Created by: yeyupiaoling

训练环境

• Python 3.7
• windows 10
• PaddlePaddle 1.7.0
• MobileNet V2

转换环境

• Ubuntu 16.04
• Python 3.5
• Paddle-Lite 2.3.0

模型： infer_model.zip

图片（提供几张）： images.zip

数据预处理：

import os
import random
from multiprocessing import cpu_count
import numpy as np
import paddle
from PIL import Image


# 测试图片的预处理
def test_mapper(sample):
    img, label, crop_size = sample
    img = Image.open(img)
    # 统一图像大小
    img = img.resize((crop_size, crop_size), Image.ANTIALIAS)
    # 转换成numpy值
    img = np.array(img).astype(np.float32)
    # 转换成CHW
    img = img.transpose((2, 0, 1))
    img = (img - 128.0) * 0.0078125
    return img, int(label)


# 测试的图片reader
def test_reader(test_list_path, crop_size):
    def reader():
        with open(test_list_path, 'r', encoding='utf-8') as f:
            lines = f.readlines()
            for line in lines:
                img, label = line.split('\t')
                img = os.path.join('..', img)
                yield img, label, crop_size

    return paddle.reader.xmap_readers(test_mapper, reader, cpu_count(), 1024)

量化训练：

from paddle.fluid.contrib.slim.quantization import PostTrainingQuantization, WeightQuantization
import paddle.fluid as fluid
import my_reader

exe = fluid.Executor(fluid.CPUPlace())
model_dir = 'infer_model'
save_model_path = 'mobilenet_v2_quant'

sample_generator = my_reader.test_reader('../dataset/test_list.txt', 224)
batch_size = 10
batch_nums = 10
algo = "KL"
quantizable_op_type = ["conv2d", "depthwise_conv2d", "mul"]
ptq = PostTrainingQuantization(
            executor=exe,
            sample_generator=sample_generator,
            model_dir=model_dir,
            batch_size=batch_size,
            batch_nums=batch_nums,
            algo=algo,
            quantizable_op_type=quantizable_op_type)
ptq.quantize()
ptq.save_quantized_model(save_model_path)

转换命令：

./opt --model_dir=./mobilenet_v2_quant \
      --optimize_out_type=naive_buffer \
      --optimize_out=mobilenet_v2_quant \
      --valid_targets=arm \
      --prefer_int8_kernel=true