An easy-to-use tool for training Pytorch deep learning models
Project description
DeepEpochs
Pytorch深度学习模型训练工具。
安装
pip install deepepochs
使用
数据要求
- 训练集、验证集和测试集是
torch.utils.data.Dataloader
对象 Dataloaer
中每个mini-batch数据是一个tuple
或list
,其中最后一个是标签- 如果数据不包含标签,则请将最后一项置为
None
- 如果数据不包含标签,则请将最后一项置为
指标计算
- 每个指标是一个函数
- 它有两个参数,分别为模型的预测结果和标签
- 返回值为当前mini-batch上的指标值
常规训练流程
from deepepochs import Trainer, Checker, rename
import torch
from torch import nn
from torch.nn import functional as F
from torchvision.datasets import MNIST
from torchvision import transforms
from torch.utils.data import DataLoader, random_split
from torchmetrics import functional as MF
# datasets
data_dir = './dataset'
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
mnist_full = MNIST(data_dir, train=True, transform=transform, download=True)
train_ds, val_ds, _ = random_split(mnist_full, [5000, 5000, 50000])
test_ds = MNIST(data_dir, train=False, transform=transform, download=True)
# dataloaders
train_dl = DataLoader(train_ds, batch_size=32)
val_dl = DataLoader(val_ds, batch_size=32)
test_dl = DataLoader(test_ds, batch_size=32)
# pytorch model
channels, width, height = (1, 28, 28)
model = nn.Sequential(
nn.Flatten(),
nn.Linear(channels * width * height, 64),
nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(64, 64),
nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(64, 10)
)
def acc(preds, targets):
return MF.accuracy(preds, targets, task='multiclass', num_classes=10)
@rename('')
def multi_metrics(preds, targets):
return {
'p': MF.precision(preds, targets, task='multiclass', num_classes=10),
'r': MF.recall(preds, targets, task='multiclass', num_classes=10)
}
checker = Checker('loss', mode='min', patience=2)
opt = torch.optim.Adam(model.parameters(), lr=2e-4)
trainer = Trainer(model, F.cross_entropy, opt=opt, epochs=100, checker=checker, metrics=[acc, multi_metrics])
progress = trainer.fit(train_dl, val_dl)
test_rst = trainer.test(test_dl)
非常规训练流程
- 方法1:
- 第1步:继承
deepepochs.TrainerBase
类,定制满足需要的Trainer
,实现train_step
方法和evaluate_step
方法 - 第2步:调用定制
Trainer
训练模型。
- 第1步:继承
- 方法2:
- 第1步:继承
deepepochs.Callback
类,定制满足需要的Callback - 第2步:使用
deepepochs.Learner
训练模型,将定制的Callback作为Learner
的参数 - 提示:
Learner
是具有Callback
功能的Trainer
- 第1步:继承
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