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