train.py 中的一些小知识点

asdf

1. def load_darknet_weights(self, weights_path) : 加载预训练权重的函数

2. dict.get(key, default=None)

说明：key – 字典中要查找的键。default – 如果指定键的值不存在时，返回该默认值值。

3. python 函数中 关于星 * 的使用例子

3.1. 用在函数的参数前，* 可以同时访问多个参数，** 可以同时访问多个 字典形式的参数

程序如下：

def multiple(arg, *args):
    print("arg: ", arg)
 
    for value in args:
        print("other args:", value)


def multiple2(**args): 
    for key in args:
        print(key + ":", str(args[key]))
        # print(key + ":" + bytes(args[key]))
 
  
def multiple3(arg, *args, **dictargs):
    print("arg: ", arg )

    for value in args:
        print("other args:", value)

    for key in dictargs:
        print("dictargs:" + key + ":" + str(dictargs[key]))
        # print("dictargs:" + key + ":" + bytes(dictargs[key]))
       
     
if __name__ == '__main__':
    print("++++++++++++")
    multiple(1,'a',True, 13212, "asdfasf ", "asdfas df", {"aaa":"asdf ", 123:123, })
    print("++++++++++++")
    multiple2(name='Amy', age=12, single=True)
    print("++++++++++++")
    multiple3(1,'a',True, name='Amy',age=12, )

print 结果如下：

++++++++++++
arg:  1

other args: a
other args: True
other args: 13212
other args: asdfasf 
other args: asdfas df
other args: {'aaa': 'asdf ', 123: 123}
++++++++++++
name: Amy
age: 12
single: True
++++++++++++
arg:  1

other args: a
other args: True

dictargs:name:Amy
dictargs:age:12

3.2 用在变量之前， * 可以提取变量中的值，** 可以提取字典中的值

程序如下：

def add(x, y): 
    return x + y 

if __name__ == "__main__":
   
    para =  (1,2)
    print([*para])  # [1, 2]

    para2 = [1, 3]
    print([*para2])  # [1, 3]

    para3 = {"x":2, "y":3}
    print([*para3])  # ['x', 'y']
    # print([**para2]) #  SyntaxError: invalid syntax
 
    print(add(*para))  # 3
    print(add(*para2))  # 4
    print(add(**para3))  # 5

列表的加法

a = [1 ,2,3]
a += [5, 6, 7, 8]

print(a)  # [1, 2, 3, 5, 6, 7, 8]

dict.item() 同时访问字典中的key 和value

a = {'loss': 75.91, 'x': 0.11}
print("a.items() = ", a.items())

for name, metric in a.items():
	print(name, metric)

显示结果如下：

a.items() = dict_items([('loss', 75.91), ('x', 0.11)])

loss 75.91
x 0.11