args.device = torch.device('cuda:'+str(args.gpu_id))
时间: 2023-05-19 16:05:37 浏览: 74
这是一行 Python 代码,用于将模型加载到指定的 GPU 上。其中 args.gpu_id 是 GPU 的 ID,可以是任何整数值。如果你的系统中有多个 GPU,你可以使用这个代码将模型加载到指定的 GPU 上。
相关问题
str_ids = args.gpu_ids.split(',') args.gpu_ids = [] for str_id in str_ids: id = int(str_id) if id >= 0: args.gpu_ids.append(id) if len(args.gpu_ids) > 0: torch.cuda.set_device(args.gpu_ids[0])什么意思
这段代码的作用是将用户从命令行输入的 GPU ID 字符串分割成单个 ID,并将这些 ID 存储为整数类型的列表。如果列表不为空,代码将设置当前使用的 CUDA 设备为列表中第一个 GPU 的 ID。这表示代码将在指定的 GPU 上运行。
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()
```
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