TFLite:`ValueError:模型输入未量化`

Question

问题

我尝试将crop_and_resize操作转换为TFLite。我正在使用下面的脚本。但转换失败，并引发错误ValueError: Model input is not quantized.。有人知道吗？我没有找到任何关于先前相关问题的相关信息。

代码片段

import tensorflow as tf
IMG_SIZE = 128
NUM_BOXES = 100
CROP_SIZE = 28
NB_DATA_SAMPLES = 32
BATCH_SIZE = 1

def create_model():
images_ = tf.keras.Input(shape=(IMG_SIZE, IMG_SIZE, 3), batch_size=BATCH_SIZE, dtype=tf.float32)
boxes_ = tf.keras.Input(shape=(NUM_BOXES, 4), batch_size=BATCH_SIZE, dtype=tf.float32)
box_indices = tf.reshape(
tf.repeat(
tf.expand_dims(tf.range(BATCH_SIZE, dtype=tf.int32), axis=-1),
NUM_BOXES,
axis=-1
),
shape=(-1,)
)
cropped_images = tf.image.crop_and_resize(
image=images_,
boxes=tf.reshape(boxes_, (-1, 4)),
box_indices=box_indices,
crop_size=(CROP_SIZE, CROP_SIZE))
model = tf.keras.models.Model(inputs=[images_, boxes_], outputs=cropped_images)
model.summary(line_length=200)
return model

model = create_model()
def representative_dataset_generator():
for _ in range(NB_DATA_SAMPLES):
image_ = tf.random.normal(shape=(1, IMG_SIZE, IMG_SIZE, 3))
bboxes_ = tf.random.uniform((1, NUM_BOXES, 4), maxval=1)
yield [image_, bboxes_]
# Converter
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS_INT8,
tf.lite.OpsSet.SELECT_TF_OPS
]
converter.target_spec.supported_types = [tf.int8]
converter.inference_input_type = tf.uint8
converter.inference_output_type = tf.uint8
converter.representative_dataset = representative_dataset_generator
quant_model = converter.convert()

感谢

Answer 1

我可以成功地执行您的代码。但是，它只量化重塑图层，而不量化裁剪图层。所以我想tflite可能无法量化他们不支持的操作。