output_dict.setdefault(args.loss_type + '_loss', train_epoch_loss) output_dict.setdefault(args.loss_type + '_preds', (preds * scalar).tolist()) 这是什么意思

这段代码是在Python中使用字典的方法，其中 `output_dict` 是一个字典对象。`setdefault()` 方法用于在字典中查找指定键的值，如果该键不存在，则将其添加到字典中，并将其默认值设置为指定的值。

在这个例子中，`args.loss_type + '_loss'` 是一个键，它的值是 `train_epoch_loss`。如果字典中不存在该键，则将其添加到字典中。

同样，`args.loss_type + '_preds'` 是另一个键，它的值是 `(preds * scalar).tolist()`。如果字典中不存在该键，则将其添加到字典中。

这段代码的目的是将两个键值对添加到字典中，以便在后续的代码中使用这些值。

相关问题

import numpy as np import pandas as pd import matplotlib as plt from model import MLP import torch.optim as optim import torch import torch.nn as nn import argparse parser = argparse.ArgumentParser() parser.add_argument('--batchSize', type=int, default=4, help='input batch size') parser.add_argument('--nEpochs', type=int, default=100, help='number of epochs to train for') parser.add_argument('--LR', type=float, default=0.001, help='learning rate for net') opt = parser.parse_args() # 数据集预处理 df = pd.read_csv("C:/Users/shen/Desktop/housing.csv", delim_whitespace=True) # Turn into Numpy arrays arr = df.to_numpy(dtype='float') # Split the features and the labels X = arr[:, :-1] y = np.expand_dims(arr[:, -1], 1) ones = np.ones((X.shape[0], 1)) X_new = np.hstack((ones, X)) # Split the dataset into train set and test set indices = np.random.permutation(X_new.shape[0]) train_indices, test_indices = indices[:int(0.9*X_new.shape[0])], indices[int(0.9*X_new.shape[0]):] X_train, X_test = X_new[train_indices, :], X_new[test_indices, :] y_train, y_test = y[train_indices, :], y[test_indices, :] model = MLP() device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model.to(device) model.train() optim_model = optim.Adam(model.parameters(), lr=opt.LR)#Adam优化器 # for epoch in range(opt.nEpochs): # # START CODE HERE # # END CODE HERE # torch.save(model.state_dict(),"C:/Users/shen/Desktop/checkpoint/net_weight.pth")帮我解释这里面每行代码的意思

这段代码主要是在导入一些Python的库（如numpy，pandas，matplotlib等），以及导入一个叫做MLP的模型。同时还使用了一些PyTorch的库（如optim，nn等）。接下来使用argparse库创建了一个命令行参数解析器，可以通过命令行来指定一些参数（比如batchSize，nEpochs和LR）。最后通过parse_args()函数解析这些参数。

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()