问如果您使用自定义锚进行培训，如何将YOLOv4暗网权重转换为Tensorflow格式？
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Stack Overflow用户

提问于 2021-03-18 23:01:30

回答 1查看 6.6K关注 0票数 1

主要问题：

我应该对回购的源代码做什么改变，以成功地将我的YOLOv4暗网重量(带有自定义锚)转换成Tensorflow格式？

背景：

我使用这个回购将我的YOLOv4暗网权重转换为Tensorflow格式。

我使用YOLOv4自定义锚(9个锚)对自定义数据集进行了培训，但每个yolo层使用的锚数分别为4、3、2。默认情况下，YOLOv4使用每个yolo层3个锚点。

主要问题：

我使用的repo编码方式只考虑了默认锚点，其中有每个[yolo]层3个锚。

我试图做什么来解决主要问题：

我试图对源代码做一些更改，这些修改在本链接中进行了总结。
我使用下面的代码试图将暗网重量转换为tf格式。以下是转换过程的日志。

python save_model.py --weights data/yolov4-512.weights --output ./checkpoints/yolov4-512 --input_size 512 --model yolov4

我使用代码：python detect.py --weights checkpoints/yolov4-512 --size 512 --model yolov4 --image data/pear.jpg测试了生成的tf模型。进程失败了，错误可以在下面看到。我见过这里可能出现的问题，但我不知道如何解决它们。

2021-03-19 15:05:03.694379: W tensorflow/core/common_runtime/bfc_allocator.cc:312] Garbage collection: deallocate free memory regions (i.e., allocations) so that we can re-allocate a larger region to avoid OOM due to memory fragmentation. If you see this message frequently, you are running near the threshold of the available device memory and re-allocation may incur great performance overhead. You may try smaller batch sizes to observe the performance impact. Set TF_ENABLE_GPU_GARBAGE_COLLECTION=false if you'd like to disable this feature.
    Traceback (most recent call last):
      File "detect.py", line 90, in <module>
        app.run(main)
      File "C:\Python37\lib\site-packages\absl\app.py", line 303, in run
        _run_main(main, args)
      File "C:\Python37\lib\site-packages\absl\app.py", line 251, in _run_main
        sys.exit(main(argv))
      File "detect.py", line 66, in main
        pred_bbox = infer(batch_data)
      File "C:\Python37\lib\site-packages\tensorflow\python\eager\function.py", line 1655, in __call__
        return self._call_impl(args, kwargs)
      File "C:\Python37\lib\site-packages\tensorflow\python\eager\function.py", line 1673, in _call_impl
        return self._call_with_flat_signature(args, kwargs, cancellation_manager)
      File "C:\Python37\lib\site-packages\tensorflow\python\eager\function.py", line 1722, in _call_with_flat_signature
        return self._call_flat(args, self.captured_inputs, cancellation_manager)
      File "C:\Python37\lib\site-packages\tensorflow\python\saved_model\load.py", line 106, in _call_flat
        cancellation_manager)
      File "C:\Python37\lib\site-packages\tensorflow\python\eager\function.py", line 1924, in _call_flat
        ctx, args, cancellation_manager=cancellation_manager))
      File "C:\Python37\lib\site-packages\tensorflow\python\eager\function.py", line 550, in call
        ctx=ctx)
      File "C:\Python37\lib\site-packages\tensorflow\python\eager\execute.py", line 60, in quick_execute
        inputs, attrs, num_outputs)
    tensorflow.python.framework.errors_impl.InvalidArgumentError:  Input to reshape is a tensor with 98304 values, but the requested shape has 73728
             [[{{node StatefulPartitionedCall/functional_1/tf_op_layer_Reshape/Reshape}}]] [Op:__inference_signature_wrapper_5589]
    
    Function call stack:
    signature_wrapper