:09:49] ppdet.engine INFO: Epoch: [0] [10/87] learning_rate: 0.000010 loss: 4.605562 loss_cls: 3.812212 loss_iou: 0.159975 loss_dfl: 0.778183 loss_l1: 0.269759 eta: 0:02:52 batch_cost: 0.0656 data_cost: 0.0011 ips: 121.8882 images/s max_mem_reserved: 4456 MB max_mem_allocated: 3664 MB [07/12 09:09:50] ppdet.engine INFO: Epoch: [0] [20/87] learning_rate: 0.000020 loss: 4.248178 loss_cls: 3.397702 loss_iou: 0.154198 loss_dfl: 0.802431 loss_l1: 0.298053 eta: 0:01:56 batch_cost: 0.0677 data_cost: 0.0031 ips: 118.1077 images/s max_mem_reserved: 5635 MB max_mem_allocated: 3985 MB [07/12 09:09:51] ppdet.engine INFO: Epoch: [0] [30/87] learning_rate: 0.000030 loss: 3.207999 loss_cls: 2.584213 loss_iou: 0.156741 loss_dfl: 0.694154 loss_l1: 0.244871 eta: 0:01:32 batch_cost: 0.0539 data_cost: 0.0006 ips: 148.4765 images/s max_mem_reserved: 5635 MB max_mem_allocated: 3985 MB [07/12 09:09:52] ppdet.engine INFO: Epoch: [0] [40/87] learning_rate: 0.000040 loss: 2.711038 loss_cls: 1.899038 loss_iou: 0.142061 loss_dfl: 0.700217 loss_l1: 0.209759 eta: 0:01:21 batch_cost: 0.0596 data_cost: 0.0011 ips: 134.1488 images/s max_mem_reserved: 5635 MB max_mem_allocated: 3985 MB [07/12 09:09:52] ppdet.engine INFO: Epoch: [0] [50/87] learning_rate: 0.000050 loss: 2.403087 loss_cls: 1.697473 loss_iou: 0.115551 loss_dfl: 0.657189 loss_l1: 0.180151 eta: 0:01:14 batch_cost: 0.0593 data_cost: 0.0013 ips: 134.8738 images/s max_mem_reserved: 5635 MB max_mem_allocated: 3985 MB [07/12 09:09:53] ppdet.engine INFO: Epoch: [0] [60/87] learning_rate: 0.000060 loss: 2.129927 loss_cls: 1.343819 loss_iou: 0.151717 loss_dfl: 0.790706 loss_l1: 0.329948 eta: 0:01:08 batch_cost: 0.0582 data_cost: 0.0012 ips: 137.5663 images/s max_mem_reserved: 5635 MB max_mem_allocated: 3985 MB [07/12 09:09:54] ppdet.engine INFO: Epoch: [0] [70/87] learning_rate: 0.000070 loss: 1.974438 loss_cls: 1.265653 loss_iou: 0.127933 loss_dfl: 0.702785 loss_l1: 0.243673 eta: 0:01:05 batch_cost: 0.0632 data_cost: 0.0017 ips: 126.5618 images/s max_mem_reserved: 5635 MB max_mem_allocated: 3985 MB [07/12 09:09:55] ppdet.engine INFO: Epoch: [0] [80/87] learning_rate: 0.000080 loss: 1.859349 loss_cls: 1.211074 loss_iou: 0.119532 loss_dfl: 0.639663 loss_l1: 0.177138 eta: 0:01:02 batch_cost: 0.0609 data_cost: 0.0018 ips: 131.2872 images/s max_mem_reserved: 5635 MB max_mem_allocated: 3985 MB [07/12 09:09:56] ppdet.utils.checkpoint INFO: Save checkpoint: /root/PaddleX/output loading annotations into memory... Done (t=0.00s) creating index... index created! [07/12 09:09:56] ppdet.data.source.coco INFO: Load [176 samples valid, 0 samples invalid] in file /root/PaddleX/dataset/det_coco_examples/annotations/instance_val.json. loading annotations into memory... Done (t=0.00s) creating index... index created! [07/12 09:09:56] ppdet.engine INFO: Eval iter: 0 [07/12 09:09:58] ppdet.metrics.metrics INFO: The bbox result is saved to bbox.json. loading annotations into memory... Done (t=0.00s) creating index... index created! [07/12 09:09:58] ppdet.metrics.coco_utils INFO: Start evaluate... Loading and preparing results... DONE (t=0.32s) creating index... index created! Running per image evaluation... Evaluate annotation type bbox DONE (t=0.64s). Accumulating evaluation results... DONE (t=0.19s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.428 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.568 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.498 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.402 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.378 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.556 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.549 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.765 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.797 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.707 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.830 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.856 [07/12 09:10:00] ppdet.engine INFO: Total sample number: 176, average FPS: 82.6468006164384 [07/12 09:10:00] ppdet.engine INFO: Best test bbox ap is 0.428. [07/12 09:10:00] ppdet.utils.checkpoint INFO: Save checkpoint: /root/PaddleX/output [07/12 09:10:00] ppdet.engine INFO: Epoch: [1] [ 0/87] learning_rate: 0.000087 loss: 1.768240 loss_cls: 1.105604 loss_iou: 0.124229 loss_dfl: 0.606990 loss_l1: 0.158132 eta: 0:01:01 batch_cost: 0.0599 data_cost: 0.0027 ips: 133.5788 images/s max_mem_reserved: 5635 MB max_mem_allocated: 3985 MB [07/12 09:10:01] ppdet.engine INFO: Epoch: [1] [10/87] learning_rate: 0.000097 loss: 1.784680 loss_cls: 1.181858 loss_iou: 0.134359 loss_dfl: 0.640814 loss_l1: 0.182603 eta: 0:00:59 batch_cost: 0.0658 data_cost: 0.0037 ips: 121.5322 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4009 MB [07/12 09:10:02] ppdet.engine INFO: Epoch: [1] [20/87] learning_rate: 0.000100 loss: 1.980097 loss_cls: 1.254821 loss_iou: 0.137246 loss_dfl: 0.710579 loss_l1: 0.200776 eta: 0:00:57 batch_cost: 0.0630 data_cost: 0.0036 ips: 126.9660 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4009 MB [07/12 09:10:02] ppdet.engine INFO: Epoch: [1] [30/87] learning_rate: 0.000100 loss: 1.605003 loss_cls: 0.974784 loss_iou: 0.116518 loss_dfl: 0.650027 loss_l1: 0.165881 eta: 0:00:55 batch_cost: 0.0571 data_cost: 0.0023 ips: 140.0083 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4009 MB [07/12 09:10:03] ppdet.engine INFO: Epoch: [1] [40/87] learning_rate: 0.000100 loss: 1.695622 loss_cls: 1.024845 loss_iou: 0.128229 loss_dfl: 0.659235 loss_l1: 0.177416 eta: 0:00:54 batch_cost: 0.0699 data_cost: 0.0025 ips: 114.4243 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:04] ppdet.engine INFO: Epoch: [1] [50/87] learning_rate: 0.000100 loss: 1.631290 loss_cls: 1.027864 loss_iou: 0.107940 loss_dfl: 0.621047 loss_l1: 0.162190 eta: 0:00:53 batch_cost: 0.0598 data_cost: 0.0019 ips: 133.8810 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:05] ppdet.engine INFO: Epoch: [1] [60/87] learning_rate: 0.000100 loss: 1.468891 loss_cls: 0.876769 loss_iou: 0.118743 loss_dfl: 0.636702 loss_l1: 0.148867 eta: 0:00:51 batch_cost: 0.0584 data_cost: 0.0012 ips: 137.0070 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:06] ppdet.engine INFO: Epoch: [1] [70/87] learning_rate: 0.000100 loss: 1.495109 loss_cls: 0.931451 loss_iou: 0.109741 loss_dfl: 0.663792 loss_l1: 0.160617 eta: 0:00:50 batch_cost: 0.0632 data_cost: 0.0017 ips: 126.6789 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:07] ppdet.engine INFO: Epoch: [1] [80/87] learning_rate: 0.000100 loss: 1.610014 loss_cls: 0.962692 loss_iou: 0.127851 loss_dfl: 0.644245 loss_l1: 0.170075 eta: 0:00:49 batch_cost: 0.0677 data_cost: 0.0045 ips: 118.2121 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:08] ppdet.utils.checkpoint INFO: Save checkpoint: /root/PaddleX/output [07/12 09:10:08] ppdet.engine INFO: Eval iter: 0 [07/12 09:10:10] ppdet.metrics.metrics INFO: The bbox result is saved to bbox.json. loading annotations into memory... Done (t=0.00s) creating index... index created! [07/12 09:10:10] ppdet.metrics.coco_utils INFO: Start evaluate... Loading and preparing results... DONE (t=0.31s) creating index... index created! Running per image evaluation... Evaluate annotation type bbox DONE (t=0.62s). Accumulating evaluation results... DONE (t=0.19s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.656 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.841 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.763 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.554 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.686 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.790 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.649 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.785 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.804 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.724 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.826 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.846 [07/12 09:10:11] ppdet.engine INFO: Total sample number: 176, average FPS: 82.94741686802439 [07/12 09:10:11] ppdet.engine INFO: Best test bbox ap is 0.656. [07/12 09:10:12] ppdet.utils.checkpoint INFO: Save checkpoint: /root/PaddleX/output [07/12 09:10:12] ppdet.engine INFO: Epoch: [2] [ 0/87] learning_rate: 0.000100 loss: 1.390771 loss_cls: 0.816167 loss_iou: 0.110740 loss_dfl: 0.609991 loss_l1: 0.151098 eta: 0:00:49 batch_cost: 0.0694 data_cost: 0.0033 ips: 115.3150 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:13] ppdet.engine INFO: Epoch: [2] [10/87] learning_rate: 0.000100 loss: 1.389389 loss_cls: 0.774846 loss_iou: 0.099172 loss_dfl: 0.613026 loss_l1: 0.142075 eta: 0:00:48 batch_cost: 0.0757 data_cost: 0.0037 ips: 105.7219 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:14] ppdet.engine INFO: Epoch: [2] [20/87] learning_rate: 0.000100 loss: 1.491619 loss_cls: 0.882632 loss_iou: 0.105861 loss_dfl: 0.650901 loss_l1: 0.174578 eta: 0:00:47 batch_cost: 0.0573 data_cost: 0.0011 ips: 139.5100 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:14] ppdet.engine INFO: Epoch: [2] [30/87] learning_rate: 0.000100 loss: 1.540402 loss_cls: 0.920521 loss_iou: 0.122485 loss_dfl: 0.642548 loss_l1: 0.179540 eta: 0:00:46 batch_cost: 0.0623 data_cost: 0.0014 ips: 128.4126 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:15] ppdet.engine INFO: Epoch: [2] [40/87] learning_rate: 0.000100 loss: 1.741574 loss_cls: 0.994562 loss_iou: 0.138730 loss_dfl: 0.681820 loss_l1: 0.192439 eta: 0:00:45 batch_cost: 0.0598 data_cost: 0.0017 ips: 133.8637 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:16] ppdet.engine INFO: Epoch: [2] [50/87] learning_rate: 0.000100 loss: 1.668370 loss_cls: 0.972257 loss_iou: 0.143522 loss_dfl: 0.686965 loss_l1: 0.211415 eta: 0:00:44 batch_cost: 0.0614 data_cost: 0.0013 ips: 130.3738 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:17] ppdet.engine INFO: Epoch: [2] [60/87] learning_rate: 0.000100 loss: 1.566314 loss_cls: 0.899278 loss_iou: 0.126620 loss_dfl: 0.663479 loss_l1: 0.198697 eta: 0:00:43 batch_cost: 0.0627 data_cost: 0.0011 ips: 127.6901 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:18] ppdet.engine INFO: Epoch: [2] [70/87] learning_rate: 0.000100 loss: 1.507144 loss_cls: 0.857531 loss_iou: 0.131449 loss_dfl: 0.657898 loss_l1: 0.220142 eta: 0:00:43 batch_cost: 0.0671 data_cost: 0.0023 ips: 119.2881 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:19] ppdet.engine INFO: Epoch: [2] [80/87] learning_rate: 0.000100 loss: 1.552018 loss_cls: 0.792345 loss_iou: 0.151174 loss_dfl: 0.689410 loss_l1: 0.212275 eta: 0:00:42 batch_cost: 0.0597 data_cost: 0.0023 ips: 133.9541 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:20] ppdet.utils.checkpoint INFO: Save checkpoint: /root/PaddleX/output [07/12 09:10:20] ppdet.engine INFO: Eval iter: 0 [07/12 09:10:22] ppdet.metrics.metrics INFO: The bbox result is saved to bbox.json. loading annotations into memory... Done (t=0.00s) creating index... index created! [07/12 09:10:22] ppdet.metrics.coco_utils INFO: Start evaluate... Loading and preparing results... DONE (t=0.32s) creating index... index created! Running per image evaluation... Evaluate annotation type bbox DONE (t=0.67s). Accumulating evaluation results... DONE (t=0.19s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.675 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.856 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.799 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.587 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.722 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.788 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.659 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.788 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.814 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.722 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.848 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.873 [07/12 09:10:23] ppdet.engine INFO: Total sample number: 176, average FPS: 80.98281835422215 [07/12 09:10:23] ppdet.engine INFO: Best test bbox ap is 0.675. [07/12 09:10:24] ppdet.utils.checkpoint INFO: Save checkpoint: /root/PaddleX/output [07/12 09:10:24] ppdet.engine INFO: Epoch: [3] [ 0/87] learning_rate: 0.000100 loss: 1.337718 loss_cls: 0.767077 loss_iou: 0.111621 loss_dfl: 0.642397 loss_l1: 0.155339 eta: 0:00:41 batch_cost: 0.0652 data_cost: 0.0038 ips: 122.7419 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:25] ppdet.engine INFO: Epoch: [3] [10/87] learning_rate: 0.000100 loss: 1.462643 loss_cls: 0.807598 loss_iou: 0.136303 loss_dfl: 0.641163 loss_l1: 0.169720 eta: 0:00:40 batch_cost: 0.0666 data_cost: 0.0013 ips: 120.0900 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:25] ppdet.engine INFO: Epoch: [3] [20/87] learning_rate: 0.000100 loss: 1.438077 loss_cls: 0.790194 loss_iou: 0.121551 loss_dfl: 0.658683 loss_l1: 0.166509 eta: 0:00:40 batch_cost: 0.0573 data_cost: 0.0007 ips: 139.5620 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:26] ppdet.engine INFO: Epoch: [3] [30/87] learning_rate: 0.000100 loss: 1.495787 loss_cls: 0.931611 loss_iou: 0.125179 loss_dfl: 0.674387 loss_l1: 0.171514 eta: 0:00:39 batch_cost: 0.0647 data_cost: 0.0012 ips: 123.6218 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:27] ppdet.engine INFO: Epoch: [3] [40/87] learning_rate: 0.000100 loss: 1.502775 loss_cls: 0.860802 loss_iou: 0.112805 loss_dfl: 0.668937 loss_l1: 0.159058 eta: 0:00:38 batch_cost: 0.0714 data_cost: 0.0045 ips: 112.0016 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:28] ppdet.engine INFO: Epoch: [3] [50/87] learning_rate: 0.000100 loss: 1.523543 loss_cls: 0.820807 loss_iou: 0.122483 loss_dfl: 0.648966 loss_l1: 0.176340 eta: 0:00:37 batch_cost: 0.0588 data_cost: 0.0012 ips: 136.1254 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:29] ppdet.engine INFO: Epoch: [3] [60/87] learning_rate: 0.000100 loss: 1.621172 loss_cls: 0.891860 loss_iou: 0.131958 loss_dfl: 0.657761 loss_l1: 0.168215 eta: 0:00:37 batch_cost: 0.0604 data_cost: 0.0024 ips: 132.5343 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:30] ppdet.engine INFO: Epoch: [3] [70/87] learning_rate: 0.000100 loss: 1.460573 loss_cls: 0.802123 loss_iou: 0.122040 loss_dfl: 0.634926 loss_l1: 0.165973 eta: 0:00:36 batch_cost: 0.0611 data_cost: 0.0012 ips: 130.8832 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:31] ppdet.engine INFO: Epoch: [3] [80/87] learning_rate: 0.000100 loss: 1.402430 loss_cls: 0.779390 loss_iou: 0.121753 loss_dfl: 0.627617 loss_l1: 0.157090 eta: 0:00:35 batch_cost: 0.0625 data_cost: 0.0012 ips: 128.0246 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:32] ppdet.utils.checkpoint INFO: Save checkpoint: /root/PaddleX/output [07/12 09:10:32] ppdet.engine INFO: Eval iter: 0 [07/12 09:10:34] ppdet.metrics.metrics INFO: The bbox result is saved to bbox.json. loading annotations into memory... Done (t=0.00s) creating index... index created! [07/12 09:10:34] ppdet.metrics.coco_utils INFO: Start evaluate... Loading and preparing results... DONE (t=0.31s) creating index... index created! Running per image evaluation... Evaluate annotation type bbox DONE (t=0.67s). Accumulating evaluation results... DONE (t=0.20s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.668 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.851 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.785 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.640 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.709 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.748 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.671 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.795 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.807 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.701 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.840 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.864 [07/12 09:10:35] ppdet.engine INFO: Total sample number: 176, average FPS: 85.31150929190167 [07/12 09:10:35] ppdet.engine INFO: Best test bbox ap is 0.675. [07/12 09:10:35] ppdet.engine INFO: Epoch: [4] [ 0/87] learning_rate: 0.000100 loss: 1.550932 loss_cls: 0.844714 loss_iou: 0.135754 loss_dfl: 0.670553 loss_l1: 0.191254 eta: 0:00:35 batch_cost: 0.0665 data_cost: 0.0023 ips: 120.3728 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:36] ppdet.engine INFO: Epoch: [4] [10/87] learning_rate: 0.000100 loss: 1.547516 loss_cls: 0.848658 loss_iou: 0.126366 loss_dfl: 0.668471 loss_l1: 0.169774 eta: 0:00:34 batch_cost: 0.0617 data_cost: 0.0024 ips: 129.5639 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:37] ppdet.engine INFO: Epoch: [4] [20/87] learning_rate: 0.000100 loss: 1.272558 loss_cls: 0.703266 loss_iou: 0.105227 loss_dfl: 0.605535 loss_l1: 0.134884 eta: 0:00:33 batch_cost: 0.0595 data_cost: 0.0012 ips: 134.4707 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:38] ppdet.engine INFO: Epoch: [4] [30/87] learning_rate: 0.000100 loss: 1.485260 loss_cls: 0.835504 loss_iou: 0.136417 loss_dfl: 0.641114 loss_l1: 0.168137 eta: 0:00:32 batch_cost: 0.0575 data_cost: 0.0008 ips: 139.2397 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:39] ppdet.engine INFO: Epoch: [4] [40/87] learning_rate: 0.000100 loss: 1.430090 loss_cls: 0.819202 loss_iou: 0.104138 loss_dfl: 0.606502 loss_l1: 0.128452 eta: 0:00:31 batch_cost: 0.0511 data_cost: 0.0008 ips: 156.6701 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:39] ppdet.engine INFO: Epoch: [4] [50/87] learning_rate: 0.000100 loss: 1.381274 loss_cls: 0.681876 loss_iou: 0.121735 loss_dfl: 0.642299 loss_l1: 0.156692 eta: 0:00:31 batch_cost: 0.0603 data_cost: 0.0031 ips: 132.7058 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:40] ppdet.engine INFO: Epoch: [4] [60/87] learning_rate: 0.000100 loss: 1.380815 loss_cls: 0.738216 loss_iou: 0.118489 loss_dfl: 0.623430 loss_l1: 0.146345 eta: 0:00:30 batch_cost: 0.0597 data_cost: 0.0012 ips: 134.0521 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:41] ppdet.engine INFO: Epoch: [4] [70/87] learning_rate: 0.000100 loss: 1.285821 loss_cls: 0.702206 loss_iou: 0.110113 loss_dfl: 0.633130 loss_l1: 0.147362 eta: 0:00:29 batch_cost: 0.0594 data_cost: 0.0007 ips: 134.6521 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:42] ppdet.engine INFO: Epoch: [4] [80/87] learning_rate: 0.000100 loss: 1.351363 loss_cls: 0.743829 loss_iou: 0.114198 loss_dfl: 0.648497 loss_l1: 0.155006 eta: 0:00:29 batch_cost: 0.0663 data_cost: 0.0030 ips: 120.5763 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:43] ppdet.utils.checkpoint INFO: Save checkpoint: /root/PaddleX/output [07/12 09:10:43] ppdet.engine INFO: Eval iter: 0 [07/12 09:10:46] ppdet.metrics.metrics INFO: The bbox result is saved to bbox.json. loading annotations into memory... Done (t=0.00s) creating index... index created! [07/12 09:10:46] ppdet.metrics.coco_utils INFO: Start evaluate... Loading and preparing results... DONE (t=0.32s) creating index... index created! Running per image evaluation... Evaluate annotation type bbox DONE (t=0.67s). Accumulating evaluation results... DONE (t=0.22s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.725 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.906 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.849 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.521 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.786 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.857 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.688 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.812 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.828 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.700 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.867 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.889 [07/12 09:10:47] ppdet.engine INFO: Total sample number: 176, average FPS: 83.0148019198503 [07/12 09:10:47] ppdet.engine INFO: Best test bbox ap is 0.725. [07/12 09:10:47] ppdet.utils.checkpoint INFO: Save checkpoint: /root/PaddleX/output [07/12 09:10:47] ppdet.engine INFO: Epoch: [5] [ 0/87] learning_rate: 0.000100 loss: 1.539663 loss_cls: 0.845392 loss_iou: 0.117514 loss_dfl: 0.684147 loss_l1: 0.172684 eta: 0:00:28 batch_cost: 0.0691 data_cost: 0.0033 ips: 115.8244 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:48] ppdet.engine INFO: Epoch: [5] [10/87] learning_rate: 0.000100 loss: 1.365917 loss_cls: 0.726142 loss_iou: 0.121519 loss_dfl: 0.642211 loss_l1: 0.194982 eta: 0:00:28 batch_cost: 0.0770 data_cost: 0.0013 ips: 103.9058 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:49] ppdet.engine INFO: Epoch: [5] [20/87] learning_rate: 0.000100 loss: 1.302887 loss_cls: 0.703819 loss_iou: 0.114993 loss_dfl: 0.646046 loss_l1: 0.163100 eta: 0:00:27 batch_cost: 0.0665 data_cost: 0.0017 ips: 120.2192 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:50] ppdet.engine INFO: Epoch: [5] [30/87] learning_rate: 0.000100 loss: 1.343019 loss_cls: 0.709335 loss_iou: 0.105727 loss_dfl: 0.646562 loss_l1: 0.147193 eta: 0:00:26 batch_cost: 0.0607 data_cost: 0.0033 ips: 131.7994 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:51] ppdet.engine INFO: Epoch: [5] [40/87] learning_rate: 0.000100 loss: 1.169549 loss_cls: 0.614753 loss_iou: 0.106422 loss_dfl: 0.596619 loss_l1: 0.136496 eta: 0:00:26 batch_cost: 0.0649 data_cost: 0.0027 ips: 123.2171 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:52] ppdet.engine INFO: Epoch: [5] [50/87] learning_rate: 0.000100 loss: 1.166546 loss_cls: 0.622307 loss_iou: 0.104742 loss_dfl: 0.635888 loss_l1: 0.156443 eta: 0:00:25 batch_cost: 0.0761 data_cost: 0.0066 ips: 105.0886 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:53] ppdet.engine INFO: Epoch: [5] [60/87] learning_rate: 0.000100 loss: 1.336002 loss_cls: 0.694902 loss_iou: 0.127036 loss_dfl: 0.639862 loss_l1: 0.145244 eta: 0:00:24 batch_cost: 0.0548 data_cost: 0.0007 ips: 145.8906 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:53] ppdet.engine INFO: Epoch: [5] [70/87] learning_rate: 0.000100 loss: 1.252199 loss_cls: 0.622877 loss_iou: 0.112993 loss_dfl: 0.586119 loss_l1: 0.124820 eta: 0:00:23 batch_cost: 0.0527 data_cost: 0.0012 ips: 151.8976 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:54] ppdet.engine INFO: Epoch: [5] [80/87] learning_rate: 0.000100 loss: 1.342439 loss_cls: 0.730094 loss_iou: 0.101224 loss_dfl: 0.616764 loss_l1: 0.132082 eta: 0:00:23 batch_cost: 0.0587 data_cost: 0.0022 ips: 136.2950 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:10:55] ppdet.utils.checkpoint INFO: Save checkpoint: /root/PaddleX/output [07/12 09:10:55] ppdet.engine INFO: Eval iter: 0 [07/12 09:10:58] ppdet.metrics.metrics INFO: The bbox result is saved to bbox.json. loading annotations into memory... Done (t=0.00s) creating index... index created! [07/12 09:10:58] ppdet.metrics.coco_utils INFO: Start evaluate... Loading and preparing results... DONE (t=0.34s) creating index... index created! Running per image evaluation... Evaluate annotation type bbox DONE (t=0.66s). Accumulating evaluation results... DONE (t=0.19s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.729 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.915 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.853 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.588 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.776 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.896 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.680 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.813 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.823 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.709 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.855 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.909 [07/12 09:10:59] ppdet.engine INFO: Total sample number: 176, average FPS: 86.09518460143579 [07/12 09:10:59] ppdet.engine INFO: Best test bbox ap is 0.729. [07/12 09:10:59] ppdet.utils.checkpoint INFO: Save checkpoint: /root/PaddleX/output [07/12 09:10:59] ppdet.engine INFO: Epoch: [6] [ 0/87] learning_rate: 0.000100 loss: 1.384937 loss_cls: 0.789926 loss_iou: 0.118200 loss_dfl: 0.649648 loss_l1: 0.182329 eta: 0:00:22 batch_cost: 0.0655 data_cost: 0.0042 ips: 122.1339 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:00] ppdet.engine INFO: Epoch: [6] [10/87] learning_rate: 0.000100 loss: 1.315218 loss_cls: 0.699129 loss_iou: 0.111649 loss_dfl: 0.640923 loss_l1: 0.145202 eta: 0:00:22 batch_cost: 0.0750 data_cost: 0.0042 ips: 106.6987 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:01] ppdet.engine INFO: Epoch: [6] [20/87] learning_rate: 0.000100 loss: 1.513419 loss_cls: 0.823316 loss_iou: 0.110493 loss_dfl: 0.638827 loss_l1: 0.156521 eta: 0:00:21 batch_cost: 0.0656 data_cost: 0.0019 ips: 122.0280 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:02] ppdet.engine INFO: Epoch: [6] [30/87] learning_rate: 0.000100 loss: 1.249289 loss_cls: 0.665539 loss_iou: 0.094675 loss_dfl: 0.597553 loss_l1: 0.131725 eta: 0:00:20 batch_cost: 0.0551 data_cost: 0.0012 ips: 145.2544 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:03] ppdet.engine INFO: Epoch: [6] [40/87] learning_rate: 0.000100 loss: 1.293702 loss_cls: 0.743678 loss_iou: 0.109693 loss_dfl: 0.627322 loss_l1: 0.150387 eta: 0:00:20 batch_cost: 0.0570 data_cost: 0.0012 ips: 140.4211 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:04] ppdet.engine INFO: Epoch: [6] [50/87] learning_rate: 0.000100 loss: 1.432692 loss_cls: 0.898931 loss_iou: 0.101426 loss_dfl: 0.616519 loss_l1: 0.138447 eta: 0:00:19 batch_cost: 0.0670 data_cost: 0.0035 ips: 119.3785 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:05] ppdet.engine INFO: Epoch: [6] [60/87] learning_rate: 0.000100 loss: 1.350745 loss_cls: 0.705949 loss_iou: 0.102724 loss_dfl: 0.659727 loss_l1: 0.178991 eta: 0:00:18 batch_cost: 0.0645 data_cost: 0.0018 ips: 123.9867 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:05] ppdet.engine INFO: Epoch: [6] [70/87] learning_rate: 0.000100 loss: 1.387840 loss_cls: 0.769666 loss_iou: 0.115262 loss_dfl: 0.678071 loss_l1: 0.176297 eta: 0:00:18 batch_cost: 0.0629 data_cost: 0.0018 ips: 127.1059 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:06] ppdet.engine INFO: Epoch: [6] [80/87] learning_rate: 0.000100 loss: 1.262626 loss_cls: 0.702466 loss_iou: 0.093557 loss_dfl: 0.624663 loss_l1: 0.137510 eta: 0:00:17 batch_cost: 0.0563 data_cost: 0.0012 ips: 142.0422 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:07] ppdet.utils.checkpoint INFO: Save checkpoint: /root/PaddleX/output [07/12 09:11:07] ppdet.engine INFO: Eval iter: 0 [07/12 09:11:10] ppdet.metrics.metrics INFO: The bbox result is saved to bbox.json. loading annotations into memory... Done (t=0.00s) creating index... index created! [07/12 09:11:10] ppdet.metrics.coco_utils INFO: Start evaluate... Loading and preparing results... DONE (t=0.33s) creating index... index created! Running per image evaluation... Evaluate annotation type bbox DONE (t=0.63s). Accumulating evaluation results... DONE (t=0.18s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.737 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.913 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.859 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.666 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.765 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.918 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.694 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.825 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.833 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.729 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.854 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.926 [07/12 09:11:11] ppdet.engine INFO: Total sample number: 176, average FPS: 83.68314836521435 [07/12 09:11:11] ppdet.engine INFO: Best test bbox ap is 0.737. [07/12 09:11:11] ppdet.utils.checkpoint INFO: Save checkpoint: /root/PaddleX/output [07/12 09:11:11] ppdet.engine INFO: Epoch: [7] [ 0/87] learning_rate: 0.000100 loss: 1.422885 loss_cls: 0.757248 loss_iou: 0.123571 loss_dfl: 0.648132 loss_l1: 0.153349 eta: 0:00:17 batch_cost: 0.0675 data_cost: 0.0007 ips: 118.5714 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:12] ppdet.engine INFO: Epoch: [7] [10/87] learning_rate: 0.000100 loss: 1.196135 loss_cls: 0.643476 loss_iou: 0.100191 loss_dfl: 0.608694 loss_l1: 0.140919 eta: 0:00:16 batch_cost: 0.0688 data_cost: 0.0031 ips: 116.2557 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:13] ppdet.engine INFO: Epoch: [7] [20/87] learning_rate: 0.000100 loss: 1.226071 loss_cls: 0.688826 loss_iou: 0.098229 loss_dfl: 0.616484 loss_l1: 0.139337 eta: 0:00:15 batch_cost: 0.0597 data_cost: 0.0006 ips: 133.9187 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:14] ppdet.engine INFO: Epoch: [7] [30/87] learning_rate: 0.000100 loss: 1.322234 loss_cls: 0.721738 loss_iou: 0.095474 loss_dfl: 0.618615 loss_l1: 0.122551 eta: 0:00:15 batch_cost: 0.0560 data_cost: 0.0012 ips: 142.7300 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:15] ppdet.engine INFO: Epoch: [7] [40/87] learning_rate: 0.000100 loss: 1.132649 loss_cls: 0.603390 loss_iou: 0.095182 loss_dfl: 0.612046 loss_l1: 0.127093 eta: 0:00:14 batch_cost: 0.0611 data_cost: 0.0017 ips: 130.9579 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:15] ppdet.engine INFO: Epoch: [7] [50/87] learning_rate: 0.000100 loss: 1.318787 loss_cls: 0.722417 loss_iou: 0.116941 loss_dfl: 0.631221 loss_l1: 0.158205 eta: 0:00:13 batch_cost: 0.0526 data_cost: 0.0007 ips: 152.2213 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:16] ppdet.engine INFO: Epoch: [7] [60/87] learning_rate: 0.000100 loss: 1.230540 loss_cls: 0.694264 loss_iou: 0.101939 loss_dfl: 0.613630 loss_l1: 0.140158 eta: 0:00:13 batch_cost: 0.0620 data_cost: 0.0020 ips: 129.0189 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:17] ppdet.engine INFO: Epoch: [7] [70/87] learning_rate: 0.000100 loss: 1.230380 loss_cls: 0.655850 loss_iou: 0.107221 loss_dfl: 0.626712 loss_l1: 0.147802 eta: 0:00:12 batch_cost: 0.0611 data_cost: 0.0006 ips: 130.9815 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:18] ppdet.engine INFO: Epoch: [7] [80/87] learning_rate: 0.000100 loss: 1.231788 loss_cls: 0.622659 loss_iou: 0.110632 loss_dfl: 0.640497 loss_l1: 0.135875 eta: 0:00:11 batch_cost: 0.0572 data_cost: 0.0012 ips: 139.7457 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:19] ppdet.utils.checkpoint INFO: Save checkpoint: /root/PaddleX/output [07/12 09:11:19] ppdet.engine INFO: Eval iter: 0 [07/12 09:11:21] ppdet.metrics.metrics INFO: The bbox result is saved to bbox.json. loading annotations into memory... Done (t=0.00s) creating index... index created! [07/12 09:11:21] ppdet.metrics.coco_utils INFO: Start evaluate... Loading and preparing results... DONE (t=0.32s) creating index... index created! Running per image evaluation... Evaluate annotation type bbox DONE (t=0.64s). Accumulating evaluation results... DONE (t=0.18s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.746 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.915 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.859 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.654 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.783 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.876 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.681 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.827 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.840 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.738 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.874 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.883 [07/12 09:11:22] ppdet.engine INFO: Total sample number: 176, average FPS: 88.77598215526974 [07/12 09:11:22] ppdet.engine INFO: Best test bbox ap is 0.746. [07/12 09:11:23] ppdet.utils.checkpoint INFO: Save checkpoint: /root/PaddleX/output [07/12 09:11:23] ppdet.engine INFO: Epoch: [8] [ 0/87] learning_rate: 0.000100 loss: 1.223972 loss_cls: 0.647248 loss_iou: 0.115358 loss_dfl: 0.654169 loss_l1: 0.158221 eta: 0:00:11 batch_cost: 0.0608 data_cost: 0.0007 ips: 131.5395 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:24] ppdet.engine INFO: Epoch: [8] [10/87] learning_rate: 0.000100 loss: 1.253777 loss_cls: 0.664291 loss_iou: 0.108572 loss_dfl: 0.600805 loss_l1: 0.130051 eta: 0:00:10 batch_cost: 0.0618 data_cost: 0.0007 ips: 129.4013 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:25] ppdet.engine INFO: Epoch: [8] [20/87] learning_rate: 0.000100 loss: 1.215144 loss_cls: 0.649968 loss_iou: 0.098036 loss_dfl: 0.628021 loss_l1: 0.150629 eta: 0:00:09 batch_cost: 0.0659 data_cost: 0.0039 ips: 121.4861 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:25] ppdet.engine INFO: Epoch: [8] [30/87] learning_rate: 0.000100 loss: 1.125804 loss_cls: 0.577423 loss_iou: 0.102836 loss_dfl: 0.607989 loss_l1: 0.133713 eta: 0:00:09 batch_cost: 0.0581 data_cost: 0.0023 ips: 137.7836 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:26] ppdet.engine INFO: Epoch: [8] [40/87] learning_rate: 0.000100 loss: 1.216511 loss_cls: 0.657649 loss_iou: 0.102880 loss_dfl: 0.602189 loss_l1: 0.135921 eta: 0:00:08 batch_cost: 0.0584 data_cost: 0.0011 ips: 136.9558 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:27] ppdet.engine INFO: Epoch: [8] [50/87] learning_rate: 0.000100 loss: 1.291992 loss_cls: 0.633064 loss_iou: 0.122280 loss_dfl: 0.645464 loss_l1: 0.168339 eta: 0:00:07 batch_cost: 0.0613 data_cost: 0.0007 ips: 130.4970 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:28] ppdet.engine INFO: Epoch: [8] [60/87] learning_rate: 0.000100 loss: 1.244390 loss_cls: 0.707732 loss_iou: 0.116866 loss_dfl: 0.660145 loss_l1: 0.149036 eta: 0:00:07 batch_cost: 0.0686 data_cost: 0.0025 ips: 116.6567 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:29] ppdet.engine INFO: Epoch: [8] [70/87] learning_rate: 0.000100 loss: 1.182690 loss_cls: 0.600389 loss_iou: 0.109270 loss_dfl: 0.628452 loss_l1: 0.140246 eta: 0:00:06 batch_cost: 0.0568 data_cost: 0.0006 ips: 140.8548 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:30] ppdet.engine INFO: Epoch: [8] [80/87] learning_rate: 0.000100 loss: 1.228379 loss_cls: 0.654501 loss_iou: 0.103048 loss_dfl: 0.635927 loss_l1: 0.154724 eta: 0:00:06 batch_cost: 0.0664 data_cost: 0.0028 ips: 120.5488 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:31] ppdet.utils.checkpoint INFO: Save checkpoint: /root/PaddleX/output [07/12 09:11:31] ppdet.engine INFO: Eval iter: 0 [07/12 09:11:33] ppdet.metrics.metrics INFO: The bbox result is saved to bbox.json. loading annotations into memory... Done (t=0.00s) creating index... index created! [07/12 09:11:33] ppdet.metrics.coco_utils INFO: Start evaluate... Loading and preparing results... DONE (t=0.33s) creating index... index created! Running per image evaluation... Evaluate annotation type bbox DONE (t=0.63s). Accumulating evaluation results... DONE (t=0.19s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.746 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.923 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.861 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.650 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.791 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.853 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.691 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.821 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.839 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.744 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.869 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.901 [07/12 09:11:34] ppdet.engine INFO: Total sample number: 176, average FPS: 84.13426652553824 [07/12 09:11:34] ppdet.engine INFO: Best test bbox ap is 0.746. [07/12 09:11:34] ppdet.engine INFO: Epoch: [9] [ 0/87] learning_rate: 0.000100 loss: 1.200734 loss_cls: 0.654749 loss_iou: 0.106560 loss_dfl: 0.634837 loss_l1: 0.154424 eta: 0:00:05 batch_cost: 0.0689 data_cost: 0.0022 ips: 116.1811 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:35] ppdet.engine INFO: Epoch: [9] [10/87] learning_rate: 0.000100 loss: 1.241560 loss_cls: 0.659439 loss_iou: 0.111410 loss_dfl: 0.623893 loss_l1: 0.157532 eta: 0:00:04 batch_cost: 0.0663 data_cost: 0.0007 ips: 120.7332 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:36] ppdet.engine INFO: Epoch: [9] [20/87] learning_rate: 0.000100 loss: 1.241023 loss_cls: 0.634497 loss_iou: 0.114327 loss_dfl: 0.592484 loss_l1: 0.148404 eta: 0:00:04 batch_cost: 0.0500 data_cost: 0.0006 ips: 159.9491 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:37] ppdet.engine INFO: Epoch: [9] [30/87] learning_rate: 0.000100 loss: 1.305959 loss_cls: 0.710079 loss_iou: 0.108491 loss_dfl: 0.627996 loss_l1: 0.159664 eta: 0:00:03 batch_cost: 0.0591 data_cost: 0.0023 ips: 135.2922 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:38] ppdet.engine INFO: Epoch: [9] [40/87] learning_rate: 0.000100 loss: 1.263560 loss_cls: 0.669567 loss_iou: 0.117143 loss_dfl: 0.625192 loss_l1: 0.148830 eta: 0:00:03 batch_cost: 0.0645 data_cost: 0.0024 ips: 123.9762 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:38] ppdet.engine INFO: Epoch: [9] [50/87] learning_rate: 0.000100 loss: 1.261001 loss_cls: 0.656496 loss_iou: 0.100584 loss_dfl: 0.608320 loss_l1: 0.113287 eta: 0:00:02 batch_cost: 0.0592 data_cost: 0.0012 ips: 135.0353 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:39] ppdet.engine INFO: Epoch: [9] [60/87] learning_rate: 0.000100 loss: 1.262122 loss_cls: 0.637243 loss_iou: 0.112913 loss_dfl: 0.613015 loss_l1: 0.142008 eta: 0:00:01 batch_cost: 0.0637 data_cost: 0.0030 ips: 125.5927 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:40] ppdet.engine INFO: Epoch: [9] [70/87] learning_rate: 0.000100 loss: 1.237639 loss_cls: 0.637313 loss_iou: 0.110222 loss_dfl: 0.640235 loss_l1: 0.131281 eta: 0:00:01 batch_cost: 0.0609 data_cost: 0.0006 ips: 131.3569 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:41] ppdet.engine INFO: Epoch: [9] [80/87] learning_rate: 0.000100 loss: 1.311582 loss_cls: 0.706572 loss_iou: 0.108260 loss_dfl: 0.633108 loss_l1: 0.152676 eta: 0:00:00 batch_cost: 0.0675 data_cost: 0.0039 ips: 118.5431 images/s max_mem_reserved: 5635 MB max_mem_allocated: 4014 MB [07/12 09:11:42] ppdet.utils.checkpoint INFO: Save checkpoint: /root/PaddleX/output [07/12 09:11:42] ppdet.engine INFO: Eval iter: 0 [07/12 09:11:45] ppdet.metrics.metrics INFO: The bbox result is saved to bbox.json. loading annotations into memory... Done (t=0.00s) creating index... index created! [07/12 09:11:45] ppdet.metrics.coco_utils INFO: Start evaluate... Loading and preparing results... DONE (t=0.33s) creating index... index created! Running per image evaluation... Evaluate annotation type bbox DONE (t=0.65s). Accumulating evaluation results... DONE (t=0.19s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.750 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.928 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.883 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.664 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.789 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.857 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.697 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.830 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.840 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.746 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.869 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.913 [07/12 09:11:46] ppdet.engine INFO: Total sample number: 176, average FPS: 83.41924504988955 [07/12 09:11:46] ppdet.engine INFO: Best test bbox ap is 0.750. [07/12 09:11:46] ppdet.utils.checkpoint INFO: Save checkpoint: /root/PaddleX/output Traceback (most recent call last): File "/root/PaddleX/paddlex/modules/base/trainer/train_deamon.py", line 34, in wrap func(self, *args, kwargs) File "/root/PaddleX/paddlex/modules/base/trainer/train_deamon.py", line 196, in update self.results[i] = self.update_result(self.results[i], File "/root/PaddleX/paddlex/modules/base/trainer/train_deamon.py", line 249, in update_result model = self.get_model(result["model_name"], config_path) File "/root/PaddleX/paddlex/modules/base/trainer/train_deamon.py", line 204, in get_model config, model = build_model( File "/root/PaddleX/paddlex/modules/base/build_model.py", line 37, in build_model config.update_device(get_device(device)) File "/root/PaddleX/paddlex/repo_apis/PaddleDetection_api/object_det/config.py", line 258, in update_device assert device_type.lower() == "cpu" AssertionError λ DESKTOP-2583UDJ ~/PaddleX λ DESKTOP-2583UDJ ~/PaddleX python main.py -c paddlex/configs/object_detection/PP-YOLOE_plus-S.yaml -o Global.mode=predict -o Predict.model_dir="/output/best_model" -o Predict.input_path="https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg" grep: warning: GREP_OPTIONS is deprecated; please use an alias or script The device id has been set to 0. Traceback (most recent call last): File "/root/PaddleX/paddlex/utils/result_saver.py", line 30, in wrap result = func(self, *args, *kwargs) File "/root/PaddleX/paddlex/engine.py", line 49, in run predictor = build_predictor(self.config) File "/root/PaddleX/paddlex/modules/base/predictor/predictor.py", line 189, in build_predictor return PredictorBuilderByConfig(args, kwargs) File "/root/PaddleX/paddlex/modules/base/predictor/predictor.py", line 173, in init self.predictor = BasePredictor.get(model_name)( File "/root/PaddleX/paddlex/modules/base/predictor/utils/node.py", line 43, in _wrapper ret = init_func(self, *args, **kwargs) File "/root/PaddleX/paddlex/modules/base/predictor/predictor.py", line 50, in init self.other_src = self.load_other_src() File "/root/PaddleX/paddlex/modules/object_detection/predictor/predictor.py", line 38, in load_other_src raise FileNotFoundError( FileNotFoundError: Cannot find config file: /output/best_model/inference.yml

在生成的output路径下只有一个文件生成: model.pdparams

cuicheng01 commented 1 month ago

您好，第一步的文档在上周已经更新为了最新的sudo nvidia-docker run --name paddlex -v $PWD:/paddle --shm-size=8G --network=host -it registry.baidubce.com/paddlex/paddlex:3.0.0b1-gpu-cuda11.8-cudnn8.9-trt8.5 /bin/bash，麻烦查看最新的文档重新试一下~

Franklinyung commented 3 weeks ago

版面分析也遇到了这个问题，也是docker 单机双卡

λ kings-System-Product-Name /paddle/PaddleDetection python
Python 3.10.14 (main, Apr  6 2024, 18:45:05) [GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> CUDA_VISIBLE_DEVICES=0
>>> paddle.utils.run_check()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'paddle' is not defined
>>> import paddle
grep: warning: GREP_OPTIONS is deprecated; please use an alias or script
>>> paddle.utils.run_check()
Running verify PaddlePaddle program ... 
I0813 05:56:29.452620  4991 program_interpreter.cc:243] New Executor is Running.
W0813 05:56:29.454569  4991 gpu_resources.cc:119] Please NOTE: device: 0, GPU Compute Capability: 8.9, Driver API Version: 12.5, Runtime API Version: 11.8
W0813 05:56:29.454983  4991 gpu_resources.cc:164] device: 0, cuDNN Version: 8.9.
I0813 05:56:29.605643  4991 interpreter_util.cc:648] Standalone Executor is Used.
PaddlePaddle works well on 1 GPU.
grep: warning: GREP_OPTIONS is deprecated; please use an alias or script
grep: warning: GREP_OPTIONS is deprecated; please use an alias or script
======================= Modified FLAGS detected =======================
======================= Modified FLAGS detected =======================
FLAGS(name='FLAGS_curand_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia/curand/lib', default_value='')
FLAGS(name='FLAGS_cusolver_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia/cusolver/lib', default_value='')
FLAGS(name='FLAGS_nccl_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia/nccl/lib', default_value='')
FLAGS(name='FLAGS_cupti_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia/cuda_cupti/lib', default_value='')
FLAGS(name='FLAGS_nvidia_package_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia', default_value='')
FLAGS(name='FLAGS_cusparse_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia/cusparse/lib', default_value='')
FLAGS(name='FLAGS_cupti_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia/cuda_cupti/lib', default_value='')
FLAGS(name='FLAGS_nvidia_package_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia', default_value='')
FLAGS(name='FLAGS_cublas_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia/cublas/lib', default_value='')
FLAGS(name='FLAGS_cublas_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia/cublas/lib', default_value='')
FLAGS(name='FLAGS_cudnn_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia/cudnn/lib', default_value='')
FLAGS(name='FLAGS_nccl_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia/nccl/lib', default_value='')
FLAGS(name='FLAGS_cusolver_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia/cusolver/lib', default_value='')
FLAGS(name='FLAGS_cudnn_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia/cudnn/lib', default_value='')
FLAGS(name='FLAGS_curand_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia/curand/lib', default_value='')
FLAGS(name='FLAGS_selected_gpus', current_value='0', default_value='')
FLAGS(name='FLAGS_cusparse_dir', current_value='/usr/local/lib/python3.10/dist-packages/paddle/../nvidia/cusparse/lib', default_value='')
=======================================================================
FLAGS(name='FLAGS_selected_gpus', current_value='1', default_value='')
=======================================================================
I0813 05:56:30.221688  5035 tcp_utils.cc:181] The server starts to listen on IP_ANY:49395
I0813 05:56:30.221735  5036 tcp_utils.cc:130] Successfully connected to 127.0.0.1:49395
I0813 05:56:30.221796  5035 tcp_utils.cc:130] Successfully connected to 127.0.0.1:49395
I0813 05:56:30.221881  5036 process_group_nccl.cc:138] ProcessGroupNCCL pg_timeout_ 1800000
I0813 05:56:30.221884  5036 process_group_nccl.cc:139] ProcessGroupNCCL nccl_comm_init_option_ 0
W0813 05:56:30.225226  5036 gpu_resources.cc:119] Please NOTE: device: 1, GPU Compute Capability: 8.9, Driver API Version: 12.5, Runtime API Version: 11.8
W0813 05:56:30.225577  5036 gpu_resources.cc:164] device: 1, cuDNN Version: 8.9.
I0813 05:56:30.309875  5035 process_group_nccl.cc:138] ProcessGroupNCCL pg_timeout_ 1800000
I0813 05:56:30.309892  5035 process_group_nccl.cc:139] ProcessGroupNCCL nccl_comm_init_option_ 0
W0813 05:56:30.313675  5035 gpu_resources.cc:119] Please NOTE: device: 0, GPU Compute Capability: 8.9, Driver API Version: 12.5, Runtime API Version: 11.8
W0813 05:56:30.314036  5035 gpu_resources.cc:164] device: 0, cuDNN Version: 8.9.
I0813 05:56:30.816231  5035 process_group_nccl.cc:143] ProcessGroupNCCL destruct 
I0813 05:56:30.816318  5036 process_group_nccl.cc:143] ProcessGroupNCCL destruct 
I0813 05:56:30.839124  5055 tcp_store.cc:293] receive shutdown event and so quit from MasterDaemon run loop
PaddlePaddle works well on 2 GPUs.
PaddlePaddle is installed successfully! Let's start deep learning with PaddlePaddle now.

训练时无法运行

PaddlePaddle / PaddleX

docker_paddlex3.0beta 目标识别报错！！在微信和直播都提问了！ #1820