问题描述
我正在尝试使用TensorFlow中的视频数据训练模型。当前的数据管道使用数据生成器，该数据生成器使用moviepy模块加载视频，然后使用dlib和其他一些软件包进行一些视频预处理。我正在进行端到端培训（而不是先对视频进行预处理并向模型加载清理后的数据），因为我希望模型能够进行随机学习。换句话说，在每个时期，我希望从视频加载的帧是不同的。但是，培训的速度非常慢，即每个纪元约17小时，因为主要瓶颈出现在数据生成器（加载和预处理视频）部分。
我搜索了tf网站，发现了分布式输入功能和数据预取功能。这是我将生成器放入tf数据集的部分，其中valid_list中的args是生成器接受并产生已处理数据的文件路径的列表：
valid_gen=tf.data.Dataset.from_generator(generate_data,output_types=((tf.float32,tf.float32,tf.float32),tf.int64),output_shapes=((tf.TensorShape([None,segments,imsize,3]),tf.TensorShape([None,68]),68,2])),n_class])),args=(valid_list,batch_size))
valid_gen.prefetch(buffer_size=batch_size)
dist_valid_data=mirrored_strategy.experimental_distribute_dataset(valid_gen)
但是，出现以下错误：
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-48-3af068b6698e> in <module>
2 validation_data=dist_valid_data,callbacks=callbacks,3 validation_steps=len(valid_dict)//batch_size,----> 4 batch_size=batch_size,epochs=1000)
~/.conda/envs/video_env_1/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self,*args,**kwargs)
106 def _method_wrapper(self,**kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
--> 108 return method(self,**kwargs)
109
110 # Running inside `run_distribute_coordinator` already.
~/.conda/envs/video_env_1/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in fit(self,x,y,batch_size,epochs,verbose,callbacks,validation_split,validation_data,shuffle,class_weight,sample_weight,initial_epoch,steps_per_epoch,validation_steps,validation_batch_size,validation_freq,max_queue_size,workers,use_multiprocessing)
1096 batch_size=batch_size):
1097 callbacks.on_train_batch_begin(step)
-> 1098 tmp_logs = train_function(iterator)
1099 if data_handler.should_sync:
1100 context.async_wait()
~/.conda/envs/video_env_1/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self,**kwds)
778 else:
779 compiler = "nonXla"
--> 780 result = self._call(*args,**kwds)
781
782 new_tracing_count = self._get_tracing_count()
~/.conda/envs/video_env_1/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self,**kwds)
838 # Lifting succeeded,so variables are initialized and we can run the
839 # stateless function.
--> 840 return self._stateless_fn(*args,**kwds)
841 else:
842 canon_args,canon_kwds = \
~/.conda/envs/video_env_1/lib/python3.7/site-packages/tensorflow/python/eager/function.py in __call__(self,**kwargs)
2827 with self._lock:
2828 graph_function,args,kwargs = self._maybe_define_function(args,kwargs)
-> 2829 return graph_function._filtered_call(args,kwargs) # pylint: disable=protected-access
2830
2831 @property
~/.conda/envs/video_env_1/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _filtered_call(self,kwargs,cancellation_manager)
1846 resource_variable_ops.BaseResourceVariable))],1847 captured_inputs=self.captured_inputs,-> 1848 cancellation_manager=cancellation_manager)
1849
1850 def _call_flat(self,captured_inputs,cancellation_manager=None):
~/.conda/envs/video_env_1/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _call_flat(self,cancellation_manager)
1922 # No tape is watching; skip to running the function.
1923 return self._build_call_outputs(self._inference_function.call(
-> 1924 ctx,cancellation_manager=cancellation_manager))
1925 forward_backward = self._select_forward_and_backward_functions(
1926 args,~/.conda/envs/video_env_1/lib/python3.7/site-packages/tensorflow/python/eager/function.py in call(self,ctx,cancellation_manager)
548 inputs=args,549 attrs=attrs,--> 550 ctx=ctx)
551 else:
552 outputs = execute.execute_with_cancellation(
~/.conda/envs/video_env_1/lib/python3.7/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name,num_outputs,inputs,attrs,name)
58 ctx.ensure_initialized()
59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle,device_name,op_name,---> 60 inputs,num_outputs)
61 except core._NotOkStatusException as e:
62 if name is not None:
InvalidArgumentError: TypeError: endswith first arg must be bytes or a tuple of bytes,not str
Traceback (most recent call last):
File "/home/dwa382/.conda/envs/video_env_1/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py",line 244,in __call__
ret = func(*args)
File "/home/dwa382/.conda/envs/video_env_1/lib/python3.7/site-packages/tensorflow/python/autograph/impl/api.py",line 302,in wrapper
return func(*args,**kwargs)
File "/home/dwa382/.conda/envs/video_env_1/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py",line 827,in generator_py_func
values = next(generator_state.get_iterator(iterator_id))
File "<ipython-input-44-34d69f4e6d29>",line 306,in generate_data
imsize=imsize,segments=segments)
File "<ipython-input-44-34d69f4e6d29>",line 221,in pre_processing
v_array=np.array(list(VideoFileClip(fp).iter_frames(fps=fps)))
File "/home/dwa382/.conda/envs/video_env_1/lib/python3.7/site-packages/moviepy/video/io/VideoFileClip.py",line 91,in __init__
fps_source=fps_source)
File "/home/dwa382/.conda/envs/video_env_1/lib/python3.7/site-packages/moviepy/video/io/ffmpeg_reader.py",line 36,in __init__
fps_source)
File "/home/dwa382/.conda/envs/video_env_1/lib/python3.7/site-packages/moviepy/video/io/ffmpeg_reader.py",in ffmpeg_parse_infos
is_GIF = filename.endswith('.gif')
TypeError: endswith first arg must be bytes or a tuple of bytes,not str
[[{{node PyFunc}}]]
[[MultideviceIteratorGetNextFromShard]]
[[RemoteCall]]
[[IteratorGetNextAsOptional]] [Op:__inference_train_function_19168]
Function call stack:
train_function
谢谢。
解决方法
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