@torch.no_grad() def main(): global args, save_path args = parser.parse_args() data_dir = Path(args.data) print("=> fetching img pairs in '{}'".format(args.data)) if args.output is None: save_path = data_dir/'flow' else: save_path = Path(args.output) print('=> will save everything to {}'.format(save_path)) save_path.makedirs_p() # Data loading code input_transform = transforms.Compose([transforms.Normalize(mean=[0,0,0], std=[255,255,255]) ])

这段代码定义了一个名为 `main` 的函数，并使用装饰器 `@torch.no_grad()` 来禁用梯度计算，以减少内存消耗。

`main` 函数首先使用 `parser.parse_args()` 解析命令行参数，并将结果保存在全局变量 `args` 中。

接下来，代码通过 `Path` 类将命令行参数中的数据文件夹路径转换为 `data_dir` 对象，并打印一条消息指示正在获取图像对。

然后，代码根据命令行参数中的输出文件夹路径创建一个目录，并将其保存在全局变量 `save_path` 中。如果未指定输出文件夹路径，则默认创建一个名为 "flow" 的子目录。

接着，代码创建了一个名为 `input_transform` 的图像转换对象。在这个例子中，它只应用了一个归一化操作，即将图像的像素值从 `[0,0,0]` 到 `[255,255,255]` 范围进行归一化。

以上就是这段代码的主要内容和功能。如果有更多问题，可以继续提问。

相关问题

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()

https://github.com/weizhepei/CasRel中run.py解读

`run.py` 是 `CasRel` 项目的入口文件，用于训练和测试模型。以下是 `run.py` 的主要代码解读和功能说明：

### 导入依赖包和模块

首先，`run.py` 导入了所需的依赖包和模块，包括 `torch`、`numpy`、`argparse`、`logging` 等。

import argparse
import logging
import os
import random
import time

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler

from casrel import CasRel
from dataset import RE_Dataset
from utils import init_logger, load_tokenizer, set_seed, collate_fn

### 解析命令行参数

接下来，`run.py` 解析了命令行参数，包括训练数据路径、模型保存路径、预训练模型路径、学习率等参数。

def set_args():
    parser = argparse.ArgumentParser()

    parser.add_argument("--train_data", default=None, type=str, required=True,
                        help="The input training data file (a text file).")
    parser.add_argument("--dev_data", default=None, type=str, required=True,
                        help="The input development data file (a text file).")
    parser.add_argument("--test_data", default=None, type=str, required=True,
                        help="The input testing data file (a text file).")

    parser.add_argument("--model_path", default=None, type=str, required=True,
                        help="Path to save, load model")
    parser.add_argument("--pretrain_path", default=None, type=str,
                        help="Path to pre-trained model")
    parser.add_argument("--vocab_path", default=None, type=str, required=True,
                        help="Path to vocabulary")

    parser.add_argument("--batch_size", default=32, type=int,
                        help="Batch size per GPU/CPU for training.")
    parser.add_argument("--gradient_accumulation_steps", default=1, type=int,
                        help="Number of updates steps to accumulate before performing a backward/update pass.")
    parser.add_argument("--learning_rate", default=5e-5, type=float,
                        help="The initial learning rate for Adam.")
    parser.add_argument("--num_train_epochs", default=3, type=int,
                        help="Total number of training epochs to perform.")
    parser.add_argument("--max_seq_length", default=256, type=int,
                        help="The maximum total input sequence length after tokenization. Sequences longer "
                             "than this will be truncated, sequences shorter will be padded.")
    parser.add_argument("--warmup_proportion", default=0.1, type=float,
                        help="Linear warmup over warmup_steps.")
    parser.add_argument("--weight_decay", default=0.01, type=float,
                        help="Weight decay if we apply some.")
    parser.add_argument("--adam_epsilon", default=1e-8, type=float,
                        help="Epsilon for Adam optimizer.")
    parser.add_argument("--max_grad_norm", default=1.0, type=float,
                        help="Max gradient norm.")
    parser.add_argument("--logging_steps", type=int, default=500,
                        help="Log every X updates steps.")
    parser.add_argument("--save_steps", type=int, default=500,
                        help="Save checkpoint every X updates steps.")
    parser.add_argument("--seed", type=int, default=42,
                        help="random seed for initialization")
    parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu",
                        help="selected device (default: cuda if available)")

    args = parser.parse_args()

    return args

### 加载数据和模型

接下来，`run.py` 加载了训练、验证和测试数据，以及 `CasRel` 模型。

def main():
    args = set_args()

    init_logger()
    set_seed(args)

    tokenizer = load_tokenizer(args.vocab_path)

    train_dataset = RE_Dataset(args.train_data, tokenizer, args.max_seq_length)
    dev_dataset = RE_Dataset(args.dev_data, tokenizer, args.max_seq_length)
    test_dataset = RE_Dataset(args.test_data, tokenizer, args.max_seq_length)

    train_sampler = RandomSampler(train_dataset)
    train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.batch_size,
                                  collate_fn=collate_fn)

    dev_sampler = SequentialSampler(dev_dataset)
    dev_dataloader = DataLoader(dev_dataset, sampler=dev_sampler, batch_size=args.batch_size,
                                collate_fn=collate_fn)

    test_sampler = SequentialSampler(test_dataset)
    test_dataloader = DataLoader(test_dataset, sampler=test_sampler, batch_size=args.batch_size,
                                  collate_fn=collate_fn)

    model = CasRel(args)
    if args.pretrain_path:
        model.load_state_dict(torch.load(args.pretrain_path, map_location="cpu"))
        logging.info(f"load pre-trained model from {args.pretrain_path}")

    model.to(args.device)

### 训练模型

接下来，`run.py` 开始训练模型，包括前向传播、反向传播、梯度更新等步骤。

    optimizer = torch.optim.Adam([{'params': model.bert.parameters(), 'lr': args.learning_rate},
                                  {'params': model.subject_fc.parameters(), 'lr': args.learning_rate},
                                  {'params': model.object_fc.parameters(), 'lr': args.learning_rate},
                                  {'params': model.predicate_fc.parameters(), 'lr': args.learning_rate},
                                  {'params': model.linear.parameters(), 'lr': args.learning_rate}],
                                 lr=args.learning_rate, eps=args.adam_epsilon, weight_decay=args.weight_decay)

    total_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
    warmup_steps = int(total_steps * args.warmup_proportion)

    scheduler = torch.optim.lr_scheduler.LambdaLR(
        optimizer,
        lr_lambda=lambda epoch: 1 / (1 + 0.05 * (epoch - 1))
    )

    global_step = 0
    best_f1 = 0
    for epoch in range(args.num_train_epochs):
        for step, batch in enumerate(train_dataloader):
            model.train()
            batch = tuple(t.to(args.device) for t in batch)

            inputs = {
                "input_ids": batch[0],
                "attention_mask": batch[1],
                "token_type_ids": batch[2],
                "subj_pos": batch[3],
                "obj_pos": batch[4],
                "subj_type": batch[5],
                "obj_type": batch[6],
                "subj_label": batch[7],
                "obj_label": batch[8],
                "predicate_label": batch[9],
            }

            outputs = model(**inputs)
            loss = outputs[0]
            loss.backward()

            if (step + 1) % args.gradient_accumulation_steps == 0:
                torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)

                optimizer.step()
                scheduler.step()
                optimizer.zero_grad()
                global_step += 1

                if global_step % args.logging_steps == 0:
                    logging.info(f"Epoch:[{epoch + 1}]/[{args.num_train_epochs}] Step:[{global_step}] "
                                 f"Train loss:{loss.item():.6f}")

                if global_step % args.save_steps == 0:
                    f1 = evaluate(model, dev_dataloader, args)
                    if f1 > best_f1:
                        best_f1 = f1
                        torch.save(model.state_dict(), os.path.join(args.model_path, "best_model.bin"))
                        logging.info(f"Save model at step [{global_step}] with best f1 {best_f1:.4f}")

### 测试模型

最后，`run.py` 对模型进行测试，输出模型在测试集上的预测结果。

    model.load_state_dict(torch.load(os.path.join(args.model_path, "best_model.bin"), map_location="cpu"))
    logging.info(f"load best model from {os.path.join(args.model_path, 'best_model.bin')}")

    f1, precision, recall = evaluate(model, test_dataloader, args)

    logging.info(f"Test f1:{f1:.4f} precision:{precision:.4f} recall:{recall:.4f}")

以上就是 `run.py` 的主要代码解读和功能说明。