import os from os.path import join, abspath from glob import glob import subprocess import argparse config_mapping = { "new-metrics-exp-23" : "./config/tf_effnet_v2_s_in21k.yaml", } import shutil def copytree(src, dst, symlinks=False, ignore=None):
时间: 2024-04-09 19:27:08 浏览: 14
def copytree(src, dst, symlinks=False, ignore=None):
"""
Recursively copy a directory tree.
Args:
src (str): Source directory path.
dst (str): Destination directory path.
symlinks (bool, optional): If True, copy symbolic links as symbolic links.
ignore (callable, optional): A function that takes a directory name and filenames as arguments
and returns a list of filenames to ignore.
"""
for item in os.listdir(src):
s = os.path.join(src, item)
d = os.path.join(dst, item)
if os.path.isdir(s):
shutil.copytree(s, d, symlinks, ignore)
else:
shutil.copy2(s, d)
相关问题
self.base_path = os.path.abspath(os.path.join(os.path.pardir))
self.base_path = os.path.abspath(os.path.join(os.path.pardir)) 是一行Python代码,用于获取当前文件所在目录的上一级目录的绝对路径。
具体解释如下:
- os.path.pardir 表示当前目录的上一级目录(即父目录)。
- os.path.join() 函数用于拼接路径,将当前目录的上一级目录与当前目录拼接在一起。
- os.path.abspath() 函数用于获取路径的绝对路径。
因此,self.base_path = os.path.abspath(os.path.join(os.path.pardir)) 的作用是将当前文件所在目录的上一级目录的绝对路径赋值给变量 self.base_path。
import cv2 from PIL import Image from Nets.mobileNet import MobileNetV1 as Net from torch.utils.data import DataLoader from torchvision import transforms from torchvision.datasets import ImageFolder import os import torch PROJECT_PATH = os.path.abspath( os.path.join(os.path.abspath(os.path.dirname(__file__)), os.pardir)) # 训练数据集 DATA_TRAIN = os.path.join(PROJECT_PATH, "MechineLearning/trainSet") # 模型保存地址 DATA_MODEL = os.path.join(PROJECT_PATH, "MechineLearning/model/alexNet.pth") DEVICE = torch.device("cuda") 将这段代码改成只用使用cpu的代码
import cv2
from PIL import Image
from Nets.mobileNet import MobileNetV1 as Net
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import ImageFolder
import os
import torch
PROJECT_PATH = os.path.abspath(
os.path.join(os.path.abspath(os.path.dirname(__file__)), os.pardir))
# 训练数据集
DATA_TRAIN = os.path.join(PROJECT_PATH, "MechineLearning/trainSet")
# 模型保存地址
DATA_MODEL = os.path.join(PROJECT_PATH, "MechineLearning/model/alexNet.pth")
DEVICE = torch.device("cpu") # 将设备改为cpu
注意,这只是将设备更改为CPU,可能需要根据你的代码的具体情况进行其他更改。