I'm facing an issue while converting the LSTM model to tflite.
I'm converting this model to use it in my flutter app.
The model is used to detect and translate Indian sign language.
Below is my conversion code.
import tensorflow as tf
from keras.models import load_model
model=load_model("action.h5")
tf.keras.models.save_model(model,'model.pbtxt')
converter =tf.lite.TFLiteConverter.from_keras_model(model=model)
lite_model=converter.convert()
with open("lite_model.tflite","wb") as f:
f.write(lite_model)
If I run this code, the following error occurs
INFO:tensorflow:Assets written to: model.pbtxt\assets
INFO:tensorflow:Assets written to: model.pbtxt\assets
INFO:tensorflow:Assets written to: C:\Users\gk\AppData\Local\Temp\tmp6276n3rh\assets
INFO:tensorflow:Assets written to: C:\Users\gk\AppData\Local\Temp\tmp6276n3rh\assets
---------------------------------------------------------------------------
ConverterError Traceback (most recent call last)
Input In [73], in <cell line: 7>()
4 tf.keras.models.save_model(model,'model.pbtxt')
5 converter =tf.lite.TFLiteConverter.from_keras_model(model=model)
----> 7 lite_model=converter.convert()
8 with open("lite_model.tflite","wb") as f:
9 f.write(lite_model)
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow\lite\python\lite.py:929, in _export_metrics.<locals>.wrapper(self, *args, **kwargs)
926 @functools.wraps(convert_func)
927 def wrapper(self, *args, **kwargs):
928 # pylint: disable=protected-access
--> 929 return self._convert_and_export_metrics(convert_func, *args, **kwargs)
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow\lite\python\lite.py:908, in TFLiteConverterBase._convert_and_export_metrics(self, convert_func, *args, **kwargs)
906 self._save_conversion_params_metric()
907 start_time = time.process_time()
--> 908 result = convert_func(self, *args, **kwargs)
909 elapsed_time_ms = (time.process_time() - start_time) * 1000
910 if result:
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow\lite\python\lite.py:1338, in TFLiteKerasModelConverterV2.convert(self)
1325 @_export_metrics
1326 def convert(self):
1327 """Converts a keras model based on instance variables.
1328
1329 Returns:
(...)
1336 Invalid quantization parameters.
1337 """
-> 1338 saved_model_convert_result = self._convert_as_saved_model()
1339 if saved_model_convert_result:
1340 return saved_model_convert_result
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow\lite\python\lite.py:1321, in TFLiteKerasModelConverterV2._convert_as_saved_model(self)
1317 graph_def, input_tensors, output_tensors = (
1318 self._convert_keras_to_saved_model(temp_dir))
1319 if self.saved_model_dir:
1320 return super(TFLiteKerasModelConverterV2,
-> 1321 self).convert(graph_def, input_tensors, output_tensors)
1322 finally:
1323 shutil.rmtree(temp_dir, True)
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow\lite\python\lite.py:1131, in TFLiteConverterBaseV2.convert(self, graph_def, input_tensors, output_tensors)
1126 logging.info("Using new converter: If you encounter a problem "
1127 "please file a bug. You can opt-out "
1128 "by setting experimental_new_converter=False")
1130 # Converts model.
-> 1131 result = _convert_graphdef(
1132 input_data=graph_def,
1133 input_tensors=input_tensors,
1134 output_tensors=output_tensors,
1135 **converter_kwargs)
1137 return self._optimize_tflite_model(
1138 result, self._quant_mode, quant_io=self.experimental_new_quantizer)
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow\lite\python\convert_phase.py:212, in convert_phase.<locals>.actual_decorator.<locals>.wrapper(*args, **kwargs)
210 else:
211 report_error_message(str(converter_error))
--> 212 raise converter_error from None # Re-throws the exception.
213 except Exception as error:
214 report_error_message(str(error))
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow\lite\python\convert_phase.py:205, in convert_phase.<locals>.actual_decorator.<locals>.wrapper(*args, **kwargs)
202 @functools.wraps(func)
203 def wrapper(*args, **kwargs):
204 try:
--> 205 return func(*args, **kwargs)
206 except ConverterError as converter_error:
207 if converter_error.errors:
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow\lite\python\convert.py:794, in convert_graphdef(input_data, input_tensors, output_tensors, **kwargs)
791 else:
792 model_flags.output_arrays.append(util.get_tensor_name(output_tensor))
--> 794 data = convert(
795 model_flags.SerializeToString(),
796 conversion_flags.SerializeToString(),
797 input_data.SerializeToString(),
798 debug_info_str=debug_info.SerializeToString() if debug_info else None,
799 enable_mlir_converter=enable_mlir_converter)
800 return data
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow\lite\python\convert.py:311, in convert(model_flags_str, conversion_flags_str, input_data_str, debug_info_str, enable_mlir_converter)
309 for error_data in _metrics_wrapper.retrieve_collected_errors():
310 converter_error.append_error(error_data)
--> 311 raise converter_error
313 return _run_deprecated_conversion_binary(model_flags_str,
314 conversion_flags_str, input_data_str,
315 debug_info_str)
ConverterError: C:\Users\gk\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow\python\saved_model\save.py:1325:0: error: 'tf.TensorListReserve' op requires element_shape to be static during TF Lite transformation pass
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
C:\Users\gk\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow\python\saved_model\save.py:1325:0: error: failed to legalize operation 'tf.TensorListReserve' that was explicitly marked illegal
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
<unknown>:0: error: Lowering tensor list ops is failed. Please consider using Select TF ops and disabling `_experimental_lower_tensor_list_ops` flag in the TFLite converter object. For example, converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]\n converter._experimental_lower_tensor_list_ops = False
!python --version
It throws a error in the converter.convert(). I'm new to deep learning and i have tried many other ways but it resulted in the same error.
If this error cannot be solved, please suggest me what can I do.....is there any other model that can be used to detect sign language efficiently and can also be used in flutter apps.
