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.gitignore

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+*.pt
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+

SetParams/DataType/BaseParam.py

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+import json
+from .TypeDef import *
+from typing import Union
+from abc import abstractmethod
+
+
+class BaseParam:
+    """
+    参数基类
+    """
+
+    def __init__(self):
+        """
+        参数基类，算法中所有需要参数的方法中，使用的参数必须继承该类型
+        """
+        self.file = ''
+        self.param_list = []
+
+    def save_to_file(self, file: str) -> None:
+        """
+        将参数保存到文件
+        :param file: 文件完整路径
+        :return: None
+        """
+        with open(file, 'w') as f:
+            f.write(str(self))
+            f.flush()
+
+    def add_param(self, p: Union[IntType, BoolType, FloatType, StringType, ListType, EnumType]) -> None:
+        """
+        将数据添加到参数列表中，以支持序列化
+        :param p: 单个数据，必须为系统支持的参数类型之一：IntType, BoolType, FloatType, StringType, ListType, EnumType
+        :return: None
+        """
+        self.param_list.append(p)
+
+    def read_from_str(self, content: str) -> None:
+        """
+        将字符串反序列化为对象中的参数
+        :param content: 要反序列化的字符串
+        :return: None
+        """
+        self.param_list.clear()
+        objs = json.loads(content)
+        for obj in objs:
+            #try:
+            item = obj #json.loads(obj)
+            t = item["type"]
+            param = None
+
+            if t == "B":
+                param = BoolType(item["name"],
+                                    item["value"],
+                                    item["description"],
+                                    item["default"],
+                                    item["show"])
+                param.index = item["index"]
+            elif t == "I":
+                param = IntType(item["name"],
+                                item["value"],
+                                item["description"],
+                                item["default"],
+                                    item["show"])
+                param.index = item["index"]
+            elif t == "F":
+                param = FloatType(item["name"],
+                                    item["value"],
+                                    item["description"],
+                                    item["default"],
+                                    item["show"])
+                param.index = item["index"]
+                #param.length = item["length"]
+            elif t == "S":
+                item['length']=len(item["value"])
+                param = StringType(item["name"],
+                                    item["value"],
+                                    item['length'],
+                                    item["description"],
+                                    item["default"],
+                                    item["show"])
+                param.index = item["index"]
+            elif t == "L":
+                param = ListType(item["name"],
+                                    item["value"],
+                                    item["description"],
+                                    item["default"],
+                                    item["show"])
+                param.index = item["index"]
+                #param.length = item["length"]
+            elif t == "E":
+                param = EnumType(item["name"],
+                                    item["value"],
+                                    item["items"],
+                                    item["description"],
+                                    item["default"],
+                                    item["show"])
+                param.index = item["index"]
+
+            if param:
+                self.param_list.append(param)
+            # except Exception as e:
+            #     pass
+
+    def read_from_file(self, file: str) -> None:
+        """
+        从文件读取序列化后的参数字符串，并反序列化为对象
+        :param file: 文件完整路径
+        :return: None
+        """
+        with open(file, 'r') as f:
+            content = f.read()
+            self.read_from_str(content)
+
+    @property
+    def param_string(self) -> str:
+        """
+        获取Json格式的参数字符串
+        :return: 返回Json格式的参数字符串
+        """
+        for i, p in enumerate(self.param_list):
+            p.index = i
+        return self.__str__()
+
+    def __str__(self) -> str:
+        """
+        重写str方法用于将对象序列化为Json字符串
+        :return: 序列化后的JSON字符串
+        """
+        ret = []
+        for index, item in enumerate(self.param_list):
+            obj = item.obj
+            if isinstance(obj, dict):
+                obj["index"] = index
+                item_obj = json.dumps(obj)
+                ret.append(item_obj)
+        return json.dumps(ret)
+
+    def get(self, name: str) -> Union[None, IntType, BoolType, FloatType, StringType, ListType, EnumType,]:
+        """
+        根据数据名称获取指定的数据对象
+        :param name: 数据名称
+        :return:
+        """
+        return next((x for x in self.param_list if x.name == name), None)

SetParams/DataType/ParamDef.py

@@ -0,0 +1,21 @@
+from .TypeDef import *
+from .BaseParam import *
+
+
+class TrainParams(BaseParam):
+    """
+    训练参数示例，使用时需要根据实际情况修改，如有其他参数（如推理函数的参数）需要自定义，并继承自 BaseParam
+    """
+
+    def __init__(self):
+        super().__init__()
+
+        # 例如训练的时候需要传入以下参数
+        gpu_num = IntType("gpu_num", 2)
+        support_cpu = BoolType("support_cpu", True)
+        labels = EnumType("labels", 0, ["dog", "cat"])
+        labels.default = 0
+
+        self.add_param(gpu_num)
+        self.add_param(support_cpu)
+        self.add_param(labels)

SetParams/DataType/TypeDef.py

@@ -0,0 +1,243 @@
+import json
+from typing import List
+from abc import abstractmethod
+
+
+class __BaseType:
+    def __init__(self,
+                 name: str,
+                 value: object,
+                 description: str,
+                 default: object,
+                 show: bool):
+        """
+        所有数据类型的基类
+        :param name:        数据名称
+        :param value:       数据值
+        :param description: 描述，用于前端UI展示
+        :param default:     该参数的默认值
+        """
+        self.name = name
+        self.description = description
+        self.index = -1
+        self.default = default
+        self.value = value
+        self.show = show
+
+    @property
+    @abstractmethod
+    def type(self) -> str:
+        """
+        参数类型，用于前端/后台的序列化与反序列化。前端展示时也需要根据此字段类型绘制不同的控件
+        :return: 一般使用单个字母，如：整型（I），布尔型（B），浮点型（F），字符串（S），列表（L），枚举（E）
+        """
+        pass
+
+    @property
+    def _base_obj(self) -> dict:
+        """
+        用于创建所有数据类型的共有对象字典
+        :return:
+        """
+        return {
+            "index": self.index,
+            "name": self.name,
+            "value": self.value,
+            "description": self.description,
+            "default": self.default,
+            "type": self.type,
+            "show": self.show
+        }
+
+    @property
+    @abstractmethod
+    def obj(self) -> dict:
+        """
+        抽象属性，获取数据类型的对象字典
+        :return:
+        """
+        return self._base_obj
+
+    def __str__(self):
+        """
+        重写数据类型的字符串序列化方法
+        :return:
+        """
+        return json.dumps(self.obj)
+
+
+class BoolType(__BaseType):
+    def __init__(self,
+                 name: str,
+                 value: bool = False,
+                 description: str = "",
+                 default: bool = False,
+                 show: bool = True):
+        """
+        布尔类型数据
+        :param name:        数据名称
+        :param value:       数据值
+        :param description: 描述，用于前端UI展示
+        :param default:     该参数的默认值
+        """
+        super().__init__(name, value, description, default, show)
+
+    @property
+    def type(self) -> str:
+        return 'B'
+
+    @property
+    def obj(self) -> dict:
+        return super()._base_obj
+
+
+class IntType(__BaseType):
+
+    def __init__(self,
+                 name: str,
+                 value: int = 0,
+                 description: str = "",
+                 default: int = 0,
+                 show: bool = True):
+        """
+        整数类型
+        :param name:        数据名称
+        :param value:       数据值
+        :param description: 描述，用于前端UI展示
+        :param default:     该参数的默认值
+        """
+        super().__init__(name, value, description, default, show)
+
+    @property
+    def type(self) -> str:
+        return 'I'
+
+    @property
+    def obj(self) -> dict:
+        return super()._base_obj
+
+
+class FloatType(__BaseType):
+    def __init__(self,
+                 name: str,
+                 value: float = 0.,
+                 description: str = "",
+                 default: float = 0.,
+                 show: bool = True):
+        """
+        浮点类型
+        :param name:        数据名称
+        :param value:       数据值
+        :param description: 描述，用于前端UI展示
+        :param default:     该参数的默认值
+        """
+        super().__init__(name, value, description, default, show)
+
+    @property
+    def type(self) -> str:
+        return 'F'
+
+    @property
+    def obj(self) -> dict:
+        return super()._base_obj
+
+
+class StringType(__BaseType):
+    def __init__(self,
+                 name: str,
+                 value: str = "",
+                 length: int = -1,
+                 description: str = "",
+                 default: str = "",
+                 show: bool = True):
+        """
+        字符串类型
+        :param name:         数据名称，用于程序内部标识该数据
+        :param value:        数据值
+        :param length:       字符串长度
+        :param description:  描述，用于前端页面对该数据进行展示
+        :param default:      默认值
+        """
+        super().__init__(name, value, description, default, show)
+        self.length = length
+        if length > 0:
+            self.value = value[:length]
+        else:
+            self.value = value
+
+    @property
+    def type(self) -> str:
+        return 'S'
+
+    @property
+    def obj(self) -> dict:
+        base_obj = super()._base_obj
+        base_obj["length"] = self.length
+        return base_obj
+
+
+class ListType(__BaseType):
+    def __init__(self,
+                 name: str,
+                 value: list = None,
+                 description: str = "",
+                 default: list = None,
+                 show: bool = True):
+        """
+        列表类型
+        :param name:         数据名称，用于程序内部标识该数据
+        :param value:        数据值
+        :param description:  描述，用于前端页面对该数据进行展示
+        :param default:      默认值
+        """
+        if not default:
+            default = []
+
+        if not value:
+            value = default
+
+        self.length = len(value)
+        super().__init__(name, value, description, default, show)
+
+    @property
+    def type(self) -> str:
+        return "L"
+
+    @property
+    def obj(self) -> dict:
+        base_obj = super()._base_obj
+        base_obj["length"] = self.length
+        return base_obj
+
+
+class EnumType(__BaseType):
+    def __init__(self,
+                 name: str,
+                 value: int = -1,
+                 items: List[str] = None,
+                 description: str = "",
+                 default: int = -1,
+                 show: bool = True):
+        """
+        枚举类型
+        :param name:         数据名称，用于程序内部标识该数据
+        :param value:        数据值，即选择了枚举列表中的第几项
+        :param items:        枚举范围，枚举项必须为字符串类型
+        :param description:  描述，用于前端页面对该数据进行展示
+        :param default:      默认值
+        """
+        if not items:
+            items = []
+            default = -1
+        self.items = items
+        super().__init__(name, value, description, default, show)
+
+    @property
+    def type(self) -> str:
+        return "E"
+
+    @property
+    def obj(self) -> dict:
+        base_obj = super()._base_obj
+        base_obj["items"] = self.items
+        return base_obj

SetParams/TrainParams-0.json

@@ -0,0 +1 @@
+["{\"index\": 0, \"name\": \"gpu_num\", \"value\": 2, \"description\": \"\", \"default\": 0, \"type\": \"I\"}", "{\"index\": 1, \"name\": \"support_cpu\", \"value\": true, \"description\": \"\", \"default\": false, \"type\": \"B\"}", "{\"index\": 2, \"name\": \"labels\", \"value\": 0, \"description\": \"\", \"default\": 0, \"type\": \"E\", \"items\": [\"dog\", \"cat\"]}"]

SetParams/TrainParams.json

@@ -0,0 +1 @@
+["{\"index\": 0, \"name\": \"num_classes\", \"value\": 0, \"description\": \"\", \"default\": 9, \"type\": \"I\"}", "{\"index\": 1, \"name\": \"lr\", \"value\": 0.0, \"description\": \"\", \"default\": 0.005, \"type\": \"F\"}", "{\"index\": 2, \"name\": \"lr_schedulerList\", \"value\": [30, 60], \"description\": \"\", \"default\": [30, 60], \"type\": \"L\", \"length\": 2}", "{\"index\": 3, \"name\": \"device\", \"value\": \"\", \"description\": \"\", \"default\": \"cpu\", \"type\": \"S\", \"length\": -1}", "{\"index\": 4, \"name\": \"DatasetDir\", \"value\": \"\", \"description\": \"\", \"default\": \"./datasets/M006B_duanmian\", \"type\": \"S\", \"length\": -1}", "{\"index\": 5, \"name\": \"saveModDir\", \"value\": \"\", \"description\": \"\", \"default\": \"./saved_model/M006B_duanmian.pt\", \"type\": \"S\", \"length\": -1}", "{\"index\": 6, \"name\": \"resumeModPath\", \"value\": \"\", \"description\": \"\", \"default\": \"\", \"type\": \"S\", \"length\": -1}", "{\"index\": 7, \"name\": \"epochnum\", \"value\": 0, \"description\": \"\", \"default\": 100, \"type\": \"I\"}", "{\"index\": 8, \"name\": \"saveEpoch\", \"value\": 0, \"description\": \"\", \"default\": 1, \"type\": \"I\"}"]

SetParams/main.py

@@ -0,0 +1,49 @@
+from DataType.TypeDef import *
+from DataType.ParamDef import *
+
+
+def train(params: TrainParams):
+    """
+    后台提交训练请求时，可以参考此方法，从前端传递的参数中取出需要的值
+    :param params:
+    :return:
+    """
+    # 存在的参数可以使用name字段获取到
+    cpu_num = params.get("gpu_num")
+    print(cpu_num)
+
+    # 不存在的参数获取的时候返回None
+    th = params.get("th")
+    print(th)
+
+
+def get_train_config_json():
+    """
+    前端获取训练所需要的参数JSON字符串时，使用str方法即可获取JSON字符串
+    :return:
+    """
+    # 第一种方法：
+    # 创建新对象
+    params = TrainParams()
+    # 从指定文件名中读取配置
+    params.read_from_file("TrainParams.json")
+    # 转为字符串
+    print(str(params))
+
+    # 第二种方法
+    # 也可以直接将文本传递给前端，但是可能需要考虑转义符号(\)的处理
+    with open("TrainParams.json", "r") as f:
+        print(f.read())
+
+    return params
+
+
+if __name__ == '__main__':
+    # 当前端需要获取参数列表时，参照此函数中的实现
+    get_train_config_json()
+
+    # 当前端传递回来参数的JSON字符串时，使用下述方法，将字符串反序列化为对象，然后传递给对应的函数
+    params = TrainParams()
+    params.read_from_str(
+        '["{\\"index\\": 0, \\"name\\": \\"gpu_num\\", \\"value\\": 2, \\"description\\": \\"\\", \\"default\\": 0, \\"type\\": \\"I\\"}", "{\\"index\\": 1, \\"name\\": \\"support_cpu\\", \\"value\\": true, \\"description\\": \\"\\", \\"default\\": false, \\"type\\": \\"B\\"}", "{\\"index\\": 2, \\"name\\": \\"labels\\", \\"value\\": 0, \\"description\\": \\"\\", \\"default\\": 0, \\"type\\": \\"E\\", \\"items\\": [\\"dog\\", \\"cat\\"]}"]')
+    train(params)

SetParams_Demo.py

@@ -0,0 +1,65 @@
+import sys
+import json
+from setparams import TrainParams
+
+
+
+
+ 
+
+
+def ppx_train(params,id):
+
+    ppx_num_classes = params.get('num_classes').value
+    ppx_epoch =  params.get('epochnum').value
+    ppx_saveEpoch =  params.get('saveEpoch').value
+    ppx_device =  params.get('device').value
+    ppx_DatasetDir =  params.get('DatasetDir').value  
+    ppx_saveModDir =  params.get('saveModDir').value
+    ppx_lr =  params.get('lr').value
+    ppx_lr_schedulerList = params.get('lr_schedulerList').value
+    ppx_resumeModPath =  params.get('resumeModPath').value
+    ppx_id = id
+    if ppx_resumeModPath == '': ppx_resumeModPath= "/mnt/sdc/algorithm/AICheck-MaskRCNN/app/maskrcnn_ppx/pretrain/mask_rcnn_r50_fpn_2x_coco.pdparams" #'COCO'
+
+
+
+    model.train(
+        num_epochs=ppx_epoch,  #***
+        save_interval_epochs=ppx_saveEpoch,  #***
+        train_dataset=train_dataset,  #***
+        train_batch_size=2,
+        eval_dataset=eval_dataset,  #***
+        pretrain_weights=ppx_resumeModPath,  #***
+        learning_rate=ppx_lr,  #***
+        lr_decay_epochs=ppx_lr_schedulerList,  #***
+        warmup_steps=10, 
+        warmup_start_lr=0.0,
+        save_dir=ppx_saveModDir,  #***    
+        use_vdl=True)
+
+
+
+
+#@start_train_algorithm
+def main(params_str):
+    params = TrainParams()
+    params.read_from_str(params_str)
+    ppx_train(params,id='1')
+
+
+if __name__ == "__main__":
+    params_list = [
+                    {"index":0,"name":"num_classes","value":9,"description":'类别数（加背景）',"default":9,"type":"I", "show":True},
+                    {"index":1,"name":"lr","value":0.0003,"description":'学习率',"default":0.0001,"type":"F", "show":True},
+                    {"index":2,"name":"lr_schedulerList","value":[30,60],"description":'学习率衰减轮次',"default":[30,60],"type":"L", "show":True},
+                    {"index":3,"name":"device","value":"cpu","description":'训练核心',"default":"cpu","type":"S", "show":True},
+                    {"index":4,"name":"DatasetDir","value":"/mnt/sdc/algorithm/PaddleX/datasets/DDX_nb","description":'数据集路径',"default":"/mnt/sdc/algorithm/PaddleX/datasets/DDX_nb","type":"S", "show":False},
+                    {"index":5,"name":"saveModDir","value":"/mnt/sdc/algorithm/PaddleX/output","description":'保存模型路径',"default":"/mnt/sdc/algorithm/PaddleX/output","type":"S", "show":False},
+                    {"index":6,"name":"resumeModPath","value":'',"description":'继续训练路径',"default":'',"type":"S", "show":False},
+                    {"index":7,"name":"epochnum","value":100,"description":'训练轮次',"default":100,"type":"I", "show":True},
+                    {"index":8,"name":"saveEpoch","value":2,"description":'保存模型轮次',"default":2,"type":"I", "show":True}]
+    params_str = json.dumps(params_list) 
+    print(params_str)
+   
+    main(params_str)

app/configs/default.py

@@ -0,0 +1,29 @@
+import os
+
+# 根目录
+ROOT_PATH = os.path.split(os.path.abspath(__name__))[0]
+# 开启debug
+DEBUG = True
+# 密钥
+SECRET_KEY = 'WugjsfiYBEVsiQfiSwEbIOEAGnOIFYqoOYHEIK'
+
+# 数据库配置
+# SQLALCHEMY_DATABASE_URI = 'postgresql+psycopg2://deepLearner:dp2021@124.71.203.3:5432/demo'
+SQLALCHEMY_DATABASE_URI = 'mysql+pymysql://demo:demo123@192.168.2.9:3306/flask_demo'
+SQLALCHEMY_TRACK_MODIFICATIONS = False
+# 查询时会显示原始SQL语句
+SQLALCHEMY_ECHO = True
+
+# SQLALCHEMY_DATABASE_URI = 'sqlite:///{}'.format(os.path.join(ROOT_PATH, 'demo.db'))
+# SQLALCHEMY_TRACK_MODIFICATIONS = False
+
+# 数据库配置
+db = {
+        'host': '127.0.0.1',
+        'user': 'root',
+        'password': '',
+        'port': 6379,
+        'database': 'school',
+        'charset': 'utf8',
+        'db': 0
+}

app/configs/development.py

@@ -0,0 +1,8 @@
+from .default import *  # NOQA F401
+
+# 数据库配置
+SQLALCHEMY_DATABASE_URI = 'mysql+pymysql://demo:demo123@192.168.2.9:3306/flask_demo'
+# SQLALCHEMY_DATABASE_URI = 'mysql+pymysql://demo:demo123@192.168.2.9:3306/flask_demo?allowPublicKeyRetrieval=true&useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai'
+SQLALCHEMY_TRACK_MODIFICATIONS = False
+# 查询时会显示原始SQL语句
+SQLALCHEMY_ECHO = True

app/configs/production.py

@@ -0,0 +1,3 @@
+from .default import *  # NOQA F401
+
+DEBUG = False

app/configs/testing.py

@@ -0,0 +1,14 @@
+import os
+
+from .default import ROOT_PATH
+from .default import *  # NOQA F401
+
+
+TEST_BASE_DIR = os.path.join(ROOT_PATH, '.test')
+SQLALCHEMY_DATABASE_URI = 'sqlite:///{}'.format(
+    os.path.join(TEST_BASE_DIR, 'demo.db'))
+# SQLALCHEMY_ECHO = True
+TESTING = True
+
+if not os.path.exists(TEST_BASE_DIR):
+    os.makedirs(TEST_BASE_DIR)

app/controller/AlgorithmController.py

@@ -0,0 +1,412 @@
+"""
+@Time ： 2022/9/20 16:17
+@Auth ： 东
+@File ：AlgorithmController.py
+@IDE ：PyCharm
+@Motto：ABC(Always Be Coding)
+@Desc：算法接口
+
+"""
+import json
+from functools import wraps
+from threading import Thread
+
+from flask import Blueprint, request
+
+from app.schemas.TrainResult import Report, ProcessValueList
+from app.utils.RedisMQTool import Task
+from app.utils.StandardizedOutput import output_wrapped
+from app.utils.redis_config import redis_client
+from app.utils.websocket_tool import manager
+import sys
+from pathlib import Path
+
+# from pynvml import *
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[0]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+# sys.path.append("/mnt/sdc/algorithm/AICheck-MaskRCNN/app/maskrcnn_ppx")
+# import ppx as pdx
+
+bp = Blueprint('AlgorithmController', __name__)
+
+
+def start_train_algorithm():
+    """
+    调用训练算法
+    """
+
+    def wrapTheFunction(func):
+        @wraps(func)
+        @bp.route('/start_train_algorithm', methods=['get'])
+        def wrapped_function():
+            param = request.args.get('param')
+            id = request.args.get('id')
+            t = Thread(target=func, args=(param, id))
+            t.start()
+            return output_wrapped(0, 'success', '成功')
+
+        return wrapped_function
+
+    return wrapTheFunction
+
+
+def start_test_algorithm():
+    """
+    调用验证算法
+    """
+
+    def wrapTheFunction(func):
+        @wraps(func)
+        @bp.route('/start_test_algorithm', methods=['get'])
+        def wrapped_function_test():
+            param = request.args.get('param')
+            id = request.args.get('id')
+            t = Thread(target=func, args=(param, id))
+            t.start()
+            return output_wrapped(0, 'success', '成功')
+
+        return wrapped_function_test
+
+    return wrapTheFunction
+
+
+def start_detect_algorithm():
+    """
+    调用检测算法
+    """
+
+    def wrapTheFunction(func):
+        @wraps(func)
+        @bp.route('/start_detect_algorithm', methods=['get'])
+        def wrapped_function_detect():
+            param = request.args.get('param')
+            id = request.args.get('id')
+            t = Thread(target=func, args=(param, id))
+            t.start()
+            return output_wrapped(0, 'success', '成功')
+
+        return wrapped_function_detect
+
+    return wrapTheFunction
+
+
+def start_download_pt():
+    """
+    下载模型
+    """
+
+    def wrapTheFunction(func):
+        @wraps(func)
+        @bp.route('/start_download_pt', methods=['get'])
+        def wrapped_function_start_download_pt():
+            param = request.args.get('param')
+            func(param)
+            return output_wrapped(0, 'success', '成功')
+
+        return wrapped_function_start_download_pt
+
+    return wrapTheFunction
+
+
+def algorithm_process_value():
+    """
+    获取中间值, redis订阅发布
+    """
+
+    def wrapTheFunction(func):
+        @wraps(func)
+        def wrapped_function(*args, **kwargs):
+            data = func(*args, **kwargs)
+            print(data)
+            Task(redis_conn=redis_client.get_redis(), channel="ceshi").publish_task(
+                data={'code': 0, 'msg': 'success', 'data': data})
+            return output_wrapped(0, 'success', data)
+
+        return wrapped_function
+
+    return wrapTheFunction
+
+
+def algorithm_process_value_websocket():
+    """
+    获取中间值, websocket发布
+    """
+
+    def wrapTheFunction(func):
+        @wraps(func)
+        def wrapped_function(*args, **kwargs):
+            data = func(*args, **kwargs)
+            id = data["id"]
+            data_res = {'code': 0, "type": 'connected', 'msg': 'success', 'data': data}
+            manager.send_message_proj_json(message=data_res, id=id)
+            return data
+
+        return wrapped_function
+
+    return wrapTheFunction
+
+
+def obtain_train_param():
+    """
+       获取训练参数
+       """
+
+    def wrapTheFunction(func):
+        @wraps(func)
+        @bp.route('/obtain_train_param', methods=['get'])
+        def wrapped_function_train_param(*args, **kwargs):
+            data = func(*args, **kwargs)
+            return output_wrapped(0, 'success', data)
+
+        return wrapped_function_train_param
+
+    return wrapTheFunction
+
+
+def obtain_test_param():
+    """
+       获取验证参数
+       """
+
+    def wrapTheFunction(func):
+        @wraps(func)
+        @bp.route('/obtain_test_param', methods=['get'])
+        def wrapped_function_test_param(*args, **kwargs):
+            data = func(*args, **kwargs)
+            return output_wrapped(0, 'success', data)
+
+        return wrapped_function_test_param
+
+    return wrapTheFunction
+
+
+def obtain_detect_param():
+    """
+       获取测试参数
+       """
+
+    def wrapTheFunction(func):
+        @wraps(func)
+        @bp.route('/obtain_detect_param', methods=['get'])
+        def wrapped_function_inf_param(*args, **kwargs):
+            data = func(*args, **kwargs)
+            return output_wrapped(0, 'success', data)
+
+        return wrapped_function_inf_param
+
+    return wrapTheFunction
+
+
+def obtain_download_pt_param():
+    """
+       获取下载模型参数
+       """
+
+    def wrapTheFunction(func):
+        @wraps(func)
+        @bp.route('/obtain_download_pt_param', methods=['get'])
+        def wrapped_function_obtain_download_pt_param(*args, **kwargs):
+            data = func(*args, **kwargs)
+            return output_wrapped(0, 'success', data)
+
+        return wrapped_function_obtain_download_pt_param
+
+    return wrapTheFunction
+
+
+# @start_train_algorithm()
+# def start(param: str):
+#     """
+#     例子
+#     """
+#     print(param)
+#     process_value_list = ProcessValueList(name='1', value=[])
+#     report = Report(rate_of_progess=0, process_value=[process_value_list], id='1')
+#
+#     @algorithm_process_value_websocket()
+#     def process(v: int):
+#         print(v)
+#         report.rate_of_progess = ((v + 1) / 10) * 100
+#         report.precision[0].value.append(v)
+#         return report.dict()
+#
+#     for i in range(10):
+#         process(i)
+#     return report.dict()
+from setparams import TrainParams
+import os
+from app.schemas.TrainResult import DetectProcessValueDice, DetectReport
+from app import file_tool
+
+
+# 启动训练
+@start_train_algorithm()
+def train_R0DY(params_str, id):
+    from app.yolov5.train_server import train_start
+    params = TrainParams()
+    params.read_from_str(params_str)
+    print(params.get('device').default)
+    data_list = file_tool.get_file(ori_path=params.get('DatasetDir').value, type_list=params.get('classname').value)
+    weights = params.get('resumeModPath').value  # 初始化模型绝对路径
+    img_size = params.get('img_size').value
+    savemodel = os.path.splitext(params.get('saveModDir').value)[0] + '_' + img_size + '.pt'  # 模型命名加上图像参数
+    epoches = params.get('epochnum').value
+    batch_size = params.get('batch_size').value
+    device = params.get('device').value
+
+    train_start(weights, savemodel, epoches, img_size, batch_size, device, data_list, id)
+
+
+# 启动验证程序
+
+@start_test_algorithm()
+def validate_RODY(params_str, id):
+    from app.yolov5.validate_server import validate_start
+    params = TrainParams()
+    params.read_from_str(params_str)
+    weights = params.get('modPath').value  # 验证模型绝对路径
+    (filename, extension) = os.path.splitext(weights)  # 文件名与后缀名分开
+    img_size = int(filename.split('ROD')[1].split('_')[2])  # 获取图像参数
+    # v_num = int(filename.split('ROD')[1].split('_')[1]) #获取版本号
+    output = params.get('outputPath').value
+    batch_size = params.get('batch_size').default
+    device = params.get('device').value
+
+    validate_start(weights, img_size, batch_size, device, output, id)
+
+
+@start_detect_algorithm()
+def detect_RODY(params_str, id):
+    from app.yolov5.detect_server import detect_start
+    params = TrainParams()
+    params.read_from_str(params_str)
+    weights = params.get('modPath').value  # 检测模型绝对路径
+    input = params.get('inputPath').value
+    outpath = params.get('outputPath').value
+    # (filename, extension) = os.path.splitext(weights)  # 文件名与后缀名分开
+    # img_size = int(filename.split('ROD')[1].split('_')[2]) #获取图像参数
+    # v_num = int(filename.split('ROD')[1].split('_')[1]) #获取版本号
+    # batch_size = params.get('batch_size').default
+    device = params.get('device').value
+
+    detect_start(input, weights, outpath, device, id)
+
+
+@start_download_pt()
+def Export_model_RODY(params_str):
+    from app.yolov5.export import Start_Model_Export
+    import zipfile
+    params = TrainParams()
+    params.read_from_str(params_str)
+    exp_inputPath = params.get('exp_inputPath').value  # 模型路径
+    exp_device = params.get('device').value
+    modellist = Start_Model_Export(exp_inputPath, exp_device)
+    exp_outputPath = exp_inputPath.replace('pt', 'zip')  # 压缩文件
+    zipf = zipfile.ZipFile(exp_outputPath, 'w')
+    for file in modellist:
+        zipf.write(file, arcname=Path(file).name)  # 将torchscript和onnx模型压缩
+
+    return exp_outputPath
+    # zipf.write(modellist[1], arcname=modellist[1])
+    # zip_inputpath = os.path.join(exp_outputPath, "inference_model")
+    # zip_outputPath = os.path.join(exp_outputPath, "inference_model.zip")
+
+
+@obtain_train_param()
+def returnTrainParams():
+    # nvmlInit()
+    # gpuDeviceCount = nvmlDeviceGetCount()  # 获取Nvidia GPU块数
+    # _kernel = [f"cuda:{a}" for a in range(gpuDeviceCount)]
+    params_list = [
+        {"index": 0, "name": "epochnum", "value": 10, "description": '训练轮次', "default": 100, "type": "I", 'show': True},
+        {"index": 1, "name": "batch_size", "value": 4, "description": '批次图像数量', "default": 1, "type": "I",
+         'show': True},
+        {"index": 2, "name": "img_size", "value": 640, "description": '训练图像大小', "default": 640, "type": "I",
+         'show': True},
+        {"index": 3, "name": "device", "value": "0", "description": '训练核心', "default": "cuda", "type": "S",
+         "items": '', 'show': True},  # _kernel
+        {"index": 4, "name": "saveModDir", "value": "E:/alg_demo-master/alg_demo/app/yolov5/best.pt",
+         "description": '保存模型路径',
+         "default": "./app/maskrcnn/saved_model/test.pt", "type": "S", 'show': False},
+        {"index": 5, "name": "resumeModPath", "value": 'E:/alg_demo-master/alg_demo/app/yolov5/yolov5s.pt',
+         "description": '继续训练路径', "default": '', "type": "S",
+         'show': False},
+        {"index": 6, "name": "resumeMod", "value": '', "description": '继续训练模型', "default": '', "type": "E", "items": '',
+         'show': True},
+        {"index": 7, "name": "classname", "value": ['logo', '3C'], "description": '类别名称', "default": '', "type": "L",
+         "items": '',
+         'show': False},
+        {"index": 8, "name": "DatasetDir", "value": "E:/aicheck/data_set/11442136178662604800/ori/",
+         "description": '数据集路径',
+         "default": "./app/maskrcnn/datasets/test", "type": "S", 'show': False}  # ORI_PATH
+    ]
+    # {"index": 9, "name": "saveEpoch", "value": 2, "description": '保存模型轮次', "default": 2, "type": "I", 'show': True}]
+    params_str = json.dumps(params_list)
+    return params_str
+
+
+@obtain_test_param()
+def returnValidateParams():
+    # nvmlInit()
+    # gpuDeviceCount = nvmlDeviceGetCount()  # 获取Nvidia GPU块数
+    # _kernel = [f"cuda:{a}" for a in range(gpuDeviceCount)]
+    params_list = [
+        {"index": 0, "name": "modPath", "value": "E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt",
+         "description": '验证模型路径', "default": "./app/maskrcnn/saved_model/test.pt", "type": "S", 'show': False},
+        {"index": 1, "name": "batch_size", "value": 1, "description": '批次图像数量', "default": 1, "type": "I",
+         'show': False},
+        {"index": 2, "name": "img_size", "value": 640, "description": '训练图像大小', "default": 640, "type": "I",
+         'show': False},
+        {"index": 3, "name": "outputPath", "value": 'E:/aicheck/data_set/11442136178662604800/val_results/',
+         "description": '输出结果路径',
+         "default": './app/maskrcnn/datasets/M006B_waibi/res', "type": "S", 'show': False},
+        {"index": 4, "name": "device", "value": "0", "description": '训练核心', "default": "cuda", "type": "S",
+         "items": '', 'show': False}  # _kernel
+    ]
+    # {"index": 9, "name": "saveEpoch", "value": 2, "description": '保存模型轮次', "default": 2, "type": "I", 'show': True}]
+    params_str = json.dumps(params_list)
+    return params_str
+
+
+@obtain_detect_param()
+def returnDetectParams():
+    # nvmlInit()
+    # gpuDeviceCount = nvmlDeviceGetCount()  # 获取Nvidia GPU块数
+    # _kernel = [f"cuda:{a}" for a in range(gpuDeviceCount)]
+    params_list = [
+        {"index": 0, "name": "inputPath", "value": 'E:/aicheck/data_set/11442136178662604800/input/',
+         "description": '输入图像路径', "default": './app/maskrcnn/datasets/M006B_waibi/JPEGImages', "type": "S",
+         'show': False},
+        {"index": 1, "name": "outputPath", "value": 'E:/aicheck/data_set/11442136178662604800/val_results/',
+         "description": '输出结果路径',
+         "default": './app/maskrcnn/datasets/M006B_waibi/res', "type": "S", 'show': False},
+        {"index": 0, "name": "modPath", "value": "E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt",
+         "description": '模型路径', "default": "./app/maskrcnn/saved_model/test.pt", "type": "S", 'show': False},
+        {"index": 3, "name": "device", "value": "0", "description": '推理核', "default": "cpu", "type": "S",
+         'show': False},
+    ]
+    # {"index": 9, "name": "saveEpoch", "value": 2, "description": '保存模型轮次', "default": 2, "type": "I", 'show': True}]
+    params_str = json.dumps(params_list)
+    return params_str
+
+
+@obtain_download_pt_param()
+def returnDownloadParams():
+    params_list = [
+        {"index": 0, "name": "exp_inputPath", "value": 'E:/alg_demo-master/alg_demo/app/yolov5/圆孔_123_RODY_1_640.pt',
+         "description": '转化模型输入路径',
+         "default": '/mnt/sdc/IntelligentizeAI/IntelligentizeAI/data_set/weights/new磁环检测test_183504733393264640_R-DDM_11.pt/',
+         "type": "S", 'show': False},
+        {"index": 1, "name": "device", "value": 'gpu', "description": 'CPU或GPU', "default": 'gpu', "type": "S",
+         'show': False}
+    ]
+    params_str = json.dumps(params_list)
+    return params_str
+
+
+if __name__ == '__main__':
+    par = returnDownloadParams()
+    print(par)
+    Export_model_RODY(par)

app/controller/WebStatusController.py

@@ -0,0 +1,33 @@
+import logging
+
+from flask import Blueprint, app
+
+from app.exts import redisClient
+from app.utils.StandardizedOutput import output_wrapped
+
+bp = Blueprint('WebStatus', __name__)
+
+
+@bp.route('/ping', methods=['GET'])
+def ping():
+    """ For health check.
+    """
+    res = output_wrapped(0, 'pong', '')
+    return res
+
+
+@bp.route('/redis/set', methods=['post'])
+def redis_set():
+    redisClient.set('foo', 'bar', ex=60*60*6)
+    res = output_wrapped(0, 'set foo', '')
+    return res
+
+
+@bp.route('/redis/get', methods=['get'])
+def redis_get():
+    """ For health check.
+    """
+    the_food = redisClient.get('foo')
+    if not the_food:
+        return output_wrapped(5006, 'foo', "")
+    return output_wrapped(0, 'foo', the_food.decode("utf-8"))

app/controller/__init__.py

@@ -0,0 +1,4 @@
+from app.core.common_utils import import_subs
+
+
+__all__ = import_subs(locals(), modules_only=True)

app/core/common_utils.py

@@ -0,0 +1,319 @@
+import orjson
+
+try:
+    from collections import Mapping
+except:  # noqa E722
+    from collections.abc import Mapping
+import inspect
+import importlib
+import os
+import re
+import sys
+from typing import Any, Dict, List, Optional
+from unicodedata import normalize
+from distutils.version import LooseVersion
+import logging
+import datetime
+
+from flask import request
+from flask_sqlalchemy import model
+from sqlalchemy import UniqueConstraint
+import marshmallow
+from marshmallow import Schema
+
+from .webargs import use_kwargs as base_use_kwargs, parser
+
+from flask.json import JSONEncoder
+
+logger = logging.getLogger(__name__)
+
+
+class ParamsDict(dict):
+    """Just available update func.
+
+    Example::
+
+        @use_kwargs(PageParams.update({...}))
+        def list_users(page, page_size, order_by):
+            pass
+
+    """
+
+    def update(self, other=None):
+        """Update self by other Mapping and return self.
+        """
+        ret = ParamsDict(self.copy())
+        if other is not None:
+            for k, v in other.items() if isinstance(other, Mapping) else other:
+                ret[k] = v
+        return ret
+
+
+# Function version
+def row2dict(row):
+    return {
+        c.name: str(getattr(row, c.name))
+        for c in row.__table__.columns
+    }
+
+
+class dict2object(dict):
+    """
+    Dict to fake object that can use getattr.
+    """
+
+    def __getattr__(self, name: str) -> Any:
+        if name in self.keys():
+            return self[name]
+        raise AttributeError('object has no attribute {}'.format(name))
+
+    def __setattr__(self, name: str, value: Any) -> None:
+        if not isinstance(name, str):
+            raise TypeError('key must be string type.')
+        self[name] = value
+
+
+def secure_filename(filename: str) -> str:
+    """Borrowed from werkzeug.utils.secure_filename.
+
+    Pass it a filename and it will return a secure version of it. This
+    filename can then safely be stored on a regular file system and passed
+    to :func:`os.path.join`.
+
+    On windows systems the function also makes sure that the file is not
+    named after one of the special device files.
+
+        >>> secure_filename(u'哈哈.zip')
+        '哈哈.zip'
+        >>> secure_filename('My cool movie.mov')
+        'My_cool_movie.mov'
+        >>> secure_filename('../../../etc/passwd')
+        'etc_passwd'
+        >>> secure_filename(u'i contain cool \xfcml\xe4uts.txt')
+        'i_contain_cool_umlauts.txt'
+
+    """
+
+    for sep in os.path.sep, os.path.altsep:
+        if sep:
+            filename = filename.replace(sep, ' ')
+
+    filename = normalize('NFKD', '_'.join(filename.split()))
+
+    filename_strip_re = re.compile(u'[^A-Za-z0-9\u4e00-\u9fa5_.-]')
+    filename = filename_strip_re.sub('', filename).strip('._')
+
+    # on nt a couple of special files are present in each folder.  We
+    # have to ensure that the target file is not such a filename.  In
+    # this case we prepend an underline
+    windows_device_files = (
+        'CON', 'AUX', 'COM1', 'COM2', 'COM3', 'COM4', 'LPT1',
+        'LPT2', 'LPT3', 'PRN', 'NUL',
+    )
+    if os.name == 'nt' and filename and \
+            filename.split('.')[0].upper() in windows_device_files:
+        filename = '_' + filename
+
+    return filename
+
+
+def _get_init_args(instance, base_class):
+    """Get instance's __init__ args and it's value when __call__.
+    """
+    getargspec = inspect.getfullargspec
+    argspec = getargspec(base_class.__init__)
+
+    defaults = argspec.defaults
+    kwargs = {}
+    if defaults is not None:
+        no_defaults = argspec.args[:-len(defaults)]
+        has_defaults = argspec.args[-len(defaults):]
+
+        kwargs = {k: getattr(instance, k) for k in no_defaults
+                  if k != 'self' and hasattr(instance, k)}
+        kwargs.update({k: getattr(instance, k) if hasattr(instance, k) else
+        getattr(instance, k, defaults[i])
+                       for i, k in enumerate(has_defaults)})
+        assert len(kwargs) == len(argspec.args) - 1, 'exclude `self`'
+    return kwargs
+
+
+def use_kwargs(argmap, schema_kwargs: Optional[Dict] = None, **kwargs: Any):
+    """For fix ``Schema(partial=True)`` not work when used with
+    ``@webargs.flaskparser.use_kwargs``. More details ``see webargs.core``.
+
+    Args:
+
+        argmap (marshmallow.Schema,dict,callable): Either a
+            `marshmallow.Schema`, `dict` of argname ->
+            `marshmallow.fields.Field` pairs, or a callable that returns a
+            `marshmallow.Schema` instance.
+        schema_kwargs (dict): kwargs for argmap.
+
+    Returns:
+        dict: A dictionary of parsed arguments.
+
+    """
+    schema_kwargs = schema_kwargs or {}
+
+    argmap = parser._get_schema(argmap, request)
+
+    if not (argmap.partial or schema_kwargs.get('partial')):
+        return base_use_kwargs(argmap, **kwargs)
+
+    def factory(request):
+        argmap_kwargs = _get_init_args(argmap, Schema)
+        argmap_kwargs.update(schema_kwargs)
+
+        # force set force_all=False
+        only = parser.parse(argmap, request).keys()
+
+        argmap_kwargs.update({
+            'partial': False,  # fix missing=None not work
+            'only': only or None,
+            'context': {"request": request},
+        })
+        if tuple(LooseVersion(marshmallow.__version__).version)[0] < 3:
+            argmap_kwargs['strict'] = True
+
+        return argmap.__class__(**argmap_kwargs)
+
+    return base_use_kwargs(factory, **kwargs)
+
+
+def import_subs(locals_, modules_only: bool = False) -> List[str]:
+    """ Auto import submodules, used in __init__.py.
+
+    Args:
+
+        locals_: `locals()`.
+        modules_only: Only collect modules to __all__.
+
+    Examples::
+
+        # app/models/__init__.py
+        from hobbit_core.utils import import_subs
+
+        __all__ = import_subs(locals())
+
+    Auto collect Model's subclass, Schema's subclass and instance.
+    Others objects must defined in submodule.__all__.
+    """
+    package = locals_['__package__']
+    path = locals_['__path__']
+    top_mudule = sys.modules[package]
+
+    all_ = []
+    for name in os.listdir(path[0]):
+        if not name.endswith(('.py', '.pyc')) or name.startswith('__init__.'):
+            continue
+
+        module_name = name.split('.')[0]
+        submodule = importlib.import_module(f".{module_name}", package)
+        all_.append(module_name)
+
+        if modules_only:
+            continue
+
+        if hasattr(submodule, '__all__'):
+            for name in getattr(submodule, '__all__'):
+                if not isinstance(name, str):
+                    raise Exception(f'Invalid object {name} in __all__, '
+                                    f'must contain only strings.')
+                setattr(top_mudule, name, getattr(submodule, name))
+                all_.append(name)
+        else:
+            for name, obj in submodule.__dict__.items():
+                if isinstance(obj, (model.DefaultMeta, Schema)) or \
+                        (inspect.isclass(obj) and
+                         (issubclass(obj, Schema) or
+                          obj.__name__.endswith('Service'))):
+                    setattr(top_mudule, name, obj)
+                    all_.append(name)
+    return all_
+
+
+def bulk_create_or_update_on_duplicate(
+        db, model_cls, items, updated_at='updated_at', batch_size=500):
+    """ Support MySQL and postgreSQL.
+    https://dev.mysql.com/doc/refman/8.0/en/insert-on-duplicate.html
+
+    Args:
+
+        db: Instance of `SQLAlchemy`.
+        model_cls: Model object.
+        items: List of data,[ example: `[{key: value}, {key: value}, ...]`.
+        updated_at: Field which recording row update time.
+        batch_size: Batch size is max rows per execute.
+
+    Returns:
+        dict: A dictionary contains rowcount and items_count.
+    """
+    if not items:
+        logger.warning("bulk_create_or_update_on_duplicate save to "
+                       f"{model_cls} failed, empty items")
+        return {'rowcount': 0, 'items_count': 0}
+
+    items_count = len(items)
+    table_name = model_cls.__tablename__
+    fields = list(items[0].keys())
+    unique_keys = [c.name for i in model_cls.__table_args__ if isinstance(
+        i, UniqueConstraint) for c in i]
+    columns = [c.name for c in model_cls.__table__.columns if c.name not in (
+        'id', 'created_at')]
+
+    if updated_at in columns and updated_at not in fields:
+        fields.append(updated_at)
+        updated_at_time = datetime.datetime.now()
+        for item in items:
+            item[updated_at] = updated_at_time
+
+    assert set(fields) == set(columns), \
+        'item fields not equal to columns in models：new: ' + \
+        f'{set(fields) - set(columns)}, delete: {set(columns) - set(fields)}'
+
+    for item in items:
+        for column in unique_keys:
+            if column in item and item[column] is None:
+                item[column] = ''
+
+    engine = db.get_engine(bind=getattr(model_cls, '__bind_key__', None))
+    if engine.name == 'postgresql':
+        sql_on_update = ', '.join([
+            f' {field} = excluded.{field}'
+            for field in fields if field not in unique_keys])
+        sql = f"""INSERT INTO {table_name} ({", ".join(fields)}) VALUES
+            ({", ".join([f':{key}' for key in fields])})
+            ON CONFLICT ({", ".join(unique_keys)}) DO UPDATE SET
+            {sql_on_update}"""
+    elif engine.name == 'mysql':
+        sql_on_update = '`, `'.join([
+            f' `{field}` = new.{field}' for field in fields
+            if field not in unique_keys])
+        sql = f"""INSERT INTO {table_name} (`{"`, `".join(fields)}`) VALUES
+            ({", ".join([f':{key}' for key in fields])}) AS new
+            ON DUPLICATE KEY UPDATE
+            {sql_on_update}"""
+    else:
+        raise Exception(f'not support db: {engine.name}')
+
+    rowcounts = 0
+    while len(items) > 0:
+        batch, items = items[:batch_size], items[batch_size:]
+        try:
+            result = db.session.execute(sql, batch, bind=engine)
+        except Exception as e:
+            logger.error(e, exc_info=True)
+            logger.info(sql)
+            raise e
+        rowcounts += result.rowcount
+    logger.info(f'{model_cls} save_data: rowcount={rowcounts}, '
+                f'items_count: {items_count}')
+    return {'rowcount': rowcounts, 'items_count': items_count}
+
+
+def orjson_serializer(obj):
+    """
+        Note that `orjson.dumps()` return byte array, while sqlalchemy expects string, thus `decode()` call.
+    """
+    return orjson.dumps(obj, option=orjson.OPT_SERIALIZE_NUMPY | orjson.OPT_NAIVE_UTC).decode()

app/core/err_handler.py

@@ -0,0 +1,68 @@
+import logging
+
+from flask import jsonify, request, make_response, abort
+from marshmallow import ValidationError
+
+logger = logging.getLogger(__name__)
+
+
+# 处理404错误
+def page_not_found(e):
+    # if a request is in our blog URL space
+    if request.path.startswith('/blog/'):
+        # we return a custom blog 404 page
+        return jsonify({
+            "code": e.code,
+            "message": e.name,
+            "description": e.description,
+            "data": ""
+        }), 404
+    else:
+        # otherwise we return our generic site-wide 404 page
+        return jsonify({
+            "code": e.code,
+            "message": e.name,
+            "description": e.description,
+            "data": ""
+        }), 404
+
+
+# 处理405错误
+def method_not_allowed(e):
+    # otherwise we return a generic site-wide 405 page
+    return jsonify({
+        "code": e.code,
+        "message": e.name,
+        "description": e.description,
+        "data": ""
+    }), 405
+
+
+# 处理其他400错误
+def exception_400(e):
+    # otherwise we return a generic site-wide 405 page
+    return jsonify({
+        "code": e.code,
+        "message": e.name,
+        "description": e.description,
+        "data": ""
+    }), 400
+
+
+# 处理其他状态码错误
+def exception_500(e):
+    print(e)
+    return jsonify({
+        "code": e.code,
+        "message": e.name,
+        "description": e.description,
+        "data": ""
+    }), e.code
+
+
+# 处理参数校验错误
+def check_data(schema, data):
+    try:
+        return schema().load(data)
+    except ValidationError as e:
+        abort(make_response(jsonify(code=400, message=str(e.messages), data=None), 400))

app/core/webargs.py

@@ -0,0 +1,26 @@
+from collections.abc import Mapping
+
+from webargs.flaskparser import FlaskParser
+
+
+def strip_whitespace(value):
+    if isinstance(value, str):
+        value = value.strip()
+    # you'll be getting a MultiDictProxy here potentially, but it should work
+    elif isinstance(value, Mapping):
+        return {k: strip_whitespace(value[k]) for k in value}
+    elif isinstance(value, (list, set)):
+        return type(value)(map(strip_whitespace, value))
+    return value
+
+
+class CustomParser(FlaskParser):
+
+    def _load_location_data(self, **kwargs):
+        data = super()._load_location_data(**kwargs)
+        return strip_whitespace(data)
+
+
+parser = CustomParser()
+use_args = parser.use_args
+use_kwargs = parser.use_kwargs

app/exts.py

@@ -0,0 +1,12 @@
+import redis
+from flask_sqlalchemy import SQLAlchemy
+from flask_migrate import Migrate
+from flask_marshmallow import Marshmallow
+
+
+db = SQLAlchemy()
+migrate = Migrate()
+ma = Marshmallow()
+
+pool = redis.ConnectionPool(host='localhost', password='sdust2020', port=6379, db=8)
+redisClient = redis.Redis(connection_pool=pool)

app/file_tool.py

@@ -0,0 +1,253 @@
+import json
+import os
+import shutil
+from math import ceil
+from typing import List, Optional, Union
+
+#from ai_platform.common.config import settings
+# from ai_platform.model.crud import image_label_crud as ilc, project_list_crud as plc, \
+#     image_dataset_curd as idc
+#from ai_platform.common.logger import logger
+# from ai_platform.model.database import session
+from app.core.common_utils import logger
+from app.json_util import write_info
+
+# root_path = settings.root_path
+
+# root_path = '/home/wd/server/ai_platform/data_set/'
+
+#db = session
+
+
+def delete_file(files: List[str]):
+    """
+    删除文件
+    :param files:
+    :return:
+    """
+    for file in files:
+        if os.path.exists(file):
+            os.remove(file)
+
+
+def get_file_then_delete_file(path: str):
+    """
+    删除指定路径下的所有文件
+    :param path:
+    :return:
+    """
+    (filedir, filename) = os.path.split(path)
+    if os.path.exists(filedir):
+        del_files = []
+        for (dirpath, dirnames, filenames) in os.walk(filedir):
+            for filename in filenames:
+                del_files.append(os.path.join(dirpath, filename))
+        # del_files = os.listdir(filedir)
+        delete_file(files=del_files)
+    return filedir
+
+
+def delete_dir_file(files: List[str], json_files: List[str]):
+    """
+    若训练集、测试机、验证集的存放文件夹不为空， 删除文件夹下所有文件
+    :param json_files:
+    :param files:
+    :return:
+    """
+    logger.info('删除图片数据')
+    train_target_path = files[0].replace('ori/images', 'trained/images/train')
+    train_filedir = get_file_then_delete_file(train_target_path)
+
+    val_target_path = files[0].replace('ori/images', 'trained/images/val')
+    val_filedir = get_file_then_delete_file(val_target_path)
+
+    test_target_path = files[0].replace('ori/images', 'trained/images/test')
+    test_filedir = get_file_then_delete_file(test_target_path)
+
+    if len(json_files) == 0:
+        logger.info('无json数据')
+    else:
+        logger.info('删除json数据')
+        train_target_path = json_files[0].replace('ori/labels', 'trained/labels/train')
+        get_file_then_delete_file(train_target_path)
+
+        val_target_path = json_files[0].replace('ori/labels', 'trained/labels/val')
+        get_file_then_delete_file(val_target_path)
+
+        val_target_path = json_files[0].replace('ori/labels', 'trained/labels/test')
+        get_file_then_delete_file(val_target_path)
+    return [train_filedir + '/', val_filedir + '/', test_filedir + '/']
+
+
+def mv_file(train_files: List[str], test_files: List[str], r_v_rate: Optional[float] = 0.9,
+            t_t_rate: Optional[float] = 0.9):
+    """
+    移动图片标签到指定位置
+    :param train_files:测试集
+    :param test_files:验证集
+    :param r_v_rate:训练集内部比例
+    :param t_t_rate:训练-验证比例
+    :return:
+    """
+    train_img_files = [i for i in train_files if not i.endswith('.json')]
+    train_json_files = [i for i in train_files if i.endswith('.json')]
+    test_img_files = [i for i in test_files if not i.endswith('.json')]
+    test_json_files = [i for i in test_files if i.endswith('.json')]
+
+    # 训练集、验证集、测试集
+    #logger.info('训练集、验证集、测试集开始划分')
+    train_len_all = len(train_img_files)
+    if t_t_rate is not None:
+        test_len_all = len(test_img_files)
+        len_all = train_len_all + test_len_all
+        t_t_rate_c = test_len_all / len_all
+        if t_t_rate_c > t_t_rate:
+            train_len_all = ceil(len_all * t_t_rate)
+            test_files.extend(train_img_files[train_len_all:])
+    train_len = ceil(train_len_all * r_v_rate)
+    # t_files: 训练集， val_files:验证集
+    t_files = train_img_files[0:train_len]
+    val_files = train_img_files[train_len:train_len_all]
+
+    # 判断目标文件夹是否存在， 存在则删除目录下文件
+    #logger.info('判断目标文件夹是否存在， 存在则删除目录下文件')
+    target_path = delete_dir_file(files=train_img_files, json_files=train_json_files)
+
+    # 放到指定文件夹
+    #logger.info('放到指定文件夹')
+    # t_files:训练集开始移动
+    for file in t_files:
+        if os.path.exists(file):
+            file_path = file.replace('ori/images', 'trained/images/train')
+            # /3148803620347904/ori/images/4.jpg
+            (filedir, filename) = os.path.split(file_path)
+            if not os.path.exists(filedir):
+                os.makedirs(filedir)
+            shutil.copyfile(file, file_path)
+            # json 放到指定文件夹下
+            json_file = os.path.splitext(file)[0].replace('images', 'labels') + '.json'
+            if json_file in train_json_files:
+                file_path = json_file.replace('ori/labels', 'trained/labels/train')
+                # /3148803620347904/ori/labels/4.jpg.json
+                (filedir, filename) = os.path.split(file_path)
+                if not os.path.exists(filedir):
+                    os.makedirs(filedir)
+                shutil.copyfile(json_file, file_path)
+
+    # 测试集开始
+    for file in val_files:
+        if os.path.exists(file):
+            file_path = file.replace('ori/images', 'trained/images/val')
+            (filedir, filename) = os.path.split(file_path)
+            if not os.path.exists(filedir):
+                os.makedirs(filedir)
+            shutil.copyfile(file, file_path)
+            # json 放到指定文件夹下
+            json_file = os.path.splitext(file)[0].replace('images', 'labels') + '.json'
+            if json_file in train_json_files:
+                file_path = json_file.replace('ori/labels', 'trained/labels/val')
+                (filedir, filename) = os.path.split(file_path)
+                if not os.path.exists(filedir):
+                    os.makedirs(filedir)
+                shutil.copyfile(json_file, file_path)
+
+    for file in test_img_files:
+        if os.path.exists(file):
+            file_path = file.replace('ori/images', 'trained/images/test')
+            # /3148803620347904/ori/images/4.jpg
+            (filedir, filename) = os.path.split(file_path)
+            if not os.path.exists(filedir):
+                os.makedirs(filedir)
+            shutil.copyfile(file, file_path)
+            # json 放到指定文件夹下
+            json_file = os.path.splitext(file)[0].replace('images', 'labels') + '.json'
+            if json_file in test_json_files:
+                file_path = json_file.replace('ori/labels', 'trained/labels/test')
+                # /3148803620347904/ori/labels/4.jpg.json
+                (filedir, filename) = os.path.split(file_path)
+                if not os.path.exists(filedir):
+                    os.makedirs(filedir)
+                shutil.copyfile(json_file, file_path)
+    return target_path
+
+
+def get_file(ori_path: str, type_list: Union[object,str]):
+    # imgs = idc.get_image_all_proj_no(proj_no=proj_no, db=db)
+    imgs = os.listdir(ori_path + '/images')
+    train_files = []
+    test_files = []
+    # 训练、测试比例强制9：1
+    for img in imgs[0:1]:
+        path = ori_path + 'images/' +img
+        # print(os.path.exists(path))
+        if os.path.exists(path):
+            test_files.append(path)
+            #label = ori_path + 'labels/' + os.path.split(path)[1]
+            (filename1, extension) = os.path.splitext(img)  # 文件名与后缀名分开
+            label = ori_path + 'labels/' + filename1 + '.json'
+            if label is not None:
+                #train_files.append(label)
+                test_files.append(label)
+    for img in imgs[1:]:
+        path = ori_path + 'images/' +img
+        if os.path.exists(path):
+            train_files.append(path)
+            (filename2, extension) = os.path.splitext(img)  # 文件名与后缀名分开
+            label = ori_path + 'labels/' + filename2 + '.json'
+            if label is not None:
+                train_files.append(label)
+
+    if len(train_files) == 0 or len(test_files) == 0:
+        return False
+    # proj = plc.get_proj_by_proj_no(proj_no=proj_no, db=db)
+    target_path = mv_file(train_files=train_files, test_files=test_files)
+    # 生成标签
+    # img_types = ilc.get_label_by_proj_no(proj_no=proj_no, db=db)
+    # type_list = []
+    # for img_type in img_types:
+    #     type_list.append(img_type.lebel_type)
+    type_dict = {'classes': type_list}
+    str_json = json.dumps(type_dict)
+    path = os.path.dirname(ori_path) + '/img_label_type'
+    # path = root_path + proj_no + '/img_label_type'
+    write_info(file_name=path, file_info=json.loads(str_json))
+    target_path.append(path + '.json')
+    return target_path
+
+
+# def get_file_path(proj_no: str):
+#     """
+#     识别算法，给算法传递图片路径
+#     :param proj_no:
+#     :return:
+#     """
+#     path = root_path + '/' + proj_no
+#     img_path = path
+#     # 创建他们所需的文件夹
+#     vgg_path = path + '/vgg'
+#     if not os.path.exists(vgg_path):
+#         # vgg不存在，创建
+#         train_path = vgg_path + '/train'
+#         test_path = vgg_path + '/test'
+#         os.makedirs(train_path)
+#         os.makedirs(test_path)
+#     # 生成标签
+#     img_types = ilc.get_label_by_proj_no(proj_no=proj_no, db=db)
+#     type_list = []
+#     for img_type in img_types:
+#         type_list.append(img_type.lebel_type)
+#     type_dict = {'classes': type_list}
+#     str_json = json.dumps(type_dict)
+#     path = root_path + proj_no + '/img_label_type'
+#     write_info(file_name=path, file_info=json.loads(str_json))
+#     return img_path, path + '.json'
+
+
+if __name__ == '__main__':
+    # s = os.path.exists('D:/pythonProject/DeepLearnAiPlatform/data_set/868503011860480/ori/images/1.png')
+    # print(s)
+    # file = 'D:/pythonProject/DeepLearnAiPlatform/data_set/3148803620347904/ori/labels/36.json'
+    # file_path = 'D:/pythonProject/DeepLearnAiPlatform/data_set/3148803620347904/trained/labels/36.json'
+    s = get_file(proj_no='3148803620347904')
+    # shutil.copyfile(file, file_path)
+    print(s)

app/json_util.py

@@ -0,0 +1,28 @@
+import json
+import os
+
+
+def write_info(file_name, file_info):
+    dir = os.path.dirname(file_name)
+    if not os.path.exists(dir):
+        os.makedirs(dir)
+    with open('{}.json'.format(file_name), 'w', encoding='UTF-8') as fp:
+        json.dump(file_info, fp, indent=4, sort_keys=False)
+    fp.close()
+
+
+def read_json(file_path):
+    with open(file_path, 'r') as f:
+        data = json.load(f)
+    f.close()
+    return data
+
+
+if __name__ == "__main__":
+    path = os.path.abspath(os.path.dirname(__file__))
+    print(path)
+    # write_info('', dict(report_data))
+    # read_json('d://report.json')
+    # s = '/group1/628740635893760/ori/images/7 (1).png'
+    # s1 = s.replace('images', 'labels')
+    # print(s[2])

app/models/__init__.py

@@ -0,0 +1,3 @@
+from app.core.common_utils import import_subs
+
+__all__ = import_subs(locals())

app/run.py

@@ -0,0 +1,130 @@
+import datetime
+import importlib
+import json
+import logging
+from typing import List, Dict, Union
+
+from flask import Flask, request
+from flask.helpers import get_env
+from flask_sockets import Sockets
+
+import sys 
+#sys.path.append("/mnt/sdc/algorithm/AICheck-MaskRCNN")
+from app.core.common_utils import logger
+from app.core.err_handler import page_not_found, method_not_allowed, exception_500, exception_400
+
+from app.exts import db, migrate, ma
+from app.utils.redis_config import redis_client
+from app.utils.websocket_tool import manager
+
+
+def register_extensions(app):
+    db.init_app(app)
+    migrate.init_app(app, db)
+    ma.init_app(app)
+
+
+def register_blueprints(app):
+    from app import controller
+    for name in controller.__all__:
+        bp = getattr(importlib.import_module(f'app.controller.{name}'), 'bp', None)
+        if bp is not None:
+            app.register_blueprint(
+                bp, url_prefix=f"/api{bp.url_prefix if bp.url_prefix else ''}")
+
+
+def register_error_handler(app):
+    app.register_error_handler(404, page_not_found)
+    app.register_error_handler(405, method_not_allowed)
+    app.register_error_handler(400, exception_400)
+    # app.register_error_handler(500, exception_500)
+
+
+def register_cmds(app):
+    pass
+
+
+def register_log(app):
+    logging.root.handlers = []
+    logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO, filename='ex.log')
+
+    # set up logging to console
+    console = logging.StreamHandler()
+    console.setLevel(logging.ERROR)
+    # set a format which is simpler for console use
+    formatter = logging.Formatter('%(asctime)s : %(levelname)s : %(message)s')
+    console.setFormatter(formatter)
+    logging.getLogger("").addHandler(console)
+    # logging.debug('debug')
+    # logging.info('info')
+    # logging.warning('warning')
+    # logging.error('error')
+    # logging.exception('exp')
+
+
+def register_redis(app):
+    redis_client.init_redis_connect()
+
+
+# socketio = SocketIO()
+
+
+def create_app():
+    app = Flask(__name__, instance_relative_config=True)
+    app.config.from_object('app.configs.{}'.format(get_env()))
+    app.debug = False
+    # app.json_encoder = CustomJSONEncoder
+    with app.app_context():
+        register_extensions(app)
+        register_blueprints(app)
+        register_error_handler(app)
+        db.create_all()
+    register_cmds(app)
+    register_log(app)
+    register_redis(app)
+
+    @app.before_request
+    def log_request_info():
+        logger = logging.getLogger('werkzeug')
+        if request.method in ['POST', 'PUT']:
+            logger.info('Request Body: %s', request.get_data())
+
+    return app
+
+
+app = create_app()
+sockets = Sockets(app)
+
+
+@sockets.route('/echo/<id>')
+def echo_socket(ws, id: str):
+    # 保存ws对象，根据
+    manager.connect(ws=ws, id=id)
+    logger.info('------进入websocket')
+    while not ws.closed:
+        msg = ws.receive()
+        print(msg)
+        res = {'code': 1, 'type': 'init', 'msg': '建立连接成功', 'data': '1'}
+        try:
+            ws.send(json.dumps(res), callable(success()))  # 发送数据
+        except:
+            manager.disconnect(ws=ws, id=id)
+        # ws.send(now)  # 发送数据
+
+
+def success():
+    logger.info('-----------回调消息成功------------')
+
+
+@app.route('/')
+def hello_world():
+    return 'Hello World!'
+
+
+if __name__ == '__main__':
+    from gevent import pywsgi
+    from geventwebsocket.handler import WebSocketHandler
+
+    #8080  6913  '192.168.0.20'
+    server = pywsgi.WSGIServer(('192.168.2.118', 6914), app, handler_class=WebSocketHandler)
+    server.serve_forever()

app/schemas/TrainResult.py

@@ -0,0 +1,64 @@
+"""
+@Time ： 2022/9/29 11:39
+@Auth ： 东
+@File ：TrainResult.py
+@IDE ：PyCharm
+@Motto：ABC(Always Be Coding)
+@Desc：训练报告结果类
+
+"""
+import datetime
+from typing import List, Dict
+
+from pydantic import BaseModel, Field
+
+
+class ProcessValueList(BaseModel):
+    name: str = Field(..., description='名称')
+    value: List[int] = Field(..., description='过程值，如损失值，精度等')
+
+
+class Report(BaseModel):
+    """
+    训练算法返回值规范
+    """
+    id: str = Field(..., description='唯一值')
+    rate_of_progess: float = Field(..., description='进度，保留一位小数')
+    precision: List[ProcessValueList] = Field(..., description="过程值列表")
+    sum: int = Field(..., description='总轮次')
+    progress: int = Field(..., description='当前轮次')
+    isfinish: int = Field(0, description="是否结束")
+    num_train_img: int = Field(..., description="参与训练图像数量")
+    train_mod_savepath: str = Field(..., description="模型保存路径")
+    start_time: datetime.date = Field(datetime.datetime.now(), description="开始时间")
+    end_time: datetime.date = Field(datetime.datetime.now(), description="结束时间")
+
+
+class ReportDict(BaseModel):
+    """
+    验证算法返回值规范
+    """
+    id: str = Field(..., description='唯一值')
+    rate_of_progess: float = Field(..., description='进度，保留一位小数')
+    precision: List[Dict] = Field(..., description="过程值列表")
+    start_time: datetime.date = Field(datetime.datetime.now(), description="开始时间")
+    end_time: datetime.date = Field(datetime.datetime.now(), description="结束时间")
+
+
+class DetectProcessValueDice(BaseModel):
+    """
+    检测算法中间值
+    """
+    ori_img: str = Field(..., description='原时图片路径， 绝对路径')
+    res_img: str = Field(..., description='结果图片路径， 绝对路径')
+
+
+class DetectReport(BaseModel):
+    """
+    检测算法返回值规范
+    """
+    id: str = Field(..., description='唯一值')
+    rate_of_progess: float = Field(..., description='进度，保留一位小数')
+    precision: List[DetectProcessValueDice] = Field(..., description="过程值列表")
+    start_time: datetime.date = Field(datetime.datetime.now(), description="开始时间")
+    end_time: datetime.date = Field(datetime.datetime.now(), description="结束时间")

app/schemas/__init__.py

@@ -0,0 +1,5 @@
+from app.core.common_utils import import_subs
+
+__all__ = import_subs(locals())
+
+

app/services/RedisClient.py

@@ -0,0 +1,46 @@
+"""
+@Time ： 2022/10/9 11:53
+@Auth ： 东
+@File ：RedisClient.py
+@IDE ：PyCharm
+@Motto：ABC(Always Be Coding)
+@Desc：
+
+"""
+# coding:utf-8
+
+import time
+import redis
+def redisClient():
+    rc = redis.StrictRedis(host="localhost", port="6379", db=0, password="sdust2020")
+    ps = rc.pubsub()
+    ps.subscribe("liao")  # 订阅消息
+    a = 0
+
+    for item in ps.listen():  # 监听状态：有消息发布了就拿过来
+        print(item)
+        data = item['data']
+        if type(data) == bytes:
+            data = item['data'].decode()
+        print(data)
+        if data == '300030 -1':
+            ps.unsubscribe("liao")
+
+    print(1)
+
+
+class Task(object):
+
+    def __init__(self, redis_conn, channel):
+        self.rcon = redis_conn
+        self.ps = self.rcon.pubsub()
+        self.key = 'task:pubsub:%s' % channel
+        self.ps.subscribe(self.key)
+
+    def listen_task(self):
+        for i in self.ps.listen():
+            if i['type'] == 'message':
+                print("Task get ", i["data"])
+
+    def del_listen(self):
+        self.ps.unsubscribe(self.key)

app/services/RedisService.py

@@ -0,0 +1,21 @@
+"""
+@Time ： 2022/10/9 11:53
+@Auth ： 东
+@File ：RedisService.py
+@IDE ：PyCharm
+@Motto：ABC(Always Be Coding)
+@Desc：
+
+"""
+# coding:utf-8
+import json
+import time
+import redis
+
+number_list = ['300033', '300032', '300031', '300030']
+signal = ['1', '-1', '1', '-1']
+
+# rc = redis.StrictRedis(host='127.0.0.1', port='6379', db=3, password='sdust2020')
+# for i in range(len(number_list)):
+#     value_new = {"ceshi": "测试"}
+#     rc.publish("liao", json.dumps(value_new))  # 发布消息到liao

app/services/RpcClient.py

@@ -0,0 +1,54 @@
+"""
+@Time ： 2022/9/30 17:09
+@Auth ： 东
+@File ：RpcClient.py
+@IDE ：PyCharm
+@Motto：ABC(Always Be Coding)
+@Desc：RPC客户端
+
+"""
+
+import json
+import socket
+import time
+
+
+class TCPClient(object):
+    def __init__(self):
+        self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+
+    def connect(self, host, port):
+        """链接Server端"""
+        self.sock.connect((host, port))
+
+    def send(self, data):
+        """将数据发送到Server端"""
+        self.sock.send(data)
+
+    def recv(self, length):
+        """接受Server端回传的数据"""
+        return self.sock.recv(length)
+
+
+class RPCStub(object):
+
+    def __getattr__(self, function):
+        def _func(*args, **kwargs):
+            d = {'method_name': function, 'method_args': args, 'method_kwargs': kwargs}
+            self.send(json.dumps(d).encode('utf-8'))  # 发送数据
+            data = self.recv(1024)  # 接收方法执行后返回的结果
+            return data.decode('utf-8')
+
+        setattr(self, function, _func)
+        return _func
+
+
+class RPCClient(TCPClient, RPCStub):
+    pass
+
+
+# c = RPCClient()
+# c.connect('127.0.0.1', 5003)
+# print(c.add(1, 2, 3))
+# print(c.setData({"sss": "ssss", "list": [5, 2, 3, 4]}))
+

app/services/RpcClient2.py

@@ -0,0 +1,55 @@
+"""
+@Time ： 2022/9/30 17:09
+@Auth ： 东
+@File ：RpcClient.py
+@IDE ：PyCharm
+@Motto：ABC(Always Be Coding)
+@Desc：RPC客户端
+
+"""
+
+import json
+import socket
+import time
+
+
+class TCPClient(object):
+    def __init__(self):
+        self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+
+    def connect(self, host, port):
+        """链接Server端"""
+        self.sock.connect((host, port))
+
+    def send(self, data):
+        """将数据发送到Server端"""
+        self.sock.send(data)
+
+    def recv(self, length):
+        """接受Server端回传的数据"""
+        return self.sock.recv(length)
+
+
+class RPCStub(object):
+
+    def __getattr__(self, function):
+        def _func(*args, **kwargs):
+            d = {'method_name': function, 'method_args': args, 'method_kwargs': kwargs}
+            self.send(json.dumps(d).encode('utf-8'))  # 发送数据
+            data = self.recv(1024)  # 接收方法执行后返回的结果
+            return data.decode('utf-8')
+
+        setattr(self, function, _func)
+        return _func
+
+
+class RPCClient(TCPClient, RPCStub):
+    pass
+
+
+# c = RPCClient()
+# c.connect('127.0.0.1', 5003)
+# print(c.start('1'))
+# print(c.add(1, 2, 3))
+# print(c.setData({"sss": "ssss", "list": [1, 2, 3, 4]}))
+

app/services/RpcService.py

@@ -0,0 +1,154 @@
+"""
+@Time ： 2022/9/30 11:28
+@Auth ： 东
+@File ：RpcService.py
+@IDE ：PyCharm
+@Motto：ABC(Always Be Coding)
+@Desc：RPC服务端
+
+"""
+import asyncio
+import json
+import socket
+from functools import wraps
+
+from app.schemas.TrainResult import ProcessValueList, Report
+from app.utils.RedisMQTool import Task
+from app.utils.StandardizedOutput import output_wrapped
+from app.utils.redis_config import redis_client
+
+funcs = {}
+
+
+def register_function(func):
+    """
+    server端方法注册，client端只能调用注册的方法
+    """
+    name = func.__name__
+    funcs[name] = func
+
+
+def mq_send(func, *args, **kwargs):
+    data = func(*args, **kwargs)
+    print(data)
+
+
+
+class TCPServer(object):
+
+    def __init__(self):
+        self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        self.client_socket = None
+
+    def bind_listen(self, port=4999):
+        self.sock.bind(('0.0.0.0', port))
+        self.sock.listen(5)
+
+    def accept_receive_close(self):
+        """
+        接收client的消息
+        """
+        if self.client_socket is None:
+            (self.client_socket, address) = self.sock.accept()
+        if self.client_socket:
+            msg = self.client_socket.recv(1024)
+            data = self.on_msg(msg)
+            self.client_socket.send(data)
+
+    def on_msg(self, msg):
+        pass
+
+
+class RPCStub(object):
+    def __init__(self):
+        self.data = None
+
+    def call_method(self, data):
+        """
+        解析函数，调用对应的方法便将该方法的执行结果返回
+        """
+        if len(data) == 0:
+            return json.dumps("something wrong").encode('utf-8')
+        self.data = json.loads(data.decode('utf-8'))
+        method_name = self.data['method_name']
+        method_args = self.data['method_args']
+        method_kwargs = self.data['method_kwargs']
+        res = funcs[method_name](*method_args, **method_kwargs)
+        return json.dumps(res).encode('utf-8')
+
+
+class RPCServer(TCPServer, RPCStub):
+    def __init__(self):
+        TCPServer.__init__(self)
+        RPCStub.__init__(self)
+
+    def loop(self, port):
+        """
+        循环监听 4999端口
+        """
+        self.bind_listen(port)
+        while True:
+            try:
+                self.accept_receive_close()
+            except Exception:
+                self.client_socket.close()
+                self.client_socket = None
+                print(Exception)
+                continue
+
+    def on_msg(self, data):
+        return self.call_method(data)
+
+
+def redisMQSend():
+    def wrapTheFunction(func):
+        @wraps(func)
+        def wrapped_function(*args, **kwargs):
+            data = func(*args, **kwargs)
+            print(data)
+            Task(redis_conn=redis_client.get_redis(), channel="ceshi").publish_task(data=output_wrapped(0, 'success', data))
+
+        return wrapped_function
+
+    return wrapTheFunction
+
+
+@register_function
+def add(a, b, c=10):
+    sum = a + b + c
+    print(sum)
+    return sum
+
+
+@register_function
+def start(param: str):
+    """
+    例子
+    """
+    print(param)
+    process_value_list = ProcessValueList(name='1', value=[])
+    report = Report(rate_of_progess=0, process_value=[process_value_list])
+
+    @mq_send
+    def process(v: int):
+        print(v)
+        report.rate_of_progess = ((v + 1) / 10) * 100
+        report.process_value[0].value.append(v)
+
+    for i in range(10):
+        process(i)
+    print(report.dict())
+    return report.dict()
+
+
+@register_function
+def setData(data):
+    print(data)
+    return data
+
+
+if __name__ == '__main__':
+    # 开启redis连接
+    redis_client.init_redis_connect()
+    s = RPCServer()
+    s.loop(5003)  # 传入要监听的端口

app/services/TokenAuthService.py

@@ -0,0 +1,134 @@
+import datetime
+from functools import wraps
+
+from flask import g, jsonify, request
+
+import jwt
+from app.configs.default import SECRET_KEY
+from flask_httpauth import HTTPTokenAuth, HTTPAuth
+
+# 生成token,有效时间为 60*60 秒
+from app.exts import db
+
+
+def generate_auth_token(user_name, expiration=3600):
+    reset_token = jwt.encode(
+        {
+            "user_name": user_name,
+            "exp": datetime.datetime.now(tz=datetime.timezone.utc) + datetime.timedelta(seconds=expiration)
+        },
+        SECRET_KEY,
+        algorithm="HS256"
+    )
+    return reset_token
+
+
+# 重写 HTTPTokenAuth
+class HTTPTokenAuthReReturn(HTTPTokenAuth):
+    def __init__(self, scheme=None, realm=None, header=None):
+        super().__init__(scheme, realm, header)
+
+        # 重写default_auth_error, 或者也可以重新定义一个函数
+        # def default_auth_error(status):
+        #     return jsonify(data={}, message="token 错误！", code=status), status
+
+        def default_auth_error(status, message):
+            return jsonify(data={}, message=message, code=status), status
+
+        # 如果重新定义函数的话，这里就传入新定义的函数名
+        super().error_handler(default_auth_error)
+        # 重写 login_required
+
+    def login_required(self, f=None, role=None, optional=None):
+        if f is not None and \
+                (role is not None or optional is not None):  # pragma: no cover
+            raise ValueError(
+                'role and optional are the only supported arguments')
+
+        def login_required_internal(f):
+            @wraps(f)
+            def decorated(*args, **kwargs):
+                auth = self.get_auth()
+
+                if request.method != 'OPTIONS':
+                    password = self.get_auth_password(auth)
+
+                    status = None  # 添加状态信息
+                    message = None
+                    user = self.authenticate(auth, password)
+                    # 这里判断verify_token的返回值是否是这里的一员
+                    if user in (False, None, 'BadSignature', 'SignatureExpired'):
+                        status = 401
+                        if user == 'BadSignature':
+                            message = "Bad Signature"
+                        elif user == 'SignatureExpired':
+                            message = "Signature Expired"
+                    elif not self.authorize(role, user, auth):
+                        status = 403
+                        message = "Forbidden"
+                    if not optional and status:
+                        # Clear TCP receive buffer of any pending data
+                        request.data
+                        try:
+                            # 因为之前重写了default_auth_error所以多传入一个message
+                            return self.auth_error_callback(status, message)
+                        except TypeError:
+                            return self.auth_error_callback()
+
+                    g.flask_httpauth_user = user if user is not True \
+                        else auth.username if auth else None
+                return f(*args, **kwargs)
+
+            return decorated
+
+        if f:
+            return login_required_internal(f)
+        return login_required_internal
+
+
+auth = HTTPTokenAuthReReturn(scheme="Bearer")
+
+
+# 获取用户权限
+@auth.get_user_roles
+def get_user_roles(user):
+    the_role = get_role_token(user["token"])
+    return the_role.role_key
+
+
+# 验证token
+@auth.verify_token
+def verify_token(token):
+    try:
+        print(token)
+        data = jwt.decode(token, SECRET_KEY, algorithms=["HS256"])
+        print(data)
+    except jwt.ExpiredSignatureError:
+        print("token过期")
+        # 这里不用False，而是用自定义字符串
+        return "SignatureExpired"
+    except jwt.PyJWTError:
+        print("token错误")
+        # 这里不用False，而是用自定义字符串
+        return "BadSignature"
+    return True
+
+
+# 解析token不加密部分
+def token_parse(token):
+    the_token = str.replace(str(token), 'Bearer ', '')
+    decoded = jwt.decode(the_token, options={"verify_signature": False})
+    return decoded
+
+
+# 根据 username 获取角色
+def get_role_username(username: str):
+    return []
+
+
+# 根据 username 获取角色
+def get_role_token(token: str):
+    user_info = token_parse(token)
+    username = user_info["user_name"]
+    the_role = get_role_username(username)
+    return the_role

app/services/__init__.py

@@ -0,0 +1,5 @@
+from app.core.common_utils import import_subs
+
+__all__ = import_subs(locals())
+
+

app/utils/DateTimeUtil.py

@@ -0,0 +1,125 @@
+# coding:utf-8
+import time
+import datetime
+
+
+def gap_day(time_str, date=None, format="%Y-%m-%d"):
+    """
+    :判断自然日差几天
+    :param time_str: 2019-02-19 21:49:20
+    :return:
+    """
+    if not date:
+        date = time.strftime(format)
+    now_day_stamps = time.mktime(time.strptime(date, format))
+    if " " in format:
+        format = format.split(" ")[0]
+    input_stamps = time.mktime(time.strptime(time_str.split(" ")[0], format))
+    return int(abs((now_day_stamps - input_stamps)) // 86400)
+
+
+def gap_stamps_day(time_str, now_day_stamps=None):
+    """
+    :判断绝对时间差几天
+    :param time_str: 2019-02-19 21:49:20
+    :return:
+    """
+    if not now_day_stamps:
+        now_day_stamps = time.time()
+    input_stamps = time.mktime(time.strptime(time_str, "%Y-%m-%d %H:%M:%S"))
+    return int(abs((now_day_stamps - input_stamps)) // 86400)
+
+
+def gap_stamps_hour(time_str):
+    """
+    :判断绝对上差几个小时
+    :param time_str: 2019-02-19 21:49:20
+    :return:
+    """
+    now = int(time.time())
+    input_stamps = time.mktime(time.strptime(time_str, "%Y-%m-%d %H:%M:%S"))
+    return int(abs((now - input_stamps)) // 3600)
+
+
+def gap_stamps_minute(time_str):
+    """
+    :判断绝对上差几个分钟
+    :param time_str: 2019-02-19 21:49:20
+    :return:
+    """
+    now = int(time.time())
+    input_stamps = time.mktime(time.strptime(time_str, "%Y-%m-%d %H:%M:%S"))
+    return int(abs((now - input_stamps)) // 60)
+
+
+def get_now():
+    """
+        获取当前时间 YYYY-MM-DD HH:mm:ss
+    :param
+    :return:
+    """
+    current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
+    return current_time
+
+
+def get_time_stamp(timeStr, format="%Y-%m-%d %H:%M:%S"):
+    """
+        获取时间戳
+    :param timeStr: 2019-05-12 09:00:00
+    :param format: "%Y-%m-%d %H:%M:%S"
+    :return:
+    """
+    timeArray = time.strptime(timeStr, format)
+    timestamp = time.mktime(timeArray)
+    return int(timestamp)
+
+
+def gap_stamps_sec(time_str):
+    """
+    :判断绝对上差几秒
+    :param time_str: 2019-02-19 21:49:20
+    :return:
+    """
+
+    now = int(time.time())
+    input_stamps = time.mktime(time.strptime(time_str, "%Y-%m-%d %H:%M:%S"))
+    return int(abs((now - input_stamps)))
+
+
+def get_day_jia(date, n, format='%Y-%m-%d'):
+    """
+    时间加n天
+    :param date:
+    :param n:
+    :return:
+    """
+    jia_date = datetime.datetime.strptime(date, format)
+    jia_date = jia_date + datetime.timedelta(days=n)
+    return jia_date.strftime(format)
+
+
+def get_sec_jia(seconds, format="%Y-%m-%d %H:%M:%S"):
+    """
+    当前时间加多少秒
+    :param seconds:
+    :param format:
+    :return: 2019-12-30 19:25:30
+    """
+    return (datetime.datetime.now() + datetime.timedelta(seconds=seconds)).strftime(format)
+
+
+def get_ts_to_time(ts):
+    """时间戳转字符串"""
+    return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(ts))
+
+
+def day_long(time1, time2):
+    """
+    计算时间差
+    :return: 相差的天数
+    """
+    ts1 = get_time_stamp(time1)
+    ts2 = get_time_stamp(time2)
+    date1 = datetime.datetime.fromtimestamp(ts1)
+    date2 = datetime.datetime.fromtimestamp(ts2)
+    return abs(int((date2 - date1).days))

app/utils/EncryptionTool.py

@@ -0,0 +1,24 @@
+import hashlib
+import uuid
+
+
+def sha256_encode(contents: str):
+    res = hashlib.sha256(b"%b" % contents.encode("utf8")).hexdigest()
+    return res
+
+
+def sha3_256_encode(contents: str):
+    res = hashlib.sha3_256(b"%b" % contents.encode("utf8")).hexdigest()
+    return res
+
+
+def generate_uuid():
+    return uuid.uuid4()
+
+
+if __name__ == "__main__":
+    print(hashlib.algorithms_guaranteed)
+    print(hashlib.algorithms_available)
+    print(sha256("res"))
+    print(sha3_256("res" + generate_uuid().hex))
+    print(generate_uuid().hex)

app/utils/JSONEncodeTools.py

@@ -0,0 +1,21 @@
+"""
+@Time ： 2022/10/17 10:12
+@Auth ： 东
+@File ：JSONEncodeTools.py
+@IDE ：PyCharm
+@Motto：ABC(Always Be Coding)
+@Desc：
+
+"""
+import datetime
+import json
+
+
+class MyEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, bytes):
+            return str(obj, encoding='utf-8')
+        if isinstance(obj, datetime.datetime):
+            return str(obj)
+
+        return json.JSONEncoder.default(self, obj)

app/utils/RedisMQTool.py

@@ -0,0 +1,42 @@
+"""
+@Time ： 2022/10/9 17:50
+@Auth ： 东
+@File ：RedisMQTool.py
+@IDE ：PyCharm
+@Motto：ABC(Always Be Coding)
+@Desc：
+
+"""
+import json
+
+import redis
+
+
+class Task(object):
+
+    def __init__(self, redis_conn, channel):
+        self.rcon = redis_conn
+        self.ps = self.rcon.pubsub()
+        self.key = 'task:pubsub:%s' % channel
+        self.ps.subscribe(self.key)
+
+    def listen_task(self):
+        for i in self.ps.listen():
+            if i['type'] == 'message':
+                print("Task get ", i["data"])
+
+    def publish_task(self, data):
+        self.rcon.publish(self.key, json.dumps(data))
+
+    def del_listen(self):
+        self.ps.unsubscribe(self.key)
+
+
+if __name__ == '__main__':
+    print("listen task channel")
+
+    pool = redis.ConnectionPool(host='127.0.0.1',
+                                port=6379, db=5,)
+
+    redis_conn = redis.StrictRedis(connection_pool=pool)
+    Task(redis_conn, 'channel').listen_task()

app/utils/SimpleSqlite3Tool.py

@@ -0,0 +1,95 @@
+# coding: utf-8
+# Author：tajochen
+
+import sqlite3
+import os
+
+
+class SimpleSQLite3Tool:
+    """
+    simpleToolSql for sqlite3
+    简单数据库工具类
+    编写这个类主要是为了封装sqlite，继承此类复用方法
+    """
+
+
+def __init__(self, filename="stsql"):
+    """
+    初始化数据库，默认文件名 stsql.db
+    filename：文件名
+    """
+    self.filename = filename
+    self.db = sqlite3.connect(self.filename)
+    self.c = self.db.cursor()
+
+
+def close(self):
+    """
+    关闭数据库
+    """
+    self.c.close()
+    self.db.close()
+
+
+def execute(self, sql, param=None):
+    """
+    执行数据库的增、删、改
+    sql：sql语句
+    param：数据，可以是list或tuple，亦可是None
+    return：成功返回True
+    """
+    try:
+        if param is None:
+            self.c.execute(sql)
+        else:
+            if type(param) is list:
+                self.c.executemany(sql, param)
+            else:
+                self.c.execute(sql, param)
+        count = self.db.total_changes
+        self.db.commit()
+    except Exception as e:
+        print(e)
+        return False, e
+    if count > 0:
+        return True
+    else:
+        return False
+
+
+def query(self, sql, param=None):
+    """
+    查询语句
+    sql：sql语句
+    param：参数,可为None
+    return：成功返回True
+    """
+    if param is None:
+        self.c.execute(sql)
+    else:
+        self.c.execute(sql, param)
+    return self.c.fetchall()
+
+
+# def set(self,table,field=" * ",where="",isWhere=False):
+#     self.table = table
+#     self.filed = field
+#     if where != "" :
+#         self.where = where
+#         self.isWhere = True
+#     return True
+
+
+if __name__ == "__main__":
+    # 数据库文件位置
+    sql = SimpleSQLite3Tool("../test.db")
+    f = sql.execute("create table test (id int not null,name text not null,age int);")
+    print("ok")
+    sql.execute("insert into test (id,name,age) values (?,?,?);", [(1, 'abc', 15), (2, 'bca', 16)])
+    res = sql.query("select * from test;")
+    print(res)
+    sql.execute("insert into test (id,name) values (?,?);", (3, 'bac'))
+    res = sql.query("select * from test where id=?;", (3,))
+    res = sql.query("select * from data_collection_info;")
+    print(res)
+    sql.close()

app/utils/SnowflakeTool.py

@@ -0,0 +1,82 @@
+# -*- coding: utf-8 -*-
+"""
+@author: cxyfreedom
+@desc: 基于 snowflake 生成分布式ID
+"""
+import time
+
+# 每一部分占用的位数
+TIMESTAMP_BIT = 41  # 时间戳占用位数
+MACHINE_BIT = 5  # 机器标识占用的位数
+DATACENTER_BIT = 5  # 数据中心占用的位数
+SEQUENCE_BIT = 12  # 序列号占用的位数
+
+# 每一部分的最大值
+MAX_DATACENTER_NUM = -1 ^ (-1 << DATACENTER_BIT)
+MAX_MACHINE_NUM = -1 ^ (-1 << MACHINE_BIT)
+MAX_SEQUENCE = -1 ^ (-1 << SEQUENCE_BIT)
+
+# 每一部分向左的位移
+MACHINE_LEFT = SEQUENCE_BIT
+DATACENTER_LEFT = MACHINE_BIT + SEQUENCE_BIT
+TIMESTAMP_LEFT = DATACENTER_LEFT + DATACENTER_BIT
+
+
+class SnowFlake:
+    class OverflowError(TypeError):
+        """
+        分布式ID生成算法占位符溢出异常，会导致生成ID为负数
+        """
+        pass
+
+    class RuntimeError(TypeError):
+        """
+        运行时间错误，在此项目中当前运行时间小于上一次运行时间。
+        """
+        pass
+
+    def __init__(self):
+        if TIMESTAMP_BIT + SEQUENCE_BIT + MACHINE_BIT + DATACENTER_BIT != 63:
+            raise self.OverflowError(
+                "TIMESTAMP_BIT + SEQUENCE_BIT + MACHINE_BIT + DATACENTER_BIT not equal to 63bit")
+        self.datacenter_id = 0  # 数据中心编号
+        self.machineId = 0  # 机器标识编号
+        self.sequence = 0  # 序列号
+        self.last_stamp = -1  # 上一次时间戳
+
+    def nextId(self):
+        """生成下一个ID"""
+        cur_stamp = self.get_new_stamp()
+        if cur_stamp < self.last_stamp:
+            raise self.RuntimeError(
+                "Clock moved backwards. Refusing to generate id")
+
+        if cur_stamp == self.last_stamp:
+            # 相同毫秒内，序列号自增
+            self.sequence = (self.sequence + 1) & MAX_SEQUENCE
+            # 同一秒的序列数已经达到最大
+            if self.sequence == 0:
+                cur_stamp = self.get_next_mill()
+        else:
+            # 不同秒内，序列号为0
+            self.sequence = 0
+
+        self.last_stamp = cur_stamp
+        return (cur_stamp << TIMESTAMP_LEFT) | (
+                self.datacenter_id << DATACENTER_LEFT) | (
+                       self.machineId << MACHINE_LEFT) | self.sequence
+
+    def get_next_mill(self):
+        mill = self.get_new_stamp()
+        while mill <= self.last_stamp:
+            mill = self.get_new_stamp()
+        return mill
+
+    @staticmethod
+    def get_new_stamp():
+        now = lambda: int(time.time() * 1000)
+        return now()
+
+
+if __name__ == "__main__":
+    print(SnowFlake().nextId())

app/utils/StandardizedOutput.py

@@ -0,0 +1,5 @@
+from flask import jsonify
+
+
+def output_wrapped(status: int = 0, message: str = "", data: object = None):
+    return jsonify(code=status, message=message, data=data)

app/utils/UDPReceive.py

@@ -0,0 +1,53 @@
+#!/usr/bin/python3
+# coding= utf-8
+
+import socket
+import sys
+import struct
+import json
+
+# 本机信息
+import time
+
+host_ip = socket.gethostbyname(socket.gethostname())
+# 组播组IP和端口
+mcast_group_ip = '224.1.1.1'
+mcast_group_port = 2234
+
+
+def receiver():
+    # 建立接收 udp socket
+    sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM, socket.IPPROTO_UDP)
+    sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+    sock.setsockopt(socket.SOL_SOCKET, socket.SO_BROADCAST, 1)
+    # linux能绑定网卡这里绑定组播IP地址不会服错，windows没法绑定网卡只能绑定本网卡IP地址
+    if "linux" in sys.platform:
+        # 绑定到的网卡名，如果自己的不是eth0则修改
+        nic_name = 0
+        # 监听的组播地址
+        sock.setsockopt(socket.SOL_SOCKET, 25, nic_name)
+        sock.bind((mcast_group_ip, mcast_group_port))
+    else:
+        sock.bind((host_ip, mcast_group_port))
+    # 加入组播组
+    mq_request = struct.pack("=4sl", socket.inet_aton(mcast_group_ip), socket.INADDR_ANY)
+    sock.setsockopt(socket.IPPROTO_IP, socket.IP_ADD_MEMBERSHIP, mq_request)
+    # 设置非阻塞
+    sock.setblocking(True)
+    while True:
+        try:
+            data, address = sock.recvfrom(4096)
+            data2 = json.loads(data)
+            # 若组播数据不正确
+            if len(data2) < 3:
+                return
+        except socket.error as e:
+            print(f"while receive message error occur:{e}")
+        else:
+            print("Receive Data!")
+            print("FROM: ", address)
+            print("DATA: ", data.decode('utf-8'))
+
+
+if __name__ == "__main__":
+    receiver()

app/utils/UDPSender.py

@@ -0,0 +1,32 @@
+#!/usr/bin/python3
+# coding= utf-8
+
+import time
+import struct
+import socket
+
+# 本机信息
+host_ip = socket.gethostname()
+host_port = 6501
+# 组播组IP和端口
+mcast_group_ip = '239.255.255.252'
+mcast_group_port = 5678
+
+
+def sender():
+    send_sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM, socket.IPPROTO_UDP)
+    send_sock.setsockopt(socket.SOL_SOCKET, socket.SO_BROADCAST, 1)
+    send_sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+    send_sock.bind((host_ip, host_port))
+    # 设置存活时长
+    ttl_bin = struct.pack('@i', 255)
+    send_sock.setsockopt(socket.IPPROTO_IP, socket.IP_MULTICAST_TTL, ttl_bin)
+    while True:
+        data = '12345 english 汉字#测试'
+        send_sock.sendto(str(data).encode('utf-8'), (mcast_group_ip, mcast_group_port))
+        print(f'{time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())}: send finish.')
+        time.sleep(10)
+
+
+if __name__ == "__main__":
+    sender()

app/utils/WebsocketClient.py

@@ -0,0 +1,29 @@
+"""
+@Time ： 2022/10/11 16:43
+@Auth ： 东
+@File ：WebsocketClient.py
+@IDE ：PyCharm
+@Motto：ABC(Always Be Coding)
+@Desc：
+
+"""
+from threading import Thread
+
+import websocket
+
+def connect(self, apiKey, secretKey, trace=False):
+    self.host = OKEX_USD_CONTRACT
+    self.apiKey = apiKey
+    self.secretKey = secretKey
+    self.trace = trace
+
+    websocket.enableTrace(trace)
+
+    self.ws = websocket.WebSocketApp(self.host,
+                                     on_message=self.onMessage,
+                                     on_error=self.onError,
+                                     on_close=self.onClose,
+                                     on_open=self.onOpen)
+
+    self.thread = Thread(target=self.ws.run_forever, args=(None, None, 60, 30))
+    self.thread.start()

app/utils/YamlTool.py

@@ -0,0 +1,40 @@
+import yaml
+import os
+
+
+# 单个文档
+def get_yaml_data(yaml_file):
+    # 打开yaml文件
+    file = open(yaml_file, 'r', encoding='utf-8')
+    file_data = file.read()
+    file.close()
+    # 将字符串转化为字典或列表
+    data = yaml.safe_load(file_data)  # safe_load，safe_load,unsafe_load
+    return data
+
+
+# yaml文件中含多个文档时，分别获取文档中数据
+def get_yaml_load_all(yaml_file):
+    # 打开文件
+    file = open(yaml_file, 'r', encoding='utf-8')
+    file_data = file.read()
+    file.close()
+
+    all_data = yaml.load_all(file_data, Loader=yaml.FullLoader)
+    for data in all_data:
+        print('data-----', data)
+
+
+# 生成yaml文档
+def generate_yaml_doc(yaml_file):
+    py_ob = {"school": "zhang", "students": ['a', 'b']}
+    file = open(yaml_file, 'w', encoding='utf-8')
+    yaml.dump(py_ob, file)
+    file.close()
+
+
+if __name__ == '__main__':
+    current_path = os.path.abspath("../../")
+    yaml_path = os.path.join(current_path, "config.yaml")
+    print('--------------------', yaml_path)
+    get_yaml_data(yaml_path)

app/utils/redis_config.py

@@ -0,0 +1,66 @@
+"""
+redis 配置类
+"""
+import sys
+
+import redis
+
+from ..configs import default
+
+
+class RedisCli(object):
+
+    def __init__(self, *, host: str, port: str, password: str, db: int, socket_timeout=5):
+        # redis对象，在 @app.on_event('startup') 中连接创建
+        self._redis_client = None
+        self.host = host
+        self.port = port
+        self.password = password
+        self.db = db
+        self.socket_timeout = socket_timeout
+
+    def init_redis_connect(self) -> None:
+        """
+        初始化链接
+        :return:
+        """
+        try:
+            r = redis.ConnectionPool(
+                host=self.host,
+                port=self.port,
+                password=self.password,
+                db=self.db,
+                socket_timeout=self.socket_timeout,
+                decode_responses=True  # 解码
+            )
+            self._redis_client = redis.StrictRedis(connection_pool=r)
+            if not self._redis_client.ping():
+                print('连接超时')
+                sys.exit()
+        except (redis.AuthenticationError, Exception) as e:
+            print('连接redis异常')
+            sys.exit()
+
+    def get_redis(self):
+        return self._redis_client
+
+    # 使实例化后的对象，赋予redis对象的方法和属性
+    def __getattr__(self, item):
+        return self._redis_client.has_key(item)
+
+    def __getitem__(self, item):
+        return self._redis_client[item]
+
+    def __setitem__(self, key, value):
+        self._redis_client[key] = value
+
+    def __delitem__(self, key):
+        del self._redis_client[key]
+
+
+redis_client = RedisCli(
+    host=default.db['host'],
+    port=default.db['port'],
+    password=default.db['password'],
+    db=default.db['db'],
+)

app/utils/websocket_tool.py

@@ -0,0 +1,60 @@
+"""
+@Time ： 2022/10/12 17:55
+@Auth ： 东
+@File ：websocket_tool.py
+@IDE ：PyCharm
+@Motto：ABC(Always Be Coding)
+@Desc：
+
+"""
+import json
+from typing import Union, List, Dict
+
+from app.core.common_utils import logger
+from app.utils.JSONEncodeTools import MyEncoder
+
+
+class WebsocketUtil:
+    def __init__(self):
+        self.active_connections: List = []
+        self.active_connections_dist: Dict = {}
+
+    def connect(self, ws, id: str):
+        # 等待连接
+        msg = ws.receive()
+        # 存储ws连接对象
+        self.active_connections.append(ws)
+        if id in self.active_connections_dist:
+            self.active_connections_dist[id].append(ws)
+        else:
+            ws_list = [ws, ]
+            self.active_connections_dist[id] = ws_list
+
+    def disconnect(self, ws, id):
+        # ws关闭时 移除ws对象
+        if ws.closed:
+            if ws in self.active_connections_dist.values():
+                self.active_connections.remove(ws)
+                self.active_connections_dist[id].pop(ws)
+
+    @staticmethod
+    async def send_personal_message(message: str, ws):
+        # 发送个人消息
+        await ws.send(message)
+
+    def broadcast(self, message: str):
+        # 广播消息
+        for connection in self.active_connections:
+            connection.send(message)
+
+    def send_message_proj_json(self, message: Union[str, int, List, Dict], id: str):
+        # 广播该项目的消息
+        for connection in self.active_connections_dist[id]:
+            try:
+                connection.send(json.dumps(message, cls=MyEncoder, indent=4), )
+            except Exception as e:
+                logger.error("websocket异常断开，{}", e)
+                self.disconnect(ws=connection, id=id)
+
+
+manager = WebsocketUtil()

app/yolov5/.dockerignore

@@ -0,0 +1,222 @@
+# Repo-specific DockerIgnore -------------------------------------------------------------------------------------------
+.git
+.cache
+.idea
+runs
+output
+coco
+storage.googleapis.com
+
+data/samples/*
+**/results*.csv
+*.jpg
+
+# Neural Network weights -----------------------------------------------------------------------------------------------
+**/*.pt
+**/*.pth
+**/*.onnx
+**/*.engine
+**/*.mlmodel
+**/*.torchscript
+**/*.torchscript.pt
+**/*.tflite
+**/*.h5
+**/*.pb
+*_saved_model/
+*_web_model/
+*_openvino_model/
+
+# Below Copied From .gitignore -----------------------------------------------------------------------------------------
+# Below Copied From .gitignore -----------------------------------------------------------------------------------------
+
+
+# GitHub Python GitIgnore ----------------------------------------------------------------------------------------------
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+wandb/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv*
+venv*/
+ENV*/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+
+# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore -----------------------------------------------
+
+# General
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+Icon?
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+
+# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff:
+.idea/*
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/dictionaries
+.html  # Bokeh Plots
+.pg  # TensorFlow Frozen Graphs
+.avi # videos
+
+# Sensitive or high-churn files:
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+
+# Gradle:
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# CMake
+cmake-build-debug/
+cmake-build-release/
+
+# Mongo Explorer plugin:
+.idea/**/mongoSettings.xml
+
+## File-based project format:
+*.iws
+
+## Plugin-specific files:
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties

app/yolov5/.gitattributes

@@ -0,0 +1,2 @@
+# this drop notebooks from GitHub language stats
+*.ipynb linguist-vendored

app/yolov5/.github/CODE_OF_CONDUCT.md

@@ -0,0 +1,128 @@
+# YOLOv5 🚀 Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, religion, or sexual identity
+and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+- Demonstrating empathy and kindness toward other people
+- Being respectful of differing opinions, viewpoints, and experiences
+- Giving and gracefully accepting constructive feedback
+- Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+- Focusing on what is best not just for us as individuals, but for the
+  overall community
+
+Examples of unacceptable behavior include:
+
+- The use of sexualized language or imagery, and sexual attention or
+  advances of any kind
+- Trolling, insulting or derogatory comments, and personal or political attacks
+- Public or private harassment
+- Publishing others' private information, such as a physical or email
+  address, without their explicit permission
+- Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at
+hello@ultralytics.com.
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series
+of actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or
+permanent ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior,  harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within
+the community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.0, available at
+https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
+
+Community Impact Guidelines were inspired by [Mozilla's code of conduct
+enforcement ladder](https://github.com/mozilla/diversity).
+
+For answers to common questions about this code of conduct, see the FAQ at
+https://www.contributor-covenant.org/faq. Translations are available at
+https://www.contributor-covenant.org/translations.
+
+[homepage]: https://www.contributor-covenant.org

app/yolov5/.github/ISSUE_TEMPLATE/bug-report.yml

@@ -0,0 +1,85 @@
+name: 🐛 Bug Report
+# title: " "
+description: Problems with YOLOv5
+labels: [bug, triage]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thank you for submitting a YOLOv5 🐛 Bug Report!
+
+  - type: checkboxes
+    attributes:
+      label: Search before asking
+      description: >
+        Please search the [issues](https://github.com/ultralytics/yolov5/issues) to see if a similar bug report already exists.
+      options:
+        - label: >
+            I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and found no similar bug report.
+          required: true
+
+  - type: dropdown
+    attributes:
+      label: YOLOv5 Component
+      description: |
+        Please select the part of YOLOv5 where you found the bug.
+      multiple: true
+      options:
+        - "Training"
+        - "Validation"
+        - "Detection"
+        - "Export"
+        - "PyTorch Hub"
+        - "Multi-GPU"
+        - "Evolution"
+        - "Integrations"
+        - "Other"
+    validations:
+      required: false
+
+  - type: textarea
+    attributes:
+      label: Bug
+      description: Provide console output with error messages and/or screenshots of the bug.
+      placeholder: |
+        💡 ProTip! Include as much information as possible (screenshots, logs, tracebacks etc.) to receive the most helpful response.
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Environment
+      description: Please specify the software and hardware you used to produce the bug.
+      placeholder: |
+        - YOLO: YOLOv5 🚀 v6.0-67-g60e42e1 torch 1.9.0+cu111 CUDA:0 (A100-SXM4-40GB, 40536MiB)
+        - OS: Ubuntu 20.04
+        - Python: 3.9.0
+    validations:
+      required: false
+
+  - type: textarea
+    attributes:
+      label: Minimal Reproducible Example
+      description: >
+        When asking a question, people will be better able to provide help if you provide code that they can easily understand and use to **reproduce** the problem.
+        This is referred to by community members as creating a [minimal reproducible example](https://stackoverflow.com/help/minimal-reproducible-example).
+      placeholder: |
+        ```
+        # Code to reproduce your issue here
+        ```
+    validations:
+      required: false
+
+  - type: textarea
+    attributes:
+      label: Additional
+      description: Anything else you would like to share?
+
+  - type: checkboxes
+    attributes:
+      label: Are you willing to submit a PR?
+      description: >
+        (Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/yolov5/pulls) (PR) to help improve YOLOv5 for everyone, especially if you have a good understanding of how to implement a fix or feature.
+        See the YOLOv5 [Contributing Guide](https://github.com/ultralytics/yolov5/blob/master/CONTRIBUTING.md) to get started.
+      options:
+        - label: Yes I'd like to help by submitting a PR!

app/yolov5/.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1,8 @@
+blank_issues_enabled: true
+contact_links:
+  - name: 💬 Forum
+    url: https://community.ultralytics.com/
+    about: Ask on Ultralytics Community Forum
+  - name: Stack Overflow
+    url: https://stackoverflow.com/search?q=YOLOv5
+    about: Ask on Stack Overflow with 'YOLOv5' tag

app/yolov5/.github/ISSUE_TEMPLATE/feature-request.yml

@@ -0,0 +1,50 @@
+name: 🚀 Feature Request
+description: Suggest a YOLOv5 idea
+# title: " "
+labels: [enhancement]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thank you for submitting a YOLOv5 🚀 Feature Request!
+
+  - type: checkboxes
+    attributes:
+      label: Search before asking
+      description: >
+        Please search the [issues](https://github.com/ultralytics/yolov5/issues) to see if a similar feature request already exists.
+      options:
+        - label: >
+            I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and found no similar feature requests.
+          required: true
+
+  - type: textarea
+    attributes:
+      label: Description
+      description: A short description of your feature.
+      placeholder: |
+        What new feature would you like to see in YOLOv5?
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Use case
+      description: |
+        Describe the use case of your feature request. It will help us understand and prioritize the feature request.
+      placeholder: |
+        How would this feature be used, and who would use it?
+
+  - type: textarea
+    attributes:
+      label: Additional
+      description: Anything else you would like to share?
+
+  - type: checkboxes
+    attributes:
+      label: Are you willing to submit a PR?
+      description: >
+        (Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/yolov5/pulls) (PR) to help improve YOLOv5 for everyone, especially if you have a good understanding of how to implement a fix or feature.
+        See the YOLOv5 [Contributing Guide](https://github.com/ultralytics/yolov5/blob/master/CONTRIBUTING.md) to get started.
+      options:
+        - label: Yes I'd like to help by submitting a PR!

app/yolov5/.github/ISSUE_TEMPLATE/question.yml

@@ -0,0 +1,33 @@
+name: ❓ Question
+description: Ask a YOLOv5 question
+# title: " "
+labels: [question]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thank you for asking a YOLOv5 ❓ Question!
+
+  - type: checkboxes
+    attributes:
+      label: Search before asking
+      description: >
+        Please search the [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) to see if a similar question already exists.
+      options:
+        - label: >
+            I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions.
+          required: true
+
+  - type: textarea
+    attributes:
+      label: Question
+      description: What is your question?
+      placeholder: |
+        💡 ProTip! Include as much information as possible (screenshots, logs, tracebacks etc.) to receive the most helpful response.
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Additional
+      description: Anything else you would like to share?

app/yolov5/.github/PULL_REQUEST_TEMPLATE.md

@@ -0,0 +1,9 @@
+<!--
+Thank you for submitting a YOLOv5 🚀 Pull Request! We want to make contributing to YOLOv5 as easy and transparent as possible. A few tips to get you started:
+
+- Search existing YOLOv5 [PRs](https://github.com/ultralytics/yolov5/pull) to see if a similar PR already exists.
+- Link this PR to a YOLOv5 [issue](https://github.com/ultralytics/yolov5/issues) to help us understand what bug fix or feature is being implemented.
+- Provide before and after profiling/inference/training results to help us quantify the improvement your PR provides (if applicable).
+
+Please see our ✅ [Contributing Guide](https://github.com/ultralytics/yolov5/blob/master/CONTRIBUTING.md) for more details.
+-->

app/yolov5/.github/README_cn.md

@@ -0,0 +1,356 @@
+<div align="center">
+  <p>
+    <a align="center" href="https://ultralytics.com/yolov5" target="_blank">
+      <img width="850" src="https://github.com/ultralytics/assets/raw/master/yolov5/v62/splash_readme.png"></a>
+    <br><br>
+    <a href="https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app" style="text-decoration:none;">
+      <img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/google-play.svg" width="15%" alt="" /></a>&nbsp;
+    <a href="https://apps.apple.com/xk/app/ultralytics/id1583935240" style="text-decoration:none;">
+      <img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/app-store.svg" width="15%" alt="" /></a>
+  </p>
+
+  [English](../README.md) | 简体中文
+  <br>
+  <div>
+    <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="CI CPU testing"></a>
+    <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
+    <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
+    <br>
+    <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+    <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
+    <a href="https://join.slack.com/t/ultralytics/shared_invite/zt-w29ei8bp-jczz7QYUmDtgo6r6KcMIAg"><img src="https://img.shields.io/badge/Slack-Join_Forum-blue.svg?logo=slack" alt="Join Forum"></a>
+  </div>
+
+  <br>
+   <p>
+   YOLOv5🚀是一个在COCO数据集上预训练的物体检测架构和模型系列，它代表了<a href="https://ultralytics.com">Ultralytics</a>对未来视觉AI方法的公开研究，其中包含了在数千小时的研究和开发中所获得的经验和最佳实践。
+   </p>
+
+  <div align="center">
+    <a href="https://github.com/ultralytics" style="text-decoration:none;">
+      <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-github.png" width="2%" alt="" /></a>
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
+    <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+      <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-linkedin.png" width="2%" alt="" /></a>
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
+    <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
+      <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-twitter.png" width="2%" alt="" /></a>
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
+    <a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
+      <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-producthunt.png" width="2%" alt="" /></a>
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
+    <a href="https://youtube.com/ultralytics" style="text-decoration:none;">
+      <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-youtube.png" width="2%" alt="" /></a>
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
+    <a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
+      <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-facebook.png" width="2%" alt="" /></a>
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
+    <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
+      <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-instagram.png" width="2%" alt="" /></a>
+  </div>
+</div>
+
+
+## <div align="center">文件</div>
+
+请参阅[YOLOv5 Docs](https://docs.ultralytics.com)，了解有关训练、测试和部署的完整文件。
+
+## <div align="center">快速开始案例</div>
+
+<details open>
+<summary>安装</summary>
+
+在[**Python>=3.7.0**](https://www.python.org/) 的环境中克隆版本仓并安装 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt)，包括[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/)。
+```bash
+git clone https://github.com/ultralytics/yolov5  # 克隆
+cd yolov5
+pip install -r requirements.txt  # 安装
+```
+
+</details>
+
+<details open>
+<summary>推理</summary>
+
+YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 推理. [模型](https://github.com/ultralytics/yolov5/tree/master/models) 自动从最新YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases)下载。
+
+```python
+import torch
+
+# 模型
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5n - yolov5x6, custom
+
+# 图像
+img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list
+
+# 推理
+results = model(img)
+
+# 结果
+results.print()  # or .show(), .save(), .crop(), .pandas(), etc.
+```
+
+</details>
+
+<details>
+<summary>用 detect.py 进行推理</summary>
+
+`detect.py` 在各种数据源上运行推理, 其会从最新的 YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases) 中自动下载 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 并将检测结果保存到 `runs/detect` 目录。
+
+```bash
+python detect.py --source 0  # 网络摄像头
+                          img.jpg  # 图像
+                          vid.mp4  # 视频
+                          path/  # 文件夹
+                          'path/*.jpg'  # glob
+                          'https://youtu.be/Zgi9g1ksQHc'  # YouTube
+                          'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP 流
+```
+
+</details>
+
+<details>
+<summary>训练</summary>
+
+以下指令再现了 YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)
+数据集结果. [模型](https://github.com/ultralytics/yolov5/tree/master/models) 和 [数据集](https://github.com/ultralytics/yolov5/tree/master/data) 自动从最新的YOLOv5 [版本](https://github.com/ultralytics/yolov5/releases) 中下载。YOLOv5n/s/m/l/x的训练时间在V100 GPU上是 1/2/4/6/8天（多GPU倍速）. 尽可能使用最大的 `--batch-size`, 或通过 `--batch-size -1` 来实现 YOLOv5 [自动批处理](https://github.com/ultralytics/yolov5/pull/5092). 批量大小显示为 V100-16GB。
+
+```bash
+python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128
+                                       yolov5s                                64
+                                       yolov5m                                40
+                                       yolov5l                                24
+                                       yolov5x                                16
+```
+
+<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
+
+</details>
+
+<details open>
+<summary>教程</summary>
+
+- [训练自定义数据集](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) 🚀 推荐
+- [获得最佳训练效果的技巧](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)  ☘️
+  推荐
+- [多GPU训练](https://github.com/ultralytics/yolov5/issues/475)
+- [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 🌟 新
+- [TFLite, ONNX, CoreML, TensorRT 输出](https://github.com/ultralytics/yolov5/issues/251) 🚀
+- [测试时数据增强 (TTA)](https://github.com/ultralytics/yolov5/issues/303)
+- [模型集成](https://github.com/ultralytics/yolov5/issues/318)
+- [模型剪枝/稀疏性](https://github.com/ultralytics/yolov5/issues/304)
+- [超参数进化](https://github.com/ultralytics/yolov5/issues/607)
+- [带有冻结层的迁移学习](https://github.com/ultralytics/yolov5/issues/1314)
+- [架构概要](https://github.com/ultralytics/yolov5/issues/6998) 🌟 新
+- [使用Weights & Biases 记录实验](https://github.com/ultralytics/yolov5/issues/1289)
+- [Roboflow：数据集，标签和主动学习](https://github.com/ultralytics/yolov5/issues/4975) 🌟 新
+- [使用ClearML 记录实验](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) 🌟 新
+- [Deci 平台](https://github.com/ultralytics/yolov5/wiki/Deci-Platform) 🌟 新
+
+</details>
+
+## <div align="center">环境</div>
+
+使用经过我们验证的环境，几秒钟就可以开始。点击下面的每个图标了解详情。
+
+<div align="center">
+    <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width="15%"/>
+    </a>
+    <a href="https://www.kaggle.com/ultralytics/yolov5">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width="15%"/>
+    </a>
+    <a href="https://hub.docker.com/r/ultralytics/yolov5">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png" width="15%"/>
+    </a>
+    <a href="https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png" width="15%"/>
+    </a>
+    <a href="https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart">
+        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width="15%"/>
+    </a>
+</div>
+
+## <div align="center">如何与第三方集成</div>
+
+<div align="center">
+  <a href="https://bit.ly/yolov5-deci-platform">
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-deci.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="14%" height="0" alt="" />
+  <a href="https://cutt.ly/yolov5-readme-clearml">
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-clearml.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="14%" height="0" alt="" />
+  <a href="https://roboflow.com/?ref=ultralytics">
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="14%" height="0" alt="" />
+  <a href="https://wandb.ai/site?utm_campaign=repo_yolo_readme">
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-wb.png" width="10%" /></a>
+</div>
+
+|Deci ⭐ NEW|ClearML ⭐ NEW|Roboflow|Weights & Biases
+|:-:|:-:|:-:|:-:|
+|在[Deci](https://bit.ly/yolov5-deci-platform)一键自动编译和量化YOLOv5以提高推理性能|使用[ClearML](https://cutt.ly/yolov5-readme-clearml) (开源!)自动追踪，可视化，以及远程训练YOLOv5|标记并将您的自定义数据直接导出到YOLOv5后，用[Roboflow](https://roboflow.com/?ref=ultralytics)进行训练 |通过[Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme)自动跟踪以及可视化你在云端所有的YOLOv5训练运行情况
+
+
+## <div align="center">为什么选择 YOLOv5</div>
+
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p>
+<details>
+  <summary>YOLOv5-P5 640 图像 (点击扩展)</summary>
+
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p>
+</details>
+<details>
+  <summary>图片注释 (点击扩展)</summary>
+
+- **COCO AP val** 表示 mAP@0.5:0.95 在5000张图像的[COCO val2017](http://cocodataset.org)数据集上，在256到1536的不同推理大小上测量的指标。
+- **GPU Speed** 衡量的是在 [COCO val2017](http://cocodataset.org) 数据集上使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100实例在批量大小为32时每张图像的平均推理时间。
+- **EfficientDet** 数据来自 [google/automl](https://github.com/google/automl) ，批量大小设置为 8。
+- 复现 mAP 方法: `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
+
+</details>
+
+### 预训练检查点
+
+| 模型                                                                                                | 规模<br><sup>(像素) | mAP<sup>验证<br>0.5:0.95 | mAP<sup>验证<br>0.5 | 速度<br><sup>CPU b1<br>(ms) | 速度<br><sup>V100 b1<br>(ms) | 速度<br><sup>V100 b32<br>(ms) | 参数<br><sup>(M) | 浮点运算<br><sup>@640 (B) |
+|------------------------------------------------------------------------------------------------------|-----------------------|-------------------------|--------------------|------------------------------|-------------------------------|--------------------------------|--------------------|------------------------|
+| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n.pt)                   | 640                   | 28.0                    | 45.7               | **45**                       | **6.3**                       | **0.6**                        | **1.9**            | **4.5**                |
+| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt)                   | 640                   | 37.4                    | 56.8               | 98                           | 6.4                           | 0.9                            | 7.2                | 16.5                   |
+| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m.pt)                   | 640                   | 45.4                    | 64.1               | 224                          | 8.2                           | 1.7                            | 21.2               | 49.0                   |
+| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l.pt)                   | 640                   | 49.0                    | 67.3               | 430                          | 10.1                          | 2.7                            | 46.5               | 109.1                  |
+| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x.pt)                   | 640                   | 50.7                    | 68.9               | 766                          | 12.1                          | 4.8                            | 86.7               | 205.7                  |
+|                                                                                                      |                       |                         |                    |                              |                               |                                |                    |                        |
+| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n6.pt)                 | 1280                  | 36.0                    | 54.4               | 153                          | 8.1                           | 2.1                            | 3.2                | 4.6                    |
+| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s6.pt)                 | 1280                  | 44.8                    | 63.7               | 385                          | 8.2                           | 3.6                            | 12.6               | 16.8                   |
+| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m6.pt)                 | 1280                  | 51.3                    | 69.3               | 887                          | 11.1                          | 6.8                            | 35.7               | 50.0                   |
+| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l6.pt)                 | 1280                  | 53.7                    | 71.3               | 1784                         | 15.8                          | 10.5                           | 76.8               | 111.4                  |
+| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x6.pt)<br>+ [TTA][TTA] | 1280<br>1536          | 55.0<br>**55.8**        | 72.7<br>**72.7**   | 3136<br>-                    | 26.2<br>-                     | 19.4<br>-                      | 140.7<br>-         | 209.8<br>-             |
+
+<details>
+  <summary>表格注释 (点击扩展)</summary>
+
+- 所有检查点都以默认设置训练到300个时期. Nano和Small模型用 [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, 其他模型使用 [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).
+- **mAP<sup>val</sup>** 值是 [COCO val2017](http://cocodataset.org) 数据集上的单模型单尺度的值。
+<br>复现方法: `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
+- 使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) 实例对COCO val图像的平均速度。不包括NMS时间（~1 ms/img)
+<br>复现方法: `python val.py --data coco.yaml --img 640 --task speed --batch 1`
+- **TTA** [测试时数据增强](https://github.com/ultralytics/yolov5/issues/303) 包括反射和比例增强.
+<br>复现方法: `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
+
+</details>
+
+
+## <div align="center">分类 ⭐ 新</div>
+
+YOLOv5发布的[v6.2版本](https://github.com/ultralytics/yolov5/releases) 支持训练，验证，预测和输出分类模型！这使得训练分类器模型非常简单。点击下面开始尝试！
+
+<details>
+  <summary>分类检查点 (点击展开)</summary>
+
+<br>
+
+我们在ImageNet上使用了4xA100的实例训练YOLOv5-cls分类模型90个epochs，并以相同的默认设置同时训练了ResNet和EfficientNet模型来进行比较。我们将所有的模型导出到ONNX FP32进行CPU速度测试，又导出到TensorRT FP16进行GPU速度测试。最后，为了方便重现，我们在[Google Colab Pro](https://colab.research.google.com/signup)上进行了所有的速度测试。
+
+| 模型                                                                                              | 规模<br><sup>(像素) | 准确度<br><sup>第一 | 准确度<br><sup>前五 | 训练<br><sup>90 epochs<br>4xA100 (小时) | 速度<br><sup>ONNX CPU<br>(ms) | 速度<br><sup>TensorRT V100<br>(ms) | 参数<br><sup>(M) | 浮点运算<br><sup>@224 (B) |
+|----------------------------------------------------------------------------------------------------|-----------------------|------------------|------------------|----------------------------------------------|--------------------------------|-------------------------------------|--------------------|------------------------|
+| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n-cls.pt)         | 224                   | 64.6             | 85.4             | 7:59                                         | **3.3**                        | **0.5**                             | **2.5**            | **0.5**                |
+| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s-cls.pt)         | 224                   | 71.5             | 90.2             | 8:09                                         | 6.6                            | 0.6                                 | 5.4                | 1.4                    |
+| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m-cls.pt)         | 224                   | 75.9             | 92.9             | 10:06                                        | 15.5                           | 0.9                                 | 12.9               | 3.9                    |
+| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l-cls.pt)         | 224                   | 78.0             | 94.0             | 11:56                                        | 26.9                           | 1.4                                 | 26.5               | 8.5                    |
+| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x-cls.pt)         | 224                   | **79.0**         | **94.4**         | 15:04                                        | 54.3                           | 1.8                                 | 48.1               | 15.9                   |
+|                                                                                                    |
+| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet18.pt)               | 224                   | 70.3             | 89.5             | **6:47**                                     | 11.2                           | 0.5                                 | 11.7               | 3.7                    |
+| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet34.pt)               | 224                   | 73.9             | 91.8             | 8:33                                         | 20.6                           | 0.9                                 | 21.8               | 7.4                    |
+| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet50.pt)               | 224                   | 76.8             | 93.4             | 11:10                                        | 23.4                           | 1.0                                 | 25.6               | 8.5                    |
+| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet101.pt)             | 224                   | 78.5             | 94.3             | 17:10                                        | 42.1                           | 1.9                                 | 44.5               | 15.9                   |
+|                                                                                                    |
+| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b0.pt) | 224                   | 75.1             | 92.4             | 13:03                                        | 12.5                           | 1.3                                 | 5.3                | 1.0                    |
+| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b1.pt) | 224                   | 76.4             | 93.2             | 17:04                                        | 14.9                           | 1.6                                 | 7.8                | 1.5                    |
+| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b2.pt) | 224                   | 76.6             | 93.4             | 17:10                                        | 15.9                           | 1.6                                 | 9.1                | 1.7                    |
+| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b3.pt) | 224                   | 77.7             | 94.0             | 19:19                                        | 18.9                           | 1.9                                 | 12.2               | 2.4                    |
+
+<details>
+  <summary>表格注释 (点击扩展)</summary>
+
+- 所有检查点都被SGD优化器训练到90 epochs, `lr0=0.001` 和 `weight_decay=5e-5`， 图像大小为224，全为默认设置。<br>运行数据记录于 https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2。
+- **准确度** 值为[ImageNet-1k](https://www.image-net.org/index.php)数据集上的单模型单尺度。<br>通过`python classify/val.py --data ../datasets/imagenet --img 224`进行复制。
+- 使用Google [Colab Pro](https://colab.research.google.com/signup) V100 High-RAM实例得出的100张推理图像的平均**速度**。<br>通过 `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`进行复制。
+- 用`export.py`**导出**到FP32的ONNX和FP16的TensorRT。<br>通过 `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`进行复制。
+</details>
+</details>
+
+<details>
+  <summary>分类使用实例 (点击展开)</summary>
+
+### 训练
+YOLOv5分类训练支持自动下载MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof和ImageNet数据集，并使用`--data` 参数. 打个比方，在MNIST上使用`--data mnist`开始训练。
+
+```bash
+# 单GPU
+python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
+
+# 多-GPU DDP
+python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
+```
+
+### 验证
+在ImageNet-1k数据集上验证YOLOv5m-cl的准确性:
+```bash
+bash data/scripts/get_imagenet.sh --val  # download ImageNet val split (6.3G, 50000 images)
+python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate
+```
+
+### 预测
+用提前训练好的YOLOv5s-cls.pt去预测bus.jpg:
+```bash
+python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg
+```
+```python
+model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s-cls.pt')  # load from PyTorch Hub
+```
+
+### 导出
+导出一组训练好的YOLOv5s-cls, ResNet和EfficientNet模型到ONNX和TensorRT:
+```bash
+python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
+```
+</details>
+
+
+## <div align="center">贡献</div>
+
+我们重视您的意见! 我们希望给大家提供尽可能的简单和透明的方式对 YOLOv5 做出贡献。开始之前请先点击并查看我们的 [贡献指南](CONTRIBUTING.md)，填写[YOLOv5调查问卷](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) 来向我们发送您的经验反馈。真诚感谢我们所有的贡献者!
+
+<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
+<a href="https://github.com/ultralytics/yolov5/graphs/contributors"><img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/image-contributors-1280.png" /></a>
+
+## <div align="center">联系</div>
+
+关于YOLOv5的漏洞和功能问题，请访问 [GitHub Issues](https://github.com/ultralytics/yolov5/issues)。商业咨询或技术支持服务请访问[https://ultralytics.com/contact](https://ultralytics.com/contact)。
+
+<br>
+<div align="center">
+  <a href="https://github.com/ultralytics" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-github.png" width="3%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="3%" alt="" />
+  <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-linkedin.png" width="3%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="3%" alt="" />
+  <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-twitter.png" width="3%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="3%" alt="" />
+  <a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-producthunt.png" width="3%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="3%" alt="" />
+  <a href="https://youtube.com/ultralytics" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-youtube.png" width="3%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="3%" alt="" />
+  <a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-facebook.png" width="3%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="3%" alt="" />
+  <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-instagram.png" width="3%" alt="" /></a>
+</div>
+
+[assets]: https://github.com/ultralytics/yolov5/releases
+[tta]: https://github.com/ultralytics/yolov5/issues/303

app/yolov5/.github/SECURITY.md

@@ -0,0 +1,7 @@
+# Security Policy
+
+We aim to make YOLOv5 🚀 as secure as possible! If you find potential vulnerabilities or have any concerns please let us know so we can investigate and take corrective action if needed.
+
+### Reporting a Vulnerability
+
+To report vulnerabilities please email us at hello@ultralytics.com or visit https://ultralytics.com/contact. Thank you!

app/yolov5/.github/dependabot.yml

@@ -0,0 +1,23 @@
+version: 2
+updates:
+  - package-ecosystem: pip
+    directory: "/"
+    schedule:
+      interval: weekly
+      time: "04:00"
+    open-pull-requests-limit: 10
+    reviewers:
+      - glenn-jocher
+    labels:
+      - dependencies
+
+  - package-ecosystem: github-actions
+    directory: "/"
+    schedule:
+      interval: weekly
+      time: "04:00"
+    open-pull-requests-limit: 5
+    reviewers:
+      - glenn-jocher
+    labels:
+      - dependencies

app/yolov5/.github/workflows/ci-testing.yml

@@ -0,0 +1,140 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 Continuous Integration (CI) GitHub Actions tests
+
+name: YOLOv5 CI
+
+on:
+  push:
+    branches: [ master ]
+  pull_request:
+    branches: [ master ]
+  schedule:
+    - cron: '0 0 * * *'  # runs at 00:00 UTC every day
+
+jobs:
+  Benchmarks:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ ubuntu-latest ]
+        python-version: [ '3.9' ]  # requires python<=3.9
+        model: [ yolov5n ]
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+      #- name: Cache pip
+      #  uses: actions/cache@v3
+      #  with:
+      #    path: ~/.cache/pip
+      #    key: ${{ runner.os }}-Benchmarks-${{ hashFiles('requirements.txt') }}
+      #    restore-keys: ${{ runner.os }}-Benchmarks-
+      - name: Install requirements
+        run: |
+          python -m pip install --upgrade pip wheel
+          pip install -r requirements.txt coremltools openvino-dev tensorflow-cpu --extra-index-url https://download.pytorch.org/whl/cpu
+          python --version
+          pip --version
+          pip list
+      - name: Run benchmarks
+        run: |
+          python utils/benchmarks.py --weights ${{ matrix.model }}.pt --img 320 --hard-fail 0.29
+
+  Tests:
+    timeout-minutes: 60
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ ubuntu-latest, windows-latest ]  # macos-latest bug https://github.com/ultralytics/yolov5/pull/9049
+        python-version: [ '3.10' ]
+        model: [ yolov5n ]
+        include:
+          - os: ubuntu-latest
+            python-version: '3.7'  # '3.6.8' min
+            model: yolov5n
+          - os: ubuntu-latest
+            python-version: '3.8'
+            model: yolov5n
+          - os: ubuntu-latest
+            python-version: '3.9'
+            model: yolov5n
+          - os: ubuntu-latest
+            python-version: '3.8'  # torch 1.7.0 requires python >=3.6, <=3.8
+            model: yolov5n
+            torch: '1.7.0'  # min torch version CI https://pypi.org/project/torchvision/
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Get cache dir
+        # https://github.com/actions/cache/blob/master/examples.md#multiple-oss-in-a-workflow
+        id: pip-cache
+        run: echo "::set-output name=dir::$(pip cache dir)"
+      - name: Cache pip
+        uses: actions/cache@v3
+        with:
+          path: ${{ steps.pip-cache.outputs.dir }}
+          key: ${{ runner.os }}-${{ matrix.python-version }}-pip-${{ hashFiles('requirements.txt') }}
+          restore-keys: ${{ runner.os }}-${{ matrix.python-version }}-pip-
+      - name: Install requirements
+        run: |
+          python -m pip install --upgrade pip wheel
+          if [ "${{ matrix.torch }}" == "1.7.0" ]; then
+              pip install -r requirements.txt torch==1.7.0 torchvision==0.8.1 --extra-index-url https://download.pytorch.org/whl/cpu
+          else
+              pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu
+          fi
+        shell: bash  # for Windows compatibility
+      - name: Check environment
+        run: |
+          python -c "import utils; utils.notebook_init()"
+          echo "RUNNER_OS is ${{ runner.os }}"
+          echo "GITHUB_EVENT_NAME is ${{ github.event_name }}"
+          echo "GITHUB_WORKFLOW is ${{ github.workflow }}"
+          echo "GITHUB_ACTOR is ${{ github.actor }}"
+          echo "GITHUB_REPOSITORY is ${{ github.repository }}"
+          echo "GITHUB_REPOSITORY_OWNER is ${{ github.repository_owner }}"
+          python --version
+          pip --version
+          pip list
+      - name: Test detection
+        shell: bash  # for Windows compatibility
+        run: |
+          # export PYTHONPATH="$PWD"  # to run '$ python *.py' files in subdirectories
+          m=${{ matrix.model }}  # official weights
+          b=runs/train/exp/weights/best  # best.pt checkpoint
+          python train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device cpu  # train
+          for d in cpu; do  # devices
+            for w in $m $b; do  # weights
+              python val.py --imgsz 64 --batch 32 --weights $w.pt --device $d  # val
+              python detect.py --imgsz 64 --weights $w.pt --device $d  # detect
+            done
+          done
+          python hubconf.py --model $m  # hub
+          # python models/tf.py --weights $m.pt  # build TF model
+          python models/yolo.py --cfg $m.yaml  # build PyTorch model
+          python export.py --weights $m.pt --img 64 --include torchscript  # export
+          python - <<EOF
+          import torch
+          for path in '$m', '$b':
+              model = torch.hub.load('.', 'custom', path=path, source='local')
+              print(model('data/images/bus.jpg'))
+          EOF
+      - name: Test classification
+        shell: bash  # for Windows compatibility
+        run: |
+          m=${{ matrix.model }}-cls.pt  # official weights
+          b=runs/train-cls/exp/weights/best.pt  # best.pt checkpoint
+          python classify/train.py --imgsz 32 --model $m --data mnist2560 --epochs 1  # train
+          python classify/val.py --imgsz 32 --weights $b --data ../datasets/mnist2560  # val
+          python classify/predict.py --imgsz 32 --weights $b --source ../datasets/mnist2560/test/7/60.png  # predict
+          python classify/predict.py --imgsz 32 --weights $m --source data/images/bus.jpg  # predict
+          python export.py --weights $b --img 64 --imgsz 224 --include torchscript  # export
+          python - <<EOF
+          import torch
+          for path in '$m', '$b':
+              model = torch.hub.load('.', 'custom', path=path, source='local')
+          EOF

app/yolov5/.github/workflows/codeql-analysis.yml

@@ -0,0 +1,54 @@
+# This action runs GitHub's industry-leading static analysis engine, CodeQL, against a repository's source code to find security vulnerabilities.
+# https://github.com/github/codeql-action
+
+name: "CodeQL"
+
+on:
+  schedule:
+    - cron: '0 0 1 * *'  # Runs at 00:00 UTC on the 1st of every month
+
+jobs:
+  analyze:
+    name: Analyze
+    runs-on: ubuntu-latest
+
+    strategy:
+      fail-fast: false
+      matrix:
+        language: ['python']
+        # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python' ]
+        # Learn more:
+        # https://docs.github.com/en/free-pro-team@latest/github/finding-security-vulnerabilities-and-errors-in-your-code/configuring-code-scanning#changing-the-languages-that-are-analyzed
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+
+      # Initializes the CodeQL tools for scanning.
+      - name: Initialize CodeQL
+        uses: github/codeql-action/init@v2
+        with:
+          languages: ${{ matrix.language }}
+          # If you wish to specify custom queries, you can do so here or in a config file.
+          # By default, queries listed here will override any specified in a config file.
+          # Prefix the list here with "+" to use these queries and those in the config file.
+          # queries: ./path/to/local/query, your-org/your-repo/queries@main
+
+      # Autobuild attempts to build any compiled languages  (C/C++, C#, or Java).
+      # If this step fails, then you should remove it and run the build manually (see below)
+      - name: Autobuild
+        uses: github/codeql-action/autobuild@v2
+
+      # ℹ️ Command-line programs to run using the OS shell.
+      # 📚 https://git.io/JvXDl
+
+      # ✏️ If the Autobuild fails above, remove it and uncomment the following three lines
+      #    and modify them (or add more) to build your code if your project
+      #    uses a compiled language
+
+      #- run: |
+      #   make bootstrap
+      #   make release
+
+      - name: Perform CodeQL Analysis
+        uses: github/codeql-action/analyze@v2

app/yolov5/.github/workflows/docker.yml

@@ -0,0 +1,54 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Builds ultralytics/yolov5:latest images on DockerHub https://hub.docker.com/r/ultralytics/yolov5
+
+name: Publish Docker Images
+
+on:
+  push:
+    branches: [ master ]
+
+jobs:
+  docker:
+    if: github.repository == 'ultralytics/yolov5'
+    name: Push Docker image to Docker Hub
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@v3
+
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v2
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+
+      - name: Login to Docker Hub
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+
+      - name: Build and push arm64 image
+        uses: docker/build-push-action@v3
+        with:
+          context: .
+          platforms: linux/arm64
+          file: utils/docker/Dockerfile-arm64
+          push: true
+          tags: ultralytics/yolov5:latest-arm64
+
+      - name: Build and push CPU image
+        uses: docker/build-push-action@v3
+        with:
+          context: .
+          file: utils/docker/Dockerfile-cpu
+          push: true
+          tags: ultralytics/yolov5:latest-cpu
+
+      - name: Build and push GPU image
+        uses: docker/build-push-action@v3
+        with:
+          context: .
+          file: utils/docker/Dockerfile
+          push: true
+          tags: ultralytics/yolov5:latest

app/yolov5/.github/workflows/greetings.yml

@@ -0,0 +1,57 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+name: Greetings
+
+on:
+  pull_request_target:
+    types: [opened]
+  issues:
+    types: [opened]
+
+jobs:
+  greeting:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/first-interaction@v1
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          pr-message: |
+            👋 Hello @${{ github.actor }}, thank you for submitting a YOLOv5 🚀 PR! To allow your work to be integrated as seamlessly as possible, we advise you to:
+
+            - ✅ Verify your PR is **up-to-date** with `ultralytics/yolov5` `master` branch. If your PR is behind you can update your code by clicking the 'Update branch' button or by running `git pull` and `git merge master` locally.
+            - ✅ Verify all YOLOv5 Continuous Integration (CI) **checks are passing**.
+            - ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_  — Bruce Lee
+
+          issue-message: |
+            👋 Hello @${{ github.actor }}, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ [Tutorials](https://github.com/ultralytics/yolov5/wiki#tutorials) to get started, where you can find quickstart guides for simple tasks like [Custom Data Training](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) all the way to advanced concepts like [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607).
+
+            If this is a 🐛 Bug Report, please provide screenshots and **minimum viable code to reproduce your issue**, otherwise we can not help you.
+
+            If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online [W&B logging](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data#visualize) if available.
+
+            For business inquiries or professional support requests please visit https://ultralytics.com or email support@ultralytics.com.
+
+            ## Requirements
+
+            [**Python>=3.7.0**](https://www.python.org/) with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/). To get started:
+            ```bash
+            git clone https://github.com/ultralytics/yolov5  # clone
+            cd yolov5
+            pip install -r requirements.txt  # install
+            ```
+
+            ## Environments
+
+            YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
+
+            - **Google Colab and Kaggle** notebooks with free GPU: <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
+            - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
+            - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)
+            - **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
+
+
+            ## Status
+
+            <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="CI CPU testing"></a>
+
+            If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), validation ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.

app/yolov5/.github/workflows/stale.yml

@@ -0,0 +1,40 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+name: Close stale issues
+on:
+  schedule:
+    - cron: '0 0 * * *'  # Runs at 00:00 UTC every day
+
+jobs:
+  stale:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/stale@v5
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          stale-issue-message: |
+            👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.
+
+            Access additional [YOLOv5](https://ultralytics.com/yolov5) 🚀 resources:
+            - **Wiki** – https://github.com/ultralytics/yolov5/wiki
+            - **Tutorials** – https://github.com/ultralytics/yolov5#tutorials
+            - **Docs** – https://docs.ultralytics.com
+
+            Access additional [Ultralytics](https://ultralytics.com) ⚡ resources:
+            - **Ultralytics HUB** – https://ultralytics.com/hub
+            - **Vision API** – https://ultralytics.com/yolov5
+            - **About Us** – https://ultralytics.com/about
+            - **Join Our Team** – https://ultralytics.com/work
+            - **Contact Us** – https://ultralytics.com/contact
+
+            Feel free to inform us of any other **issues** you discover or **feature requests** that come to mind in the future. Pull Requests (PRs) are also always welcomed!
+
+            Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐!
+
+          stale-pr-message: 'This pull request has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions YOLOv5 🚀 and Vision AI ⭐.'
+          days-before-issue-stale: 30
+          days-before-issue-close: 10
+          days-before-pr-stale: 90
+          days-before-pr-close: 30
+          exempt-issue-labels: 'documentation,tutorial,TODO'
+          operations-per-run: 300  # The maximum number of operations per run, used to control rate limiting.

app/yolov5/.gitignore

@@ -0,0 +1,256 @@
+# Repo-specific GitIgnore ----------------------------------------------------------------------------------------------
+*.jpg
+*.jpeg
+*.png
+*.bmp
+*.tif
+*.tiff
+*.heic
+*.JPG
+*.JPEG
+*.PNG
+*.BMP
+*.TIF
+*.TIFF
+*.HEIC
+*.mp4
+*.mov
+*.MOV
+*.avi
+*.data
+*.json
+*.cfg
+!setup.cfg
+!cfg/yolov3*.cfg
+
+storage.googleapis.com
+runs/*
+data/*
+data/images/*
+!data/*.yaml
+!data/hyps
+!data/scripts
+!data/images
+!data/images/zidane.jpg
+!data/images/bus.jpg
+!data/*.sh
+
+results*.csv
+
+# Datasets -------------------------------------------------------------------------------------------------------------
+coco/
+coco128/
+VOC/
+
+# MATLAB GitIgnore -----------------------------------------------------------------------------------------------------
+*.m~
+*.mat
+!targets*.mat
+
+# Neural Network weights -----------------------------------------------------------------------------------------------
+*.weights
+*.pt
+*.pb
+*.onnx
+*.engine
+*.mlmodel
+*.torchscript
+*.tflite
+*.h5
+*_saved_model/
+*_web_model/
+*_openvino_model/
+darknet53.conv.74
+yolov3-tiny.conv.15
+
+# GitHub Python GitIgnore ----------------------------------------------------------------------------------------------
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+/wandb/
+.installed.cfg
+*.egg
+
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv*
+venv*/
+ENV*/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+
+# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore -----------------------------------------------
+
+# General
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+Icon?
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+
+# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff:
+.idea/*
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/dictionaries
+.html  # Bokeh Plots
+.pg  # TensorFlow Frozen Graphs
+.avi # videos
+
+# Sensitive or high-churn files:
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+
+# Gradle:
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# CMake
+cmake-build-debug/
+cmake-build-release/
+
+# Mongo Explorer plugin:
+.idea/**/mongoSettings.xml
+
+## File-based project format:
+*.iws
+
+## Plugin-specific files:
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties

app/yolov5/.pre-commit-config.yaml

@@ -0,0 +1,64 @@
+# Define hooks for code formations
+# Will be applied on any updated commit files if a user has installed and linked commit hook
+
+default_language_version:
+  python: python3.8
+
+# Define bot property if installed via https://github.com/marketplace/pre-commit-ci
+ci:
+  autofix_prs: true
+  autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions'
+  autoupdate_schedule: monthly
+  # submodules: true
+
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+      # - id: end-of-file-fixer
+      - id: trailing-whitespace
+      - id: check-case-conflict
+      - id: check-yaml
+      - id: check-toml
+      - id: pretty-format-json
+      - id: check-docstring-first
+
+  - repo: https://github.com/asottile/pyupgrade
+    rev: v2.37.3
+    hooks:
+      - id: pyupgrade
+        name: Upgrade code
+        args: [ --py37-plus ]
+
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.10.1
+    hooks:
+      - id: isort
+        name: Sort imports
+
+  - repo: https://github.com/pre-commit/mirrors-yapf
+    rev: v0.32.0
+    hooks:
+      - id: yapf
+        name: YAPF formatting
+
+  - repo: https://github.com/executablebooks/mdformat
+    rev: 0.7.14
+    hooks:
+      - id: mdformat
+        name: MD formatting
+        additional_dependencies:
+          - mdformat-gfm
+          - mdformat-black
+        exclude: "README.md|README_cn.md"
+
+  - repo: https://github.com/asottile/yesqa
+    rev: v1.3.0
+    hooks:
+      - id: yesqa
+
+  - repo: https://github.com/PyCQA/flake8
+    rev: 5.0.2
+    hooks:
+      - id: flake8
+        name: PEP8

app/yolov5/CONTRIBUTING.md

@@ -0,0 +1,93 @@
+## Contributing to YOLOv5 🚀
+
+We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible, whether it's:
+
+- Reporting a bug
+- Discussing the current state of the code
+- Submitting a fix
+- Proposing a new feature
+- Becoming a maintainer
+
+YOLOv5 works so well due to our combined community effort, and for every small improvement you contribute you will be
+helping push the frontiers of what's possible in AI 😃!
+
+## Submitting a Pull Request (PR) 🛠️
+
+Submitting a PR is easy! This example shows how to submit a PR for updating `requirements.txt` in 4 steps:
+
+### 1. Select File to Update
+
+Select `requirements.txt` to update by clicking on it in GitHub.
+
+<p align="center"><img width="800" alt="PR_step1" src="https://user-images.githubusercontent.com/26833433/122260847-08be2600-ced4-11eb-828b-8287ace4136c.png"></p>
+
+### 2. Click 'Edit this file'
+
+Button is in top-right corner.
+
+<p align="center"><img width="800" alt="PR_step2" src="https://user-images.githubusercontent.com/26833433/122260844-06f46280-ced4-11eb-9eec-b8a24be519ca.png"></p>
+
+### 3. Make Changes
+
+Change `matplotlib` version from `3.2.2` to `3.3`.
+
+<p align="center"><img width="800" alt="PR_step3" src="https://user-images.githubusercontent.com/26833433/122260853-0a87e980-ced4-11eb-9fd2-3650fb6e0842.png"></p>
+
+### 4. Preview Changes and Submit PR
+
+Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch**
+for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose
+changes** button. All done, your PR is now submitted to YOLOv5 for review and approval 😃!
+
+<p align="center"><img width="800" alt="PR_step4" src="https://user-images.githubusercontent.com/26833433/122260856-0b208000-ced4-11eb-8e8e-77b6151cbcc3.png"></p>
+
+### PR recommendations
+
+To allow your work to be integrated as seamlessly as possible, we advise you to:
+
+- ✅ Verify your PR is **up-to-date** with `ultralytics/yolov5` `master` branch. If your PR is behind you can update
+  your code by clicking the 'Update branch' button or by running `git pull` and `git merge master` locally.
+
+<p align="center"><img width="751" alt="Screenshot 2022-08-29 at 22 47 15" src="https://user-images.githubusercontent.com/26833433/187295893-50ed9f44-b2c9-4138-a614-de69bd1753d7.png"></p>
+
+- ✅ Verify all YOLOv5 Continuous Integration (CI) **checks are passing**.
+
+<p align="center"><img width="751" alt="Screenshot 2022-08-29 at 22 47 03" src="https://user-images.githubusercontent.com/26833433/187296922-545c5498-f64a-4d8c-8300-5fa764360da6.png"></p>
+
+- ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase
+  but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_  — Bruce Lee
+
+## Submitting a Bug Report 🐛
+
+If you spot a problem with YOLOv5 please submit a Bug Report!
+
+For us to start investigating a possible problem we need to be able to reproduce it ourselves first. We've created a few
+short guidelines below to help users provide what we need in order to get started.
+
+When asking a question, people will be better able to provide help if you provide **code** that they can easily
+understand and use to **reproduce** the problem. This is referred to by community members as creating
+a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example). Your code that reproduces
+the problem should be:
+
+- ✅ **Minimal** – Use as little code as possible that still produces the same problem
+- ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself
+- ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem
+
+In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code
+should be:
+
+- ✅ **Current** – Verify that your code is up-to-date with current
+  GitHub [master](https://github.com/ultralytics/yolov5/tree/master), and if necessary `git pull` or `git clone` a new
+  copy to ensure your problem has not already been resolved by previous commits.
+- ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this
+  repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️.
+
+If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛
+**Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and providing
+a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) to help us better
+understand and diagnose your problem.
+
+## License
+
+By contributing, you agree that your contributions will be licensed under
+the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/)

app/yolov5/LICENSE

@@ -0,0 +1,674 @@
+GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
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+free programs, and that you know you can do these things.
+
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+these rights or asking you to surrender the rights.  Therefore, you have
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+you modify it: responsibilities to respect the freedom of others.
+
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+
+  Developers that use the GNU GPL protect your rights with two steps:
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app/yolov5/README.md

@@ -0,0 +1,363 @@
+<div align="center">
+  <p>
+    <a align="center" href="https://ultralytics.com/yolov5" target="_blank">
+      <img width="850" src="https://github.com/ultralytics/assets/raw/master/yolov5/v62/splash_readme.png"></a>
+    <br><br>
+    <a href="https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app" style="text-decoration:none;">
+      <img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/google-play.svg" width="15%" alt="" /></a>&nbsp;
+    <a href="https://apps.apple.com/xk/app/ultralytics/id1583935240" style="text-decoration:none;">
+      <img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/app-store.svg" width="15%" alt="" /></a>
+  </p>
+
+  English | [简体中文](.github/README_cn.md)
+  <br>
+  <div>
+    <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="CI CPU testing"></a>
+    <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
+    <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
+    <br>
+    <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+    <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
+    <a href="https://join.slack.com/t/ultralytics/shared_invite/zt-w29ei8bp-jczz7QYUmDtgo6r6KcMIAg"><img src="https://img.shields.io/badge/Slack-Join_Forum-blue.svg?logo=slack" alt="Join Forum"></a>
+  </div>
+
+  <br>
+  <p>
+    YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents <a href="https://ultralytics.com">Ultralytics</a>
+    open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
+  </p>
+
+  <div align="center">
+    <a href="https://github.com/ultralytics" style="text-decoration:none;">
+      <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-github.png" width="2%" alt="" /></a>
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
+    <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+      <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-linkedin.png" width="2%" alt="" /></a>
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
+    <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
+      <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-twitter.png" width="2%" alt="" /></a>
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
+    <a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
+      <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-producthunt.png" width="2%" alt="" /></a>
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
+    <a href="https://youtube.com/ultralytics" style="text-decoration:none;">
+      <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-youtube.png" width="2%" alt="" /></a>
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
+    <a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
+      <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-facebook.png" width="2%" alt="" /></a>
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
+    <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
+      <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-instagram.png" width="2%" alt="" /></a>
+  </div>
+</div>
+
+
+## <div align="center">Documentation</div>
+
+See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment.
+
+## <div align="center">Quick Start Examples</div>
+
+<details open>
+<summary>Install</summary>
+
+Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a
+[**Python>=3.7.0**](https://www.python.org/) environment, including
+[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
+
+```bash
+git clone https://github.com/ultralytics/yolov5  # clone
+cd yolov5
+pip install -r requirements.txt  # install
+```
+
+</details>
+
+<details open>
+<summary>Inference</summary>
+
+YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest
+YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
+
+```python
+import torch
+
+# Model
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5n - yolov5x6, custom
+
+# Images
+img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list
+
+# Inference
+results = model(img)
+
+# Results
+results.print()  # or .show(), .save(), .crop(), .pandas(), etc.
+```
+
+</details>
+
+<details>
+<summary>Inference with detect.py</summary>
+
+`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from
+the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
+
+```bash
+python detect.py --source 0  # webcam
+                          img.jpg  # image
+                          vid.mp4  # video
+                          path/  # directory
+                          'path/*.jpg'  # glob
+                          'https://youtu.be/Zgi9g1ksQHc'  # YouTube
+                          'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
+```
+
+</details>
+
+<details>
+<summary>Training</summary>
+
+The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)
+results. [Models](https://github.com/ultralytics/yolov5/tree/master/models)
+and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest
+YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are
+1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the
+largest `--batch-size` possible, or pass `--batch-size -1` for
+YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.
+
+```bash
+python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128
+                                       yolov5s                                64
+                                       yolov5m                                40
+                                       yolov5l                                24
+                                       yolov5x                                16
+```
+
+<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
+
+</details>
+
+<details open>
+<summary>Tutorials</summary>
+
+- [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)  🚀 RECOMMENDED
+- [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)  ☘️
+  RECOMMENDED
+- [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
+- [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 🌟 NEW
+- [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) 🚀
+- [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
+- [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
+- [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
+- [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)
+- [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314)
+- [Architecture Summary](https://github.com/ultralytics/yolov5/issues/6998) 🌟 NEW
+- [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)
+- [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975)  🌟 NEW
+- [ClearML Logging](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) 🌟 NEW
+- [Deci Platform](https://github.com/ultralytics/yolov5/wiki/Deci-Platform) 🌟 NEW
+
+</details>
+
+## <div align="center">Environments</div>
+
+Get started in seconds with our verified environments. Click each icon below for details.
+
+<div align="center">
+  <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb">
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="5%" alt="" />
+  <a href="https://www.kaggle.com/ultralytics/yolov5">
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="5%" alt="" />
+  <a href="https://hub.docker.com/r/ultralytics/yolov5">
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="5%" alt="" />
+  <a href="https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart">
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="5%" alt="" />
+  <a href="https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart">
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width="10%" /></a>
+</div>
+
+## <div align="center">Integrations</div>
+
+<div align="center">
+  <a href="https://bit.ly/yolov5-deci-platform">
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-deci.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="14%" height="0" alt="" />
+  <a href="https://cutt.ly/yolov5-readme-clearml">
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-clearml.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="14%" height="0" alt="" />
+  <a href="https://roboflow.com/?ref=ultralytics">
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="14%" height="0" alt="" />
+  <a href="https://wandb.ai/site?utm_campaign=repo_yolo_readme">
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-wb.png" width="10%" /></a>
+</div>
+
+|Deci ⭐ NEW|ClearML ⭐ NEW|Roboflow|Weights & Biases
+|:-:|:-:|:-:|:-:|
+|Automatically compile and quantize YOLOv5 for better inference performance in one click at [Deci](https://bit.ly/yolov5-deci-platform)|Automatically track, visualize and even remotely train YOLOv5 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!)|Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) |Automatically track and visualize all your YOLOv5 training runs in the cloud with [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme)
+
+
+## <div align="center">Why YOLOv5</div>
+
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p>
+<details>
+  <summary>YOLOv5-P5 640 Figure (click to expand)</summary>
+
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p>
+</details>
+<details>
+  <summary>Figure Notes (click to expand)</summary>
+
+- **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536.
+- **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32.
+- **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8.
+- **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
+
+</details>
+
+### Pretrained Checkpoints
+
+| Model                                                                                                | size<br><sup>(pixels) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Speed<br><sup>CPU b1<br>(ms) | Speed<br><sup>V100 b1<br>(ms) | Speed<br><sup>V100 b32<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@640 (B) |
+|------------------------------------------------------------------------------------------------------|-----------------------|-------------------------|--------------------|------------------------------|-------------------------------|--------------------------------|--------------------|------------------------|
+| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n.pt)                   | 640                   | 28.0                    | 45.7               | **45**                       | **6.3**                       | **0.6**                        | **1.9**            | **4.5**                |
+| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt)                   | 640                   | 37.4                    | 56.8               | 98                           | 6.4                           | 0.9                            | 7.2                | 16.5                   |
+| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m.pt)                   | 640                   | 45.4                    | 64.1               | 224                          | 8.2                           | 1.7                            | 21.2               | 49.0                   |
+| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l.pt)                   | 640                   | 49.0                    | 67.3               | 430                          | 10.1                          | 2.7                            | 46.5               | 109.1                  |
+| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x.pt)                   | 640                   | 50.7                    | 68.9               | 766                          | 12.1                          | 4.8                            | 86.7               | 205.7                  |
+|                                                                                                      |                       |                         |                    |                              |                               |                                |                    |                        |
+| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n6.pt)                 | 1280                  | 36.0                    | 54.4               | 153                          | 8.1                           | 2.1                            | 3.2                | 4.6                    |
+| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s6.pt)                 | 1280                  | 44.8                    | 63.7               | 385                          | 8.2                           | 3.6                            | 12.6               | 16.8                   |
+| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m6.pt)                 | 1280                  | 51.3                    | 69.3               | 887                          | 11.1                          | 6.8                            | 35.7               | 50.0                   |
+| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l6.pt)                 | 1280                  | 53.7                    | 71.3               | 1784                         | 15.8                          | 10.5                           | 76.8               | 111.4                  |
+| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x6.pt)<br>+ [TTA][TTA] | 1280<br>1536          | 55.0<br>**55.8**        | 72.7<br>**72.7**   | 3136<br>-                    | 26.2<br>-                     | 19.4<br>-                      | 140.7<br>-         | 209.8<br>-             |
+
+<details>
+  <summary>Table Notes (click to expand)</summary>
+
+- All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).
+- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
+- **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.<br>Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1`
+- **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.<br>Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
+
+</details>
+
+## <div align="center">Classification ⭐ NEW</div>
+
+YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) brings support for classification model training, validation, prediction and export! We've made training classifier models super simple. Click below to get started.
+
+<details>
+  <summary>Classification Checkpoints (click to expand)</summary>
+
+<br>
+
+We trained YOLOv5-cls classification models on ImageNet for 90 epochs using a 4xA100 instance, and we trained ResNet and EfficientNet models alongside with the same default training settings to compare. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) for easy reproducibility.
+
+| Model                                                                                              | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Training<br><sup>90 epochs<br>4xA100 (hours) | Speed<br><sup>ONNX CPU<br>(ms) | Speed<br><sup>TensorRT V100<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@224 (B) |
+|----------------------------------------------------------------------------------------------------|-----------------------|------------------|------------------|----------------------------------------------|--------------------------------|-------------------------------------|--------------------|------------------------|
+| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n-cls.pt)         | 224                   | 64.6             | 85.4             | 7:59                                         | **3.3**                        | **0.5**                             | **2.5**            | **0.5**                |
+| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s-cls.pt)         | 224                   | 71.5             | 90.2             | 8:09                                         | 6.6                            | 0.6                                 | 5.4                | 1.4                    |
+| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m-cls.pt)         | 224                   | 75.9             | 92.9             | 10:06                                        | 15.5                           | 0.9                                 | 12.9               | 3.9                    |
+| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l-cls.pt)         | 224                   | 78.0             | 94.0             | 11:56                                        | 26.9                           | 1.4                                 | 26.5               | 8.5                    |
+| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x-cls.pt)         | 224                   | **79.0**         | **94.4**         | 15:04                                        | 54.3                           | 1.8                                 | 48.1               | 15.9                   |
+|                                                                                                    |
+| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet18.pt)               | 224                   | 70.3             | 89.5             | **6:47**                                     | 11.2                           | 0.5                                 | 11.7               | 3.7                    |
+| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet34.pt)               | 224                   | 73.9             | 91.8             | 8:33                                         | 20.6                           | 0.9                                 | 21.8               | 7.4                    |
+| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet50.pt)               | 224                   | 76.8             | 93.4             | 11:10                                        | 23.4                           | 1.0                                 | 25.6               | 8.5                    |
+| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet101.pt)             | 224                   | 78.5             | 94.3             | 17:10                                        | 42.1                           | 1.9                                 | 44.5               | 15.9                   |
+|                                                                                                    |
+| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b0.pt) | 224                   | 75.1             | 92.4             | 13:03                                        | 12.5                           | 1.3                                 | 5.3                | 1.0                    |
+| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b1.pt) | 224                   | 76.4             | 93.2             | 17:04                                        | 14.9                           | 1.6                                 | 7.8                | 1.5                    |
+| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b2.pt) | 224                   | 76.6             | 93.4             | 17:10                                        | 15.9                           | 1.6                                 | 9.1                | 1.7                    |
+| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b3.pt) | 224                   | 77.7             | 94.0             | 19:19                                        | 18.9                           | 1.9                                 | 12.2               | 2.4                    |
+
+<details>
+  <summary>Table Notes (click to expand)</summary>
+
+- All checkpoints are trained to 90 epochs with SGD optimizer with `lr0=0.001` and `weight_decay=5e-5` at image size 224 and all default settings.<br>Runs logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2
+- **Accuracy** values are for single-model single-scale on [ImageNet-1k](https://www.image-net.org/index.php) dataset.<br>Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224`
+- **Speed** averaged over 100 inference images using a Google [Colab Pro](https://colab.research.google.com/signup) V100 High-RAM instance.<br>Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`
+- **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`. <br>Reproduce by `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`
+</details>
+</details>
+
+<details>
+  <summary>Classification Usage Examples (click to expand)</summary>
+
+### Train
+YOLOv5 classification training supports auto-download of MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof, and ImageNet datasets with the `--data` argument. To start training on MNIST for example use `--data mnist`.
+
+```bash
+# Single-GPU
+python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
+
+# Multi-GPU DDP
+python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
+```
+
+### Val
+Validate YOLOv5m-cls accuracy on ImageNet-1k dataset:
+```bash
+bash data/scripts/get_imagenet.sh --val  # download ImageNet val split (6.3G, 50000 images)
+python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate
+```
+
+### Predict
+Use pretrained YOLOv5s-cls.pt to predict bus.jpg:
+```bash
+python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg
+```
+```python
+model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s-cls.pt')  # load from PyTorch Hub
+```
+
+### Export
+Export a group of trained YOLOv5s-cls, ResNet and EfficientNet models to ONNX and TensorRT:
+```bash
+python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
+```
+</details>
+
+
+## <div align="center">Contribute</div>
+
+We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our [Contributing Guide](CONTRIBUTING.md) to get started, and fill out the [YOLOv5 Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experiences. Thank you to all our contributors!
+
+<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
+<a href="https://github.com/ultralytics/yolov5/graphs/contributors"><img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/image-contributors-1280.png" /></a>
+
+## <div align="center">Contact</div>
+
+For YOLOv5 bugs and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business inquiries or
+professional support requests please visit [https://ultralytics.com/contact](https://ultralytics.com/contact).
+
+<br>
+<div align="center">
+  <a href="https://github.com/ultralytics" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-github.png" width="3%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="3%" alt="" />
+  <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-linkedin.png" width="3%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="3%" alt="" />
+  <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-twitter.png" width="3%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="3%" alt="" />
+  <a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-producthunt.png" width="3%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="3%" alt="" />
+  <a href="https://youtube.com/ultralytics" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-youtube.png" width="3%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="3%" alt="" />
+  <a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-facebook.png" width="3%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="3%" alt="" />
+  <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-instagram.png" width="3%" alt="" /></a>
+</div>
+
+[assets]: https://github.com/ultralytics/yolov5/releases
+[tta]: https://github.com/ultralytics/yolov5/issues/303

app/yolov5/classify/predict.py

@@ -0,0 +1,214 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Run YOLOv5 classification inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
+
+Usage - sources:
+    $ python classify/predict.py --weights yolov5s-cls.pt --source 0                               # webcam
+                                                                   img.jpg                         # image
+                                                                   vid.mp4                         # video
+                                                                   path/                           # directory
+                                                                   'path/*.jpg'                    # glob
+                                                                   'https://youtu.be/Zgi9g1ksQHc'  # YouTube
+                                                                   'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
+
+Usage - formats:
+    $ python classify/predict.py --weights yolov5s-cls.pt                 # PyTorch
+                                           yolov5s-cls.torchscript        # TorchScript
+                                           yolov5s-cls.onnx               # ONNX Runtime or OpenCV DNN with --dnn
+                                           yolov5s-cls.xml                # OpenVINO
+                                           yolov5s-cls.engine             # TensorRT
+                                           yolov5s-cls.mlmodel            # CoreML (macOS-only)
+                                           yolov5s-cls_saved_model        # TensorFlow SavedModel
+                                           yolov5s-cls.pb                 # TensorFlow GraphDef
+                                           yolov5s-cls.tflite             # TensorFlow Lite
+                                           yolov5s-cls_edgetpu.tflite     # TensorFlow Edge TPU
+"""
+
+import argparse
+import os
+import platform
+import sys
+from pathlib import Path
+
+import torch
+import torch.nn.functional as F
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from models.common import DetectMultiBackend
+from utils.augmentations import classify_transforms
+from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
+from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
+                           increment_path, print_args, strip_optimizer)
+from utils.plots import Annotator
+from utils.torch_utils import select_device, smart_inference_mode
+
+
+@smart_inference_mode()
+def run(
+        weights=ROOT / 'yolov5s-cls.pt',  # model.pt path(s)
+        source=ROOT / 'data/images',  # file/dir/URL/glob, 0 for webcam
+        data=ROOT / 'data/coco128.yaml',  # dataset.yaml path
+        imgsz=(224, 224),  # inference size (height, width)
+        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+        view_img=False,  # show results
+        save_txt=False,  # save results to *.txt
+        nosave=False,  # do not save images/videos
+        augment=False,  # augmented inference
+        visualize=False,  # visualize features
+        update=False,  # update all models
+        project=ROOT / 'runs/predict-cls',  # save results to project/name
+        name='exp',  # save results to project/name
+        exist_ok=False,  # existing project/name ok, do not increment
+        half=False,  # use FP16 half-precision inference
+        dnn=False,  # use OpenCV DNN for ONNX inference
+        vid_stride=1,  # video frame-rate stride
+):
+    source = str(source)
+    save_img = not nosave and not source.endswith('.txt')  # save inference images
+    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
+    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
+    webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
+    if is_url and is_file:
+        source = check_file(source)  # download
+
+    # Directories
+    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
+    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
+
+    # Load model
+    device = select_device(device)
+    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
+    stride, names, pt = model.stride, model.names, model.pt
+    imgsz = check_img_size(imgsz, s=stride)  # check image size
+
+    # Dataloader
+    if webcam:
+        view_img = check_imshow()
+        dataset = LoadStreams(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
+        bs = len(dataset)  # batch_size
+    else:
+        dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
+        bs = 1  # batch_size
+    vid_path, vid_writer = [None] * bs, [None] * bs
+
+    # Run inference
+    model.warmup(imgsz=(1 if pt else bs, 3, *imgsz))  # warmup
+    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
+    for path, im, im0s, vid_cap, s in dataset:
+        with dt[0]:
+            im = torch.Tensor(im).to(device)
+            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
+            if len(im.shape) == 3:
+                im = im[None]  # expand for batch dim
+
+        # Inference
+        with dt[1]:
+            results = model(im)
+
+        # Post-process
+        with dt[2]:
+            pred = F.softmax(results, dim=1)  # probabilities
+
+        # Process predictions
+        for i, prob in enumerate(pred):  # per image
+            seen += 1
+            if webcam:  # batch_size >= 1
+                p, im0 = path[i], im0s[i].copy()
+                s += f'{i}: '
+            else:
+                p, im0 = path, im0s.copy()
+
+            p = Path(p)  # to Path
+            save_path = str(save_dir / p.name)  # im.jpg
+            s += '%gx%g ' % im.shape[2:]  # print string
+            annotator = Annotator(im0, example=str(names), pil=True)
+
+            # Print results
+            top5i = prob.argsort(0, descending=True)[:5].tolist()  # top 5 indices
+            s += f"{', '.join(f'{names[j]} {prob[j]:.2f}' for j in top5i)}, "
+
+            # Write results
+            if save_img or view_img:  # Add bbox to image
+                text = '\n'.join(f'{prob[j]:.2f} {names[j]}' for j in top5i)
+                annotator.text((32, 32), text, txt_color=(255, 255, 255))
+
+            # Stream results
+            im0 = annotator.result()
+            if view_img:
+                if platform.system() == 'Linux' and p not in windows:
+                    windows.append(p)
+                    cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
+                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
+                cv2.imshow(str(p), im0)
+                cv2.waitKey(1)  # 1 millisecond
+
+            # Save results (image with detections)
+            if save_img:
+                if dataset.mode == 'image':
+                    cv2.imwrite(save_path, im0)
+                else:  # 'video' or 'stream'
+                    if vid_path[i] != save_path:  # new video
+                        vid_path[i] = save_path
+                        if isinstance(vid_writer[i], cv2.VideoWriter):
+                            vid_writer[i].release()  # release previous video writer
+                        if vid_cap:  # video
+                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
+                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                        else:  # stream
+                            fps, w, h = 30, im0.shape[1], im0.shape[0]
+                        save_path = str(Path(save_path).with_suffix('.mp4'))  # force *.mp4 suffix on results videos
+                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
+                    vid_writer[i].write(im0)
+
+        # Print time (inference-only)
+        LOGGER.info(f"{s}{dt[1].dt * 1E3:.1f}ms")
+
+    # Print results
+    t = tuple(x.t / seen * 1E3 for x in dt)  # speeds per image
+    LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
+    if save_txt or save_img:
+        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
+        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
+    if update:
+        strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)
+
+
+def parse_opt():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-cls.pt', help='model path(s)')
+    parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam')
+    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
+    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[224], help='inference size h,w')
+    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    parser.add_argument('--view-img', action='store_true', help='show results')
+    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
+    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
+    parser.add_argument('--augment', action='store_true', help='augmented inference')
+    parser.add_argument('--visualize', action='store_true', help='visualize features')
+    parser.add_argument('--update', action='store_true', help='update all models')
+    parser.add_argument('--project', default=ROOT / 'runs/predict-cls', help='save results to project/name')
+    parser.add_argument('--name', default='exp', help='save results to project/name')
+    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
+    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
+    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
+    parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
+    opt = parser.parse_args()
+    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
+    print_args(vars(opt))
+    return opt
+
+
+def main(opt):
+    check_requirements(exclude=('tensorboard', 'thop'))
+    run(**vars(opt))
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

app/yolov5/classify/train.py

@@ -0,0 +1,331 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Train a YOLOv5 classifier model on a classification dataset
+
+Usage - Single-GPU training:
+    $ python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 128
+
+Usage - Multi-GPU DDP training:
+    $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
+
+Datasets:           --data mnist, fashion-mnist, cifar10, cifar100, imagenette, imagewoof, imagenet, or 'path/to/data'
+YOLOv5-cls models:  --model yolov5n-cls.pt, yolov5s-cls.pt, yolov5m-cls.pt, yolov5l-cls.pt, yolov5x-cls.pt
+Torchvision models: --model resnet50, efficientnet_b0, etc. See https://pytorch.org/vision/stable/models.html
+"""
+
+import argparse
+import os
+import subprocess
+import sys
+import time
+from copy import deepcopy
+from datetime import datetime
+from pathlib import Path
+
+import torch
+import torch.distributed as dist
+import torch.hub as hub
+import torch.optim.lr_scheduler as lr_scheduler
+import torchvision
+from torch.cuda import amp
+from tqdm import tqdm
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from classify import val as validate
+from models.experimental import attempt_load
+from models.yolo import ClassificationModel, DetectionModel
+from utils.dataloaders import create_classification_dataloader
+from utils.general import (DATASETS_DIR, LOGGER, WorkingDirectory, check_git_status, check_requirements, colorstr,
+                           download, increment_path, init_seeds, print_args, yaml_save)
+from utils.loggers import GenericLogger
+from utils.plots import imshow_cls
+from utils.torch_utils import (ModelEMA, model_info, reshape_classifier_output, select_device, smart_DDP,
+                               smart_optimizer, smartCrossEntropyLoss, torch_distributed_zero_first)
+
+LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
+RANK = int(os.getenv('RANK', -1))
+WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
+
+
+def train(opt, device):
+    init_seeds(opt.seed + 1 + RANK, deterministic=True)
+    save_dir, data, bs, epochs, nw, imgsz, pretrained = \
+        opt.save_dir, Path(opt.data), opt.batch_size, opt.epochs, min(os.cpu_count() - 1, opt.workers), \
+        opt.imgsz, str(opt.pretrained).lower() == 'true'
+    cuda = device.type != 'cpu'
+
+    # Directories
+    wdir = save_dir / 'weights'
+    wdir.mkdir(parents=True, exist_ok=True)  # make dir
+    last, best = wdir / 'last.pt', wdir / 'best.pt'
+
+    # Save run settings
+    yaml_save(save_dir / 'opt.yaml', vars(opt))
+
+    # Logger
+    logger = GenericLogger(opt=opt, console_logger=LOGGER) if RANK in {-1, 0} else None
+
+    # Download Dataset
+    with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
+        data_dir = data if data.is_dir() else (DATASETS_DIR / data)
+        if not data_dir.is_dir():
+            LOGGER.info(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...')
+            t = time.time()
+            if str(data) == 'imagenet':
+                subprocess.run(f"bash {ROOT / 'data/scripts/get_imagenet.sh'}", shell=True, check=True)
+            else:
+                url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{data}.zip'
+                download(url, dir=data_dir.parent)
+            s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n"
+            LOGGER.info(s)
+
+    # Dataloaders
+    nc = len([x for x in (data_dir / 'train').glob('*') if x.is_dir()])  # number of classes
+    trainloader = create_classification_dataloader(path=data_dir / 'train',
+                                                   imgsz=imgsz,
+                                                   batch_size=bs // WORLD_SIZE,
+                                                   augment=True,
+                                                   cache=opt.cache,
+                                                   rank=LOCAL_RANK,
+                                                   workers=nw)
+
+    test_dir = data_dir / 'test' if (data_dir / 'test').exists() else data_dir / 'val'  # data/test or data/val
+    if RANK in {-1, 0}:
+        testloader = create_classification_dataloader(path=test_dir,
+                                                      imgsz=imgsz,
+                                                      batch_size=bs // WORLD_SIZE * 2,
+                                                      augment=False,
+                                                      cache=opt.cache,
+                                                      rank=-1,
+                                                      workers=nw)
+
+    # Model
+    with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT):
+        if Path(opt.model).is_file() or opt.model.endswith('.pt'):
+            model = attempt_load(opt.model, device='cpu', fuse=False)
+        elif opt.model in torchvision.models.__dict__:  # TorchVision models i.e. resnet50, efficientnet_b0
+            model = torchvision.models.__dict__[opt.model](weights='IMAGENET1K_V1' if pretrained else None)
+        else:
+            m = hub.list('ultralytics/yolov5')  # + hub.list('pytorch/vision')  # models
+            raise ModuleNotFoundError(f'--model {opt.model} not found. Available models are: \n' + '\n'.join(m))
+        if isinstance(model, DetectionModel):
+            LOGGER.warning("WARNING: pass YOLOv5 classifier model with '-cls' suffix, i.e. '--model yolov5s-cls.pt'")
+            model = ClassificationModel(model=model, nc=nc, cutoff=opt.cutoff or 10)  # convert to classification model
+        reshape_classifier_output(model, nc)  # update class count
+    for m in model.modules():
+        if not pretrained and hasattr(m, 'reset_parameters'):
+            m.reset_parameters()
+        if isinstance(m, torch.nn.Dropout) and opt.dropout is not None:
+            m.p = opt.dropout  # set dropout
+    for p in model.parameters():
+        p.requires_grad = True  # for training
+    model = model.to(device)
+
+    # Info
+    if RANK in {-1, 0}:
+        model.names = trainloader.dataset.classes  # attach class names
+        model.transforms = testloader.dataset.torch_transforms  # attach inference transforms
+        model_info(model)
+        if opt.verbose:
+            LOGGER.info(model)
+        images, labels = next(iter(trainloader))
+        file = imshow_cls(images[:25], labels[:25], names=model.names, f=save_dir / 'train_images.jpg')
+        logger.log_images(file, name='Train Examples')
+        logger.log_graph(model, imgsz)  # log model
+
+    # Optimizer
+    optimizer = smart_optimizer(model, opt.optimizer, opt.lr0, momentum=0.9, decay=opt.decay)
+
+    # Scheduler
+    lrf = 0.01  # final lr (fraction of lr0)
+    # lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - lrf) + lrf  # cosine
+    lf = lambda x: (1 - x / epochs) * (1 - lrf) + lrf  # linear
+    scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
+    # scheduler = lr_scheduler.OneCycleLR(optimizer, max_lr=lr0, total_steps=epochs, pct_start=0.1,
+    #                                    final_div_factor=1 / 25 / lrf)
+
+    # EMA
+    ema = ModelEMA(model) if RANK in {-1, 0} else None
+
+    # DDP mode
+    if cuda and RANK != -1:
+        model = smart_DDP(model)
+
+    # Train
+    t0 = time.time()
+    criterion = smartCrossEntropyLoss(label_smoothing=opt.label_smoothing)  # loss function
+    best_fitness = 0.0
+    scaler = amp.GradScaler(enabled=cuda)
+    val = test_dir.stem  # 'val' or 'test'
+    LOGGER.info(f'Image sizes {imgsz} train, {imgsz} test\n'
+                f'Using {nw * WORLD_SIZE} dataloader workers\n'
+                f"Logging results to {colorstr('bold', save_dir)}\n"
+                f'Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n'
+                f"{'Epoch':>10}{'GPU_mem':>10}{'train_loss':>12}{f'{val}_loss':>12}{'top1_acc':>12}{'top5_acc':>12}")
+    for epoch in range(epochs):  # loop over the dataset multiple times
+        tloss, vloss, fitness = 0.0, 0.0, 0.0  # train loss, val loss, fitness
+        model.train()
+        if RANK != -1:
+            trainloader.sampler.set_epoch(epoch)
+        pbar = enumerate(trainloader)
+        if RANK in {-1, 0}:
+            pbar = tqdm(enumerate(trainloader), total=len(trainloader), bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')
+        for i, (images, labels) in pbar:  # progress bar
+            images, labels = images.to(device, non_blocking=True), labels.to(device)
+
+            # Forward
+            with amp.autocast(enabled=cuda):  # stability issues when enabled
+                loss = criterion(model(images), labels)
+
+            # Backward
+            scaler.scale(loss).backward()
+
+            # Optimize
+            scaler.unscale_(optimizer)  # unscale gradients
+            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0)  # clip gradients
+            scaler.step(optimizer)
+            scaler.update()
+            optimizer.zero_grad()
+            if ema:
+                ema.update(model)
+
+            if RANK in {-1, 0}:
+                # Print
+                tloss = (tloss * i + loss.item()) / (i + 1)  # update mean losses
+                mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0)  # (GB)
+                pbar.desc = f"{f'{epoch + 1}/{epochs}':>10}{mem:>10}{tloss:>12.3g}" + ' ' * 36
+
+                # Test
+                if i == len(pbar) - 1:  # last batch
+                    top1, top5, vloss = validate.run(model=ema.ema,
+                                                     dataloader=testloader,
+                                                     criterion=criterion,
+                                                     pbar=pbar)  # test accuracy, loss
+                    fitness = top1  # define fitness as top1 accuracy
+
+        # Scheduler
+        scheduler.step()
+
+        # Log metrics
+        if RANK in {-1, 0}:
+            # Best fitness
+            if fitness > best_fitness:
+                best_fitness = fitness
+
+            # Log
+            metrics = {
+                "train/loss": tloss,
+                f"{val}/loss": vloss,
+                "metrics/accuracy_top1": top1,
+                "metrics/accuracy_top5": top5,
+                "lr/0": optimizer.param_groups[0]['lr']}  # learning rate
+            logger.log_metrics(metrics, epoch)
+
+            # Save model
+            final_epoch = epoch + 1 == epochs
+            if (not opt.nosave) or final_epoch:
+                ckpt = {
+                    'epoch': epoch,
+                    'best_fitness': best_fitness,
+                    'model': deepcopy(ema.ema).half(),  # deepcopy(de_parallel(model)).half(),
+                    'ema': None,  # deepcopy(ema.ema).half(),
+                    'updates': ema.updates,
+                    'optimizer': None,  # optimizer.state_dict(),
+                    'opt': vars(opt),
+                    'date': datetime.now().isoformat()}
+
+                # Save last, best and delete
+                torch.save(ckpt, last)
+                if best_fitness == fitness:
+                    torch.save(ckpt, best)
+                del ckpt
+
+    # Train complete
+    if RANK in {-1, 0} and final_epoch:
+        LOGGER.info(f'\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)'
+                    f"\nResults saved to {colorstr('bold', save_dir)}"
+                    f"\nPredict:         python classify/predict.py --weights {best} --source im.jpg"
+                    f"\nValidate:        python classify/val.py --weights {best} --data {data_dir}"
+                    f"\nExport:          python export.py --weights {best} --include onnx"
+                    f"\nPyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')"
+                    f"\nVisualize:       https://netron.app\n")
+
+        # Plot examples
+        images, labels = (x[:25] for x in next(iter(testloader)))  # first 25 images and labels
+        pred = torch.max(ema.ema(images.to(device)), 1)[1]
+        file = imshow_cls(images, labels, pred, model.names, verbose=False, f=save_dir / 'test_images.jpg')
+
+        # Log results
+        meta = {"epochs": epochs, "top1_acc": best_fitness, "date": datetime.now().isoformat()}
+        logger.log_images(file, name='Test Examples (true-predicted)', epoch=epoch)
+        logger.log_model(best, epochs, metadata=meta)
+
+
+def parse_opt(known=False):
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model', type=str, default='yolov5s-cls.pt', help='initial weights path')
+    parser.add_argument('--data', type=str, default='imagenette160', help='cifar10, cifar100, mnist, imagenet, ...')
+    parser.add_argument('--epochs', type=int, default=10, help='total training epochs')
+    parser.add_argument('--batch-size', type=int, default=64, help='total batch size for all GPUs')
+    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=128, help='train, val image size (pixels)')
+    parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
+    parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')
+    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
+    parser.add_argument('--project', default=ROOT / 'runs/train-cls', help='save to project/name')
+    parser.add_argument('--name', default='exp', help='save to project/name')
+    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
+    parser.add_argument('--pretrained', nargs='?', const=True, default=True, help='start from i.e. --pretrained False')
+    parser.add_argument('--optimizer', choices=['SGD', 'Adam', 'AdamW', 'RMSProp'], default='Adam', help='optimizer')
+    parser.add_argument('--lr0', type=float, default=0.001, help='initial learning rate')
+    parser.add_argument('--decay', type=float, default=5e-5, help='weight decay')
+    parser.add_argument('--label-smoothing', type=float, default=0.1, help='Label smoothing epsilon')
+    parser.add_argument('--cutoff', type=int, default=None, help='Model layer cutoff index for Classify() head')
+    parser.add_argument('--dropout', type=float, default=None, help='Dropout (fraction)')
+    parser.add_argument('--verbose', action='store_true', help='Verbose mode')
+    parser.add_argument('--seed', type=int, default=0, help='Global training seed')
+    parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify')
+    return parser.parse_known_args()[0] if known else parser.parse_args()
+
+
+def main(opt):
+    # Checks
+    if RANK in {-1, 0}:
+        print_args(vars(opt))
+        check_git_status()
+        check_requirements()
+
+    # DDP mode
+    device = select_device(opt.device, batch_size=opt.batch_size)
+    if LOCAL_RANK != -1:
+        assert opt.batch_size != -1, 'AutoBatch is coming soon for classification, please pass a valid --batch-size'
+        assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'
+        assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
+        torch.cuda.set_device(LOCAL_RANK)
+        device = torch.device('cuda', LOCAL_RANK)
+        dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")
+
+    # Parameters
+    opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)  # increment run
+
+    # Train
+    train(opt, device)
+
+
+def run(**kwargs):
+    # Usage: from yolov5 import classify; classify.train.run(data=mnist, imgsz=320, model='yolov5m')
+    opt = parse_opt(True)
+    for k, v in kwargs.items():
+        setattr(opt, k, v)
+    main(opt)
+    return opt
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

app/yolov5/classify/val.py

@@ -0,0 +1,168 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Validate a trained YOLOv5 classification model on a classification dataset
+
+Usage:
+    $ bash data/scripts/get_imagenet.sh --val  # download ImageNet val split (6.3G, 50000 images)
+    $ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate ImageNet
+
+Usage - formats:
+    $ python classify/val.py --weights yolov5s-cls.pt                 # PyTorch
+                                       yolov5s-cls.torchscript        # TorchScript
+                                       yolov5s-cls.onnx               # ONNX Runtime or OpenCV DNN with --dnn
+                                       yolov5s-cls.xml                # OpenVINO
+                                       yolov5s-cls.engine             # TensorRT
+                                       yolov5s-cls.mlmodel            # CoreML (macOS-only)
+                                       yolov5s-cls_saved_model        # TensorFlow SavedModel
+                                       yolov5s-cls.pb                 # TensorFlow GraphDef
+                                       yolov5s-cls.tflite             # TensorFlow Lite
+                                       yolov5s-cls_edgetpu.tflite     # TensorFlow Edge TPU
+"""
+
+import argparse
+import os
+import sys
+from pathlib import Path
+
+import torch
+from tqdm import tqdm
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from models.common import DetectMultiBackend
+from utils.dataloaders import create_classification_dataloader
+from utils.general import LOGGER, Profile, check_img_size, check_requirements, colorstr, increment_path, print_args
+from utils.torch_utils import select_device, smart_inference_mode
+
+
+@smart_inference_mode()
+def run(
+    data=ROOT / '../datasets/mnist',  # dataset dir
+    weights=ROOT / 'yolov5s-cls.pt',  # model.pt path(s)
+    batch_size=128,  # batch size
+    imgsz=224,  # inference size (pixels)
+    device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+    workers=8,  # max dataloader workers (per RANK in DDP mode)
+    verbose=False,  # verbose output
+    project=ROOT / 'runs/val-cls',  # save to project/name
+    name='exp',  # save to project/name
+    exist_ok=False,  # existing project/name ok, do not increment
+    half=False,  # use FP16 half-precision inference
+    dnn=False,  # use OpenCV DNN for ONNX inference
+    model=None,
+    dataloader=None,
+    criterion=None,
+    pbar=None,
+):
+    # Initialize/load model and set device
+    training = model is not None
+    if training:  # called by train.py
+        device, pt, jit, engine = next(model.parameters()).device, True, False, False  # get model device, PyTorch model
+        half &= device.type != 'cpu'  # half precision only supported on CUDA
+        model.half() if half else model.float()
+    else:  # called directly
+        device = select_device(device, batch_size=batch_size)
+
+        # Directories
+        save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
+        save_dir.mkdir(parents=True, exist_ok=True)  # make dir
+
+        # Load model
+        model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half)
+        stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
+        imgsz = check_img_size(imgsz, s=stride)  # check image size
+        half = model.fp16  # FP16 supported on limited backends with CUDA
+        if engine:
+            batch_size = model.batch_size
+        else:
+            device = model.device
+            if not (pt or jit):
+                batch_size = 1  # export.py models default to batch-size 1
+                LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
+
+        # Dataloader
+        data = Path(data)
+        test_dir = data / 'test' if (data / 'test').exists() else data / 'val'  # data/test or data/val
+        dataloader = create_classification_dataloader(path=test_dir,
+                                                      imgsz=imgsz,
+                                                      batch_size=batch_size,
+                                                      augment=False,
+                                                      rank=-1,
+                                                      workers=workers)
+
+    model.eval()
+    pred, targets, loss, dt = [], [], 0, (Profile(), Profile(), Profile())
+    n = len(dataloader)  # number of batches
+    action = 'validating' if dataloader.dataset.root.stem == 'val' else 'testing'
+    desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}"
+    bar = tqdm(dataloader, desc, n, not training, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}', position=0)
+    with torch.cuda.amp.autocast(enabled=device.type != 'cpu'):
+        for images, labels in bar:
+            with dt[0]:
+                images, labels = images.to(device, non_blocking=True), labels.to(device)
+
+            with dt[1]:
+                y = model(images)
+
+            with dt[2]:
+                pred.append(y.argsort(1, descending=True)[:, :5])
+                targets.append(labels)
+                if criterion:
+                    loss += criterion(y, labels)
+
+    loss /= n
+    pred, targets = torch.cat(pred), torch.cat(targets)
+    correct = (targets[:, None] == pred).float()
+    acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1)  # (top1, top5) accuracy
+    top1, top5 = acc.mean(0).tolist()
+
+    if pbar:
+        pbar.desc = f"{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}"
+    if verbose:  # all classes
+        LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}")
+        LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}")
+        for i, c in model.names.items():
+            aci = acc[targets == i]
+            top1i, top5i = aci.mean(0).tolist()
+            LOGGER.info(f"{c:>24}{aci.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}")
+
+        # Print results
+        t = tuple(x.t / len(dataloader.dataset.samples) * 1E3 for x in dt)  # speeds per image
+        shape = (1, 3, imgsz, imgsz)
+        LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}' % t)
+        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
+
+    return top1, top5, loss
+
+
+def parse_opt():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data', type=str, default=ROOT / '../datasets/mnist', help='dataset path')
+    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-cls.pt', help='model.pt path(s)')
+    parser.add_argument('--batch-size', type=int, default=128, help='batch size')
+    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=224, help='inference size (pixels)')
+    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
+    parser.add_argument('--verbose', nargs='?', const=True, default=True, help='verbose output')
+    parser.add_argument('--project', default=ROOT / 'runs/val-cls', help='save to project/name')
+    parser.add_argument('--name', default='exp', help='save to project/name')
+    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
+    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
+    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
+    opt = parser.parse_args()
+    print_args(vars(opt))
+    return opt
+
+
+def main(opt):
+    check_requirements(exclude=('tensorboard', 'thop'))
+    run(**vars(opt))
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

app/yolov5/data/Argoverse.yaml

@@ -0,0 +1,74 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
+# Example usage: python train.py --data Argoverse.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── Argoverse  ← downloads here (31.3 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/Argoverse  # dataset root dir
+train: Argoverse-1.1/images/train/  # train images (relative to 'path') 39384 images
+val: Argoverse-1.1/images/val/  # val images (relative to 'path') 15062 images
+test: Argoverse-1.1/images/test/  # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: bus
+  5: truck
+  6: traffic_light
+  7: stop_sign
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import json
+
+  from tqdm import tqdm
+  from utils.general import download, Path
+
+
+  def argoverse2yolo(set):
+      labels = {}
+      a = json.load(open(set, "rb"))
+      for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."):
+          img_id = annot['image_id']
+          img_name = a['images'][img_id]['name']
+          img_label_name = f'{img_name[:-3]}txt'
+
+          cls = annot['category_id']  # instance class id
+          x_center, y_center, width, height = annot['bbox']
+          x_center = (x_center + width / 2) / 1920.0  # offset and scale
+          y_center = (y_center + height / 2) / 1200.0  # offset and scale
+          width /= 1920.0  # scale
+          height /= 1200.0  # scale
+
+          img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']]
+          if not img_dir.exists():
+              img_dir.mkdir(parents=True, exist_ok=True)
+
+          k = str(img_dir / img_label_name)
+          if k not in labels:
+              labels[k] = []
+          labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
+
+      for k in labels:
+          with open(k, "w") as f:
+              f.writelines(labels[k])
+
+
+  # Download
+  dir = Path('../datasets/Argoverse')  # dataset root dir
+  urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']
+  download(urls, dir=dir, delete=False)
+
+  # Convert
+  annotations_dir = 'Argoverse-HD/annotations/'
+  (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images')  # rename 'tracking' to 'images'
+  for d in "train.json", "val.json":
+      argoverse2yolo(dir / annotations_dir / d)  # convert VisDrone annotations to YOLO labels

app/yolov5/data/GlobalWheat2020.yaml

@@ -0,0 +1,54 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
+# Example usage: python train.py --data GlobalWheat2020.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── GlobalWheat2020  ← downloads here (7.0 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/GlobalWheat2020  # dataset root dir
+train: # train images (relative to 'path') 3422 images
+  - images/arvalis_1
+  - images/arvalis_2
+  - images/arvalis_3
+  - images/ethz_1
+  - images/rres_1
+  - images/inrae_1
+  - images/usask_1
+val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
+  - images/ethz_1
+test: # test images (optional) 1276 images
+  - images/utokyo_1
+  - images/utokyo_2
+  - images/nau_1
+  - images/uq_1
+
+# Classes
+names:
+  0: wheat_head
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  from utils.general import download, Path
+
+
+  # Download
+  dir = Path(yaml['path'])  # dataset root dir
+  urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
+          'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
+  download(urls, dir=dir)
+
+  # Make Directories
+  for p in 'annotations', 'images', 'labels':
+      (dir / p).mkdir(parents=True, exist_ok=True)
+
+  # Move
+  for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
+           'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
+      (dir / p).rename(dir / 'images' / p)  # move to /images
+      f = (dir / p).with_suffix('.json')  # json file
+      if f.exists():
+          f.rename((dir / 'annotations' / p).with_suffix('.json'))  # move to /annotations

app/yolov5/data/Objects365.yaml

@@ -0,0 +1,438 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Objects365 dataset https://www.objects365.org/ by Megvii
+# Example usage: python train.py --data Objects365.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── Objects365  ← downloads here (712 GB = 367G data + 345G zips)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/Objects365  # dataset root dir
+train: images/train  # train images (relative to 'path') 1742289 images
+val: images/val # val images (relative to 'path') 80000 images
+test:  # test images (optional)
+
+# Classes
+names:
+  0: Person
+  1: Sneakers
+  2: Chair
+  3: Other Shoes
+  4: Hat
+  5: Car
+  6: Lamp
+  7: Glasses
+  8: Bottle
+  9: Desk
+  10: Cup
+  11: Street Lights
+  12: Cabinet/shelf
+  13: Handbag/Satchel
+  14: Bracelet
+  15: Plate
+  16: Picture/Frame
+  17: Helmet
+  18: Book
+  19: Gloves
+  20: Storage box
+  21: Boat
+  22: Leather Shoes
+  23: Flower
+  24: Bench
+  25: Potted Plant
+  26: Bowl/Basin
+  27: Flag
+  28: Pillow
+  29: Boots
+  30: Vase
+  31: Microphone
+  32: Necklace
+  33: Ring
+  34: SUV
+  35: Wine Glass
+  36: Belt
+  37: Monitor/TV
+  38: Backpack
+  39: Umbrella
+  40: Traffic Light
+  41: Speaker
+  42: Watch
+  43: Tie
+  44: Trash bin Can
+  45: Slippers
+  46: Bicycle
+  47: Stool
+  48: Barrel/bucket
+  49: Van
+  50: Couch
+  51: Sandals
+  52: Basket
+  53: Drum
+  54: Pen/Pencil
+  55: Bus
+  56: Wild Bird
+  57: High Heels
+  58: Motorcycle
+  59: Guitar
+  60: Carpet
+  61: Cell Phone
+  62: Bread
+  63: Camera
+  64: Canned
+  65: Truck
+  66: Traffic cone
+  67: Cymbal
+  68: Lifesaver
+  69: Towel
+  70: Stuffed Toy
+  71: Candle
+  72: Sailboat
+  73: Laptop
+  74: Awning
+  75: Bed
+  76: Faucet
+  77: Tent
+  78: Horse
+  79: Mirror
+  80: Power outlet
+  81: Sink
+  82: Apple
+  83: Air Conditioner
+  84: Knife
+  85: Hockey Stick
+  86: Paddle
+  87: Pickup Truck
+  88: Fork
+  89: Traffic Sign
+  90: Balloon
+  91: Tripod
+  92: Dog
+  93: Spoon
+  94: Clock
+  95: Pot
+  96: Cow
+  97: Cake
+  98: Dinning Table
+  99: Sheep
+  100: Hanger
+  101: Blackboard/Whiteboard
+  102: Napkin
+  103: Other Fish
+  104: Orange/Tangerine
+  105: Toiletry
+  106: Keyboard
+  107: Tomato
+  108: Lantern
+  109: Machinery Vehicle
+  110: Fan
+  111: Green Vegetables
+  112: Banana
+  113: Baseball Glove
+  114: Airplane
+  115: Mouse
+  116: Train
+  117: Pumpkin
+  118: Soccer
+  119: Skiboard
+  120: Luggage
+  121: Nightstand
+  122: Tea pot
+  123: Telephone
+  124: Trolley
+  125: Head Phone
+  126: Sports Car
+  127: Stop Sign
+  128: Dessert
+  129: Scooter
+  130: Stroller
+  131: Crane
+  132: Remote
+  133: Refrigerator
+  134: Oven
+  135: Lemon
+  136: Duck
+  137: Baseball Bat
+  138: Surveillance Camera
+  139: Cat
+  140: Jug
+  141: Broccoli
+  142: Piano
+  143: Pizza
+  144: Elephant
+  145: Skateboard
+  146: Surfboard
+  147: Gun
+  148: Skating and Skiing shoes
+  149: Gas stove
+  150: Donut
+  151: Bow Tie
+  152: Carrot
+  153: Toilet
+  154: Kite
+  155: Strawberry
+  156: Other Balls
+  157: Shovel
+  158: Pepper
+  159: Computer Box
+  160: Toilet Paper
+  161: Cleaning Products
+  162: Chopsticks
+  163: Microwave
+  164: Pigeon
+  165: Baseball
+  166: Cutting/chopping Board
+  167: Coffee Table
+  168: Side Table
+  169: Scissors
+  170: Marker
+  171: Pie
+  172: Ladder
+  173: Snowboard
+  174: Cookies
+  175: Radiator
+  176: Fire Hydrant
+  177: Basketball
+  178: Zebra
+  179: Grape
+  180: Giraffe
+  181: Potato
+  182: Sausage
+  183: Tricycle
+  184: Violin
+  185: Egg
+  186: Fire Extinguisher
+  187: Candy
+  188: Fire Truck
+  189: Billiards
+  190: Converter
+  191: Bathtub
+  192: Wheelchair
+  193: Golf Club
+  194: Briefcase
+  195: Cucumber
+  196: Cigar/Cigarette
+  197: Paint Brush
+  198: Pear
+  199: Heavy Truck
+  200: Hamburger
+  201: Extractor
+  202: Extension Cord
+  203: Tong
+  204: Tennis Racket
+  205: Folder
+  206: American Football
+  207: earphone
+  208: Mask
+  209: Kettle
+  210: Tennis
+  211: Ship
+  212: Swing
+  213: Coffee Machine
+  214: Slide
+  215: Carriage
+  216: Onion
+  217: Green beans
+  218: Projector
+  219: Frisbee
+  220: Washing Machine/Drying Machine
+  221: Chicken
+  222: Printer
+  223: Watermelon
+  224: Saxophone
+  225: Tissue
+  226: Toothbrush
+  227: Ice cream
+  228: Hot-air balloon
+  229: Cello
+  230: French Fries
+  231: Scale
+  232: Trophy
+  233: Cabbage
+  234: Hot dog
+  235: Blender
+  236: Peach
+  237: Rice
+  238: Wallet/Purse
+  239: Volleyball
+  240: Deer
+  241: Goose
+  242: Tape
+  243: Tablet
+  244: Cosmetics
+  245: Trumpet
+  246: Pineapple
+  247: Golf Ball
+  248: Ambulance
+  249: Parking meter
+  250: Mango
+  251: Key
+  252: Hurdle
+  253: Fishing Rod
+  254: Medal
+  255: Flute
+  256: Brush
+  257: Penguin
+  258: Megaphone
+  259: Corn
+  260: Lettuce
+  261: Garlic
+  262: Swan
+  263: Helicopter
+  264: Green Onion
+  265: Sandwich
+  266: Nuts
+  267: Speed Limit Sign
+  268: Induction Cooker
+  269: Broom
+  270: Trombone
+  271: Plum
+  272: Rickshaw
+  273: Goldfish
+  274: Kiwi fruit
+  275: Router/modem
+  276: Poker Card
+  277: Toaster
+  278: Shrimp
+  279: Sushi
+  280: Cheese
+  281: Notepaper
+  282: Cherry
+  283: Pliers
+  284: CD
+  285: Pasta
+  286: Hammer
+  287: Cue
+  288: Avocado
+  289: Hamimelon
+  290: Flask
+  291: Mushroom
+  292: Screwdriver
+  293: Soap
+  294: Recorder
+  295: Bear
+  296: Eggplant
+  297: Board Eraser
+  298: Coconut
+  299: Tape Measure/Ruler
+  300: Pig
+  301: Showerhead
+  302: Globe
+  303: Chips
+  304: Steak
+  305: Crosswalk Sign
+  306: Stapler
+  307: Camel
+  308: Formula 1
+  309: Pomegranate
+  310: Dishwasher
+  311: Crab
+  312: Hoverboard
+  313: Meat ball
+  314: Rice Cooker
+  315: Tuba
+  316: Calculator
+  317: Papaya
+  318: Antelope
+  319: Parrot
+  320: Seal
+  321: Butterfly
+  322: Dumbbell
+  323: Donkey
+  324: Lion
+  325: Urinal
+  326: Dolphin
+  327: Electric Drill
+  328: Hair Dryer
+  329: Egg tart
+  330: Jellyfish
+  331: Treadmill
+  332: Lighter
+  333: Grapefruit
+  334: Game board
+  335: Mop
+  336: Radish
+  337: Baozi
+  338: Target
+  339: French
+  340: Spring Rolls
+  341: Monkey
+  342: Rabbit
+  343: Pencil Case
+  344: Yak
+  345: Red Cabbage
+  346: Binoculars
+  347: Asparagus
+  348: Barbell
+  349: Scallop
+  350: Noddles
+  351: Comb
+  352: Dumpling
+  353: Oyster
+  354: Table Tennis paddle
+  355: Cosmetics Brush/Eyeliner Pencil
+  356: Chainsaw
+  357: Eraser
+  358: Lobster
+  359: Durian
+  360: Okra
+  361: Lipstick
+  362: Cosmetics Mirror
+  363: Curling
+  364: Table Tennis
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  from tqdm import tqdm
+
+  from utils.general import Path, check_requirements, download, np, xyxy2xywhn
+
+  check_requirements(('pycocotools>=2.0',))
+  from pycocotools.coco import COCO
+
+  # Make Directories
+  dir = Path(yaml['path'])  # dataset root dir
+  for p in 'images', 'labels':
+      (dir / p).mkdir(parents=True, exist_ok=True)
+      for q in 'train', 'val':
+          (dir / p / q).mkdir(parents=True, exist_ok=True)
+
+  # Train, Val Splits
+  for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:
+      print(f"Processing {split} in {patches} patches ...")
+      images, labels = dir / 'images' / split, dir / 'labels' / split
+
+      # Download
+      url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
+      if split == 'train':
+          download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False)  # annotations json
+          download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8)
+      elif split == 'val':
+          download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False)  # annotations json
+          download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8)
+          download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8)
+
+      # Move
+      for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):
+          f.rename(images / f.name)  # move to /images/{split}
+
+      # Labels
+      coco = COCO(dir / f'zhiyuan_objv2_{split}.json')
+      names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
+      for cid, cat in enumerate(names):
+          catIds = coco.getCatIds(catNms=[cat])
+          imgIds = coco.getImgIds(catIds=catIds)
+          for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
+              width, height = im["width"], im["height"]
+              path = Path(im["file_name"])  # image filename
+              try:
+                  with open(labels / path.with_suffix('.txt').name, 'a') as file:
+                      annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
+                      for a in coco.loadAnns(annIds):
+                          x, y, w, h = a['bbox']  # bounding box in xywh (xy top-left corner)
+                          xyxy = np.array([x, y, x + w, y + h])[None]  # pixels(1,4)
+                          x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0]  # normalized and clipped
+                          file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
+              except Exception as e:
+                  print(e)

app/yolov5/data/SKU-110K.yaml

@@ -0,0 +1,53 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
+# Example usage: python train.py --data SKU-110K.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── SKU-110K  ← downloads here (13.6 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/SKU-110K  # dataset root dir
+train: train.txt  # train images (relative to 'path')  8219 images
+val: val.txt  # val images (relative to 'path')  588 images
+test: test.txt  # test images (optional)  2936 images
+
+# Classes
+names:
+  0: object
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import shutil
+  from tqdm import tqdm
+  from utils.general import np, pd, Path, download, xyxy2xywh
+
+
+  # Download
+  dir = Path(yaml['path'])  # dataset root dir
+  parent = Path(dir.parent)  # download dir
+  urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']
+  download(urls, dir=parent, delete=False)
+
+  # Rename directories
+  if dir.exists():
+      shutil.rmtree(dir)
+  (parent / 'SKU110K_fixed').rename(dir)  # rename dir
+  (dir / 'labels').mkdir(parents=True, exist_ok=True)  # create labels dir
+
+  # Convert labels
+  names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height'  # column names
+  for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv':
+      x = pd.read_csv(dir / 'annotations' / d, names=names).values  # annotations
+      images, unique_images = x[:, 0], np.unique(x[:, 0])
+      with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f:
+          f.writelines(f'./images/{s}\n' for s in unique_images)
+      for im in tqdm(unique_images, desc=f'Converting {dir / d}'):
+          cls = 0  # single-class dataset
+          with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f:
+              for r in x[images == im]:
+                  w, h = r[6], r[7]  # image width, height
+                  xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0]  # instance
+                  f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n")  # write label

app/yolov5/data/VOC.yaml

@@ -0,0 +1,100 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
+# Example usage: python train.py --data VOC.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── VOC  ← downloads here (2.8 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/VOC
+train: # train images (relative to 'path')  16551 images
+  - images/train2012
+  - images/train2007
+  - images/val2012
+  - images/val2007
+val: # val images (relative to 'path')  4952 images
+  - images/test2007
+test: # test images (optional)
+  - images/test2007
+
+# Classes
+names:
+  0: aeroplane
+  1: bicycle
+  2: bird
+  3: boat
+  4: bottle
+  5: bus
+  6: car
+  7: cat
+  8: chair
+  9: cow
+  10: diningtable
+  11: dog
+  12: horse
+  13: motorbike
+  14: person
+  15: pottedplant
+  16: sheep
+  17: sofa
+  18: train
+  19: tvmonitor
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import xml.etree.ElementTree as ET
+
+  from tqdm import tqdm
+  from utils.general import download, Path
+
+
+  def convert_label(path, lb_path, year, image_id):
+      def convert_box(size, box):
+          dw, dh = 1. / size[0], 1. / size[1]
+          x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
+          return x * dw, y * dh, w * dw, h * dh
+
+      in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml')
+      out_file = open(lb_path, 'w')
+      tree = ET.parse(in_file)
+      root = tree.getroot()
+      size = root.find('size')
+      w = int(size.find('width').text)
+      h = int(size.find('height').text)
+
+      names = list(yaml['names'].values())  # names list
+      for obj in root.iter('object'):
+          cls = obj.find('name').text
+          if cls in names and int(obj.find('difficult').text) != 1:
+              xmlbox = obj.find('bndbox')
+              bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
+              cls_id = names.index(cls)  # class id
+              out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n')
+
+
+  # Download
+  dir = Path(yaml['path'])  # dataset root dir
+  url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
+  urls = [f'{url}VOCtrainval_06-Nov-2007.zip',  # 446MB, 5012 images
+          f'{url}VOCtest_06-Nov-2007.zip',  # 438MB, 4953 images
+          f'{url}VOCtrainval_11-May-2012.zip']  # 1.95GB, 17126 images
+  download(urls, dir=dir / 'images', delete=False, curl=True, threads=3)
+
+  # Convert
+  path = dir / 'images/VOCdevkit'
+  for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):
+      imgs_path = dir / 'images' / f'{image_set}{year}'
+      lbs_path = dir / 'labels' / f'{image_set}{year}'
+      imgs_path.mkdir(exist_ok=True, parents=True)
+      lbs_path.mkdir(exist_ok=True, parents=True)
+
+      with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f:
+          image_ids = f.read().strip().split()
+      for id in tqdm(image_ids, desc=f'{image_set}{year}'):
+          f = path / f'VOC{year}/JPEGImages/{id}.jpg'  # old img path
+          lb_path = (lbs_path / f.name).with_suffix('.txt')  # new label path
+          f.rename(imgs_path / f.name)  # move image
+          convert_label(path, lb_path, year, id)  # convert labels to YOLO format

app/yolov5/data/VisDrone.yaml

@@ -0,0 +1,70 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
+# Example usage: python train.py --data VisDrone.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── VisDrone  ← downloads here (2.3 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/VisDrone  # dataset root dir
+train: VisDrone2019-DET-train/images  # train images (relative to 'path')  6471 images
+val: VisDrone2019-DET-val/images  # val images (relative to 'path')  548 images
+test: VisDrone2019-DET-test-dev/images  # test images (optional)  1610 images
+
+# Classes
+names:
+  0: pedestrian
+  1: people
+  2: bicycle
+  3: car
+  4: van
+  5: truck
+  6: tricycle
+  7: awning-tricycle
+  8: bus
+  9: motor
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  from utils.general import download, os, Path
+
+  def visdrone2yolo(dir):
+      from PIL import Image
+      from tqdm import tqdm
+
+      def convert_box(size, box):
+          # Convert VisDrone box to YOLO xywh box
+          dw = 1. / size[0]
+          dh = 1. / size[1]
+          return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
+
+      (dir / 'labels').mkdir(parents=True, exist_ok=True)  # make labels directory
+      pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
+      for f in pbar:
+          img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
+          lines = []
+          with open(f, 'r') as file:  # read annotation.txt
+              for row in [x.split(',') for x in file.read().strip().splitlines()]:
+                  if row[4] == '0':  # VisDrone 'ignored regions' class 0
+                      continue
+                  cls = int(row[5]) - 1
+                  box = convert_box(img_size, tuple(map(int, row[:4])))
+                  lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
+                  with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
+                      fl.writelines(lines)  # write label.txt
+
+
+  # Download
+  dir = Path(yaml['path'])  # dataset root dir
+  urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip',
+          'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
+          'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
+          'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
+  download(urls, dir=dir, curl=True, threads=4)
+
+  # Convert
+  for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
+      visdrone2yolo(dir / d)  # convert VisDrone annotations to YOLO labels

app/yolov5/data/coco.yaml

@@ -0,0 +1,116 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# COCO 2017 dataset http://cocodataset.org by Microsoft
+# Example usage: python train.py --data coco.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── coco  ← downloads here (20.1 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco  # dataset root dir
+train: train2017.txt  # train images (relative to 'path') 118287 images
+val: val2017.txt  # val images (relative to 'path') 5000 images
+test: test-dev2017.txt  # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  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: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  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
+
+
+# Download script/URL (optional)
+download: |
+  from utils.general import download, Path
+
+
+  # Download labels
+  segments = False  # segment or box labels
+  dir = Path(yaml['path'])  # dataset root dir
+  url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
+  urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')]  # labels
+  download(urls, dir=dir.parent)
+
+  # Download data
+  urls = ['http://images.cocodataset.org/zips/train2017.zip',  # 19G, 118k images
+          'http://images.cocodataset.org/zips/val2017.zip',  # 1G, 5k images
+          'http://images.cocodataset.org/zips/test2017.zip']  # 7G, 41k images (optional)
+  download(urls, dir=dir / 'images', threads=3)

app/yolov5/data/coco128 - 副本.yaml

@@ -0,0 +1,101 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# Example usage: python train.py --data coco128.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── coco128  ← downloads here (7 MB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco128  # dataset root dir
+train: images/train2017  # train images (relative to 'path') 128 images
+val: images/train2017  # val images (relative to 'path') 128 images
+test:  # test images (optional)
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  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: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  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
+
+
+# Download script/URL (optional)
+download: https://ultralytics.com/assets/coco128.zip

app/yolov5/data/coco128.yaml

@@ -0,0 +1,7 @@
+path: null
+train: E:/aicheck/data_set/11442136178662604800/trained/images/train/
+val: E:/aicheck/data_set/11442136178662604800/trained/images/val/
+test: null
+names:
+  0: logo
+  1: 3C

app/yolov5/data/hyps/hyp.Objects365.yaml

@@ -0,0 +1,34 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for Objects365 training
+# python train.py --weights yolov5m.pt --data Objects365.yaml --evolve
+# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.00258
+lrf: 0.17
+momentum: 0.779
+weight_decay: 0.00058
+warmup_epochs: 1.33
+warmup_momentum: 0.86
+warmup_bias_lr: 0.0711
+box: 0.0539
+cls: 0.299
+cls_pw: 0.825
+obj: 0.632
+obj_pw: 1.0
+iou_t: 0.2
+anchor_t: 3.44
+anchors: 3.2
+fl_gamma: 0.0
+hsv_h: 0.0188
+hsv_s: 0.704
+hsv_v: 0.36
+degrees: 0.0
+translate: 0.0902
+scale: 0.491
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.0
+copy_paste: 0.0

app/yolov5/data/hyps/hyp.VOC.yaml

@@ -0,0 +1,40 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for VOC training
+# python train.py --batch 128 --weights yolov5m6.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve
+# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
+
+# YOLOv5 Hyperparameter Evolution Results
+# Best generation: 467
+# Last generation: 996
+#    metrics/precision,       metrics/recall,      metrics/mAP_0.5, metrics/mAP_0.5:0.95,         val/box_loss,         val/obj_loss,         val/cls_loss
+#              0.87729,              0.85125,              0.91286,              0.72664,            0.0076739,            0.0042529,            0.0013865
+
+lr0: 0.00334
+lrf: 0.15135
+momentum: 0.74832
+weight_decay: 0.00025
+warmup_epochs: 3.3835
+warmup_momentum: 0.59462
+warmup_bias_lr: 0.18657
+box: 0.02
+cls: 0.21638
+cls_pw: 0.5
+obj: 0.51728
+obj_pw: 0.67198
+iou_t: 0.2
+anchor_t: 3.3744
+fl_gamma: 0.0
+hsv_h: 0.01041
+hsv_s: 0.54703
+hsv_v: 0.27739
+degrees: 0.0
+translate: 0.04591
+scale: 0.75544
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+mosaic: 0.85834
+mixup: 0.04266
+copy_paste: 0.0
+anchors: 3.412

app/yolov5/data/hyps/hyp.scratch-high.yaml

@@ -0,0 +1,34 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for high-augmentation COCO training from scratch
+# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.1  # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937  # SGD momentum/Adam beta1
+weight_decay: 0.0005  # optimizer weight decay 5e-4
+warmup_epochs: 3.0  # warmup epochs (fractions ok)
+warmup_momentum: 0.8  # warmup initial momentum
+warmup_bias_lr: 0.1  # warmup initial bias lr
+box: 0.05  # box loss gain
+cls: 0.3  # cls loss gain
+cls_pw: 1.0  # cls BCELoss positive_weight
+obj: 0.7  # obj loss gain (scale with pixels)
+obj_pw: 1.0  # obj BCELoss positive_weight
+iou_t: 0.20  # IoU training threshold
+anchor_t: 4.0  # anchor-multiple threshold
+# anchors: 3  # anchors per output layer (0 to ignore)
+fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4  # image HSV-Value augmentation (fraction)
+degrees: 0.0  # image rotation (+/- deg)
+translate: 0.1  # image translation (+/- fraction)
+scale: 0.9  # image scale (+/- gain)
+shear: 0.0  # image shear (+/- deg)
+perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0  # image flip up-down (probability)
+fliplr: 0.5  # image flip left-right (probability)
+mosaic: 1.0  # image mosaic (probability)
+mixup: 0.1  # image mixup (probability)
+copy_paste: 0.1  # segment copy-paste (probability)

app/yolov5/data/hyps/hyp.scratch-low.yaml

@@ -0,0 +1,34 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for low-augmentation COCO training from scratch
+# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.01  # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937  # SGD momentum/Adam beta1
+weight_decay: 0.0005  # optimizer weight decay 5e-4
+warmup_epochs: 3.0  # warmup epochs (fractions ok)
+warmup_momentum: 0.8  # warmup initial momentum
+warmup_bias_lr: 0.1  # warmup initial bias lr
+box: 0.05  # box loss gain
+cls: 0.5  # cls loss gain
+cls_pw: 1.0  # cls BCELoss positive_weight
+obj: 1.0  # obj loss gain (scale with pixels)
+obj_pw: 1.0  # obj BCELoss positive_weight
+iou_t: 0.20  # IoU training threshold
+anchor_t: 4.0  # anchor-multiple threshold
+# anchors: 3  # anchors per output layer (0 to ignore)
+fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4  # image HSV-Value augmentation (fraction)
+degrees: 0.0  # image rotation (+/- deg)
+translate: 0.1  # image translation (+/- fraction)
+scale: 0.5  # image scale (+/- gain)
+shear: 0.0  # image shear (+/- deg)
+perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0  # image flip up-down (probability)
+fliplr: 0.5  # image flip left-right (probability)
+mosaic: 1.0  # image mosaic (probability)
+mixup: 0.0  # image mixup (probability)
+copy_paste: 0.0  # segment copy-paste (probability)

app/yolov5/data/hyps/hyp.scratch-med.yaml

@@ -0,0 +1,34 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for medium-augmentation COCO training from scratch
+# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.1  # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937  # SGD momentum/Adam beta1
+weight_decay: 0.0005  # optimizer weight decay 5e-4
+warmup_epochs: 3.0  # warmup epochs (fractions ok)
+warmup_momentum: 0.8  # warmup initial momentum
+warmup_bias_lr: 0.1  # warmup initial bias lr
+box: 0.05  # box loss gain
+cls: 0.3  # cls loss gain
+cls_pw: 1.0  # cls BCELoss positive_weight
+obj: 0.7  # obj loss gain (scale with pixels)
+obj_pw: 1.0  # obj BCELoss positive_weight
+iou_t: 0.20  # IoU training threshold
+anchor_t: 4.0  # anchor-multiple threshold
+# anchors: 3  # anchors per output layer (0 to ignore)
+fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4  # image HSV-Value augmentation (fraction)
+degrees: 0.0  # image rotation (+/- deg)
+translate: 0.1  # image translation (+/- fraction)
+scale: 0.9  # image scale (+/- gain)
+shear: 0.0  # image shear (+/- deg)
+perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0  # image flip up-down (probability)
+fliplr: 0.5  # image flip left-right (probability)
+mosaic: 1.0  # image mosaic (probability)
+mixup: 0.1  # image mixup (probability)
+copy_paste: 0.0  # segment copy-paste (probability)

app/yolov5/data/scripts/download_weights.sh

@@ -0,0 +1,21 @@
+#!/bin/bash
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Download latest models from https://github.com/ultralytics/yolov5/releases
+# Example usage: bash data/scripts/download_weights.sh
+# parent
+# └── yolov5
+#     ├── yolov5s.pt  ← downloads here
+#     ├── yolov5m.pt
+#     └── ...
+
+python - <<EOF
+from utils.downloads import attempt_download
+
+p5 = ['n', 's', 'm', 'l', 'x']  # P5 models
+p6 = [f'{x}6' for x in p5]  # P6 models
+cls = [f'{x}-cls' for x in p5]  # classification models
+
+for x in p5 + p6 + cls:
+    attempt_download(f'weights/yolov5{x}.pt')
+
+EOF

app/yolov5/data/scripts/get_coco.sh

@@ -0,0 +1,56 @@
+#!/bin/bash
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Download COCO 2017 dataset http://cocodataset.org
+# Example usage: bash data/scripts/get_coco.sh
+# parent
+# ├── yolov5
+# └── datasets
+#     └── coco  ← downloads here
+
+# Arguments (optional) Usage: bash data/scripts/get_coco.sh --train --val --test --segments
+if [ "$#" -gt 0 ]; then
+  for opt in "$@"; do
+    case "${opt}" in
+    --train) train=true ;;
+    --val) val=true ;;
+    --test) test=true ;;
+    --segments) segments=true ;;
+    esac
+  done
+else
+  train=true
+  val=true
+  test=false
+  segments=false
+fi
+
+# Download/unzip labels
+d='../datasets' # unzip directory
+url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
+if [ "$segments" == "true" ]; then
+  f='coco2017labels-segments.zip' # 168 MB
+else
+  f='coco2017labels.zip' # 168 MB
+fi
+echo 'Downloading' $url$f ' ...'
+curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
+
+# Download/unzip images
+d='../datasets/coco/images' # unzip directory
+url=http://images.cocodataset.org/zips/
+if [ "$train" == "true" ]; then
+  f='train2017.zip' # 19G, 118k images
+  echo 'Downloading' $url$f '...'
+  curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
+fi
+if [ "$val" == "true" ]; then
+  f='val2017.zip' # 1G, 5k images
+  echo 'Downloading' $url$f '...'
+  curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
+fi
+if [ "$test" == "true" ]; then
+  f='test2017.zip' # 7G, 41k images (optional)
+  echo 'Downloading' $url$f '...'
+  curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
+fi
+wait # finish background tasks

app/yolov5/data/scripts/get_coco128.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
+# Example usage: bash data/scripts/get_coco128.sh
+# parent
+# ├── yolov5
+# └── datasets
+#     └── coco128  ← downloads here
+
+# Download/unzip images and labels
+d='../datasets' # unzip directory
+url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
+f='coco128.zip' # or 'coco128-segments.zip', 68 MB
+echo 'Downloading' $url$f ' ...'
+curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
+
+wait # finish background tasks

app/yolov5/data/scripts/get_imagenet.sh

@@ -0,0 +1,51 @@
+#!/bin/bash
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Download ILSVRC2012 ImageNet dataset https://image-net.org
+# Example usage: bash data/scripts/get_imagenet.sh
+# parent
+# ├── yolov5
+# └── datasets
+#     └── imagenet  ← downloads here
+
+# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val
+if [ "$#" -gt 0 ]; then
+  for opt in "$@"; do
+    case "${opt}" in
+    --train) train=true ;;
+    --val) val=true ;;
+    esac
+  done
+else
+  train=true
+  val=true
+fi
+
+# Make dir
+d='../datasets/imagenet' # unzip directory
+mkdir -p $d && cd $d
+
+# Download/unzip train
+if [ "$train" == "true" ]; then
+  wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_train.tar # download 138G, 1281167 images
+  mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train
+  tar -xf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
+  find . -name "*.tar" | while read NAME; do
+    mkdir -p "${NAME%.tar}"
+    tar -xf "${NAME}" -C "${NAME%.tar}"
+    rm -f "${NAME}"
+  done
+  cd ..
+fi
+
+# Download/unzip val
+if [ "$val" == "true" ]; then
+  wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar # download 6.3G, 50000 images
+  mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xf ILSVRC2012_img_val.tar
+  wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash # move into subdirs
+fi
+
+# Delete corrupted image (optional: PNG under JPEG name that may cause dataloaders to fail)
+# rm train/n04266014/n04266014_10835.JPEG
+
+# TFRecords (optional)
+# wget https://raw.githubusercontent.com/tensorflow/models/master/research/slim/datasets/imagenet_lsvrc_2015_synsets.txt

app/yolov5/data/xView.yaml

@@ -0,0 +1,153 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
+# --------  DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command!  --------
+# Example usage: python train.py --data xView.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── xView  ← downloads here (20.7 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/xView  # dataset root dir
+train: images/autosplit_train.txt  # train images (relative to 'path') 90% of 847 train images
+val: images/autosplit_val.txt  # train images (relative to 'path') 10% of 847 train images
+
+# Classes
+names:
+  0: Fixed-wing Aircraft
+  1: Small Aircraft
+  2: Cargo Plane
+  3: Helicopter
+  4: Passenger Vehicle
+  5: Small Car
+  6: Bus
+  7: Pickup Truck
+  8: Utility Truck
+  9: Truck
+  10: Cargo Truck
+  11: Truck w/Box
+  12: Truck Tractor
+  13: Trailer
+  14: Truck w/Flatbed
+  15: Truck w/Liquid
+  16: Crane Truck
+  17: Railway Vehicle
+  18: Passenger Car
+  19: Cargo Car
+  20: Flat Car
+  21: Tank car
+  22: Locomotive
+  23: Maritime Vessel
+  24: Motorboat
+  25: Sailboat
+  26: Tugboat
+  27: Barge
+  28: Fishing Vessel
+  29: Ferry
+  30: Yacht
+  31: Container Ship
+  32: Oil Tanker
+  33: Engineering Vehicle
+  34: Tower crane
+  35: Container Crane
+  36: Reach Stacker
+  37: Straddle Carrier
+  38: Mobile Crane
+  39: Dump Truck
+  40: Haul Truck
+  41: Scraper/Tractor
+  42: Front loader/Bulldozer
+  43: Excavator
+  44: Cement Mixer
+  45: Ground Grader
+  46: Hut/Tent
+  47: Shed
+  48: Building
+  49: Aircraft Hangar
+  50: Damaged Building
+  51: Facility
+  52: Construction Site
+  53: Vehicle Lot
+  54: Helipad
+  55: Storage Tank
+  56: Shipping container lot
+  57: Shipping Container
+  58: Pylon
+  59: Tower
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import json
+  import os
+  from pathlib import Path
+
+  import numpy as np
+  from PIL import Image
+  from tqdm import tqdm
+
+  from utils.datasets import autosplit
+  from utils.general import download, xyxy2xywhn
+
+
+  def convert_labels(fname=Path('xView/xView_train.geojson')):
+      # Convert xView geoJSON labels to YOLO format
+      path = fname.parent
+      with open(fname) as f:
+          print(f'Loading {fname}...')
+          data = json.load(f)
+
+      # Make dirs
+      labels = Path(path / 'labels' / 'train')
+      os.system(f'rm -rf {labels}')
+      labels.mkdir(parents=True, exist_ok=True)
+
+      # xView classes 11-94 to 0-59
+      xview_class2index = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 1, 2, -1, 3, -1, 4, 5, 6, 7, 8, -1, 9, 10, 11,
+                           12, 13, 14, 15, -1, -1, 16, 17, 18, 19, 20, 21, 22, -1, 23, 24, 25, -1, 26, 27, -1, 28, -1,
+                           29, 30, 31, 32, 33, 34, 35, 36, 37, -1, 38, 39, 40, 41, 42, 43, 44, 45, -1, -1, -1, -1, 46,
+                           47, 48, 49, -1, 50, 51, -1, 52, -1, -1, -1, 53, 54, -1, 55, -1, -1, 56, -1, 57, -1, 58, 59]
+
+      shapes = {}
+      for feature in tqdm(data['features'], desc=f'Converting {fname}'):
+          p = feature['properties']
+          if p['bounds_imcoords']:
+              id = p['image_id']
+              file = path / 'train_images' / id
+              if file.exists():  # 1395.tif missing
+                  try:
+                      box = np.array([int(num) for num in p['bounds_imcoords'].split(",")])
+                      assert box.shape[0] == 4, f'incorrect box shape {box.shape[0]}'
+                      cls = p['type_id']
+                      cls = xview_class2index[int(cls)]  # xView class to 0-60
+                      assert 59 >= cls >= 0, f'incorrect class index {cls}'
+
+                      # Write YOLO label
+                      if id not in shapes:
+                          shapes[id] = Image.open(file).size
+                      box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True)
+                      with open((labels / id).with_suffix('.txt'), 'a') as f:
+                          f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n")  # write label.txt
+                  except Exception as e:
+                      print(f'WARNING: skipping one label for {file}: {e}')
+
+
+  # Download manually from https://challenge.xviewdataset.org
+  dir = Path(yaml['path'])  # dataset root dir
+  # urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip',  # train labels
+  #         'https://d307kc0mrhucc3.cloudfront.net/train_images.zip',  # 15G, 847 train images
+  #         'https://d307kc0mrhucc3.cloudfront.net/val_images.zip']  # 5G, 282 val images (no labels)
+  # download(urls, dir=dir, delete=False)
+
+  # Convert labels
+  convert_labels(dir / 'xView_train.geojson')
+
+  # Move images
+  images = Path(dir / 'images')
+  images.mkdir(parents=True, exist_ok=True)
+  Path(dir / 'train_images').rename(dir / 'images' / 'train')
+  Path(dir / 'val_images').rename(dir / 'images' / 'val')
+
+  # Split
+  autosplit(dir / 'images' / 'train')