When using generator for representative dataset in quantization it "Failed to convert value into readable tensor"

Question

I am quantizing a model. The model takes 224x224 input.

I preprocess the data with a build-in function preprocess_input() which subtracts some center pixels.

Now when using a simple image with this preprocessing function in the representative_dataset_gen() everything works fine

def representative_dataset_gen():
        pfad='./000001.jpg'
        img = cv2.imread(pfad)
        img = np.expand_dims(img,0).astype(np.float32) 
        img = preprocess_input(img) 
        yield [img]

But when I use a generator function to use more than one image

def prepare(img):
    img = np.expand_dims(img,0).astype(np.float32)
    img = preprocess_input(img)
    return arg
      
    
repDatagen=tf.keras.preprocessing.image.ImageDataGenerator(preprocessing_function=prepare)
datagen=repDatagen.flow_from_directory(folderpath,target_size=size,batch_size=1)


def representative_dataset_gen():
  for _ in range(10):
    img = datagen.next()
    yield [img]

I get following error:

ValueError: Failed to convert value into readable tensor.

My guess: This is due to ImageDataGenerator(preprocessing_function=prepare) . In the tensorflow description it says:

function that will be applied on each input. The function will run after the image is resized and augmented. The function should take one argument: one image (Numpy tensor with rank 3), and should output a Numpy tensor with the same shape.

I tried to adjust the shape of the img output of the "prepare" function without and with np.squeez(). This results in either (1,244,244,3) or (224,224,3). But I still get the error. I also tried tf.convert_to_tensor() with the same error.

def prepare(img):
    img = np.expand_dims(img,0).astype(np.float32)
    img = preprocess_input(img, version=2)
    img = np.squeeze(img)
    arg = tf.convert_to_tensor(img, dtype=tf.float32)
    return arg

Does anyone know how I have to prepare the output to get the correct tensor?

Thanks

score 1 · Answer 1 · answered Nov 12 '20 at 13:55

The problem was actually the generator output. The output is not a np.array() . Converting with np.asarray() was not possible as it throws an error.

So the answer is quite simple:

the data_generator output is a tuple

so simply yield only the first element img[0].

The code in total:

size=(224,224)

def prepare(img):
    img = np.expand_dims(img,0).astype(np.float32)
    img = preprocess_input(img, version=2)
    return img
      
repDatagen=tf.keras.preprocessing.image.ImageDataGenerator(preprocessing_function=prepare)
datagen=repDatagen.flow_from_directory(folderpath,target_size=size,batch_size=1)

def representative_dataset_gen():
  for _ in range(10):
    img = datagen.next()
    yield [img[0]]
    #yield [np.array(img[0], dtype=np.float32)] # also possible

When using generator for representative dataset in quantization it "Failed to convert value into readable tensor"

1 Answers1