keras-yolo3 훈련 에러

안녕하세요. 선생님 keras-yolo3 raccoon 데이터셋 훈련 때 오류가 발생했습니다. freeze시에는 잘 동작을 하는데, 모든 레이어를 unfreeze 할 때는 처음부터 에러가 바로 발생하네요. 메모리 용량 문제로 보이는데, 개인 GPU라서 해결할 방법은 GPU 메모리가 높은 것을 사용하는 것이겠죠?

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ResourceExhaustedError                    Traceback (most recent call last)
<ipython-input-6-b92f17d39649> in <module>
     82         epochs=100,
     83         initial_epoch=50,
---> 84         callbacks=[logging, checkpoint, reduce_lr, early_stopping])
     85     model.save_weights(log_dir + 'trained_weights_final.h5')

~\anaconda3\envs\tf113\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name +
     90                               '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

~\anaconda3\envs\tf113\lib\site-packages\keras\engine\training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
   1413             use_multiprocessing=use_multiprocessing,
   1414             shuffle=shuffle,
-> 1415             initial_epoch=initial_epoch)
   1416 
   1417     @interfaces.legacy_generator_methods_support

~\anaconda3\envs\tf113\lib\site-packages\keras\engine\training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
    211                 outs = model.train_on_batch(x, y,
    212                                             sample_weight=sample_weight,
--> 213                                             class_weight=class_weight)
    214 
    215                 outs = to_list(outs)

~\anaconda3\envs\tf113\lib\site-packages\keras\engine\training.py in train_on_batch(self, x, y, sample_weight, class_weight)
   1213             ins = x + y + sample_weights
   1214         self._make_train_function()
-> 1215         outputs = self.train_function(ins)
   1216         return unpack_singleton(outputs)
   1217 

~\anaconda3\envs\tf113\lib\site-packages\keras\backend\tensorflow_backend.py in __call__(self, inputs)
   2664                 return self._legacy_call(inputs)
   2665 
-> 2666             return self._call(inputs)
   2667         else:
   2668             if py_any(is_tensor(x) for x in inputs):

~\anaconda3\envs\tf113\lib\site-packages\keras\backend\tensorflow_backend.py in _call(self, inputs)
   2634                                 symbol_vals,
   2635                                 session)
-> 2636         fetched = self._callable_fn(*array_vals)
   2637         return fetched[:len(self.outputs)]
   2638 

~\anaconda3\envs\tf113\lib\site-packages\tensorflow\python\client\session.py in __call__(self, *args, **kwargs)
   1437           ret = tf_session.TF_SessionRunCallable(
   1438               self._session._session, self._handle, args, status,
-> 1439               run_metadata_ptr)
   1440         if run_metadata:
   1441           proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

~\anaconda3\envs\tf113\lib\site-packages\tensorflow\python\framework\errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
    526             None, None,
    527             compat.as_text(c_api.TF_Message(self.status.status)),
--> 528             c_api.TF_GetCode(self.status.status))
    529     # Delete the underlying status object from memory otherwise it stays alive
    530     # as there is a reference to status from this from the traceback due to

ResourceExhaustedError: OOM when allocating tensor with shape[1024,512,3,3] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
	 [[{{node training_1/Adam/gradients/conv2d_58/convolution_grad/Conv2DBackpropFilter}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

그리고 추가로 궁금한 것이, 처음에 훈련할 때 freeze는 따로 코드로 설정하지 않아도 첫번째 레이어는 고정되어지는 것인가요?

감사합니다