7_27 11min 22min 上下管理协议

本文详细解析了Python中with语句的工作原理,包括其如何通过__enter__和__exit__方法来管理资源,以及如何处理with块内的异常情况。通过具体代码示例展示了with语句在实际应用中的行为。
class Foo:
def __init__(self,name):
self.name=name

def __enter__(self):
print('执行enter')
return self

def __exit__(self, exc_type, exc_val, exc_tb):
print('执行exit')
print(exc_type)
print(exc_val)
print(exc_tb)
return True
with Foo('a.txt') as f:# with触发 enter
print(f)
print(asdfsaasdfasdfasdfasdfasfasdfasdfasdfasdfasfdasfd) #没有这个属性,触发__exit__
print(f.name)
print('-----------------')
print('-----------------')
print('-----------------')
print('-----------------')
print('-----------------')
print('-----------------')
print('-----------------')如果执行exist
print('000000000000000000000000000000000000000000000')exist结束后执行






with obj as f:
'代码块'

1.with obj ----》触发obj.__enter__(),拿到返回值

2.as f----->f=返回值、

3.with obj as f 等同于 f=obj.__enter__()

4.执行代码块
一:没有异常的情况下,整个代码块运行完毕后去触发__exit__,它的三个参数都为None
二:有异常的情况下,从异常出现的位置直接触发__exit__
a:如果__exit__的返回值为True,代表吞掉了异常
b:如果__exit__的返回值不为True,代表吐出了异常
c:__exit__的的运行完毕就代表了整个with语句的执行完毕











转载于:https://www.cnblogs.com/yikedashuyikexiaocao/p/9375384.html

``` def get_mask_single_level(self, coord_x, coord_y, gt_boxes, level_idx): # gt_label: (m,) gt_boxes: (m, 4) # coord_x: (h*w, ) left_border_distance = coord_x[:, None] - gt_boxes[None, :, 0] # (h*w, m) top_border_distance = coord_y[:, None] - gt_boxes[None, :, 1] right_border_distance = gt_boxes[None, :, 2] - coord_x[:, None] bottom_border_distance = gt_boxes[None, :, 3] - coord_y[:, None] border_distances = torch.stack( [left_border_distance, top_border_distance, right_border_distance, bottom_border_distance], dim=-1, ) # [h*w, m, 4] # the foreground queries must satisfy two requirements: # 1. the quereis located in bounding boxes # 2. the distance from queries to the box center match the feature map stride min_border_distances = torch.min(border_distances, dim=-1)[0] # [h*w, m] max_border_distances = torch.max(border_distances, dim=-1)[0] mask_in_gt_boxes = min_border_distances > 0 min_limit, max_limit = self.limit_range[level_idx] mask_in_level = (max_border_distances > min_limit) & (max_border_distances <= max_limit) mask_pos = mask_in_gt_boxes & mask_in_level # scale-independent salience confidence row_factor = left_border_distance + right_border_distance col_factor = top_border_distance + bottom_border_distance delta_x = (left_border_distance - right_border_distance) / row_factor delta_y = (top_border_distance - bottom_border_distance) / col_factor confidence = torch.sqrt(delta_x**2 + delta_y**2) / 2 confidence_per_box = 1 - confidence confidence_per_box[~mask_in_gt_boxes] = 0 # process positive coordinates if confidence_per_box.numel() != 0: mask = confidence_per_box.max(-1)[0] else: mask = torch.zeros(coord_y.shape, device=confidence.device, dtype=confidence.dtype) # process negative coordinates mask_pos = mask_pos.long().sum(dim=-1) >= 1 mask[~mask_pos] = 0 # add noise to add randomness mask = (1 - self.noise_scale) * mask + self.noise_scale * torch.rand_like(mask) return mask ———————————————— 版权声明:本文为博主原创文章,遵循 CC 4.0 BY-SA 版权协议,转载请附上原文出处链接和本声明。 原文链接:https://blog.youkuaiyun.com/github_72654535/article/details/140577454```给我每个代码和参数的含义
03-11
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值