pytorch_lightning安装版本 pytorch light

发现了一个神器，减少代码量，界面优美。

于是乎，开撸

Pytorch Lightning

导入下列包：

import os
import torch
from torch import nn
import torch.nn.functional as F
from torchvision import transforms
from torchvision.datasets import MNIST
from torch.utils.data import DataLoader, random_split
import pytorch_lightning as pl

Step 1: 定义Lightning模型

class LitAutoEncoder(pl.LightningModule):

    def __init__(self):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Linear(28*28, 64),
            nn.ReLU(),
            nn.Linear(64, 3)
        )
        self.decoder = nn.Sequential(
            nn.Linear(3, 64),
            nn.ReLU(),
            nn.Linear(64, 28*28)
        )

    def forward(self, x):
        # in lightning, forward defines the prediction(预测)/inference(推理) actions
        embedding = self.encoder(x)
        return embedding

    def training_step(self, batch, batch_idx):
        # training_step defined the train loop.
        # It is independent of forward
        x, y = batch
        x = x.view(x.size(0), -1)
        z = self.encoder(x)
        x_hat = self.decoder(z)
        loss = F.mse_loss(x_hat, x)
        # Logging to TensorBoard by default
        self.log('train_loss', loss)   
        #self.log('my_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
        return loss

    def configure_optimizers(self):
        optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
        return optimizer

SYSTEM VS MODEL

LightningModule 定义了一个系统而不是一个模型。

FORWARD vs TRAINING_STEP

在Lightning中，将训练步骤和推理分离，训练步骤定义了完整的训练过程。

例如，我们可以定义autoencoder去实现一个编码提取器。

def forward(self, x):
    embeddings = self.encoder(x)
    return embeddings

当然，没有什么可以阻止你在forward中使用训练步骤。

def training_step(self, batch, batch_idx):
    ...
    z = self(x)

这取决与你的应用，但是，我们建议你把这两种分开

• 使用forward去推理（预测）
• 使用训练步骤去训练

Step 2: Fit with Lightning Trainer

首先，按照你想要的方式来定义数据，Lightning只需要一个DataLoader对与训练集/交叉验证集/测试集分割

dataset = MNIST(os.getcwd(), download=True, transform=transforms.ToTensor())
train_loader = DataLoader(dataset)

接下来，初始化LightningModule和 Pytorch Lightning Trainer，然后开始训练。

# init model
autoencoder = LitAutoEncoder()

# most basic trainer, uses good defaults (auto-tensorboard, checkpoints, logs, and more)
# trainer = pl.Trainer(gpus=8) (if you have GPUs)
trainer = pl.Trainer()
trainer.fit(autoencoder, train_loader)

The Trainer automates：

• Epoch and batch iteration
• Calling of optimizer.step(), backward, zero_grad()
• Calling of .eval（), enabling/disabling grads
• Saving and loading weights
• Tensorboard (see Loggers options)
• Multi-GPU training support
• TPU support
• 16-bit training support

Predict or Deploy

当结束训练后，有3个选项去使用你训练好的LightningModule

Option 1: Sub-models

使用系统内任何模型去预测

# ----------------------------------
# to use as embedding extractor
# ----------------------------------
autoencoder = LitAutoEncoder.load_from_checkpoint('path/to/checkpoint_file.ckpt')
encoder_model = autoencoder.encoder
encoder_model.eval（)

# ----------------------------------
# to use as image generator
# ----------------------------------
decoder_model = autoencoder.decoder
decoder_model.eval（)

Option 2: Forward

# ----------------------------------
# using the AE to extract embeddings
# ----------------------------------
class LitAutoEncoder(pl.LightningModule):
    def forward(self, x):
        embedding = self.encoder(x)
        return embedding

autoencoder = LitAutoencoder()
autoencoder = autoencoder(torch.rand(1, 28 * 28))

# ----------------------------------
# or using the AE to generate images
# ----------------------------------
class LitAutoEncoder(pl.LightningModule):
    def forward(self):
        z = torch.rand(1, 3)
        image = self.decoder(z)
        image = image.view(1, 1, 28, 28)
        return image

autoencoder = LitAutoencoder()
image_sample = autoencoder()

Option 3: Production

确保已经添加了forward mathod ， 或者仅仅需要跟踪子模块

# ----------------------------------
# torchscript
# ----------------------------------
autoencoder = LitAutoEncoder()
torch.jit.save(autoencoder.to_torchscript(), "model.pt")
os.path.isfile("model.pt")

# ----------------------------------
# onnx
# ----------------------------------
with tempfile.NamedTemporaryFile(suffix='.onnx', delete=False) as tmpfile:
     autoencoder = LitAutoEncoder()
     input_sample = torch.randn((1, 28 * 28))
     autoencoder.to_onnx(tmpfile.name, input_sample, export_params=True)
     os.path.isfile(tmpfile.name)

Using CPUs/GPUs/TPUs

在Lightning中使用cpu、gpu或tpu是很简单的，无需改变代码，只需要改变训练选项。

# train on CPU
trainer = pl.Trainer()

# train on 8 CPUs
trainer = pl.Trainer(num_processes=8)

# train on 1024 CPUs across 128 machines
trainer = pl.Trainer(
    num_processes=8,
    num_nodes=128
)

# train on 1 GPU
trainer = pl.Trainer(gpus=1

# train on multiple GPUs across nodes (32 gpus here)
trainer = pl.Trainer(
    gpus=4,
    num_nodes=8
)

# train on gpu 1, 3, 5 (3 gpus total)
trainer = pl.Trainer(gpus=[1, 3, 5])

# Multi GPU with mixed precision
trainer = pl.Trainer(gpus=2, precision=16)

# Train on TPUs
trainer = pl.Trainer(tpu_cores=8)

无需修改代码中的任意一行，就可以使用上面的代码执行以下操作。

# train on TPUs using 16 bit precision
# using only half the training data and checking validation every quarter of a training epoch
trainer = pl.Trainer(
    tpu_cores=8,
    precision=16,
    limit_train_batches=0.5,
    val_check_interval=0.25
)

Checkpoints

Lightning会自动保存你的模型，一旦你训练好了，你可以通过下面代码来加载检查点

model = LitModel.load_from_checkpoint(path)

上面的检查点包含了初始化模型和设置状态字典所需的所有参数

# load the ckpt
ckpt = torch.load('path/to/checkpoint.ckpt')

# 等效与上述代码
model = LitModel()
model.load_state_dict(ckpt['state_dict'])

Logging

要记录日志到Tensorboard或进度条，可以在LighningModule中的任何地方调用use()方法。

def training_step(self, batch, batch_idx):
    self.log('my_metric', x)

The log() 有一些选项。

• on_step (在训练的那个步骤记录metric)
• on_epoch (在epoch结束时，自动积累并且记录)
• prog_bar (记录到进度条中)
• logger (记录到tensorboard中)

Setting on_epoch=True will accumulate your logged values over the full training epoch.

def training_step(self, batch, batch_idx):
    self.log('my_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)

进度条中显示的损失值在最后的值上被平滑处理，因此不同于在训练/验证步骤中返回的实际损失。

你也可以直接使用记录器的任意方法

def training_step(self, batch, batch_idx):
    tensorboard = self.logger.experiment
    tensorboard.any_summary_writer_method_you_want())

一旦你的训练开始，你就可以通过使用你最喜欢的记录器启动Tensorboard查看日志。

!tensorboard --logdir ./lightning_logs  #在命令行中查看

From: https://pytorch-lightning.readthedocs.io/en/latest/new-project.html#

def training_step(self, batch, batch_idx):
    tensorboard = self.logger.experiment
    tensorboard.any_summary_writer_method_you_want())

一旦你的训练开始，你就可以通过使用你最喜欢的记录器启动Tensorboard查看日志。

!tensorboard --logdir ./lightning_logs  #在命令行中查看

From: https://pytorch-lightning.readthedocs.io/en/latest/new-project.html#