args.data set在python中什么意思
时间: 2024-03-11 13:48:58 浏览: 39
string Id { get; set; }
public string UserName { get; set; }
public List<string> Roles { get; set在 Python 中,`args` 通常是指函数或方法的可变参数。而 `args.data` 则需要结合; }
}
}
```
UserClaimsViewModel.cs:
```csharp
using System.Collections.Generic;
using System.Security.Claims;
namespace Permission具体的上下文来理解。一般来说,`args.data` 可能是指一个包含数据集的ManagementSystem
{
public class UserClaimsViewModel
{
public string Id { get; set; }
public string UserName {对象或者数据集的路径。例如,在使用 TensorFlow 或 PyTorch 进行深度学习时,`args.data` 可 get; set; }
public List<Claim> Claims { get; set; }
}
}
```
ClaimViewModel.cs:
```c能是指数据集的路径或者一个数据集对象,用于加载训练数据。
相关问题
for batch_data in tqdm(data_set):与 if (epoch_num + 1) % args.verbose == 0: 这两行代码什么意思
`for batch_data in tqdm(data_set):` 这行代码是一个循环语句,用于遍历数据集中的批量数据。`data_set`是一个可迭代对象,每次迭代返回一个批量的数据。`batch_data`是一个变量,用于存储每次迭代返回的批量数据。
`tqdm` 是一个Python库,可以在循环中显示进度条。在这里,它用于包装 `data_set`,以便在循环遍历中显示一个进度条,表示当前处理的批量数据的进度。
`if (epoch_num + 1) % args.verbose == 0:` 这行代码是一个条件语句,用于检查当前迭代的轮数是否满足显示详细信息的条件。
`epoch_num` 是一个变量,表示当前的迭代轮数。
`args.verbose` 是一个参数,表示指定的详细信息的显示频率。
如果当前迭代的轮数加1除以 `args.verbose` 的结果等于0,即当前轮数是 `args.verbose` 的整数倍,那么条件成立。
在这个条件成立的情况下,可以执行一些需要详细信息显示的操作,例如打印训练过程中的某些指标或输出一些调试信息。
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()
```