def map_label(label, classes): mapped_label = torch.LongTensor(label.size()) for i in range(classes.size(0)): mapped_label[label==classes[i]] = i return mapped_label
时间: 2024-04-13 08:27:30 浏览: 20
这段代码定义了一个函数map_label,用于将标签映射到给定的一组类别。让我来解释一下这段代码的逻辑:
该函数接受两个输入参数:label和classes。label是一个包含标签的张量,classes是一个包含类别的张量。
首先,代码创建了一个与label相同大小的空白张量mapped_label,用于存储映射后的标签。
然后,代码通过一个循环遍历classes中的每个类别。在每次循环中,代码使用[label==classes[i]]来创建一个布尔掩码,该掩码将与label中与当前类别相等的元素对应位置为True,其余位置为False。
接下来,代码将该布尔掩码应用于mapped_label,并将对应位置的元素设置为当前类别的索引i。
最后,代码返回mapped_label,即映射后的标签。
这段代码的目的是将原始标签映射到给定的类别集合中。它可以用于处理多类别分类问题中的标签映射任务。
相关问题
def map_data(data_map, axis_num): data_map = np.max(data_map, axis=axis_num) data_map -= data_map.min() data_map = data_map / data_map.max() data_map = np.array(data_map * 255, dtype=np.uint8) return data_map 转为c++ Eigen::Tensor
这里提供一种使用 Eigen::Tensor 库实现的 C++ 版本代码:
```cpp
#include <Eigen/Dense>
#include <Eigen/Tensor>
Eigen::Tensor<uint8_t, 2> map_data(const Eigen::Tensor<float, 3>& data_map, int axis_num) {
int rows = data_map.dimension(0);
int cols = data_map.dimension(1);
Eigen::Tensor<float, 2> max_vals;
if (axis_num == 0) {
max_vals = data_map.maximum(Eigen::array<int, 1>{2}).reshape(Eigen::array<int, 2>{rows, cols});
}
else {
max_vals = data_map.maximum(Eigen::array<int, 1>{1}).reshape(Eigen::array<int, 2>{rows, cols});
}
Eigen::Tensor<uint8_t, 2> mapped_data = (data_map / max_vals).maximum(0.0f).minimum(1.0f) * 255.0f;
return mapped_data;
}
```
这里使用了 Eigen::Tensor 库来处理多维数组。输入数据类型为 `Eigen::Tensor<float, 3>`,输出数据类型为 `Eigen::Tensor<uint8_t, 2>`。函数中使用 `maximum()` 和 `minimum()` 函数来进行数值的归一化,并使用 `reshape()` 函数来将三维数组转换为二维数组。
class UserSigninLog(db.Model): """登录日志表""" bind_key = 'db2' tablename = 'user_signin_log' id = mapped_column(Integer, primary_key=True) userid = mapped_column(String(16), index=True) username = mapped_column(String(32)) ip_addr = mapped_column(String(32)) request_user_agent = mapped_column(String(256)) c_date = mapped_column(DateTime) @staticmethod def add_user_signin_log(userid: str, username: str): """新增登录日志""" user_signin_log = UserSigninLog() user_signin_log.userid = userid user_signin_log.username = username user_signin_log.ip_addr = request.access_route[0] user_signin_log.request_user_agent = request.headers.get("User-Agent") user_signin_log.c_date = datetime.now() db.session.add(user_signin_log) db.session.commit() return True @staticmethod def check_ip_and_header_within_x_days(userid: str, days=7): """检查x天内登录过的ip地址和请求头""" sql_query = select(UserSigninLog) \ .where(UserSigninLog.userid == userid, UserSigninLog.ip_addr == request.access_route[0], UserSigninLog.request_user_agent == request.headers.get("User-Agent"), UserSigninLog.c_date >= datetime.now() - timedelta(days=days)).limit(1) user_ip_addr_log = db.session.execute(sql_query).scalar_one_or_none() if user_ip_addr_log: return True return False 请帮我建议并优化这段代码,python3.8
以下是我对这段代码的建议和优化:
1. 使用 Flask 的上下文对象获取 request 对象,避免在静态方法中使用全局变量
```python
from flask import request
class UserSigninLog(db.Model):
# ...
@staticmethod
def add_user_signin_log(userid: str, username: str):
"""新增登录日志"""
user_signin_log = UserSigninLog()
user_signin_log.userid = userid
user_signin_log.username = username
user_signin_log.ip_addr = request.access_route[0]
user_signin_log.request_user_agent = request.headers.get("User-Agent")
user_signin_log.c_date = datetime.now()
db.session.add(user_signin_log)
db.session.commit()
return True
@staticmethod
def check_ip_and_header_within_x_days(userid: str, days=7):
"""检查x天内登录过的ip地址和请求头"""
user_agent = request.headers.get("User-Agent")
ip_addr = request.access_route[0]
c_date = datetime.now() - timedelta(days=days)
user_ip_addr_log = UserSigninLog.query.filter_by(userid=userid, ip_addr=ip_addr, request_user_agent=user_agent).filter(UserSigninLog.c_date >= c_date).first()
if user_ip_addr_log:
return True
return False
```
2. 使用 SQLAlchemy 的 ORM 语法查询,避免手写 SQL 语句
```python
from sqlalchemy import and_
class UserSigninLog(db.Model):
# ...
@staticmethod
def check_ip_and_header_within_x_days(userid: str, days=7):
"""检查x天内登录过的ip地址和请求头"""
user_agent = request.headers.get("User-Agent")
ip_addr = request.access_route[0]
c_date = datetime.now() - timedelta(days=days)
user_ip_addr_log = UserSigninLog.query.filter(and_(UserSigninLog.userid == userid, UserSigninLog.ip_addr == ip_addr, UserSigninLog.request_user_agent == user_agent, UserSigninLog.c_date >= c_date)).first()
if user_ip_addr_log:
return True
return False
```
3. 让静态方法更加灵活,接收 request 对象作为参数
```python
class UserSigninLog(db.Model):
# ...
@staticmethod
def add_user_signin_log(userid: str, username: str, request):
"""新增登录日志"""
user_signin_log = UserSigninLog()
user_signin_log.userid = userid
user_signin_log.username = username
user_signin_log.ip_addr = request.access_route[0]
user_signin_log.request_user_agent = request.headers.get("User-Agent")
user_signin_log.c_date = datetime.now()
db.session.add(user_signin_log)
db.session.commit()
return True
@staticmethod
def check_ip_and_header_within_x_days(userid: str, request, days=7):
"""检查x天内登录过的ip地址和请求头"""
user_agent = request.headers.get("User-Agent")
ip_addr = request.access_route[0]
c_date = datetime.now() - timedelta(days=days)
user_ip_addr_log = UserSigninLog.query.filter(and_(UserSigninLog.userid == userid, UserSigninLog.ip_addr == ip_addr, UserSigninLog.request_user_agent == user_agent, UserSigninLog.c_date >= c_date)).first()
if user_ip_addr_log:
return True
return False
```
这些是我对这段代码的建议和优化,希望能对你有所帮助。