电子商务官方网站,网站设计哪家口碑好,wordpress评论改成微博,一般app需要多少钱文章目录 pytorch基础知识和basicSR中用到的语法1.Sampler类与4种采样方式2.python dict的get方法使用3.prefetch_dataloader.py4. pytorch 并行和分布式训练4.1 选择要使用的cuda4.2 DataParallel使用方法常规使用方法保存和载入 4.3 DistributedDataParallel 5.wangdb 入门5.… 文章目录 pytorch基础知识和basicSR中用到的语法1.Sampler类与4种采样方式2.python dict的get方法使用3.prefetch_dataloader.py4. pytorch 并行和分布式训练4.1 选择要使用的cuda4.2 DataParallel使用方法常规使用方法保存和载入 4.3 DistributedDataParallel 5.wangdb 入门5.1 sign up(https://wandb.ai/site)5.2 安装和login5.3 demo 5.model and train5.1 create model5.2 opt中设置5.2 SRModel 类 pytorch基础知识和basicSR中用到的语法
1.Sampler类与4种采样方式
一文弄懂Pytorch的DataLoader, DataSet, Sampler之间的关系 pytorch源码阅读三Sampler类与4种采样方式
下面代码是自定义的一个采样器: ratio控制扩充数据集的倍数 num_replicas是进程数一般是world_size rank: 当前进程的rank
其实目的就是把数据集的索引划分为num_replicas组供每个进程process 处理 至于ratio是为了使每个epoch训练的数据增多for saving time when restart the dataloader after each epoch
import math
import torch
from torch.utils.data.sampler import Samplerclass EnlargedSampler(Sampler):Sampler that restricts data loading to a subset of the dataset.Modified from torch.utils.data.distributed.DistributedSamplerSupport enlarging the dataset for iteration-based training, for savingtime when restart the dataloader after each epochArgs:dataset (torch.utils.data.Dataset): Dataset used for sampling.num_replicas (int | None): Number of processes participating inthe training. It is usually the world_size.rank (int | None): Rank of the current process within num_replicas.ratio (int): Enlarging ratio. Default: 1.def __init__(self, dataset, num_replicas, rank, ratio1):self.dataset datasetself.num_replicas num_replicasself.rank rankself.epoch 0self.num_samples math.ceil(len(self.dataset) * ratio / self.num_replicas)self.total_size self.num_samples * self.num_replicasdef __iter__(self):# deterministically shuffle based on epochg torch.Generator()g.manual_seed(self.epoch)indices torch.randperm(self.total_size, generatorg).tolist()dataset_size len(self.dataset)indices [v % dataset_size for v in indices]# subsampleindices indices[self.rank:self.total_size:self.num_replicas]assert len(indices) self.num_samplesreturn iter(indices)def __len__(self):return self.num_samplesdef set_epoch(self, epoch):self.epoch epoch测试一下
import numpy as np
if __name__ __main__:data np.arange(20).tolist()en_sample EnlargedSampler(data, 2, 0)en_sample.set_epoch(1)for i in en_sample:print(i)print(\n------------------\n)en_sample EnlargedSampler(data, 2, 1)en_sample.set_epoch(1) # 设置为同一个epoch . rank0或者1时生成的index是互补的# 或者不用设置默认为0即可。for i in en_sample:print(i)结果
2.python dict的get方法使用 3.prefetch_dataloader.py 载入本批数据的时候预先载入下一批数据。主要看next函数
import queue as Queue
import threading
import torch
from torch.utils.data import DataLoaderclass PrefetchGenerator(threading.Thread):A general prefetch generator.Reference: https://stackoverflow.com/questions/7323664/python-generator-pre-fetchArgs:generator: Python generator.num_prefetch_queue (int): Number of prefetch queue.def __init__(self, generator, num_prefetch_queue):threading.Thread.__init__(self)self.queue Queue.Queue(num_prefetch_queue)self.generator generatorself.daemon Trueself.start()def run(self):for item in self.generator:self.queue.put(item)self.queue.put(None)def __next__(self):next_item self.queue.get()if next_item is None:raise StopIterationreturn next_itemdef __iter__(self):return selfclass PrefetchDataLoader(DataLoader):Prefetch version of dataloader.Reference: https://github.com/IgorSusmelj/pytorch-styleguide/issues/5#TODO:Need to test on single gpu and ddp (multi-gpu). There is a known issue inddp.Args:num_prefetch_queue (int): Number of prefetch queue.kwargs (dict): Other arguments for dataloader.def __init__(self, num_prefetch_queue, **kwargs):self.num_prefetch_queue num_prefetch_queuesuper(PrefetchDataLoader, self).__init__(**kwargs)def __iter__(self):return PrefetchGenerator(super().__iter__(), self.num_prefetch_queue)class CPUPrefetcher():CPU prefetcher.Args:loader: Dataloader.def __init__(self, loader):self.ori_loader loaderself.loader iter(loader)def next(self):try:return next(self.loader)except StopIteration:return Nonedef reset(self):self.loader iter(self.ori_loader)class CUDAPrefetcher():CUDA prefetcher.Reference: https://github.com/NVIDIA/apex/issues/304#It may consume more GPU memory.Args:loader: Dataloader.opt (dict): Options.def __init__(self, loader, opt):self.ori_loader loaderself.loader iter(loader)self.opt optself.stream torch.cuda.Stream()self.device torch.device(cuda if opt[num_gpu] ! 0 else cpu)self.preload()def preload(self):try:self.batch next(self.loader) # self.batch is a dictexcept StopIteration:self.batch Nonereturn None# put tensors to gpuwith torch.cuda.stream(self.stream):for k, v in self.batch.items():if torch.is_tensor(v):self.batch[k] self.batch[k].to(deviceself.device, non_blockingTrue)def next(self):torch.cuda.current_stream().wait_stream(self.stream) # 等待下一批处理完毕batch self.batch # 赋值self.preload() # 预先载入下一批return batchdef reset(self):self.loader iter(self.ori_loader)self.preload()4. pytorch 并行和分布式训练
4.1 选择要使用的cuda
当我们的服务器上有多个GPU我们应该指明我们使用的GPU是哪一块如果我们不设置的话tensor.cuda()方法会默认将tensor保存到第一块GPU上等价于tensor.cuda(0)这将会导致爆出out of memory的错误。我们可以通过以下两种方式继续设置。
在文件最开始部分#设置在文件最开始部分
import os
os.environ[CUDA_VISIBLE_DEVICE] 0,1,2 # 设置默认的显卡在命令行运行的时候设置 CUDA_VISBLE_DEVICE0,1 python train.py # 使用01两块GPU4.2 DataParallel使用方法
常规使用方法 model UNetSeeInDark()model._initialize_weights()gpus [0123]model nn.DataParallel(model, device_idsgpus)device torch.device(cuda:0)model model.to(device)# 如果不使用并行只需要注释掉 model nn.DataParallel(model, device_idsgpus)# 如果要更改要使用的gpu 更改gpus,和device中的torch.device(cuda:0)中的number即可保存和载入
保存可以使用
# 因为model被DP wrap了得先取出模型
save_model_path os.path.join(save_model_dir, fcheckpoint_{epoch:05d}.pth)
# torch.save(model.state_dict(), save_model_path)
torch.save(model.module.state_dict(), save_model_path)然后载入模型
model_copy.load_state_dict(torch.load(m_path, map_locationdevice))如果没有提出model.module进行保存 在载入的时候可能需要如下方式
checkpoint torch.load(m_path)
model_copy.load_state_dict({k.replace(module., ): v for k, v in checkpoint.items()})4.3 DistributedDataParallel
首先DataParallel是单进程多线程的方法并且仅能工作在单机多卡的情况。而DistributedDataParallel方法是多进程多线程的并且适用与单机多卡和多机多卡的情况。即使在在单机多卡的情况下DistributedDataParallell也比DataParallel的速度更快。 目前还未深入理解 深入理解Pytorch中的分布式训练 pytorch分布式训练 Pytorch中多GPU并行计算教程 PyTorch 并行训练极简 Demo
5.wangdb 入门
直接参看https://docs.wandb.ai/quickstart 最详细的介绍和入门
5.1 sign up(https://wandb.ai/site) 5.2 安装和login
pip install wandb wandb.login() 然后复制API key
5.3 demo
import wandb
import random# start a new wandb run to track this script
wandb.init(# set the wandb project where this run will be loggedprojectmy-awesome-project,# track hyperparameters and run metadataconfig{learning_rate: 0.02,architecture: CNN,dataset: CIFAR-100,epochs: 10,}
)# simulate training
epochs 10
offset random.random() / 5
for epoch in range(2, epochs):acc 1 - 2 ** -epoch - random.random() / epoch - offsetloss 2 ** -epoch random.random() / epoch offset# log metrics to wandbwandb.log({acc: acc, loss: loss})# [optional] finish the wandb run, necessary in notebooks5b1bb8a27da51a7375b4b52c24a82fe1807877f1
wandb.finish()运行之后
wandb: Currently logged in as: wangty537. Use wandb login --relogin to force relogin
wandb: Tracking run with wandb version 0.15.10
wandb: Run data is saved locally in D:\code\denoise\noise-synthesis-main\wandb\run-20230921_103737-j9ezjcqo
wandb: Run wandb offline to turn off syncing.
wandb: Syncing run wobbly-jazz-1
wandb: View project at https://wandb.ai/wangty537/my-awesome-project
wandb: View run at https://wandb.ai/wangty537/my-awesome-project/runs/j9ezjcqo
wandb: Waiting for WB process to finish... (success).
wandb:
wandb: Run history:
wandb: acc ▁▆▇██▇▇█
wandb: loss █▄█▁▅▁▄▁
wandb:
wandb: Run summary:
wandb: acc 0.88762
wandb: loss 0.12236
wandb:
wandb: View run wobbly-jazz-1 at: https://wandb.ai/wangty537/my-awesome-project/runs/j9ezjcqo
wandb: Synced 5 WB file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
wandb: Find logs at: .\wandb\run-20230921_103737-j9ezjcqo\logs然后可以在 https://wandb.ai/home 查看相关信息
https://docs.wandb.ai/quickstart 还介绍了更多高阶应用。
5.model and train
5.1 create model
利用注册机制
# create model
model build_model(opt)
def build_model(opt):Build model from options.Args:opt (dict): Configuration. It must contain:model_type (str): Model type.opt deepcopy(opt)model MODEL_REGISTRY.get(opt[model_type])(opt)logger get_root_logger()logger.info(fModel [{model.__class__.__name__}] is created.)return model5.2 opt中设置
model_type: SRModel
scale: 25.2 SRModel 类
BaseModel是基类
MODEL_REGISTRY.register()
class SRModel(BaseModel):xxx
