带--eval 运行后报错

D:\PaddleDetection-release-2.2>python tools/train.py -c configs/dota/s2anet_1x_spine.yml --eval -o use_gpu=true --use_vdl=True --vdl_log_dir=Vdl_dir/log
Traceback (most recent call last):
File "tools/train.py", line 32, in
from ppdet.core.workspace import load_config, merge_config
File "D:\PaddleDetection-release-2.2\ppdet\__init__.py", line 15, in
from . import (core, data, engine, modeling, model_zoo, optimizer, metrics,
File "D:\PaddleDetection-release-2.2\ppdet\data\__init__.py", line 15, in
from . import source
File "D:\PaddleDetection-release-2.2\ppdet\data\source\__init__.py", line 15, in
from . import coco
File "D:\PaddleDetection-release-2.2\ppdet\data\source\coco.py", line 17, in
from ppdet.core.workspace import register, serializable
File "D:\PaddleDetection-release-2.2\ppdet\core\workspace.py", line 23, in
import yaml
ModuleNotFoundError: No module named 'yaml'

D:\PaddleDetection-release-2.2>
D:\PaddleDetection-release-2.2>python tools/train.py -c configs/dota/s2anet_1x_spine.yml --eval -o use_gpu=true --use_vdl=True --vdl_log_dir=Vdl_dir/log
Traceback (most recent call last):
File "tools/train.py", line 32, in
from ppdet.core.workspace import load_config, merge_config
File "D:\PaddleDetection-release-2.2\ppdet\__init__.py", line 15, in
from . import (core, data, engine, modeling, model_zoo, optimizer, metrics,
File "D:\PaddleDetection-release-2.2\ppdet\data\__init__.py", line 15, in
from . import source
File "D:\PaddleDetection-release-2.2\ppdet\data\source\__init__.py", line 15, in
from . import coco
File "D:\PaddleDetection-release-2.2\ppdet\data\source\coco.py", line 18, in
from .dataset import DetDataset
File "D:\PaddleDetection-release-2.2\ppdet\data\source\dataset.py", line 24, in
from ppdet.utils.download import get_dataset_path
File "D:\PaddleDetection-release-2.2\ppdet\utils\download.py", line 26, in
import tqdm
ModuleNotFoundError: No module named 'tqdm'

D:\PaddleDetection-release-2.2>pip install -r requirements.txt -i https://pypi.douban.com/simple
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Installing collected packages: opencv-python, shapely, scipy, terminaltables, cycler, pyparsing, python-dateutil, kiwisolver, matplotlib, pycocotools, MarkupSafe, Ji
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cv-python-4.5.3.56 packaging-21.0 pandas-1.3.3 platformdirs-2.4.0 pluggy-1.0.0 pre-commit-2.15.0 py-1.10.0 py-cpuinfo-8.0.0 pycocotools-2.0.2 pycodestyle-2.7.0 pycry
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WARNING: You are using pip version 20.1.1; however, version 21.2.4 is available.
You should consider upgrading via the 'c:\users\administrator\appdata\local\programs\python\python37\python.exe -m pip install --upgrade pip' command.

D:\PaddleDetection-release-2.2>python tools/train.py -c configs/dota/s2anet_1x_spine.yml --eval -o use_gpu=true --use_vdl=True --vdl_log_dir=Vdl_dir/log
D:\PaddleDetection-release-2.2\ppdet\modeling\ops.py:542: DeprecationWarning: invalid escape sequence \_
"""
D:\PaddleDetection-release-2.2\ppdet\modeling\ops.py:1375: DeprecationWarning: invalid escape sequence \l
"""
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
W0928 15:49:11.203294 3316 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 6.1, Driver API Version: 11.4, Runtime API Version: 10.2
W0928 15:49:11.222311 3316 device_context.cc:422] device: 0, cuDNN Version: 7.6.
[09/28 15:49:15] ppdet.utils.checkpoint INFO: Finish loading model weights: C:\Users\Administrator/.cache/paddle/weights\ResNet50_vd_ssld_v2_pretrained.pdparams
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
import custom_ops error, try install rbox_iou_ops following ppdet/ext_op/README.md No module named 'rbox_iou_ops'

D:\PaddleDetection-release-2.2>python tools/train.py -c configs/dota/s2anet_1x_spine.yml --eval -o use_gpu=true --use_vdl=True --vdl_log_dir=Vdl_dir/log
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
W0928 15:50:34.392117 8004 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 6.1, Driver API Version: 11.4, Runtime API Version: 10.2
W0928 15:50:34.412135 8004 device_context.cc:422] device: 0, cuDNN Version: 7.6.
[09/28 15:50:38] ppdet.utils.checkpoint INFO: Finish loading model weights: C:\Users\Administrator/.cache/paddle/weights\ResNet50_vd_ssld_v2_pretrained.pdparams
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
[09/28 15:50:41] ppdet.engine INFO: Epoch: [0] [ 0/32] learning_rate: 0.003333 fam_cls_loss: 1.237142 fam_reg_loss: 1.374504 odm_cls_loss: 1.178282 odm_reg_loss: 1.3
68945 loss: 5.158874 eta: 13:38:03 batch_cost: 2.5565 data_cost: 0.0010 ips: 0.3912 images/s
[09/28 15:50:55] ppdet.engine INFO: Epoch: [0] [20/32] learning_rate: 0.003600 fam_cls_loss: 1.214454 fam_reg_loss: 1.192971 odm_cls_loss: 1.165017 odm_reg_loss: 1.2
23456 loss: 4.698532 eta: 4:21:43 batch_cost: 0.7319 data_cost: 0.0002 ips: 1.3664 images/s
[09/28 15:51:04] ppdet.utils.checkpoint INFO: Save checkpoint: output\s2anet_1x_spine
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
[09/28 15:51:04] ppdet.engine INFO: Eval iter: 0
[09/28 15:51:05] ppdet.engine INFO: Total sample number: 4, averge FPS: 3.041216775583134
[09/28 15:51:05] ppdet.engine WARNING: Evaluation results empty, this may be due to training iterations being too few or not loading the correct weights.
[09/28 15:51:06] ppdet.engine INFO: Epoch: [1] [ 0/32] learning_rate: 0.003760 fam_cls_loss: 1.115531 fam_reg_loss: 0.801266 odm_cls_loss: 1.140743 odm_reg_loss: 1.1
75973 loss: 4.255508 eta: 4:10:20 batch_cost: 0.7224 data_cost: 0.0002 ips: 1.3842 images/s
[09/28 15:51:20] ppdet.engine INFO: Epoch: [1] [20/32] learning_rate: 0.004027 fam_cls_loss: 1.057243 fam_reg_loss: 0.618059 odm_cls_loss: 1.010921 odm_reg_loss: 0.7
39421 loss: 3.700317 eta: 3:59:49 batch_cost: 0.6984 data_cost: 0.0002 ips: 1.4318 images/s
[09/28 15:51:28] ppdet.utils.checkpoint INFO: Save checkpoint: output\s2anet_1x_spine
[09/28 15:51:29] ppdet.engine INFO: Eval iter: 0
[09/28 15:51:29] ppdet.engine INFO: Total sample number: 4, averge FPS: 3.7842502151831807
[09/28 15:51:29] ppdet.engine WARNING: Evaluation results empty, this may be due to training iterations being too few or not loading the correct weights.
[09/28 15:51:30] ppdet.engine INFO: Epoch: [2] [ 0/32] learning_rate: 0.004187 fam_cls_loss: 1.091923 fam_reg_loss: 0.747975 odm_cls_loss: 0.965288 odm_reg_loss: 0.9
03533 loss: 3.603977 eta: 3:57:03 batch_cost: 0.7036 data_cost: 0.0002 ips: 1.4213 images/s
[09/28 15:51:44] ppdet.engine INFO: Epoch: [2] [20/32] learning_rate: 0.004453 fam_cls_loss: 1.073099 fam_reg_loss: 0.659383 odm_cls_loss: 1.037109 odm_reg_loss: 1.1
64573 loss: 3.917665 eta: 3:53:35 batch_cost: 0.7004 data_cost: 0.0001 ips: 1.4278 images/s
[09/28 15:51:53] ppdet.utils.checkpoint INFO: Save checkpoint: output\s2anet_1x_spine
[09/28 15:51:53] ppdet.engine INFO: Eval iter: 0
[09/28 15:51:54] ppdet.engine INFO: Total sample number: 4, averge FPS: 3.853582738413388
[09/28 15:51:54] ppdet.engine WARNING: Evaluation results empty, this may be due to training iterations being too few or not loading the correct weights.
[09/28 15:51:54] ppdet.engine INFO: Epoch: [3] [ 0/32] learning_rate: 0.004613 fam_cls_loss: 1.037857 fam_reg_loss: 0.682860 odm_cls_loss: 1.024731 odm_reg_loss: 0.9
74949 loss: 3.724231 eta: 3:52:19 batch_cost: 0.7027 data_cost: 0.0001 ips: 1.4230 images/s
[09/28 15:52:08] ppdet.engine INFO: Epoch: [3] [20/32] learning_rate: 0.004880 fam_cls_loss: 0.996933 fam_reg_loss: 0.498990 odm_cls_loss: 0.986139 odm_reg_loss: 0.7
07003 loss: 3.270152 eta: 3:50:27 batch_cost: 0.6999 data_cost: 0.0002 ips: 1.4287 images/s
[09/28 15:52:17] ppdet.utils.checkpoint INFO: Save checkpoint: output\s2anet_1x_spine
[09/28 15:52:17] ppdet.engine INFO: Eval iter: 0
[09/28 15:52:18] ppdet.engine INFO: Total sample number: 4, averge FPS: 3.7842493616120962
[09/28 15:52:18] ppdet.engine WARNING: Evaluation results empty, this may be due to training iterations being too few or not loading the correct weights.
[09/28 15:52:19] ppdet.engine INFO: Epoch: [4] [ 0/32] learning_rate: 0.005040 fam_cls_loss: 1.105944 fam_reg_loss: 0.649619 odm_cls_loss: 1.008118 odm_reg_loss: 0.7
88578 loss: 3.809409 eta: 3:49:46 batch_cost: 0.7036 data_cost: 0.0003 ips: 1.4213 images/s
[09/28 15:52:33] ppdet.engine INFO: Epoch: [4] [20/32] learning_rate: 0.005307 fam_cls_loss: 1.118393 fam_reg_loss: 0.620102 odm_cls_loss: 1.029244 odm_reg_loss: 0.8
43047 loss: 3.669367 eta: 3:48:38 batch_cost: 0.7023 data_cost: 0.0003 ips: 1.4239 images/s
[09/28 15:52:41] ppdet.utils.checkpoint INFO: Save checkpoint: output\s2anet_1x_spine
[09/28 15:52:42] ppdet.engine INFO: Eval iter: 0
[09/28 15:52:42] ppdet.engine INFO: Total sample number: 4, averge FPS: 3.8910997775814122
[09/28 15:52:42] ppdet.engine WARNING: Evaluation results empty, this may be due to training iterations being too few or not loading the correct weights.
[09/28 15:52:43] ppdet.engine INFO: Epoch: [5] [ 0/32] learning_rate: 0.005467 fam_cls_loss: 1.097872 fam_reg_loss: 0.636788 odm_cls_loss: 1.050236 odm_reg_loss: 0.9
15670 loss: 3.765637 eta: 3:48:16 batch_cost: 0.7068 data_cost: 0.0003 ips: 1.4148 images/s
[09/28 15:52:57] ppdet.engine INFO: Epoch: [5] [20/32] learning_rate: 0.005733 fam_cls_loss: 1.056859 fam_reg_loss: 0.573235 odm_cls_loss: 0.952704 odm_reg_loss: 0.7
85765 loss: 3.357337 eta: 3:47:32 batch_cost: 0.7055 data_cost: 0.0001 ips: 1.4175 images/s
[09/28 15:53:06] ppdet.utils.checkpoint INFO: Save checkpoint: output\s2anet_1x_spine
[09/28 15:53:06] ppdet.engine INFO: Eval iter: 0
[09/28 15:53:07] ppdet.metrics.metrics INFO: The bbox result is saved to bbox.json.
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
[09/28 15:53:07] ppdet.metrics.coco_utils INFO: Start evaluate...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
Traceback (most recent call last):
File "tools/train.py", line 138, in
main()
File "tools/train.py", line 134, in main
run(FLAGS, cfg)
File "tools/train.py", line 109, in run
trainer.train(FLAGS.eval)
File "D:\PaddleDetection-release-2.2\ppdet\engine\trainer.py", line 407, in train
self._eval_with_loader(self._eval_loader)
File "D:\PaddleDetection-release-2.2\ppdet\engine\trainer.py", line 439, in _eval_with_loader
metric.accumulate()
File "D:\PaddleDetection-release-2.2\ppdet\metrics\metrics.py", line 132, in accumulate
classwise=self.classwise)
File "D:\PaddleDetection-release-2.2\ppdet\metrics\coco_utils.py", line 112, in cocoapi_eval
coco_eval.evaluate()
File "C:\Users\Administrator\AppData\Local\Programs\Python\Python37\lib\site-packages\pycocotools\cocoeval.py", line 149, in evaluate
for imgId in p.imgIds
File "C:\Users\Administrator\AppData\Local\Programs\Python\Python37\lib\site-packages\pycocotools\cocoeval.py", line 150, in
for catId in catIds}
File "C:\Users\Administrator\AppData\Local\Programs\Python\Python37\lib\site-packages\pycocotools\cocoeval.py", line 189, in computeIoU
ious = maskUtils.iou(d,g,iscrowd)
File "pycocotools\_mask.pyx", line 214, in pycocotools._mask.iou
File "pycocotools\_mask.pyx", line 193, in pycocotools._mask.iou._preproc
Exception: list input can be bounding box (Nx4) or RLEs ([RLE])

救命