为TFliteconverter创建代表性数据集的正确方法是什么? [英] What is the correct way to create representative dataset for TFliteconverter?
问题描述
我正在尝试使用 INT8
的权重和激活来推断 tinyYOLO-V2
。我可以使用TFliteConverter将权重转换为INT8。对于 INT8
激活,我必须给出代表性的数据集以估计比例因子。我创建此类数据集的方法似乎不对。
I am trying to infer tinyYOLO-V2
with INT8
weights and activation. I can convert the weights to INT8 with TFliteConverter. For INT8
activation, I have to give representative dataset to estimate the scaling factor. My method of creating such dataset seems wrong.
什么是正确的程序?
def rep_data_gen():
a = []
for i in range(160):
inst = anns[i]
file_name = inst['filename']
img = cv2.imread(img_dir + file_name)
img = cv2.resize(img, (NORM_H, NORM_W))
img = img / 255.0
img = img.astype('float32')
a.append(img)
a = np.array(a)
print(a.shape) # a is np array of 160 3D images
img = tf.data.Dataset.from_tensor_slices(a).batch(1)
for i in img.take(BATCH_SIZE):
print(i)
yield [i]
# https://www.tensorflow.org/lite/performance/post_training_quantization
converter = tf.lite.TFLiteConverter.from_keras_model_file("./yolo.h5")
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = [tf.int8]
converter.inference_output_type = [tf.int8]
converter.representative_dataset=rep_data_gen
tflite_quant_model = converter.convert()
ValueError:无法设置张量:得到类型为STRING的张量,但预期输入27为FLOAT32类型,名称:input_1
ValueError: Cannot set tensor: Got tensor of type STRING but expected type FLOAT32 for input 27, name: input_1
推荐答案
我使用您的代码读取数据集并发现错误:
I used your code for reading in a dataset and found the error:
img = img .astype('float32')应该
img = img.astype('float32') should be
img = img.astype(np.float32)
img = img.astype(np.float32)
希望这有帮助
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