使用用于Tensorflow的Edgetpu编译器编译时出错
使用用于Tensorflow的Edgetpu编译器编译时出错
提问于 2020-06-29 21:39:03
我正在尝试将一个两个输出的keras模型转换成一个编译的，量化的，tflite模型，它将在Google Coral上工作。我以前在一个只有1个输出的Keras网络中使用了这个确切的过程，它是有效的。
下面是我的流程：
import tensorflow as tf
from tensorflow.keras.applications.mobilenet import preprocess_input
file = 'path/to/model-01.h5'
model = tf.keras.models.load_model(file)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
os.chdir('/path/to/image/directories')#Where image directories are
directory = os.listdir()
directory
def representative_dataset_gen():
for i in directory:
count = 0
os.chdir(i)
files = os.listdir()
print(i)
for j in files:
if count<500:
img = Image.open(j)
width, height = img.size
bands = img.getbands()
array = np.asarray(img, dtype=np.float32)
array = preprocess_input(array)
count=count+1
yield[np.expand_dims(array, axis=0)]
else:
break
os.chdir('../')
converter.representative_dataset = representative_dataset_gen
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.int8 # or tf.uint8
converter.inference_output_type = tf.int8 # or tf.uint8
tflite_quant_model = converter.convert()
tflite_model_dir = pathlib.Path('where/i/want/to/save/')
tflite_quant_model_file = tflite_model_dir/'quantized.tflite'
tflite_quant_model_file.write_bytes(tflite_quant_model)然后，我尝试在终端中使用edgetpu_compiler
edgetpu_compiler quantizedmodel.tflite并收到此错误：
ERROR: :129 std::abs(input_product_scale - bias_scale) <= 1e-6 * std::min(input_product_scale, bias_scale) was not true.
ERROR: Node number 40 (FULLY_CONNECTED) failed to prepare.
Internal compiler error. Aborting! 在尝试验证模型时尝试interpreter.allocate_tensors()时，我也得到了相同的错误。
#Load Model
interpreter = tf.lite.Interpreter(model_path='path/to/model/quantized.tflite')
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
interpreter.resize_tensor_input(input_details[0]['index'], (32, 200, 200, 3))
interpreter.resize_tensor_input(output_details[0]['index'], (32, 5))
interpreter.allocate_tensors()它返回
RuntimeError Traceback (most recent call last)
in
2 interpreter.resize_tensor_input(input_details[0]['index'], (32, 200, 200, 3))
3 interpreter.resize_tensor_input(output_details[0]['index'], (32, 5))
----> 4 interpreter.allocate_tensors()
5
~/Software/anaconda3/envs/Tensorflow2/lib/python3.7/site-packages/tensorflow_core/lite/python/interpreter.py in allocate_tensors(self)
245 def allocate_tensors(self):
246 self._ensure_safe()
--> 247 return self._interpreter.AllocateTensors()
248
249 def _safe_to_run(self):
~/Software/anaconda3/envs/Tensorflow2/lib/python3.7/site-packages/tensorflow_core/lite/python/interpreter_wrapper/tensorflow_wrap_interpreter_wrapper.py in AllocateTensors(self)
108
109 def AllocateTensors(self):
--> 110 return _tensorflow_wrap_interpreter_wrapper.InterpreterWrapper_AllocateTensors(self)
111
112 def Invoke(self):
RuntimeError: tensorflow/lite/kernels/kernel_util.cc:106 std::abs(input_product_scale - bias_scale) <= 1e-6 * std::min(input_product_scale, bias_scale) was not true.Node number 40 (FULLY_CONNECTED) failed to prepare.我使用的是tensorflow 2.2.0
回答 1
Stack Overflow用户
回答已采纳
发布于 2020-06-29 22:25:39
我会为这个打开一个问题here，因为它是tflite量化过程中的一个实际错误。我非常肯定我以前见过这种情况，但不确定是否有解决方法:/
编辑基本上，你可以尝试使用这个脚本进行一个虚拟推理运行，如果在你的CPU模型上失败了，那么很明显模型在tflite转换后被破坏了。
import numpy as np
import sys
from tflite_runtime.interpreter import Interpreter
from tflite_runtime.interpreter import load_delegate
if len(sys.argv) < 2:
print('Usage:', sys.argv[0], 'model_path')
exit()
def main():
"""Runs inference with an input tflite model."""
model_path = str(sys.argv[1])
if model_path.endswith('edgetpu.tflite'):
print('initialized for edgetpu')
delegates = [load_delegate('libedgetpu.so.1.0')]
interpreter = Interpreter(model_path, experimental_delegates=delegates)
else:
print('initialized for cpu')
interpreter = Interpreter(model_path)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
images = np.zeros(input_details[0]['shape'], input_details[0]['dtype'])
#print(images)
interpreter.set_tensor(input_details[0]['index'], images)
interpreter.invoke()
output_details = interpreter.get_output_details()
outputs = interpreter.get_tensor(output_details[0]['index'])
print(outputs)
print('Success.')
if __name__== '__main__':
main()我在pass中看到了这个问题，但不确定是否有解决方案。打开一个bug实际上是修复这个问题的最好方法。
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/62647070
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