from_data_dir = r'./beijing air quality data' dest_dir = '../data' read_data(from_data_dir, dest_dir)
时间: 2024-04-03 22:35:46 浏览: 9
根据提供的代码片段,这似乎是一个 Python 代码块。这段代码定义了两个变量 `from_data_dir` 和 `dest_dir`,并将它们分别初始化为字符串 `'./beijing air quality data'` 和 `'../data'`。
此外,它调用了一个名为 `read_data` 的函数,并传入了这两个变量作为参数。由于这里只提供了代码片段,所以我无法确定 `read_data` 函数的具体实现,但它可能是用于读取某个数据集或文件夹中的数据的自定义函数。
最后值得注意的是,这里的字符串前面都带有一个 `r`,表示这是一个原始字符串,其中的转义字符将会被直接输出,而不会被转义。这在处理文件路径等字符串时比较常见。
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下面代码转化为paddle2.2.2代码 :from __future__ import division import os, time, scipy.io import torch import torch.nn as nn import torch.optim as optim import numpy as np import glob import cv2 import argparse from PIL import Image from skimage.measure import compare_psnr,compare_ssim from tensorboardX import SummaryWriter from models import RViDeNet from utils import * parser = argparse.ArgumentParser(description='Pretrain denoising model') parser.add_argument('--gpu_id', dest='gpu_id', type=int, default=0, help='gpu id') parser.add_argument('--num_epochs', dest='num_epochs', type=int, default=33, help='num_epochs') parser.add_argument('--patch_size', dest='patch_size', type=int, default=128, help='patch_size') parser.add_argument('--batch_size', dest='batch_size', type=int, default=1, help='batch_size') args = parser.parse_args() os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_id) save_dir = './pretrain_model' if not os.path.isdir(save_dir): os.makedirs(save_dir) gt_paths1 = glob.glob('./data/SRVD_data/raw_clean/MOT17-02_raw/*.tiff') gt_paths2 = glob.glob('./data/SRVD_data/raw_clean/MOT17-09_raw/*.tiff') gt_paths3 = glob.glob('./data/SRVD_data/raw_clean/MOT17-10_raw/*.tiff') gt_paths4 = glob.glob('./data/SRVD_data/raw_clean/MOT17-11_raw/*.tiff') gt_paths = gt_paths1 + gt_paths2 + gt_paths3 + gt_paths4 ps = args.patch_size # patch size for training batch_size = args.batch_size # batch size for training
把原始代码转换为PaddlePaddle 2.2.2 的代码如下:
```python
import os
import time
import argparse
import glob
import cv2
import numpy as np
from PIL import Image
from skimage.measure import compare_psnr, compare_ssim
from tensorboardX import SummaryWriter
from models import RViDeNet
from utils import *
import paddle
from paddle import nn
from paddle.optimizer import optim
paddle.set_device('gpu')
parser = argparse.ArgumentParser(description='Pretrain denoising model')
parser.add_argument('--gpu_id', dest='gpu_id', type=int, default=0, help='gpu id')
parser.add_argument('--num_epochs', dest='num_epochs', type=int, default=33, help='num_epochs')
parser.add_argument('--patch_size', dest='patch_size', type=int, default=128, help='patch_size')
parser.add_argument('--batch_size', dest='batch_size', type=int, default=1, help='batch_size')
args = parser.parse_args()
save_dir = './pretrain_model'
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
gt_paths1 = glob.glob('./data/SRVD_data/raw_clean/MOT17-02_raw/*.tiff')
gt_paths2 = glob.glob('./data/SRVD_data/raw_clean/MOT17-09_raw/*.tiff')
gt_paths3 = glob.glob('./data/SRVD_data/raw_clean/MOT17-10_raw/*.tiff')
gt_paths4 = glob.glob('./data/SRVD_data/raw_clean/MOT17-11_raw/*.tiff')
gt_paths = gt_paths1 + gt_paths2 + gt_paths3 + gt_paths4
ps = args.patch_size # patch size for training
batch_size = args.batch_size # batch size for training
num_epochs = args.num_epochs
train_dataset = DatasetDenoising(gt_paths, ps=ps)
train_loader = paddle.io.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
model = RViDeNet()
model.train()
optimizer = optim.Adam(learning_rate=1e-4, parameters=model.parameters())
writer = SummaryWriter()
for epoch in range(num_epochs):
epoch_start_time = time.time()
epoch_loss = 0
for i, (noisy_patches, gt_patches) in enumerate(train_loader()):
noisy_patches = paddle.to_tensor(noisy_patches)
gt_patches = paddle.to_tensor(gt_patches)
output = model(noisy_patches)
loss = nn.functional.mse_loss(output, gt_patches)
optimizer.clear_grad()
loss.backward()
optimizer.step()
epoch_loss += loss
epoch_time = time.time() - epoch_start_time
epoch_loss = epoch_loss / len(train_loader)
print("Epoch [{}/{}] Loss: {:.5f} [{:.2f}s]".format(epoch + 1, num_epochs, epoch_loss, epoch_time))
writer.add_scalar("Loss/train", epoch_loss, epoch + 1)
if (epoch + 1) % 10 == 0:
model_path = os.path.join(save_dir, 'RViDeNet_epoch{}.pdparams'.format(epoch + 1))
paddle.save(model.state_dict(), model_path)
print("Saving model to: {}".format(model_path))
writer.close()
```
os.makedirs(r'../people', exist_ok=True) os.makedirs(r'../animal', exist_ok=True) dest_folder1 = r'../people' dest_folder2 = r'../animal'这个代码什么意思能不能优化一下
这段代码的意思是在当前工作目录的上一级目录中创建两个文件夹"people"和"animal",如果它们已经存在,则不会抛出异常。然后将"people"文件夹路径赋给变量dest_folder1,将"animal"文件夹路径赋给变量dest_folder2。
如果想要优化这段代码,可以将文件夹路径和文件夹名称用变量表示,以便更灵活地使用。示例如下:
```
import os
# 定义文件夹路径和名称
parent_folder = '../' # 上一级目录
folders = ['people', 'animal'] # 文件夹名称列表
# 创建文件夹
for folder in folders:
folder_path = os.path.join(parent_folder, folder) # 拼接文件夹路径
os.makedirs(folder_path, exist_ok=True) # 创建文件夹
print(f'文件夹"{folder_path}"创建成功!')
```
该代码将文件夹路径和名称都用变量表示,并使用os.path.join()函数拼接文件夹路径。然后使用for循环遍历文件夹名称列表,在每个循环中创建文件夹。如果创建成功,会输出"文件夹{folder_path}创建成功!"。