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WongKinYiu / yolov7

Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
GNU General Public License v3.0
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I can't testing! #1464

Open abdalluhahmed opened 1 year ago

abdalluhahmed commented 1 year ago

/content/yolov7 Namespace(augment=False, batch_size=16, conf_thres=0.001, data='/content/yolov7/yolov7/PV-EL-images-1/data.yaml', device='0', exist_ok=False, img_size=640, iou_thres=0.65, name='yolov7_640_val', project='runs/test', save_conf=False, save_hybrid=False, save_json=False, save_txt=False, single_cls=False, task='val', trace=False, verbose=False, weights=['yolov7.pt']) YOLOR 🚀 v0.1-121-g2fdc7f1 torch 1.13.1+cu116 CUDA:0 (Tesla T4, 15109.875MB)

Fusing layers... RepConv.fuse_repvgg_block RepConv.fuse_repvgg_block RepConv.fuse_repvgg_block Model Summary: 306 layers, 36905341 parameters, 36905341 gradients /usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py:675: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at aten/src/ATen/core/TensorBody.h:480.) if param.grad is not None: /usr/local/lib/python3.8/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] val: Scanning 'PV-EL-images-1/valid/labels.cache' images and labels... 443 found, 0 missing, 0 empty, 0 corrupted: 100% 443/443 [00:00<?, ?it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 25% 7/28 [00:02<00:08, 2.47it/s] Traceback (most recent call last): File "test.py", line 314, in test(opt.data, File "test.py", line 184, in test confusion_matrix.process_batch(predn, torch.cat((labels[:, 0:1], tbox), 1)) File "/content/yolov7/utils/metrics.py", line 152, in process_batch self.matrix[gc, detection_classes[m1[j]]] += 1 # correct IndexError: index 67 is out of bounds for axis 1 with size 9

yulin010101 commented 1 year ago

Does your training model data have the same label category as the test data?