import argparseparser = argparse.ArgumentParser()parser.add_argument('--num_epochs', type=int, default=10, help='number of epochs to train (default: 10)')args = parser.parse_args()print(args.num_epochs)这段代码中想要指定num-opochs为8,该如何添加代码
时间: 2024-02-09 18:08:38 浏览: 30
你可以在运行程序时指定 `--num_epochs` 参数的值为 `8`,例如:
```
python your_script.py --num_epochs 8
```
这样程序就会使用你指定的值 `8` 来覆盖默认值 `10`。
如果你想在程序中直接修改参数的值,可以使用 `args` 对象来修改。例如:
```python
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--num_epochs', type=int, default=10, help='number of epochs to train (default: 10)')
args = parser.parse_args()
args.num_epochs = 8 # 修改 num_epochs 参数的值为 8
print(args.num_epochs) # 输出 8
```
注意,如果你在程序中修改了参数的值,那么该参数的默认值将不再起作用。在上面的例子中,我们将 `num_epochs` 的值修改为 `8`,因此程序输出的结果为 `8`,而不是默认值 `10`。
相关问题
parser.add_argument('--epochs', dest='epochs', type=int, default=200, help='# of epochs')
在给出的代码中,使用了`argparse`库中的`add_argument`函数来添加一个名为`--epochs`的命令行参数。这个参数用于指定训练的总轮数。具体代码如下:
```python
import argparse
# 创建一个ArgumentParser对象
parser = argparse.ArgumentParser()
# 添加一个--epochs参数
parser.add_argument('--epochs', dest='epochs', type=int, default=200, help='# of epochs')
# 解析命令行参数
args = parser.parse_args()
# 使用args.epochs获取训练的总轮数
num_epochs = args.epochs
```
在上述代码中,`--epochs`参数的默认值被设置为200,表示如果在命令行中没有指定该参数,将使用默认值。通过`args.epochs`可以获取到训练的总轮数。你可以根据实际需要在命令行中指定`--epochs`参数,例如`python train.py --epochs 100`表示训练100轮。
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()
```