【yolov5】onnx的INT8量化engine-编程知识

GitHub上有大佬写好代码，理论上直接克隆仓库里下来使用

git clone https://github.com/Wulingtian/yolov5_tensorrt_int8_tools.git

然后在yolov5_tensorrt_int8_tools的convert_trt_quant.py 修改如下参数

BATCH_SIZE 模型量化一次输入多少张图片

BATCH 模型量化次数

height width 输入图片宽和高

CALIB_IMG_DIR 训练图片路径，用于量化

onnx_model_path onnx模型路径

engine_model_path 模型保存路径

其中这个batch_size不能超过照片的数量，然后跑这个convert_trt_quant.py

出问题了吧@_@

这是因为tensor的版本更新原因，这个代码的tensorrt版本是7系列的，而目前新的tensorrt版本已经没有了一些属性，所以我们需要对这个大佬写的代码进行一些修改

如何修改呢，其实tensorrt官方给出了一个caffe量化INT8的例子

https://github.com/NVIDIA/TensorRT/tree/master/samples/python/int8_caffe_mnist

如果足够NB是可以根据官方的这个例子修改一下直接实现onnx的INT8量化的

但是奈何我连半桶水都没有，只有一滴水，但是这个例子中的tensorrt版本是新的，于是我尝试将上面那位大佬的代码修改为使用新版的tensorrt

居然成功了？？！!

成功量化后的模型大小只有4MB，相比之下的FP16的大小为6MB，FP32的大小为9MB

再看看检测速度，速度和FP16差不太多

但是效果要差上一些了

那肯定不能忘记送上修改的代码，折腾一晚上的结果如下，主要是 util_trt程序

# tensorrt-libimport os
import tensorrt as trt
import pycuda.autoinit
import pycuda.driver as cuda
from calibrator import Calibrator
from torch.autograd import Variable
import torch
import numpy as np
import time
# add verbose
TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE) # ** engine可视化 **# create tensorrt-engine# fixed and dynamic
def get_engine(max_batch_size=1, onnx_file_path="", engine_file_path="",\fp16_mode=False, int8_mode=False, calibration_stream=None, calibration_table_path="", save_engine=False):"""Attempts to load a serialized engine if available, otherwise builds a new TensorRT engine and saves it."""def build_engine(max_batch_size, save_engine):"""Takes an ONNX file and creates a TensorRT engine to run inference with"""with trt.Builder(TRT_LOGGER) as builder, \builder.create_network(1) as network,\trt.OnnxParser(network, TRT_LOGGER) as parser:# parse onnx model fileif not os.path.exists(onnx_file_path):quit('ONNX file {} not found'.format(onnx_file_path))print('Loading ONNX file from path {}...'.format(onnx_file_path))with open(onnx_file_path, 'rb') as model:print('Beginning ONNX file parsing')parser.parse(model.read())assert network.num_layers > 0, 'Failed to parse ONNX model. \Please check if the ONNX model is compatible 'print('Completed parsing of ONNX file')print('Building an engine from file {}; this may take a while...'.format(onnx_file_path))        # build trt enginebuilder.max_batch_size = max_batch_sizeconfig = builder.create_builder_config()config.max_workspace_size = 1 << 20if int8_mode:config.set_flag(trt.BuilderFlag.INT8)assert calibration_stream, 'Error: a calibration_stream should be provided for int8 mode'config.int8_calibrator  = Calibrator(calibration_stream, calibration_table_path)print('Int8 mode enabled')runtime=trt.Runtime(TRT_LOGGER)plan = builder.build_serialized_network(network, config)engine = runtime.deserialize_cuda_engine(plan)if engine is None:print('Failed to create the engine')return None   print("Completed creating the engine")if save_engine:with open(engine_file_path, "wb") as f:f.write(engine.serialize())return engineif os.path.exists(engine_file_path):# If a serialized engine exists, load it instead of building a new one.print("Reading engine from file {}".format(engine_file_path))with open(engine_file_path, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime:return runtime.deserialize_cuda_engine(f.read())else:return build_engine(max_batch_size, save_engine)

唔，convert_trt_quant.py的代码也给一下吧

import numpy as np
import torch
import torch.nn as nn
import util_trt
import glob,os,cv2BATCH_SIZE = 1
BATCH = 79
height = 640
width = 640
CALIB_IMG_DIR = '/content/drive/MyDrive/yolov5/ikunData/images'
onnx_model_path = "runs/train/exp4/weights/FP32.onnx"
def preprocess_v1(image_raw):h, w, c = image_raw.shapeimage = cv2.cvtColor(image_raw, cv2.COLOR_BGR2RGB)# Calculate widht and height and paddingsr_w = width / wr_h = height / hif r_h > r_w:tw = widthth = int(r_w * h)tx1 = tx2 = 0ty1 = int((height - th) / 2)ty2 = height - th - ty1else:tw = int(r_h * w)th = heighttx1 = int((width - tw) / 2)tx2 = width - tw - tx1ty1 = ty2 = 0# Resize the image with long side while maintaining ratioimage = cv2.resize(image, (tw, th))# Pad the short side with (128,128,128)image = cv2.copyMakeBorder(image, ty1, ty2, tx1, tx2, cv2.BORDER_CONSTANT, (128, 128, 128))image = image.astype(np.float32)# Normalize to [0,1]image /= 255.0# HWC to CHW format:image = np.transpose(image, [2, 0, 1])# CHW to NCHW format#image = np.expand_dims(image, axis=0)# Convert the image to row-major order, also known as "C order":#image = np.ascontiguousarray(image)return imagedef preprocess(img):img = cv2.resize(img, (640, 640))img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)img = img.transpose((2, 0, 1)).astype(np.float32)img /= 255.0return imgclass DataLoader:def __init__(self):self.index = 0self.length = BATCHself.batch_size = BATCH_SIZE# self.img_list = [i.strip() for i in open('calib.txt').readlines()]self.img_list = glob.glob(os.path.join(CALIB_IMG_DIR, "*.jpg"))assert len(self.img_list) > self.batch_size * self.length, '{} must contains more than '.format(CALIB_IMG_DIR) + str(self.batch_size * self.length) + ' images to calib'print('found all {} images to calib.'.format(len(self.img_list)))self.calibration_data = np.zeros((self.batch_size,3,height,width), dtype=np.float32)def reset(self):self.index = 0def next_batch(self):if self.index < self.length:for i in range(self.batch_size):assert os.path.exists(self.img_list[i + self.index * self.batch_size]), 'not found!!'img = cv2.imread(self.img_list[i + self.index * self.batch_size])img = preprocess_v1(img)self.calibration_data[i] = imgself.index += 1# example onlyreturn np.ascontiguousarray(self.calibration_data, dtype=np.float32)else:return np.array([])def __len__(self):return self.lengthdef main():# onnx2trtfp16_mode = Falseint8_mode = True print('*** onnx to tensorrt begin ***')# calibrationcalibration_stream = DataLoader()engine_model_path = "runs/train/exp4/weights/int8.engine"calibration_table = 'yolov5_tensorrt_int8_tools/models_save/calibration.cache'# fixed_engine,校准产生校准表engine_fixed = util_trt.get_engine(BATCH_SIZE, onnx_model_path, engine_model_path, fp16_mode=fp16_mode, int8_mode=int8_mode, calibration_stream=calibration_stream, calibration_table_path=calibration_table, save_engine=True)assert engine_fixed, 'Broken engine_fixed'print('*** onnx to tensorrt completed ***\n')if __name__ == '__main__':main()