一、Paddle Lite简介

Paddle Lite 是一种轻量级、灵活性强、易于扩展的高性能的深度学习预测框架，它可以支持诸如 ARM、OpenCL 、NPU 等等多种终端，同时拥有强大的图优化及预测加速能力。

二、环境安装

2.1 本地环境安装（基于python3.6）：

pip3 install paddlelite==2.12 -i http://pypi.douban.com/simple/pip3 install x2paddle -i http://pypi.douban.com/simple/

2.2 开发板Paddle Lite编译（基于python3.7）:

2.2.1 已经编译好的whl包（arm，支持python、耗时分析功能）下载链接

https://download.csdn.net/download/m0_46303486/87364716https://download.csdn.net/download/m0_46303486/87364716

2.2.2 自己编译(本地编译)

（1）基本环境安装（如已安装，请跳过）

sudo apt updatesudo apt-get install -y --no-install-recommends \gcc g++ make wget python unzip patchelf python-dev

（2） cmake安装，推荐使用3.10及以上版本（如已安装，请跳过）

wget https://www.cmake.org/files/v3.10/cmake-3.10.3.tar.gztar -zxvf cmake-3.10.3.tar.gzcd cmake-3.10.3./configuremakesudo make install

（3）下载Paddle Lite源码并编译

git clone https://github.com/PaddlePaddle/Paddle-Lite.gitcd Paddle-Litesudo rm -rf third-party#  --with_python=ON和--with_profile=ON为编译参数，编译过程中的可选参数见（4）常用编译参数，
本教程基于python，故使用python编译包sudo ./lite/tools/build_linux.sh --with_python=ON --with_profile=ON

（4）常用编译参数

参数	说明	可选范围	默认值
arch	目标硬件的架构版本	armv8 / armv7hf / armv7	armv8
toolchain	C++ 语言的编译器工具链	gcc	gcc
with_python	是否包含 python 编译包，目标应用程序是 python 语言时需配置为 ON	OFF / ON	OFF
with_cv	是否将 cv 函数加入编译包中	OFF / ON	OFF
with_log	是否在执行过程打印日志	OFF / ON	ON
with_exception	是否开启 C++ 异常	OFF / ON	OFF
with_profile	是否打开执行耗时分析	OFF / ON	OFF
with_precision_profile	是否打开逐层精度结果分析	OFF / ON	OFF
with_opencl	是否编译支持 OpenCL 的预测库	OFF / ON	OFF

（5）编译产物

编译成功后，会在/Paddle-Lite/build.lite.linux.armv8.gcc/

inference_lite_lib.armlinux.armv8/python/install/dist 目录下生成对应的.whl包，安装即可。

并且会生成相应的python版本的demo。

三、模型转换（在本地环境中进行）

如果想用 Paddle Lite 运行第三方来源（TensorFlow、Caffe、ONNX、PyTorch）模型，一般需要经过两次转化。即使用 X2paddle 工具将第三方模型转化为 PaddlePaddle 格式，再使用 opt工具将 PaddlePaddle 模型转化为Padde Lite 可支持格式。

为了简化这一过程，X2Paddle 集成了 opt 工具，提供一键转换 API，以 ONNX 为例（大部分模型都可以转换成ONNX）：

TensorFlow、Caffe、PyTorch直接转Padde Lite相关部分的API可参考：https://github.com/PaddlePaddle/X2Paddle/blob/develop/docs/inference_model_convertor/convert2lite_api.mdhttps://github.com/PaddlePaddle/X2Paddle/blob/develop/docs/inference_model_convertor/convert2lite_api.md

from x2paddle.convert import onnx2paddlemodel_path = "/pose/light_pose_sim.onnx"
save_dir = "./paddleLite_models/light_pose_sim_paddle"onnx2paddle(model_path, save_dir,convert_to_lite=True,lite_valid_places="arm",lite_model_type="naive_buffer")# model_path(str) 为 ONNX 模型路径
# save_dir(str) 为转换后模型保存路径
# convert_to_lite(bool) 表示是否使用 opt 工具，默认为 False# lite_valid_places(str) 指定转换类型，默认为 arm
# lite_valid_places参数目前可支持 arm、 opencl、 x86、 metal、 xpu、 bm、 mlu、 
# intel_fpga、 huawei_ascend_npu、imagination_nna、
# rockchip_npu、 mediatek_apu、 huawei_kirin_npu、 amlogic_npu，可以同时指定多个硬件平台 
# (以逗号分隔，优先级高的在前)，opt 将会自动选择最佳方式。# lite_model_type(str) 指定模型转化类型，目前支持两种类型：protobuf 和 naive_buffer，默认为 naive_buffer

转换后，会在指定目录下生成.nb文件，该文件就是在部署PaddleLite时需要用到的模型

四、模型部署，推理及应用

经过以上步骤，你已经成功完成了所有准备步骤，接下来就是将相关代码和模型移植到开发板上即可。

4.1 使用 Paddle Lite 执行推理的主要步骤

# (1) 设置配置信息config = MobileConfig()config.set_model_from_file("Your dictionary/opt.nb")# (2) 创建预测器predictor = create_paddle_predictor(config)# (3) 获取输入Tensor的引用，用来设置输入数据,参数表示第几个输入，单输入时为0input_tensor = predictor.get_input(0)input_tensor.from_numpy(input_data)# (4) 执行推理，需要在设置输入数据后使用predictor.run()# (5) 获取输出Tensor的引用，用来设置输出数据,参数表示第几个输出，单输出时为0output_tensor = predictor.get_output(0)# 将tensor数据类型转为ndarray类型ort_outs = output_tensor.numpy()

4.2 以Yolov5为例，使用.nb模型进行推理

代码是从Yolov5官方源码中扣出来的，修改main函数中的路径即可！

import cv2
import numpy as np
import onnxruntime as rt
from paddlelite.lite import *CLASSES = {0: 'person',1: 'bicycle',2: 'car',3: 'motorbike',4: 'aeroplane',5: 'bus',6: 'train',7: 'truck',8: 'boat',9: 'traffic light',10: 'fire hydrant',11: 'stop sign',12: 'parking meter',13: 'bench',14: 'bird',15: 'cat',16: 'dog',17: 'horse',18: 'sheep',19: 'cow',20: 'elephant',21: 'bear',22: 'zebra',23: 'giraffe',24: 'backpack',25: 'umbrella',26: 'handbag',27: 'tie',28: 'suitcase',29: 'frisbee',30: 'skis',31: 'snowboard',32: 'sports ball',33: 'kite',34: 'baseball bat',35: 'baseball glove',36: 'skateboard',37: 'surfboard',38: 'tennis racket',39: 'bottle',40: 'wine glass',41: 'cup',42: 'fork',43: 'knife',44: 'spoon',45: 'bowl',46: 'banana',47: 'apple',48: 'sandwich',49: 'orange',50: 'broccoli',51: 'carrot',52: 'hot dog',53: 'pizza',54: 'donut',55: 'cake',56: 'chair',57: 'sofa',58: 'potted plant',59: 'bed',60: 'dining table',61: 'toilet',62: 'tvmonitor',63: 'laptop',64: 'mouse',65: 'remote',66: 'keyboard',67: 'cell phone',68: 'microwave',69: 'oven',70: 'toaster',71: 'sink',72: 'refrigerator',73: 'book',74: 'clock',75: 'vase',76: 'scissors',77: 'teddy bear',78: 'hair drier',79: 'toothbrush'
}
def box_iou(box1, box2, eps=1e-7):(a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)inter = (np.min(a2, b2) - np.max(a1, b1)).clamp(0).prod(2)return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):# Resize and pad image while meeting stride-multiple constraintsshape = im.shape[:2]  # current shape [height, width]if isinstance(new_shape, int):new_shape = (new_shape, new_shape)# Scale ratio (new / old)r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])if not scaleup:  # only scale down, do not scale up (for better val mAP)r = min(r, 1.0)# Compute paddingratio = r, r  # width, height ratiosnew_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh paddingif auto:  # minimum rectangledw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh paddingelif scaleFill:  # stretchdw, dh = 0.0, 0.0new_unpad = (new_shape[1], new_shape[0])ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratiosdw /= 2  # divide padding into 2 sidesdh /= 2if shape[::-1] != new_unpad:  # resizeim = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))left, right = int(round(dw - 0.1)), int(round(dw + 0.1))im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add borderreturn im, ratio, (dw, dh)def onnx_inf(onnxModulePath, data):sess = rt.InferenceSession(onnxModulePath)input_name = sess.get_inputs()[0].nameoutput_name = sess.get_outputs()[0].namepred_onnx = sess.run([output_name], {input_name: data.reshape(1, 3, 640, 640).astype(np.float32)})return pred_onnxdef xywh2xyxy(x):# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right# isinstance 用来判断某个变量是否属于某种类型y = np.copy(x)y[..., 0] = x[..., 0] - x[..., 2] / 2  # top left xy[..., 1] = x[..., 1] - x[..., 3] / 2  # top left yy[..., 2] = x[..., 0] + x[..., 2] / 2  # bottom right xy[..., 3] = x[..., 1] + x[..., 3] / 2  # bottom right yreturn ydef nms_boxes(boxes, scores):x = boxes[:, 0]y = boxes[:, 1]w = boxes[:, 2] - boxes[:, 0]h = boxes[:, 3] - boxes[:, 1]areas = w * horder = scores.argsort()[::-1]keep = []while order.size > 0:i = order[0]keep.append(i)xx1 = np.maximum(x[i], x[order[1:]])yy1 = np.maximum(y[i], y[order[1:]])xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)inter = w1 * h1ovr = inter / (areas[i] + areas[order[1:]] - inter)inds = np.where(ovr <= 0.45)[0]order = order[inds + 1]keep = np.array(keep)return keepdef non_max_suppression(prediction,conf_thres=0.25,iou_thres=0.45,classes=None,agnostic=False,multi_label=False,labels=(),max_det=300,nm=0,  # number of masks
):"""Non-Maximum Suppression (NMS) on inference results to reject overlapping detectionsReturns:list of detections, on (n,6) tensor per image [xyxy, conf, cls]"""# Checksassert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'if isinstance(prediction, (list, tuple)):  # YOLOv5 model in validation model, output = (inference_out, loss_out)prediction = prediction[0]  # select only inference outputbs = prediction.shape[0]  # batch sizenc = prediction.shape[2] - nm - 5  # number of classesxc = prediction[..., 4] > conf_thres  # candidates# Settingsmax_wh = 7680  # (pixels) maximum box width and heightmax_nms = 30000  # maximum number of boxes into torchvision.ops.nms()redundant = True  # require redundant detectionsmulti_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)merge = False  # use merge-NMSmi = 5 + nc  # mask start indexoutput = [np.zeros((0, 6 + nm))] * bsfor xi, x in enumerate(prediction):  # image index, image inferencex = x[xc[xi]]  # confidenceif labels and len(labels[xi]):lb = labels[xi]v = np.zeros(len(lb), nc + nm + 5)v[:, :4] = lb[:, 1:5]  # boxv[:, 4] = 1.0  # confv[range(len(lb)), lb[:, 0].long() + 5] = 1.0  # clsx = np.concatenate((x, v), 0)# If none remain process next imageif not x.shape[0]:continuex[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf# Box/Maskbox = xywh2xyxy(x[:, :4])  # center_x, center_y, width, height) to (x1, y1, x2, y2)mask = x[:, mi:]  # zero columns if no masks# Detections matrix nx6 (xyxy, conf, cls)if multi_label:i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).Tx = np.concatenate((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1)else:  # best class onlyconf = np.max(x[:, 5:mi], 1).reshape(box.shape[:1][0], 1)j = np.argmax(x[:, 5:mi], 1).reshape(box.shape[:1][0], 1)x = np.concatenate((box, conf, j, mask), 1)[conf.reshape(box.shape[:1][0]) > conf_thres]# Filter by classif classes is not None:x = x[(x[:, 5:6] == np.array(classes, device=x.device)).any(1)]# Check shapen = x.shape[0]  # number of boxesif not n:  # no boxescontinueindex = x[:, 4].argsort(axis=0)[:max_nms][::-1]x = x[index]# Batched NMSc = x[:, 5:6] * (0 if agnostic else max_wh)  # classesboxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scoresi = nms_boxes(boxes, scores)i = i[:max_det]  # limit detections# 用来合并框的if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrixweights = iou * scores[None]  # box weightsx[i, :4] = np.multiply(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxesif redundant:i = i[iou.sum(1) > 1]  # require redundancyoutput[xi] = x[i]return outputdef clip_boxes(boxes, shape):# Clip boxes (xyxy) to image shape (height, width)boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1])  # x1, x2boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0])  # y1, y2def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):# Rescale boxes (xyxy) from img1_shape to img0_shapeif ratio_pad is None:  # calculate from img0_shapegain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / newpad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh paddingelse:gain = ratio_pad[0][0]pad = ratio_pad[1]boxes[..., [0, 2]] -= pad[0]  # x paddingboxes[..., [1, 3]] -= pad[1]  # y paddingboxes[..., :4] /= gainclip_boxes(boxes, img0_shape)return boxesif __name__ == "__main__":PaddleLite_ModulePath = "/PATH_to_nb_Model"IMG_Path = "/PATH_to_test.jpg"imgsz = (640, 640)img = cv2.imread(IMG_Path)img = cv2.resize(img, (640, 640))# preprocessim = letterbox(img, imgsz, auto=True)[0]  # padded resizeim = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGBim = np.ascontiguousarray(im)  # contiguousim = im.astype(np.float32)im /= 255  # 0 - 255 to 0.0 - 1.0if len(im.shape) == 3:im = im[None]  # expand for batch dim# 1. 设置配置信息config = MobileConfig()config.set_model_from_file(PaddleLite_ModulePath)# 2. 创建预测器predictor = create_paddle_predictor(config)# 3. 获取输入Tensor的引用，用来设置输入数据,参数表示第几个输入，单输入时为0input_tensor = predictor.get_input(0)input_tensor.from_numpy(im)# 4. 执行推理，需要在设置输入数据后使用predictor.run()print("predictor:", predictor)# 5. 获取输出Tensor的引用，用来设置输出数据,参数表示第几个输出，单输出时为0output_tensor = predictor.get_output(0)pred = output_tensor.numpy()# NMSconf_thres = 0.25  # confidence thresholdiou_thres = 0.45  # NMS IOU thresholdmax_det = 1000  # maximum detections per imageclasses = None  # filter by class: --class 0, or --class 0 2 3agnostic_nms = False  # class-agnostic NMSpred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)# Process predictionsseen = 0for i, det in enumerate(pred):  # per imageseen += 1if len(det):# Rescale boxes from img_size to im0 sizedet[:, :4] = scale_boxes(im.shape[2:], det[:, :4], img.shape).round()# print(pred)outputs = pred[0][:, :6]if len(outputs[:, 4:] > 0):for i in outputs:prob = i[4]cls = int(i[5])prob = np.around(prob, decimals=2)if prob >= 0.4:all_pred_boxes = i[:4]for b in range(len(all_pred_boxes)):x1 = int(all_pred_boxes[0])y1 = int(all_pred_boxes[1])x2 = int(all_pred_boxes[2])y2 = int(all_pred_boxes[3])cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 1)cv2.putText(img, CLASSES[cls]+' '+str(prob), (x1, y1), cv2.FONT_HERSHEY_TRIPLEX, 0.8, (0, 255, 0), 1, 4)cv2.imwrite('./data/images/test_paddle_03.png', img)