args = parser.parse_args() args.cuda = not args.no_cuda and torch.cuda.is_available()

这是argparse模块的用法示例的一部分，它使用parse_args()方法从命令行中解析参数，并将结果保存在args变量中。接下来，它根据解析出的--no-cuda参数的值和当前系统是否支持CUDA来设置args.cuda变量的值。

具体来说，这里的代码将args.no_cuda取反并与torch.cuda.is_available()的值做与运算，如果结果为True，则将args.cuda设置为True，否则设置为False。这样，args.cuda将表示是否启用CUDA训练。

例如，如果我们在命令行中输入：

python myscript.py --no-cuda

而当前系统不支持CUDA，则解析后的结果将是：

args.no_cuda = True
args.cuda = False

这样，我们就可以使用args.cuda来确定是否需要在GPU上运行代码。

相关问题

def main(): args = parser.parse_args() if args.seed is not None: random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) cudnn.deterministic = True main_worker(args)

这是一段 Python 代码，它定义了一个名为 "main" 的函数。该函数首先调用 "parser.parse_args()" 方法来解析命令行参数。如果参数 "args.seed" 不为空，则通过设置相关随机数生成器（random、numpy.random 和 torch）的种子来确保生成的随机数不受外部因素的影响。最后，它调用 "main_worker(args)" 函数。

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：

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()