问使用用于Tensorflow的Edgetpu编译器编译时出错
EN

Stack Overflow用户

提问于 2020-06-29 21:39:03

回答 1查看 193关注 0票数 2

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

google-coral

python

tensorflow

keras

tensorflow-lite

腾讯特效SDK 2.5折起

美颜基础/原子能力套餐低至1500元/月，提供丰富的美化能力，支持全平台集成

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()