machineLearningTemplate/train_model.py

import time

import numpy
import pytorch_lightning as pl
from pytorch_lightning.utilities.types import EPOCH_OUTPUT
from torch import nn
import torch

from network.res_net import resnet56, accuracy


class TrainModule(pl.LightningModule):
    def __init__(self, config=None):
        super().__init__()
        self.time_sum = None
        self.config = config
        self.net = resnet56()
        self.loss = nn.CrossEntropyLoss()

    # 返回值必须包含loss, loss可以作为dict中的key, 或者直接返回loss
    def training_step(self, batch, batch_idx):
        _, input, label = batch
        label = label.flatten()
        pred = self.net(input)
        loss = self.loss(pred, label)
        self.log("Training loss", loss)
        acc = accuracy(pred, label)[0]
        self.log("Training acc", acc)
        return loss

    def validation_step(self, batch, batch_idx):
        _, input, label = batch
        label = label.flatten()
        pred = self.net(input)
        loss = self.loss(pred, label)
        self.log("Validation loss", loss)
        acc = accuracy(pred, label)[0]
        self.log("Validation acc", acc)
        return loss

    def test_step(self, batch, batch_idx):
        _, input, label = batch
        label = label.flatten()
        if self.time_sum is None:
            time_start = time.time()
            pred = self.net(input)
            time_end = time.time()
            self.time_sum = time_end - time_start
            print(f'\n推理时间为: {self.time_sum:f}')
        else:
            pred = self.net(input)
        loss = self.loss(pred, label)
        self.log("Test loss", loss)
        acc = accuracy(pred, label)[0]
        self.log("Test acc", acc)
        return input, label, pred

    def configure_optimizers(self):
        optimizer = torch.optim.SGD(self.parameters(), lr=0.1, momentum=0.9, weight_decay=1e-4)
        return optimizer

    def load_pretrain_parameters(self):
        """
        载入预训练参数
        """
        pass