args = parser.parse_args() args.root_model = f'{args.root_path}/{args.dataset}/{args.mark}' os.makedirs(args.root_model, exist_ok=True) if args.gpu is not None: warnings.warn('You have chosen a specific GPU. This will completely ' 'disable data parallelism.') if args.dist_url == "env://" and args.world_size == -1: args.world_size = int(os.environ["WORLD_SIZE"]) args.distributed = args.world_size > 1 or args.multiprocessing_distributed
时间: 2024-04-17 12:22:58 浏览: 43
这段代码是用于解析命令行参数,并根据参数设置一些变量和环境。具体的功能如下:
- `args = parser.parse_args()`:通过解析命令行参数,将参数值赋给`args`对象。
- `args.root_model = f'{args.root_path}/{args.dataset}/{args.mark}'`:根据命令行参数的值,构建一个路径字符串,并将其赋值给`args.root_model`。
- `os.makedirs(args.root_model, exist_ok=True)`:创建一个目录,如果目录已存在则不会抛出异常。
- `if args.gpu is not None: ...`:如果命令行参数中指定了GPU设备,则发出一个警告信息。
- `if args.dist_url == "env://" and args.world_size == -1: ...`:如果分布式训练的URL参数是默认值且world_size参数也是默认值,则尝试从环境变量中获取world_size的值。
- `args.distributed = args.world_size > 1 or args.multiprocessing_distributed`:根据world_size和multiprocessing_distributed参数的值,设置distributed变量的布尔值。
这段代码主要是对命令行参数进行解析和设置相应的变量,以便后续使用。
相关问题
args = parser.parse_args() dataset = args.event_log n_layer = args.n_layers
I'm sorry, as an AI language model, I cannot provide the exact explanation of the code without the complete context. However, I can provide a general explanation of the code.
The first line of the code `args = parser.parse_args()` is used to parse the command-line arguments passed to the script. It creates an `args` object that contains the arguments and their values.
The second line of the code `dataset = args.event_log` assigns the value of the `event_log` argument to the `dataset` variable.
The third line of the code `n_layer = args.n_layers` assigns the value of the `n_layers` argument to the `n_layer` variable.
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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