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我正在尝试将我在其官方网站上使用量化感知训练教程训练和微调的 keras 模型转换为 int tflite 模型。在我必须将模型转换为 tflite 格式之前,我可以按照他们的步骤进行操作。然后它给了我这个输出:

`Traceback (most recent call last):
  File "/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/lite/python/convert.py", line 185, in toco_convert_protos
    enable_mlir_converter)
  File "/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/lite/python/wrap_toco.py", line 38, in wrapped_toco_convert
    enable_mlir_converter)
Exception: /home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/python/keras/layers/ops/core.py:56:1: error: 'std.constant' op requires attribute's type ('tensor<48x64xf32>') to match op's return type ('tensor<*xf32>')
    outputs = standard_ops.tensordot(inputs, kernel, [[rank - 1], [0]])
^
/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/python/keras/layers/core.py:1194:1: note: called from
        dtype=self._compute_dtype_object)
^
/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow_model_optimization/python/core/quantization/keras/quantize_wrapper.py:162:1: note: called from
    outputs = self.layer.call(inputs)
^
/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:302:1: note: called from
      return func(*args, **kwargs)
^
/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:961:1: note: called from
          outputs = call_fn(inputs, *args, **kwargs)
^
/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:507:1: note: called from
        outputs = node.layer(*args, **kwargs)
^
/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:385:1: note: called from
        inputs, training=training, mask=mask)
^
/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:961:1: note: called from
          outputs = call_fn(inputs, *args, **kwargs)
^
/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py:132:1: note: called from
      outputs = model(inputs, training=False)
^
/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py:600:1: note: called from
        return weak_wrapped_fn().__wrapped__(*args, **kwds)
^
/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/python/keras/layers/ops/core.py:56:1: note: see current operation: %cst_8 = "std.constant"() {value = dense<"0x38211AB .. A3E"> : tensor<48x64xf32>} : () -> tensor<*xf32>
    outputs = standard_ops.tensordot(inputs, kernel, [[rank - 1], [0]])
^


During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/student1/kvantizacija/tensorflow_example.py", line 58, in <module>
    tflite_model_quant = converter.convert()
  File "/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/lite/python/lite.py", line 778, in convert
    self).convert(graph_def, input_tensors, output_tensors)
  File "/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/lite/python/lite.py", line 595, in convert
    **converter_kwargs)
  File "/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/lite/python/convert.py", line 560, in toco_convert_impl
    enable_mlir_converter=enable_mlir_converter)
  File "/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/lite/python/convert.py", line 188, in toco_convert_protos
    raise ConverterError(str(e))
tensorflow.lite.python.convert.ConverterError: /home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/python/keras/layers/ops/core.py:56:1: error: 'std.constant' op requires attribute's type ('tensor<48x64xf32>') to match op's return type ('tensor<*xf32>')
    outputs = standard_ops.tensordot(inputs, kernel, [[rank - 1], [0]])
^
/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/python/keras/layers/core.py:1194:1: note: called from
        dtype=self._compute_dtype_object)
^
/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow_model_optimization/python/core/quantization/keras/quantize_wrapper.py:162:1: note: called from
    outputs = self.layer.call(inputs)
^
/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:302:1: note: called from
      return func(*args, **kwargs)
^
/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:961:1: note: called from
          outputs = call_fn(inputs, *args, **kwargs)
^
/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:507:1: note: called from
        outputs = node.layer(*args, **kwargs)
^
/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:385:1: note: called from
        inputs, training=training, mask=mask)
^
/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:961:1: note: called from
          outputs = call_fn(inputs, *args, **kwargs)
^
/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py:132:1: note: called from
      outputs = model(inputs, training=False)
^
/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py:600:1: note: called from
        return weak_wrapped_fn().__wrapped__(*args, **kwds)
^
/home/student1/venv_kvant/lib/python3.6/site-packages/tensorflow/python/keras/layers/ops/core.py:56:1: note: see current operation: %cst_8 = "std.constant"() {value = dense<"0x38211ABEE ... 6D3DE88D49BE40211A3E"> : tensor<48x64xf32>} : () -> tensor<*xf32>
    outputs = standard_ops.tensordot(inputs, kernel, [[rank - 1], [0]])
^


Process finished with exit code 1

如果我删除优化标志,它会给我一个 tflite 模型,但它没有给我我需要的 int8 模型。我可以成功发布训练量化相同的模型,我使用 quant 感知训练进行微调,但由于某种原因,当我将模型包装在 Quantize 包装器中并尝试转换它时,它不起作用。我正在使用最新的夜间版本,并尝试在访问和不访问 GPU 的情况下运行脚本。`

如果您需要更多信息,请随时询问。该模型还有一个是 4 个 CNN + Max_pool 块,最后有一些 Dense 层。如果需要,我可以提供模型的可视化。

PS。这是摘要:

__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input (InputLayer)              [(None, 48, 48, 3)]  0                                            
__________________________________________________________________________________________________
quantize_layer (QuantizeLayer)  (None, 48, 48, 3)    3           input[0][0]                      
__________________________________________________________________________________________________
quant_conv_1 (QuantizeWrapper)  (None, 46, 46, 16)   483         quantize_layer[0][0]             
__________________________________________________________________________________________________
quant_relu_1 (QuantizeWrapper)  (None, 46, 46, 16)   3           quant_conv_1[0][0]               
__________________________________________________________________________________________________
quant_pool_1 (QuantizeWrapper)  (None, 22, 22, 16)   1           quant_relu_1[0][0]               
__________________________________________________________________________________________________
quant_conv_2 (QuantizeWrapper)  (None, 20, 20, 32)   4707        quant_pool_1[0][0]               
__________________________________________________________________________________________________
quant_relu_2 (QuantizeWrapper)  (None, 20, 20, 32)   3           quant_conv_2[0][0]               
__________________________________________________________________________________________________
quant_pool_2 (QuantizeWrapper)  (None, 9, 9, 32)     1           quant_relu_2[0][0]               
__________________________________________________________________________________________________
quant_conv_3 (QuantizeWrapper)  (None, 7, 7, 32)     9315        quant_pool_2[0][0]               
__________________________________________________________________________________________________
quant_relu_3 (QuantizeWrapper)  (None, 7, 7, 32)     3           quant_conv_3[0][0]               
__________________________________________________________________________________________________
quant_pool_3 (QuantizeWrapper)  (None, 3, 3, 32)     1           quant_relu_3[0][0]               
__________________________________________________________________________________________________
quant_conv_4 (QuantizeWrapper)  (None, 2, 2, 64)     8387        quant_pool_3[0][0]               
__________________________________________________________________________________________________
quant_pool_4 (QuantizeWrapper)  (None, 1, 1, 64)     1           quant_conv_4[0][0]               
__________________________________________________________________________________________________
quant_relu_4 (QuantizeWrapper)  (None, 1, 1, 64)     3           quant_pool_4[0][0]               
__________________________________________________________________________________________________
quant_fc_yaw (QuantizeWrapper)  (None, 1, 1, 48)     3125        quant_relu_4[0][0]               
__________________________________________________________________________________________________
quant_fc_pitch (QuantizeWrapper (None, 1, 1, 48)     3125        quant_relu_4[0][0]               
__________________________________________________________________________________________________
quant_fc_roll (QuantizeWrapper) (None, 1, 1, 48)     3125        quant_relu_4[0][0]               
__________________________________________________________________________________________________
quant_relu_yaw (QuantizeWrapper (None, 1, 1, 48)     3           quant_fc_yaw[0][0]               
__________________________________________________________________________________________________
quant_relu_pitch (QuantizeWrapp (None, 1, 1, 48)     3           quant_fc_pitch[0][0]             
__________________________________________________________________________________________________
quant_relu_roll (QuantizeWrappe (None, 1, 1, 48)     3           quant_fc_roll[0][0]              
__________________________________________________________________________________________________
quant_flatten_yaw (QuantizeWrap (None, 48)           1           quant_relu_yaw[0][0]             
__________________________________________________________________________________________________
quant_flatten_pitch (QuantizeWr (None, 48)           1           quant_relu_pitch[0][0]           
__________________________________________________________________________________________________
quant_flatten_roll (QuantizeWra (None, 48)           1           quant_relu_roll[0][0]            
__________________________________________________________________________________________________
quant_output_yaw (QuantizeWrapp (None, 61)           2994        quant_flatten_yaw[0][0]          
__________________________________________________________________________________________________
quant_output_pitch (QuantizeWra (None, 61)           2994        quant_flatten_pitch[0][0]        
__________________________________________________________________________________________________
quant_output_roll (QuantizeWrap (None, 61)           2994        quant_flatten_roll[0][0]         
__________________________________________________________________________________________________
quant_yaw (QuantizeWrapper)     (None, 1)            54          quant_flatten_yaw[0][0]          
__________________________________________________________________________________________________
quant_pitch (QuantizeWrapper)   (None, 1)            54          quant_flatten_pitch[0][0]        
__________________________________________________________________________________________________
quant_roll (QuantizeWrapper)    (None, 1)            54          quant_flatten_roll[0][0]         
==================================================================================================
Total params: 41,442
Trainable params: 41,066
Non-trainable params: 376
4

1 回答 1

0

由于找到另一种解决问题的方法,因此已关闭。问题是有一系列层:->密集->扁平->密集,因此发生了这个错误。目前我使用的解决方案是切换 Flatten 层和 1st Dense 层的位置。如果有人知道如何使用原始序列解决,请告诉我。

于 2020-06-15T08:41:43.723 回答