1/1.8" 400 万像素CMOS 传感器支持24 倍电动变焦、自动聚焦支持AIISP 图像增强，增强低噪效果支持标准ONVIF、GB28181 协议内嵌智能深度学习算力2.0Tops开放AI算法部署二次开发   

想问下各位大佬在mdc610上摄像头配置的intanceid为25,用的森云233的相机，相机上有数据流，为什么mviz上没有对应可选的话题呢1.查询摄像头的event信息2.查询event频率信息3.在MDC系统上执行如下命令，启动camera_mviz服务4.在本地ubuntu上启动mviz,没有可以选择的topic  

 你好，这边在安装mindstudio 的时候提示cann 没有安装版本如下：ubuntu 18.04Ascend-cann-toolkit_5.20.t6.2.b060_linux-x86_64.runmindstudio=3.0.4 

MDC610，开发环境版本：8.10.T0.1.B205执行yolov5模型推理本地图片遇到报错：[ERROR]   execute model failed, modelId is 1, error code: 500002在相同环境用ascend/sample中yolov3 caffemodel转出来的模型可以推理，官方yolov5的pt转onnx转om报错，请问是什么原因？再请问平台最高支持yolov几？附转模型过程：pt转onnx：官方yolov5s 6.0版本，转了2版，有concat和无concat版本，转换参数：--weights yolov5s1.pt --opset 11 --include onnx --simplifyonnx转om：转换指令：atc --model=yolov5s1_concat.onnx --framework=5 --output=/home/eddie/zls/demo/model/v5/yolov5s1_concat --soc_version=BS9SX1AA --log=warning >log.txt （log在附件）运行环境的log也附上，但是没有细节报错。

 一直报错，加环境变量，改权限后还是报错  

使用rpa，登录系统的时候，输入用户名和密码后还需要输入验证码，如何获取验证码？请指教，谢谢！

图像识别技术原理是什么样的？

mdc610 上是否支持 Ascend sample 代码中samples/cplusplus/common/acllite目录下的 ACLLite库的编译。如果支持该怎么修改sample中对应的，1、INSTALL_DIR 2、THIRDPART_PATH 两个变量，看了下自带MakeFile中设置的路径在我当前mdc目录下找不到对应的，理论上肯定能支持acllite这个库编译的，请问我该如何操作！！！！！谢谢。。。。。。。。

第九期 最后提交错了  能重新提交一次吗

安装了910b的cann，RC2，安装了torch2.3.1和torch_npu2.3.1，python3.10.15，一直报错python3 -c "import torch;import torch_npu; a = torch.randn(3, 4).npu(); print(a + a);" 报错  [W compiler_depend.ts:615] Warning: expandable_segments currently defaults to false. You can enable this feature by `export PYTORCH_NPU_ALLOC_CONF = expandable_segments:True`. (function operator()) EH9999: Inner Error! EH9999  [Init][Env]init env failed![FUNC:ReportInnerError][FILE:log_inner.cpp][LINE:145]         TraceBack (most recent call last):         build op model failed, result = 500001[FUNC:ReportInnerError][FILE:log_inner.cpp][LINE:145]  Traceback (most recent call last):   File "<string>", line 1, in <module>   File "/root/.local/lib/python3.10/site-packages/torch/_tensor.py", line 464, in __repr__     return torch._tensor_str._str(self, tensor_contents=tensor_contents)   File "/root/.local/lib/python3.10/site-packages/torch/_tensor_str.py", line 697, in _str     return _str_intern(self, tensor_contents=tensor_contents)   File "/root/.local/lib/python3.10/site-packages/torch/_tensor_str.py", line 617, in _str_intern     tensor_str = _tensor_str(self, indent)   File "/root/.local/lib/python3.10/site-packages/torch/_tensor_str.py", line 349, in _tensor_str     formatter = _Formatter(get_summarized_data(self) if summarize else self)   File "/root/.local/lib/python3.10/site-packages/torch/_tensor_str.py", line 138, in __init__     tensor_view, torch.isfinite(tensor_view) & tensor_view.ne(0) RuntimeError: The Inner error is reported as above. The process exits for this inner error, and the current working operator name is IsFinite. Since the operator is called asynchronously, the stacktrace may be inaccurate. If you want to get the accurate stacktrace, pleace set the environment variable ASCEND_LAUNCH_BLOCKING=1. [ERROR] 2024-11-21-10:49:01 (PID:20848, Device:0, RankID:0) ERR00100 PTA call acl api failed 

第九期问题求助：给的4000多张测试集图片可以直接标注后拿来训练吗

python=3.8, torch=2.1.0 torch-npu=2.1.0,CANN_toolkit=7.0.1.1，版本应该都是对应的python是miniconda下的环境，有source set_env.sh ，可以find到libhccl.so这个文件，但是export LD_LIBRARY_PATH没有用报错信息：Traceback (most recent call last):   File "/home/x/miniconda3/envs/pytorch38/lib/python3.8/site-packages/torch_npu/__init__.py", line 14, in <module>     import torch_npu.npu   File "/home/x/miniconda3/envs/pytorch38/lib/python3.8/site-packages/torch_npu/npu/__init__.py", line 110, in <module>     from .utils import (synchronize, device_count, can_device_access_peer, set_device, current_device, get_device_name,   File "/home/x/miniconda3/envs/pytorch38/lib/python3.8/site-packages/torch_npu/npu/utils.py", line 11, in <module>     import torch_npu._CImportError: libhccl.so: cannot open shared object file: No such file or directory. Please check that the cann package is installed. Please run 'source set_env.sh' in the CANN installation path.

请问下文档中的ImageData结构体中的rawData 图像数据指针 mbufData Mbuf数据指针 这两个数据有啥区别呢？，是1)、里面存的东西是一样的，只是存储的位置不一样？2)、里面的数据不一样，存储的位置也不一样。文档中没有详细的说明。

 2.6 学习率  提供了4种不同的学习率形式选择，分别是：exponential，cosine_annealing，cosine_annealing_V2，cosine_annealing_sample。这里选择的是cosine_annealing  """Learning rate scheduler.""" import math from collections import Counter  import numpy as np  def linear_warmup_lr(current_step, warmup_steps, base_lr, init_lr):     """Linear learning rate."""     lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)     lr = float(init_lr) + lr_inc * current_step     return lr  def warmup_step_lr(lr, lr_epochs, steps_per_epoch, warmup_epochs, max_epoch, gamma=0.1):     """Warmup step learning rate."""     base_lr = lr     warmup_init_lr = 0     total_steps = int(max_epoch * steps_per_epoch)     warmup_steps = int(warmup_epochs * steps_per_epoch)     milestones = lr_epochs     milestones_steps = []     for milestone in milestones:         milestones_step = milestone * steps_per_epoch         milestones_steps.append(milestones_step)      lr_each_step = []     lr = base_lr     milestones_steps_counter = Counter(milestones_steps)     for i in range(total_steps):         if i < warmup_steps:             lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)         else:             lr = lr * gamma**milestones_steps_counter[i]         lr_each_step.append(lr)      return np.array(lr_each_step).astype(np.float32)  def multi_step_lr(lr, milestones, steps_per_epoch, max_epoch, gamma=0.1):     return warmup_step_lr(lr, milestones, steps_per_epoch, 0, max_epoch, gamma=gamma)  def step_lr(lr, epoch_size, steps_per_epoch, max_epoch, gamma=0.1):     lr_epochs = []     for i in range(1, max_epoch):         if i % epoch_size == 0:             lr_epochs.append(i)     return multi_step_lr(lr, lr_epochs, steps_per_epoch, max_epoch, gamma=gamma)  def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch, T_max, eta_min=0):     """Cosine annealing learning rate."""     base_lr = lr     warmup_init_lr = 0     total_steps = int(max_epoch * steps_per_epoch)     warmup_steps = int(warmup_epochs * steps_per_epoch)      lr_each_step = []     for i in range(total_steps):         last_epoch = i // steps_per_epoch         if i < warmup_steps:             lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)         else:             lr = eta_min + (base_lr - eta_min) * (1. + math.cos(math.pi * last_epoch / T_max)) / 2         lr_each_step.append(lr)      return np.array(lr_each_step).astype(np.float32)  def warmup_cosine_annealing_lr_V2(lr, steps_per_epoch, warmup_epochs, max_epoch, T_max, eta_min=0):     """Cosine annealing learning rate V2."""     base_lr = lr     warmup_init_lr = 0     total_steps = int(max_epoch * steps_per_epoch)     warmup_steps = int(warmup_epochs * steps_per_epoch)      last_lr = 0     last_epoch_V1 = 0      T_max_V2 = int(max_epoch * 1 / 3)      lr_each_step = []     for i in range(total_steps):         last_epoch = i // steps_per_epoch         if i < warmup_steps:             lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)         else:             if i < total_steps * 2 / 3:                 lr = eta_min + (base_lr - eta_min) * (1. + math.cos(math.pi * last_epoch / T_max)) / 2                 last_lr = lr                 last_epoch_V1 = last_epoch             else:                 base_lr = last_lr                 last_epoch = last_epoch - last_epoch_V1                 lr = eta_min + (base_lr - eta_min) * (1. + math.cos(math.pi * last_epoch / T_max_V2)) / 2          lr_each_step.append(lr)     return np.array(lr_each_step).astype(np.float32)  def warmup_cosine_annealing_lr_sample(lr, steps_per_epoch, warmup_epochs, max_epoch, T_max, eta_min=0):     """Warmup cosine annealing learning rate."""     start_sample_epoch = 60     step_sample = 2     tobe_sampled_epoch = 60     end_sampled_epoch = start_sample_epoch + step_sample * tobe_sampled_epoch     max_sampled_epoch = max_epoch + tobe_sampled_epoch     T_max = max_sampled_epoch      base_lr = lr     warmup_init_lr = 0     total_steps = int(max_epoch * steps_per_epoch)     total_sampled_steps = int(max_sampled_epoch * steps_per_epoch)     warmup_steps = int(warmup_epochs * steps_per_epoch)      lr_each_step = []      for i in range(total_sampled_steps):         last_epoch = i // steps_per_epoch         if last_epoch in range(start_sample_epoch, end_sampled_epoch, step_sample):             continue         if i < warmup_steps:             lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)         else:             lr = eta_min + (base_lr - eta_min) * (1. + math.cos(math.pi * last_epoch / T_max)) / 2         lr_each_step.append(lr)      assert total_steps == len(lr_each_step)     return np.array(lr_each_step).astype(np.float32)  def get_lr(args, steps_per_epoch):     """generate learning rate."""     if args.lr_scheduler == 'exponential':         lr = warmup_step_lr(args.lr, args.lr_epochs, steps_per_epoch, args.warmup_epochs, args.max_epoch,                             gamma=args.lr_gamma)     elif args.lr_scheduler == 'cosine_annealing':         lr = warmup_cosine_annealing_lr(args.lr, steps_per_epoch, args.warmup_epochs,                                         args.max_epoch, args.T_max, args.eta_min)     elif args.lr_scheduler == 'cosine_annealing_V2':         lr = warmup_cosine_annealing_lr_V2(args.lr, steps_per_epoch, args.warmup_epochs,                                            args.max_epoch, args.T_max, args.eta_min)     elif args.lr_scheduler == 'cosine_annealing_sample':         lr = warmup_cosine_annealing_lr_sample(args.lr, steps_per_epoch, args.warmup_epochs,                                                args.max_epoch, args.T_max, args.eta_min)     else:         raise NotImplementedError(args.lr_scheduler)     return lr 2.7 模型训练 import os import time import mindspore as ms import mindspore.nn as nn import mindspore.communication as comm  from src.logger import get_logger from src.util import AverageMeter, get_param_groups, cpu_affinity from src.initializer import default_recurisive_init, load_yolov5_params  ms.set_seed(1)  def train_preprocess():     if args.lr_scheduler == 'cosine_annealing' and args.max_epoch > args.T_max:         args.T_max = args.max_epoch      args.lr_epochs = list(map(int, args.lr_epochs.split(',')))     args.data_root = os.path.join(args.data_dir, 'train2017')     args.annFile = os.path.join(args.data_dir, 'annotations/instances_train2017.json')     if args.pretrained_checkpoint:         args.pretrained_checkpoint = args.pretrained_checkpoint     args.device_id = int(os.getenv('DEVICE_ID', '1'))     ms.set_context(mode=ms.GRAPH_MODE, device_target=args.device_target)      if args.is_distributed:         # init distributed         init_distribute()      # for promoting performance in GPU device     if args.device_target == "GPU" and args.bind_cpu:         cpu_affinity(args.rank, min(args.group_size, 8))          # logger module is managed by config, it is used in other function. e.x. config.logger.info("xxx")     args.logger = get_logger(args.output_dir, args.rank)   def run_train():     train_preprocess()      loss_meter = AverageMeter('loss')     dict_version = {'yolov5s': 0, 'yolov5m': 1, 'yolov5l': 2, 'yolov5x': 3}     network = YOLOV5(is_training=True, version=dict_version[args.yolov5_version])     # default is kaiming-normal     default_recurisive_init(network)     load_yolov5_params(args, network)     network = YoloWithLossCell(network)      ds = create_yolo_dataset(image_dir=args.data_root, anno_path=args.annFile, is_training=True,                              batch_size=args.per_batch_size, device_num=args.group_size,                              rank=args.rank, config=args)     args.logger.info('Finish loading dataset')      steps_per_epoch = ds.get_dataset_size()     lr = get_lr(args, steps_per_epoch)     opt = nn.Momentum(params=get_param_groups(network), momentum=args.momentum, learning_rate=ms.Tensor(lr),                       weight_decay=args.weight_decay, loss_scale=args.loss_scale)     network = nn.TrainOneStepCell(network, opt, args.loss_scale // 2)     network.set_train()      data_loader = ds.create_tuple_iterator(do_copy=False)     first_step = True     t_end = time.time()      for epoch_idx in range(args.max_epoch):         for step_idx, data in enumerate(data_loader):             images = data[0]             input_shape = images.shape[2:4]             input_shape = ms.Tensor(tuple(input_shape[::-1]), ms.float32)             loss = network(images, data[2], data[3], data[4], data[5], data[6],                            data[7], input_shape)             loss_meter.update(loss.asnumpy())              # it is used for loss, performance output per config.log_interval steps.             if (epoch_idx * steps_per_epoch + step_idx) % args.log_interval == 0:                 time_used = time.time() - t_end                 if first_step:                     fps = args.per_batch_size * args.group_size / time_used                     per_step_time = time_used * 1000                     first_step = False                 else:                     fps = args.per_batch_size * args.log_interval * args.group_size / time_used                     per_step_time = time_used / args.log_interval * 1000                 args.logger.info('epoch[{}], iter[{}], {}, fps:{:.2f} imgs/sec, '                                    'lr:{}, per step time: {}ms'.format(                                     epoch_idx + 1, step_idx + 1, loss_meter, fps,                                      lr[epoch_idx * steps_per_epoch + step_idx], per_step_time))                 t_end = time.time()                 loss_meter.reset()         if args.rank == 0:             ckpt_name = os.path.join(args.output_dir, "yolov5_{}_{}.ckpt".format(epoch_idx + 1, steps_per_epoch))             ms.save_checkpoint(network, ckpt_name)      args.logger.info('==========end training===============')  if __name__ == "__main__":     run_train() （一大堆结果）2.8 模型测试 import os import time  import numpy as np  import mindspore as ms # from model_utils.config import config # from src.yolo import YOLOV5 from src.logger import get_logger from src.util import DetectionEngine # from src.yolo_dataset import create_yolo_dataset  from src.util import DetectionEngine  def eval_preprocess():     args.data_root = os.path.join(args.data_dir, 'val2017')     args.ann_file = os.path.join(args.data_dir, 'annotations/instances_val2017.json')     ms.set_context(mode=ms.GRAPH_MODE, device_target=args.device_target)      # logger module is managed by config, it is used in other function. e.x. config.logger.info("xxx")     args.logger = get_logger(args.output_dir, args.rank)  def load_parameters(network, filename):     args.logger.info("yolov5 pretrained network model: %s", filename)     param_dict = ms.load_checkpoint(filename)     param_dict_new = {}     for key, values in param_dict.items():         if key.startswith('moments.'):             continue         elif key.startswith('yolo_network.'):             param_dict_new[key[13:]] = values         else:             param_dict_new[key] = values     ms.load_param_into_net(network, param_dict_new)     args.logger.info('load_model %s success', filename)  def run_eval():     eval_preprocess()     start_time = time.time()     args.logger.info('Creating Network....')     dict_version = {'yolov5s': 0, 'yolov5m': 1, 'yolov5l': 2, 'yolov5x': 3}     network = YOLOV5(is_training=False, version=dict_version[args.yolov5_version])      if os.path.isfile(args.pretrained):         load_parameters(network, args.pretrained)     else:         raise FileNotFoundError(f"{args.pretrained} is not a filename.")      ds = create_yolo_dataset(args.data_root, args.ann_file, is_training=False, batch_size=args.per_batch_size,                              device_num=1, rank=0, shuffle=False, config=args)      args.logger.info('testing shape : %s', args.test_img_shape)     args.logger.info('total %d images to eval', ds.get_dataset_size() * args.per_batch_size)      network.set_train(False)      # init detection engine     detection = DetectionEngine(args, args.test_ignore_threshold)      input_shape = ms.Tensor(tuple(args.test_img_shape), ms.float32)     args.logger.info('Start inference....')     for index, data in enumerate(ds.create_dict_iterator(output_numpy=True, num_epochs=1)):         image = data["image"]         # adapt network shape of input data         image = np.concatenate((image[..., ::2, ::2], image[..., 1::2, ::2],                                 image[..., ::2, 1::2], image[..., 1::2, 1::2]), axis=1)         image = ms.Tensor(image)         image_shape_ = data["image_shape"]         image_id_ = data["img_id"]         output_big, output_me, output_small = network(image, input_shape)         output_big = output_big.asnumpy()         output_me = output_me.asnumpy()         output_small = output_small.asnumpy()         detection.detect([output_small, output_me, output_big], args.per_batch_size, image_shape_, image_id_)          if index % 50 == 0:             args.logger.info('Processing... {:.2f}% '.format(index / ds.get_dataset_size() * 100))      args.logger.info('Calculating mAP...')     detection.do_nms_for_results()     result_file_path = detection.write_result()     args.logger.info('result file path: %s', result_file_path)     eval_result = detection.get_eval_result()      cost_time = time.time() - start_time     eval_log_string = '\n=============coco eval result=========\n' + eval_result     args.logger.info(eval_log_string)     args.logger.info('testing cost time %.2f h', cost_time / 3600.)  if __name__ == "__main__":     run_eval() （一大堆结果）

 1、算法介绍 YOLOv5是一种单阶段目标检测算法，该算法在YOLOv4的基础上添加了一些新的改进思路，使其速度与精度都得到了极大的性能提升。需要说明的是，YOLOv5没有论文，其作者是Mosaic Augmentation 的创造者，YOLOv5在gtihub上的链接为：https://github.com/ultralytics/yolov5  1.1 模型结构： YOLOv5网络模型主要有四个部分组成，分别为：输入端，Backbone，Neck，Prediction。和YOLOv4相比，YOLOv5做了一些优化，主要有(1) 输入端：Mosaic数据增强，（2）Backbone：Focus结构，CSP结构, (3）Neck：FPN+PAN结构,（4）Prediction：GIOU_Loss。下面是YOLOv5的整体网络结构图： %E5%9B%BE%E7%89%87-4.png  2、模型实现 2.1 环境准备与数据读取  案例基于MindSpore1.8的GPU版本实现，在GXT1080TI单卡上完成训练。 案例使用数据集为coco_mini，是从COCO数据集中分离出来的一小部分数据,其中训练集50张图像，测试集10张图像，数据格式为图像和json文件。coco_mini数据集的下载链接为: https://pan.baidu.com/s/1FJ_Css0KoXqKqifmUzmBUw 提取码: g55f。下载好的数据集包括3个文件，分别对应数据标签，训练数据，测试数据，文件路径结构如下：    .datasets/     └── coco_mini_dataset         ├── annotations             ├──instances_train2017.json             └──instances_val2017.json          ├── train2017         └── val2017          > 下面是数据的可视化展示:  import numpy as np import matplotlib import os import glob import math from PIL import Image, ImageSequence from matplotlib import pyplot as plt  #显示下载好的数据 train_image_path = "dataset/mini_coco_dataset/train2017/"  image = [] for root, dirs, files in os.walk(train_image_path):     for i in range(6):         image.append(files[i])  def show_image(image_list,num = 6):     '''     #image_list: 图像序列，numpy数组     #num: 显示图片的数量     '''     img_titles = []     for ind,img in enumerate(image_list):         if ind == num:             break         img_titles.append(ind)      for i in range(len(img_titles)):         if len(img_titles) > 6:             row = 3         elif 3<len(img_titles)<=6:             row = 2         else:             row = 1         col = math.ceil(len(img_titles)/row)         plt.subplot(row,col,i+1),plt.imshow(Image.open(os.path.join(train_image_path, image[i])))         plt.title(img_titles[i])         plt.xticks([]),plt.yticks([])     plt.show()      show_image(image,num=4) 2.2 参数定义（包括lr,epoch, pretrained_checkpoints) import argparse import mindspore as ms  import sys sys.argv=[''] del sys  ms.set_seed(1)  parser = argparse.ArgumentParser('mindspore coco training')  # device related parser.add_argument('--device_target', type=str, default='GPU', help='device where the code will be implemented.')  # dataset related parser.add_argument('--data_dir', default='./dataset/mini_coco_dataset/', type=str, help='Train dataset directory.') parser.add_argument('--output_dir', default='./output', type=str, help='output') parser.add_argument('--pretrained_checkpoint', default='',type=str, help='pretrained_checkpoint') parser.add_argument('--per_batch_size', default=8, type=int, help='Batch size for Training. Default: 8')  # network related parser.add_argument('--yolov5_version', default='yolov5s', type=str,                     help='The version of YOLOv5, options: yolov5s, yolov5m, yolov5l, yolov5x') parser.add_argument('--pretrained_backbone', default='', type=str, help='The pretrained file of yolov5. Default: "".') parser.add_argument('--resume_yolov5', default='', type=str,                     help='The ckpt file of YOLOv5, which used to fine tune. Default: ""')  # optimizer and lr related parser.add_argument('--lr_scheduler', default='cosine_annealing', type=str,                     help='Learning rate scheduler, options: exponential, cosine_annealing. Default: exponential') parser.add_argument('--lr', default=0.0005, type=float, help='Learning rate. Default: 0.01') parser.add_argument('--lr_epochs', type=str, default='220,250',                     help='Epoch of changing of lr changing, split with ",". Default: 220,250') parser.add_argument('--lr_gamma', type=float, default=0.1,                     help='Decrease lr by a factor of exponential lr_scheduler. Default: 0.1') parser.add_argument('--eta_min', type=float, default=0., help='Eta_min in cosine_annealing scheduler. Default: 0') parser.add_argument('--T_max', type=int, default=300, help='T-max in cosine_annealing scheduler. Default: 320') parser.add_argument('--max_epoch', type=int, default=300, help='Max epoch num to train the model. Default: 320') parser.add_argument('--warmup_epochs', default=4, type=float, help='Warmup epochs. Default: 0') parser.add_argument('--weight_decay', type=float, default=0.0005, help='Weight decay factor. Default: 0.0005') parser.add_argument('--momentum', type=float, default=0.9, help='Momentum. Default: 0.9') parser.add_argument('--bind_cpu', default= True, help='Whether bind cpu when distributed training. Default: True') parser.add_argument('--resize_rate', default= 10, help='resize_rate') parser.add_argument('--anchor_scales', default= [[12, 16],[19, 36],[40, 28],[36, 75],[76, 55],[72, 146],[142, 110],                                                 [192, 243],                                                 [459, 401]], help='resize_rate') parser.add_argument('--input_shape', default= [[3, 32, 64, 128, 256, 512, 1],[3, 48, 96, 192, 384, 768, 2],                                               [3, 64, 128, 256, 512, 1024, 3],[3, 80, 160, 320, 640, 1280, 4]],                                                help='resize_rate') parser.add_argument('--num_classes', default= 80, help='num_classes') parser.add_argument('--max_box', default= 150, help='num_classes') parser.add_argument('--hue', default= 0.015, help='num_classes') parser.add_argument('--saturation', default= 1.5, help='num_classes') parser.add_argument('--value', default= 0.4, help='num_classes') parser.add_argument('--jitter', default= 0.3, help='num_classes')  # loss related parser.add_argument('--loss_scale', type=int, default=1024, help='Static loss scale. Default: 1024') parser.add_argument('--label_smooth', type=int, default=0, help='Whether to use label smooth in CE. Default:0') parser.add_argument('--label_smooth_factor', type=float, default=0.1,                     help='Smooth strength of original one-hot. Default: 0.1')  # logging related parser.add_argument('--log_interval', type=int, default=6, help='Logging interval steps. Default: 100') parser.add_argument('--ckpt_path', type=str, default='outputs/', help='Checkpoint save location. Default: outputs/') parser.add_argument('--ckpt_interval', type=int, default=None, help='Save checkpoint interval. Default: None')  # distributed related parser.add_argument('--is_distributed', type=int, default=0,                     help='Distribute train or not, 1 for yes, 0 for no. Default: 1') parser.add_argument('--rank', type=int, default=0, help='Local rank of distributed. Default: 0') parser.add_argument('--group_size', type=int, default=1, help='World size of device. Default: 1')  # test related parser.add_argument('--pretrained',default='./output/yolov5_300_6.ckpt', type=str, help='checkpoints') parser.add_argument('--test_img_shape',default=[640,640], help='test image shape') parser.add_argument('--test_ignore_threshold',default=0.001, help='test_ignore_threshold') parser.add_argument('--eval_nms_thresh',default=0.5, help='eval_nms_thresh') parser.add_argument('--ignore_threshold',default=0.5, help='ignore_threshold') parser.add_argument('--multi_label',default=True, help='ignore_threshold') parser.add_argument('--multi_label_thresh',default=0.1, help='ignore_threshold') parser.add_argument('--labels',default=[ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat',           'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat',           'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack',           'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',           'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',           'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',           'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',           'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',           'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book',           'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'], help='labels') parser.add_argument('--coco_ids',default=[ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27,             28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53,             54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80,             81, 82, 84, 85, 86, 87, 88, 89, 90 ], help='coco_ids')  args, _ = parser.parse_known_args()  2.3 数据集加载  由于YOLOv5主要目的是进行目标检测，因此在这里构建的数据集格式是参照COCO数据集格式进行构建，还定义了一些函数来对没有标注的数据进行筛选。  YOLOv5的数据集加载中最主要是利用了mosaic实现数据增强手段。Mosaic利用了四张图片，对四张图片进行拼接，每一张图片都有其对应的框，将四张图片拼接之后就获得一张新的图片，同时也获得这张图片对应的框，然后将这样一张新的图片传入到神经网络当中去学习。  """YOLOV5 dataset.""" from __future__ import division import os import multiprocessing import random import numpy as np import cv2 from PIL import Image import math import numpy as np from pycocotools.coco import COCO import mindspore.dataset as ds import mindspore.dataset.vision.c_transforms as CV from mindspore.dataset.vision import Normalize,HWC2CHW from src.transforms import reshape_fn, MultiScaleTrans, PreprocessTrueBox  min_keypoints_per_image = 10 GENERATOR_PARALLEL_WORKER = 8  class DistributedSampler:     """Distributed sampler."""      def __init__(self, dataset_size, num_replicas=None, rank=None, shuffle=True):         if num_replicas is None:             print("***********Setting world_size to 1 since it is not passed in ******************")             num_replicas = 1         if rank is None:             print("***********Setting rank to 0 since it is not passed in ******************")             rank = 0         self.dataset_size = dataset_size         self.num_replicas = num_replicas         self.rank = rank         self.epoch = 0         self.num_samples = int(math.ceil(dataset_size * 1.0 / self.num_replicas))         self.total_size = self.num_samples * self.num_replicas         self.shuffle = shuffle      def __iter__(self):         # deterministically shuffle based on epoch         if self.shuffle:             indices = np.random.RandomState(seed=self.epoch).permutation(self.dataset_size)             # np.array type. number from 0 to len(dataset_size)-1, used as             # index of dataset             indices = indices.tolist()             self.epoch += 1             # change to list type         else:             indices = list(range(self.dataset_size))          # add extra samples to make it evenly divisible         indices += indices[:(self.total_size - len(indices))]         assert len(indices) == self.total_size          # subsample         indices = indices[self.rank:self.total_size:self.num_replicas]         assert len(indices) == self.num_samples          return iter(indices)      def __len__(self):         return self.num_samples  def _has_only_empty_bbox(anno):     return all(any(o <= 1 for o in obj["bbox"][2:]) for obj in anno)  def _count_visible_keypoints(anno):     return sum(sum(1 for v in ann["keypoints"][2::3] if v > 0) for ann in anno)  def has_valid_annotation(anno):     """Check annotation file."""     # if it's empty, there is no annotation     if not anno:         return False     # if all boxes have close to zero area, there is no annotation     if _has_only_empty_bbox(anno):         return False     # keypoints task have a slight different criteria for considering     # if an annotation is valid     if "keypoints" not in anno[0]:         return True     # for keypoint detection tasks, only consider valid images those     # containing at least min_keypoints_per_image     if _count_visible_keypoints(anno) >= min_keypoints_per_image:         return True     return False  class COCOYoloDataset:     """YOLOV5 Dataset for COCO."""     def __init__(self, root, ann_file, remove_images_without_annotations=True,                  filter_crowd_anno=True, is_training=True):         self.coco = COCO(ann_file)         self.root = root         self.img_ids = list(sorted(self.coco.imgs.keys()))         self.filter_crowd_anno = filter_crowd_anno         self.is_training = is_training         self.mosaic = True         # filter images without any annotations         if remove_images_without_annotations:             img_ids = []             for img_id in self.img_ids:                 ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=None)                 anno = self.coco.loadAnns(ann_ids)                 if has_valid_annotation(anno):                     img_ids.append(img_id)             self.img_ids = img_ids          self.categories = {cat["id"]: cat["name"] for cat in self.coco.cats.values()}          self.cat_ids_to_continuous_ids = {             v: i for i, v in enumerate(self.coco.getCatIds())         }         self.continuous_ids_cat_ids = {             v: k for k, v in self.cat_ids_to_continuous_ids.items()         }         self.count = 0      def _mosaic_preprocess(self, index, input_size):         labels4 = []         s = 384         self.mosaic_border = [-s // 2, -s // 2]         yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border]         indices = [index] + [random.randint(0, len(self.img_ids) - 1) for _ in range(3)]         for i, img_ids_index in enumerate(indices):             coco = self.coco             img_id = self.img_ids[img_ids_index]             img_path = coco.loadImgs(img_id)[0]["file_name"]             img = Image.open(os.path.join(self.root, img_path)).convert("RGB")             img = np.array(img)             h, w = img.shape[:2]              if i == 0:  # top left                 img4 = np.full((s * 2, s * 2, img.shape[2]), 128, dtype=np.uint8)  # base image with 4 tiles                 x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc  # xmin, ymin, xmax, ymax (large image)                 x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h  # xmin, ymin, xmax, ymax (small image)             elif i == 1:  # top right                 x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc                 x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h             elif i == 2:  # bottom left                 x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)                 x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)             elif i == 3:  # bottom right                 x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)                 x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)              img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]  # img4[ymin:ymax, xmin:xmax]              padw = x1a - x1b             padh = y1a - y1b              ann_ids = coco.getAnnIds(imgIds=img_id)             target = coco.loadAnns(ann_ids)             # filter crowd annotations             if self.filter_crowd_anno:                 annos = [anno for anno in target if anno["iscrowd"] == 0]             else:                 annos = [anno for anno in target]              target = {}             boxes = [anno["bbox"] for anno in annos]             target["bboxes"] = boxes              classes = [anno["category_id"] for anno in annos]             classes = [self.cat_ids_to_continuous_ids[cl] for cl in classes]             target["labels"] = classes              bboxes = target['bboxes']             labels = target['labels']             out_target = []              for bbox, label in zip(bboxes, labels):                 tmp = []                 # convert to [x_min y_min x_max y_max]                 bbox = self._convetTopDown(bbox)                 tmp.extend(bbox)                 tmp.append(int(label))                 # tmp [x_min y_min x_max y_max, label]                 out_target.append(tmp)  # 这里out_target是label的实际宽高，对应于图片中的实际度量              labels = out_target.copy()             labels = np.array(labels)             out_target = np.array(out_target)              labels[:, 0] = out_target[:, 0] + padw             labels[:, 1] = out_target[:, 1] + padh             labels[:, 2] = out_target[:, 2] + padw             labels[:, 3] = out_target[:, 3] + padh             labels4.append(labels)          if labels4:             labels4 = np.concatenate(labels4, 0)             np.clip(labels4[:, :4], 0, 2 * s, out=labels4[:, :4])  # use with random_perspective         flag = np.array([1])         return img4, labels4, input_size, flag      def __getitem__(self, index):         """         Args:             index (int): Index          Returns:             (img, target) (tuple): target is a dictionary contains "bbox", "segmentation" or "keypoints",                 generated by the image's annotation. img is a PIL image.         """         coco = self.coco         img_id = self.img_ids[index]         img_path = coco.loadImgs(img_id)[0]["file_name"]         if not self.is_training:             img = Image.open(os.path.join(self.root, img_path)).convert("RGB")             return img, img_id          input_size = [640, 640]         if self.mosaic and random.random() < 0.5:             return self._mosaic_preprocess(index, input_size)         img = np.fromfile(os.path.join(self.root, img_path), dtype='int8')         ann_ids = coco.getAnnIds(imgIds=img_id)         target = coco.loadAnns(ann_ids)         # filter crowd annotations         if self.filter_crowd_anno:             annos = [anno for anno in target if anno["iscrowd"] == 0]         else:             annos = [anno for anno in target]          target = {}         boxes = [anno["bbox"] for anno in annos]         target["bboxes"] = boxes          classes = [anno["category_id"] for anno in annos]         classes = [self.cat_ids_to_continuous_ids[cl] for cl in classes]         target["labels"] = classes          bboxes = target['bboxes']         labels = target['labels']         out_target = []         for bbox, label in zip(bboxes, labels):             tmp = []             # convert to [x_min y_min x_max y_max]             bbox = self._convetTopDown(bbox)             tmp.extend(bbox)             tmp.append(int(label))             # tmp [x_min y_min x_max y_max, label]             out_target.append(tmp)         flag = np.array([0])         return img, out_target, input_size, flag      def __len__(self):         return len(self.img_ids)      def _convetTopDown(self, bbox):         x_min = bbox[0]         y_min = bbox[1]         w = bbox[2]         h = bbox[3]         return [x_min, y_min, x_min+w, y_min+h]  def create_yolo_dataset(image_dir, anno_path, batch_size, device_num, rank,                         config=args, is_training=True, shuffle=True):     """Create dataset for YOLOV5."""     cv2.setNumThreads(0)     ds.config.set_enable_shared_mem(True)     if is_training:         filter_crowd = True         remove_empty_anno = True     else:         filter_crowd = False         remove_empty_anno = False      yolo_dataset = COCOYoloDataset(root=image_dir, ann_file=anno_path, filter_crowd_anno=filter_crowd,                                    remove_images_without_annotations=remove_empty_anno, is_training=is_training)     distributed_sampler = DistributedSampler(len(yolo_dataset), device_num, rank, shuffle=shuffle)     yolo_dataset.size = len(distributed_sampler)     hwc_to_chw = HWC2CHW()      args.dataset_size = len(yolo_dataset)      cores = multiprocessing.cpu_count()     num_parallel_workers = int(cores / device_num)     # num_parallel_workers = 1     if is_training:         multi_scale_trans = MultiScaleTrans(args, device_num)         yolo_dataset.transforms = multi_scale_trans          dataset_column_names = ["image", "annotation", "input_size", "mosaic_flag"]         output_column_names = ["image", "annotation", "bbox1", "bbox2", "bbox3",                                "gt_box1", "gt_box2", "gt_box3"]         map1_out_column_names = ["image", "annotation", "size"]         map2_in_column_names = ["annotation", "size"]         map2_out_column_names = ["annotation", "bbox1", "bbox2", "bbox3",                                  "gt_box1", "gt_box2", "gt_box3"]          dataset = ds.GeneratorDataset(yolo_dataset, column_names=dataset_column_names, sampler=distributed_sampler,                                       python_multiprocessing=True, num_parallel_workers=min(4, num_parallel_workers))         dataset = dataset.map(operations=multi_scale_trans, input_columns=dataset_column_names,                               output_columns=map1_out_column_names, column_order=map1_out_column_names,                               num_parallel_workers=min(12, num_parallel_workers), python_multiprocessing=True)         dataset = dataset.map(operations=PreprocessTrueBox(args), input_columns=map2_in_column_names,                               output_columns=map2_out_column_names, column_order=output_column_names,                               num_parallel_workers=min(4, num_parallel_workers), python_multiprocessing=False)         mean = [m * 255 for m in [0.485, 0.456, 0.406]]         std = [s * 255 for s in [0.229, 0.224, 0.225]]         dataset = dataset.map([Normalize(mean, std), hwc_to_chw],                               num_parallel_workers=min(4, num_parallel_workers))          def concatenate(images):             images = np.concatenate((images[..., ::2, ::2], images[..., 1::2, ::2],                                      images[..., ::2, 1::2], images[..., 1::2, 1::2]), axis=0)             return images         dataset = dataset.map(operations=concatenate, input_columns="image",                               num_parallel_workers=min(4, num_parallel_workers))         dataset = dataset.batch(batch_size, num_parallel_workers=min(4, num_parallel_workers), drop_remainder=True)     else:         dataset = ds.GeneratorDataset(yolo_dataset, column_names=["image", "img_id"],                                       sampler=distributed_sampler)         compose_map_func = (lambda image, img_id: reshape_fn(image, img_id, args))         dataset = dataset.map(operations=compose_map_func, input_columns=["image", "img_id"],                               output_columns=["image", "image_shape", "img_id"],                               column_order=["image", "image_shape", "img_id"],                               num_parallel_workers=8)         dataset = dataset.map(operations=hwc_to_chw, input_columns=["image"], num_parallel_workers=8)         dataset = dataset.batch(batch_size, drop_remainder=True)     return dataset  2.4 模型实现 Backbone  （1）Focus结构：其中比较关键是切片操作。比如下图的切片示意图，4 * 4 * 3的图像切片后变成2 * 2 * 12的特征图  %E5%9B%BE%E7%89%87-4.png  （2）CSP结构： Yolov5中设计了两种CSP结构，CSP1_X结构应用于Backbone主干网络，另一种CSP2_X结构则应用于Neck中。  %E5%9B%BE%E7%89%87-3.png  """DarkNet model.""" import mindspore.nn as nn import mindspore.ops as ops  class Bottleneck(nn.Cell):     # Standard bottleneck     # ch_in, ch_out, shortcut, groups, expansion     def __init__(self, c1, c2, shortcut=True, e=0.5):         super(Bottleneck, self).__init__()         c_ = int(c2 * e)  # hidden channels         self.conv1 = Conv(c1, c_, 1, 1)         self.conv2 = Conv(c_, c2, 3, 1)         self.add = shortcut and c1 == c2      def construct(self, x):         c1 = self.conv1(x)         c2 = self.conv2(c1)         out = c2         if self.add:             out = x + out         return out  class BottleneckCSP(nn.Cell):     # CSP Bottleneck with 3 convolutions     def __init__(self, c1, c2, n=1, shortcut=True, e=0.5):         super(BottleneckCSP, self).__init__()         c_ = int(c2 * e)  # hidden channels         self.conv1 = Conv(c1, c_, 1, 1)         self.conv2 = Conv(c1, c_, 1, 1)         self.conv3 = Conv(2 * c_, c2, 1)  # act=FReLU(c2)         self.m = nn.SequentialCell(             [Bottleneck(c_, c_, shortcut, e=1.0) for _ in range(n)])         self.concat = ops.Concat(axis=1)      def construct(self, x):         c1 = self.conv1(x)         c2 = self.m(c1)         c3 = self.conv2(x)         c4 = self.concat((c2, c3))         c5 = self.conv3(c4)          return c5  class SPP(nn.Cell):     # Spatial pyramid pooling layer used in YOLOv3-SPP     def __init__(self, c1, c2, k=(5, 9, 13)):         super(SPP, self).__init__()         c_ = c1 // 2  # hidden channels         self.conv1 = Conv(c1, c_, 1, 1)         self.conv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)          self.maxpool1 = nn.MaxPool2d(kernel_size=5, stride=1, pad_mode='same')         self.maxpool2 = nn.MaxPool2d(kernel_size=9, stride=1, pad_mode='same')         self.maxpool3 = nn.MaxPool2d(kernel_size=13, stride=1, pad_mode='same')         self.concat = ops.Concat(axis=1)      def construct(self, x):         c1 = self.conv1(x)         m1 = self.maxpool1(c1)         m2 = self.maxpool2(c1)         m3 = self.maxpool3(c1)         c4 = self.concat((c1, m1, m2, m3))         c5 = self.conv2(c4)         return c5  class Focus(nn.Cell):     # Focus wh information into c-space     def __init__(self, c1, c2, k=1, s=1, p=None, act=True):         super(Focus, self).__init__()         self.conv = Conv(c1 * 4, c2, k, s, p, act)      def construct(self, x):         c1 = self.conv(x)         return c1  class SiLU(nn.Cell):     def __init__(self):         super(SiLU, self).__init__()         self.sigmoid = ops.Sigmoid()      def construct(self, x):         return x * self.sigmoid(x)  def auto_pad(k, p=None):  # kernel, padding     # Pad to 'same'     if p is None:         p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad     return p  class Conv(nn.Cell):     # Standard convolution     def __init__(self, c1, c2, k=1, s=1, p=None,                  dilation=1,                  alpha=0.1,                  momentum=0.97,                  eps=1e-3,                  pad_mode="same",                  act=True):  # ch_in, ch_out, kernel, stride, padding         super(Conv, self).__init__()         self.padding = auto_pad(k, p)         self.pad_mode = None         if self.padding == 0:             self.pad_mode = 'same'         elif self.padding == 1:             self.pad_mode = 'pad'         self.conv = nn.Conv2d(             c1,             c2,             k,             s,             padding=self.padding,             pad_mode=self.pad_mode,             has_bias=False)         self.bn = nn.BatchNorm2d(c2, momentum=momentum, eps=eps)         self.act = SiLU() if act is True else (             act if isinstance(act, nn.Cell) else ops.Identity())      def construct(self, x):         return self.act(self.bn(self.conv(x)))  class YOLOv5Backbone(nn.Cell):     def __init__(self, shape):         super(YOLOv5Backbone, self).__init__()         self.focus = Focus(shape[0], shape[1], k=3, s=1)         self.conv1 = Conv(shape[1], shape[2], k=3, s=2)         self.CSP1 = BottleneckCSP(shape[2], shape[2], n=1 * shape[6])         self.conv2 = Conv(shape[2], shape[3], k=3, s=2)         self.CSP2 = BottleneckCSP(shape[3], shape[3], n=3 * shape[6])         self.conv3 = Conv(shape[3], shape[4], k=3, s=2)         self.CSP3 = BottleneckCSP(shape[4], shape[4], n=3 * shape[6])         self.conv4 = Conv(shape[4], shape[5], k=3, s=2)         self.spp = SPP(shape[5], shape[5], k=[5, 9, 13])         self.CSP4 = BottleneckCSP(shape[5], shape[5], n=1 * shape[6], shortcut=False)      def construct(self, x):         """construct method"""         c1 = self.focus(x)         c2 = self.conv1(c1)         c3 = self.CSP1(c2)         c4 = self.conv2(c3)         # out         c5 = self.CSP2(c4)         c6 = self.conv3(c5)         # out         c7 = self.CSP3(c6)         c8 = self.conv4(c7)         c9 = self.spp(c8)         # out         c10 = self.CSP4(c9)         return c5, c7, c10  2.4 模型实现 Neck  这是Pytorch版本的YOLOv5代码中对neck部分的配置文件，从配置文件中可以看出Neck部分的组件较为单一，基本上就由CBS（ConV）、Upsample、Concat和不带shortcut的CSP（C3)组成:    [[-1, 1, Conv, [512, 1, 1]],    [-1, 1, nn.Upsample, [None, 2, 'nearest']],    [[-1, 6], 1, Concat, [1]],  # cat backbone P4    [-1, 3, C3, [512, False]],  # 13     [-1, 1, Conv, [256, 1, 1]],    [-1, 1, nn.Upsample, [None, 2, 'nearest']],    [[-1, 4], 1, Concat, [1]],  # cat backbone P3    [-1, 3, C3, [256, False]],  # 17 (P3/8-small)     [-1, 1, Conv, [256, 3, 2]],    [[-1, 14], 1, Concat, [1]],  # cat head P4    [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)     [-1, 1, Conv, [512, 3, 2]],    [[-1, 10], 1, Concat, [1]],  # cat head P5    [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)   ] > 另外，Neck的网络结构设计也是沿用了FPN+PAN的结构。FPN就是使用一种 自顶向下的侧边连接在所有尺度上构建出高级语义特征图，构造了特征金字塔的经典结构；PAN的结构也不稀奇，对于PAN，底层的目标信息已经非常模糊了，因此PAN又加入了自底向上的路线，弥补并加强了定位信息  jupyter  #  model import mindspore as ms import mindspore.nn as nn import mindspore.ops as ops  class YoloBlock(nn.Cell):      def __init__(self, in_channels, out_channels):         super(YoloBlock, self).__init__()          self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, has_bias=True)      def construct(self, x):         """construct method"""          out = self.conv(x)         return out  class YOLO(nn.Cell):     def __init__(self, backbone, shape):         super(YOLO, self).__init__()         self.backbone = backbone         self.out_channel = (80 + 5) * 3          self.conv1 = Conv(shape[5], shape[4], k=1, s=1)         self.CSP5 = BottleneckCSP(shape[5], shape[4], n=1*shape[6], shortcut=False)         self.conv2 = Conv(shape[4], shape[3], k=1, s=1)         self.CSP6 = BottleneckCSP(shape[4], shape[3], n=1*shape[6], shortcut=False)         self.conv3 = Conv(shape[3], shape[3], k=3, s=2)         self.CSP7 = BottleneckCSP(shape[4], shape[4], n=1*shape[6], shortcut=False)         self.conv4 = Conv(shape[4], shape[4], k=3, s=2)         self.CSP8 = BottleneckCSP(shape[5], shape[5], n=1*shape[6], shortcut=False)         self.back_block1 = YoloBlock(shape[3], self.out_channel)         self.back_block2 = YoloBlock(shape[4], self.out_channel)         self.back_block3 = YoloBlock(shape[5], self.out_channel)          self.concat = ops.Concat(axis=1)      def construct(self, x):                  img_height = x.shape[2] * 2         img_width = x.shape[3] * 2          feature_map1, feature_map2, feature_map3 = self.backbone(x)          c1 = self.conv1(feature_map3)         ups1 = ops.ResizeNearestNeighbor((img_height // 16, img_width // 16))(c1)         c2 = self.concat((ups1, feature_map2))         c3 = self.CSP5(c2)         c4 = self.conv2(c3)         ups2 = ops.ResizeNearestNeighbor((img_height // 8, img_width // 8))(c4)         c5 = self.concat((ups2, feature_map1))         # out         c6 = self.CSP6(c5)         c7 = self.conv3(c6)          c8 = self.concat((c7, c4))         # out         c9 = self.CSP7(c8)         c10 = self.conv4(c9)         c11 = self.concat((c10, c1))         # out         c12 = self.CSP8(c11)         small_object_output = self.back_block1(c6)         medium_object_output = self.back_block2(c9)         big_object_output = self.back_block3(c12)         return small_object_output, medium_object_output, big_object_output  2.4 模型构建 backbone+Neck+Head  Backbone作用：特征提取  Neck作用：对特征进行一波混合与组合，并且把这些特征传递给预测层  Head作用：进行最终的预测输出  jupyter  # backbone+Neck+Head  class DetectionBlock(nn.Cell):      def __init__(self, scale, is_training=True):         super(DetectionBlock, self).__init__()         if scale == 's':             idx = (0, 1, 2)             self.scale_x_y = 1.2             self.offset_x_y = 0.1         elif scale == 'm':             idx = (3, 4, 5)             self.scale_x_y = 1.1             self.offset_x_y = 0.05         elif scale == 'l':             idx = (6, 7, 8)             self.scale_x_y = 1.05             self.offset_x_y = 0.025         else:             raise KeyError("Invalid scale value for DetectionBlock")         self.anchors = ms.Tensor([args.anchor_scales[i] for i in idx], ms.float32)         self.num_anchors_per_scale = 3         self.num_attrib = 4+1+args.num_classes         self.lambda_coord = 1          self.sigmoid = nn.Sigmoid()         self.reshape = ops.Reshape()         self.tile = ops.Tile()         self.concat = ops.Concat(axis=-1)         self.pow = ops.Pow()         self.transpose = ops.Transpose()         self.exp = ops.Exp()         self.conf_training = is_training      def construct(self, x, input_shape):         """construct method"""         num_batch = x.shape[0]         grid_size = x.shape[2:4]          # Reshape and transpose the feature to [n, grid_size[0], grid_size[1], 3, num_attrib]         prediction = self.reshape(x, (num_batch,                                       self.num_anchors_per_scale,                                       self.num_attrib,                                       grid_size[0],                                       grid_size[1]))         prediction = self.transpose(prediction, (0, 3, 4, 1, 2))          grid_x = ms.numpy.arange(grid_size[1])         grid_y = ms.numpy.arange(grid_size[0])         # Tensor of shape [grid_size[0], grid_size[1], 1, 1] representing the coordinate of x/y axis for each grid         # [batch, gridx, gridy, 1, 1]         grid_x = self.tile(self.reshape(grid_x, (1, 1, -1, 1, 1)), (1, grid_size[0], 1, 1, 1))         grid_y = self.tile(self.reshape(grid_y, (1, -1, 1, 1, 1)), (1, 1, grid_size[1], 1, 1))         # Shape is [grid_size[0], grid_size[1], 1, 2]         grid = self.concat((grid_x, grid_y))          box_xy = prediction[:, :, :, :, :2]         box_wh = prediction[:, :, :, :, 2:4]         box_confidence = prediction[:, :, :, :, 4:5]         box_probs = prediction[:, :, :, :, 5:]          # gridsize1 is x         # gridsize0 is y         box_xy = (self.scale_x_y * self.sigmoid(box_xy) - self.offset_x_y + grid) / \                  ops.cast(ops.tuple_to_array((grid_size[1], grid_size[0])), ms.float32)         # box_wh is w->h         box_wh = self.exp(box_wh) * self.anchors / input_shape          box_confidence = self.sigmoid(box_confidence)         box_probs = self.sigmoid(box_probs)          if self.conf_training:             return prediction, box_xy, box_wh         return self.concat((box_xy, box_wh, box_confidence, box_probs))  class YOLOV5(nn.Cell):     """     YOLOV5 network.      Args:         is_training: Bool. Whether train or not.      Returns:         Cell, cell instance of YOLOV5 neural network.      Examples:         YOLOV5s(True)     """      def __init__(self, is_training, version=0):         super(YOLOV5, self).__init__()          # YOLOv5 network         self.shape = args.input_shape[version]         self.feature_map = YOLO(backbone=YOLOv5Backbone(shape=self.shape), shape=self.shape)          # prediction on the default anchor boxes         self.detect_1 = DetectionBlock('l', is_training=is_training)         self.detect_2 = DetectionBlock('m', is_training=is_training)         self.detect_3 = DetectionBlock('s', is_training=is_training)      def construct(self, x, input_shape):         small_object_output, medium_object_output, big_object_output = self.feature_map(x)         output_big = self.detect_1(big_object_output, input_shape)         output_me = self.detect_2(medium_object_output, input_shape)         output_small = self.detect_3(small_object_output, input_shape)         # big is the final output which has smallest feature map         return output_big, output_me, output_small  2.5 损失定义  YOLOv5一共有三种损失函数：  分类损失cls_loss：计算锚框与对应的标定分类是否正确 定位损失box_loss：预测框与标定框之间的误差（GIoU） 置信度损失obj_loss：计算网络的置信度 总的损失函数=分类损失+定位损失+置信度损失  分类损失cls_loss和置信度损失obj_loss使用的是二元交叉熵损失函数BCEWithLogitsLoss计算。  BCEwithlogitsloss = BCELoss + Sigmoid。  定位损失box_loss使用的是GIoU loss(可以的话，也可以替换成CIoU，EIoU，SIoU等IoU损失) > IOU损失     IOU Loss的定义是先求出预测框和真实框之间的交集和并集之比，再求负对数，但是在实际使用中我们常常将IOU Loss写成1-IOU。如果两个框重合则交并比等于1，Loss为0说明重合度非常高。IOU满足非负性、同一性、对称性、三角不等性，相比于L1/L2等损失函数还具有尺度不变性，不论box的尺度大小，输出的iou损失总是在0-1之间。所以能够较好的反映预测框与真实框的检测效果。     IOU的公式如下： jupyter     IOU的图示如下：     jupyter     普通IOU的优缺点很明显，优点：1、IOU具有尺度不变性2、满足非负性。同时，由于IOU并没有考虑框之间的距离，所以它的作为loss函数的时候也有相应的缺点：1、在A框与B框不重合的时候IOU为0，不能正确反映两者的距离大小。2. IoU无法精确的反映两者的重合度大小。    GIOU是为克服IOU的缺点同时充分利用优点而提出的.(论文：Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression)        GIOU的公式如下：     jupyter     GIOU的图示如下：   jupyter     计算过程如下：1.假设A为预测框，B为真实框，S是所有框的集合，2.不管A与B是否相交，C是包含A与B的最小框(包含A与B的最小凸闭合框)，C也属于S集合，3.首先计算IoU，A与B的交并比，4.再计算C框中没有A与B的面积，比上C框面积；5.IoU减去前面算出的比；得到GIoU。  # loss  class ConfidenceLoss(nn.Cell):     """Loss for confidence."""      def __init__(self):         super(ConfidenceLoss, self).__init__()         self.cross_entropy = ops.SigmoidCrossEntropyWithLogits()         self.reduce_sum = ops.ReduceSum()      def construct(self, object_mask, predict_confidence, ignore_mask):         confidence_loss = self.cross_entropy(predict_confidence, object_mask)         confidence_loss = object_mask * confidence_loss + (1 - object_mask) * confidence_loss * ignore_mask         confidence_loss = self.reduce_sum(confidence_loss, ())         return confidence_loss  class ClassLoss(nn.Cell):     """Loss for classification."""      def __init__(self):         super(ClassLoss, self).__init__()         self.cross_entropy = ops.SigmoidCrossEntropyWithLogits()         self.reduce_sum = ops.ReduceSum()      def construct(self, object_mask, predict_class, class_probs):         class_loss = object_mask * self.cross_entropy(predict_class, class_probs)         class_loss = self.reduce_sum(class_loss, ())         return class_loss  class Iou(nn.Cell):     """Calculate the iou of boxes"""     def __init__(self):         super(Iou, self).__init__()         self.min = ops.Minimum()         self.max = ops.Maximum()         self.squeeze = ops.Squeeze(-1)      def construct(self, box1, box2):         """         box1: pred_box [batch, gx, gy, anchors, 1,      4] ->4: [x_center, y_center, w, h]         box2: gt_box   [batch, 1,  1,  1,       maxbox, 4]         convert to topLeft and rightDown         """         box1_xy = box1[:, :, :, :, :, :2]         box1_wh = box1[:, :, :, :, :, 2:4]         box1_mins = box1_xy - box1_wh / ops.scalar_to_array(2.0) # topLeft         box1_maxs = box1_xy + box1_wh / ops.scalar_to_array(2.0) # rightDown          box2_xy = box2[:, :, :, :, :, :2]         box2_wh = box2[:, :, :, :, :, 2:4]         box2_mins = box2_xy - box2_wh / ops.scalar_to_array(2.0)         box2_maxs = box2_xy + box2_wh / ops.scalar_to_array(2.0)          intersect_mins = self.max(box1_mins, box2_mins)         intersect_maxs = self.min(box1_maxs, box2_maxs)         intersect_wh = self.max(intersect_maxs - intersect_mins, ops.scalar_to_array(0.0))         # self.squeeze: for effiecient slice         intersect_area = self.squeeze(intersect_wh[:, :, :, :, :, 0:1]) * \                          self.squeeze(intersect_wh[:, :, :, :, :, 1:2])         box1_area = self.squeeze(box1_wh[:, :, :, :, :, 0:1]) * \                     self.squeeze(box1_wh[:, :, :, :, :, 1:2])         box2_area = self.squeeze(box2_wh[:, :, :, :, :, 0:1]) * \                     self.squeeze(box2_wh[:, :, :, :, :, 1:2])         iou = intersect_area / (box1_area + box2_area - intersect_area)         # iou : [batch, gx, gy, anchors, maxboxes]         return iou      class GIou(nn.Cell):     """Calculating giou"""     def __init__(self):         super(GIou, self).__init__()         self.reshape = ops.Reshape()         self.min = ops.Minimum()         self.max = ops.Maximum()         self.concat = ops.Concat(axis=1)         self.mean = ops.ReduceMean()         self.div = ops.RealDiv()         self.eps = 0.000001      def construct(self, box_p, box_gt):         """construct method"""         box_p_area = (box_p[..., 2:3] - box_p[..., 0:1]) * (box_p[..., 3:4] - box_p[..., 1:2])         box_gt_area = (box_gt[..., 2:3] - box_gt[..., 0:1]) * (box_gt[..., 3:4] - box_gt[..., 1:2])         x_1 = self.max(box_p[..., 0:1], box_gt[..., 0:1])         x_2 = self.min(box_p[..., 2:3], box_gt[..., 2:3])         y_1 = self.max(box_p[..., 1:2], box_gt[..., 1:2])         y_2 = self.min(box_p[..., 3:4], box_gt[..., 3:4])         intersection = (y_2 - y_1) * (x_2 - x_1)         xc_1 = self.min(box_p[..., 0:1], box_gt[..., 0:1])         xc_2 = self.max(box_p[..., 2:3], box_gt[..., 2:3])         yc_1 = self.min(box_p[..., 1:2], box_gt[..., 1:2])         yc_2 = self.max(box_p[..., 3:4], box_gt[..., 3:4])         c_area = (xc_2 - xc_1) * (yc_2 - yc_1)         union = box_p_area + box_gt_area - intersection         union = union + self.eps         c_area = c_area + self.eps         iou = self.div(ops.cast(intersection, ms.float32), ops.cast(union, ms.float32))         res_mid0 = c_area - union         res_mid1 = self.div(ops.cast(res_mid0, ms.float32), ops.cast(c_area, ms.float32))         giou = iou - res_mid1         giou = ops.clip_by_value(giou, -1.0, 1.0)         return giou  def xywh2x1y1x2y2(box_xywh):     boxes_x1 = box_xywh[..., 0:1] - box_xywh[..., 2:3] / 2     boxes_y1 = box_xywh[..., 1:2] - box_xywh[..., 3:4] / 2     boxes_x2 = box_xywh[..., 0:1] + box_xywh[..., 2:3] / 2     boxes_y2 = box_xywh[..., 1:2] + box_xywh[..., 3:4] / 2     boxes_x1y1x2y2 = ops.Concat(-1)((boxes_x1, boxes_y1, boxes_x2, boxes_y2))      return boxes_x1y1x2y2  class YoloLossBlock(nn.Cell):     """     Loss block cell of YOLOV5 network.     """     def __init__(self, scale, config=args):         super(YoloLossBlock, self).__init__()         self.config = config         if scale == 's':             # anchor mask             idx = (0, 1, 2)         elif scale == 'm':             idx = (3, 4, 5)         elif scale == 'l':             idx = (6, 7, 8)         else:             raise KeyError("Invalid scale value for DetectionBlock")         self.anchors = ms.Tensor([self.config.anchor_scales[i] for i in idx], ms.float32)         self.ignore_threshold = ms.Tensor(self.config.ignore_threshold, ms.float32)         self.concat = ops.Concat(axis=-1)         self.iou = Iou()         self.reduce_max = ops.ReduceMax(keep_dims=False)         self.confidence_loss = ConfidenceLoss()         self.class_loss = ClassLoss()          self.reduce_sum = ops.ReduceSum()         self.select = ops.Select()         self.equal = ops.Equal()         self.reshape = ops.Reshape()         self.expand_dims = ops.ExpandDims()         self.ones_like = ops.OnesLike()         self.log = ops.Log()         self.tuple_to_array = ops.TupleToArray()         self.g_iou = GIou()      def construct(self, prediction, pred_xy, pred_wh, y_true, gt_box, input_shape):         """         prediction : origin output from yolo         pred_xy: (sigmoid(xy)+grid)/grid_size         pred_wh: (exp(wh)*anchors)/input_shape         y_true : after normalize         gt_box: [batch, maxboxes, xyhw] after normalize         """         object_mask = y_true[:, :, :, :, 4:5]         class_probs = y_true[:, :, :, :, 5:]         true_boxes = y_true[:, :, :, :, :4]          grid_shape = prediction.shape[1:3]         grid_shape = ops.cast(self.tuple_to_array(grid_shape[::-1]), ms.float32)          pred_boxes = self.concat((pred_xy, pred_wh))         true_wh = y_true[:, :, :, :, 2:4]         true_wh = self.select(self.equal(true_wh, 0.0),                               self.ones_like(true_wh),                               true_wh)         true_wh = self.log(true_wh / self.anchors * input_shape)         # 2-w*h for large picture, use small scale, since small obj need more precise         box_loss_scale = 2 - y_true[:, :, :, :, 2:3] * y_true[:, :, :, :, 3:4]          gt_shape = gt_box.shape         gt_box = self.reshape(gt_box, (gt_shape[0], 1, 1, 1, gt_shape[1], gt_shape[2]))          # add one more dimension for broadcast         iou = self.iou(self.expand_dims(pred_boxes, -2), gt_box)         # gt_box is x,y,h,w after normalize         # [batch, grid[0], grid[1], num_anchor, num_gt]         best_iou = self.reduce_max(iou, -1)         # [batch, grid[0], grid[1], num_anchor]          # ignore_mask IOU too small         ignore_mask = best_iou < self.ignore_threshold         ignore_mask = ops.cast(ignore_mask, ms.float32)         ignore_mask = self.expand_dims(ignore_mask, -1)         # ignore_mask backpro will cause a lot maximunGrad and minimumGrad time consume.         # so we turn off its gradient         ignore_mask = ops.stop_gradient(ignore_mask)          confidence_loss = self.confidence_loss(object_mask, prediction[:, :, :, :, 4:5], ignore_mask)         class_loss = self.class_loss(object_mask, prediction[:, :, :, :, 5:], class_probs)          object_mask_me = self.reshape(object_mask, (-1, 1))  # [8, 72, 72, 3, 1]         box_loss_scale_me = self.reshape(box_loss_scale, (-1, 1))         pred_boxes_me = xywh2x1y1x2y2(pred_boxes)         pred_boxes_me = self.reshape(pred_boxes_me, (-1, 4))         true_boxes_me = xywh2x1y1x2y2(true_boxes)         true_boxes_me = self.reshape(true_boxes_me, (-1, 4))         c_iou = self.g_iou(pred_boxes_me, true_boxes_me)         c_iou_loss = object_mask_me * box_loss_scale_me * (1 - c_iou)         c_iou_loss_me = self.reduce_sum(c_iou_loss, ())         loss = c_iou_loss_me * 4 + confidence_loss + class_loss         batch_size = prediction.shape[0]         return loss / batch_size  class YoloWithLossCell(nn.Cell):     """YOLOV5 loss."""     def __init__(self, network):         super(YoloWithLossCell, self).__init__()         self.yolo_network = network         self.config = args         self.loss_big = YoloLossBlock('l', self.config)         self.loss_me = YoloLossBlock('m', self.config)         self.loss_small = YoloLossBlock('s', self.config)         self.tenser_to_array = ops.TupleToArray()      def construct(self, x, y_true_0, y_true_1, y_true_2, gt_0, gt_1, gt_2, input_shape):         input_shape = x.shape[2:4]         input_shape = ops.cast(self.tenser_to_array(input_shape) * 2, ms.float32)          yolo_out = self.yolo_network(x, input_shape)         loss_l = self.loss_big(*yolo_out[0], y_true_0, gt_0, input_shape)         loss_m = self.loss_me(*yolo_out[1], y_true_1, gt_1, input_shape)         loss_s = self.loss_small(*yolo_out[2], y_true_2, gt_2, input_shape)         return loss_l + loss_m + loss_s * 0.2