Yolov7_OBB_KFIOUrun-time error #6
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featuring KFIOU
featuring KFIOU
Added KFIOU
Disabled mmdetection calls
KFIOU Enabled
Removed the weight loss module
KFIOU Implemented in the OTA Loss
KFIOU can now return 2 variables
xyxy2xywh redefined inside to avoid circular import
revised data source
class index pre-defined before indexing the p_theta
Run on CPU only to avoid device transfer error
device transfer experiment on line 250
Attempted to move newbox1 & 2 to GPU (Line 247)
Mean angular method + KFIOU prep revised
Float32 support for SIGMA
Sigma_p&t converted to float32 before processing & finalised with float16
mmrotate's funtions removed, leaving only LX-CLY's
The uselessness of the internal variable "p_theta" has been identified
You must manually define the angular mode (CSL for LE-def [-90,+90) & OOCV [-90, 0) for KLD & KFIOU). Otherwise, the rboxs_utils.py file will not run.
You must manually define the angular mode (CSL for LE-def [-90,+90) & OOCV [-90, 0) for KLD & KFIOU). Otherwise, the rboxs_utils.py file will not run.
To compare and contrast the metrics in each loss mode & different angulation, you must manually define the angular mode (CSL for LE-def [-90,+90) & OOCV [-90, 0) for KLD & KFIOU). Otherwise, the rboxs_utils.py file will not run.
added samename functionality
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Traceback (most recent call last):
File "train.py", line 656, in
main(opt)
File "train.py", line 553, in main
train(opt.hyp, opt, device, callbacks)
File "train.py", line 350, in train
scaler.scale(loss).backward()
File "/usr/local/lib/python3.8/dist-packages/torch/tensor.py", line 307, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
File "/usr/local/lib/python3.8/dist-packages/torch/autograd/init.py", line 150, in backward
grad_tensors = make_grads(tensors, grad_tensors)
File "/usr/local/lib/python3.8/dist-packages/torch/autograd/init.py", line 51, in _make_grads
raise RuntimeError("grad can be implicitly created only for scalar outputs")
RuntimeError: grad can be implicitly created only for scalar outputs
Hello author, I run according to your instructions and there is an error