model=MyLightningModule.load_from_checkpoint("/path/to/checkpoint.ckpt")# disable randomness, dropout, etc...model.eval()# 基于训练好的权重得到预测值y_hat=model(x) LightningModule 允许通过调用 self.save_hyperparameters()自动保存传递给 init 的所有超参数: classMyLightningModule(LightningModule):def...
# most casesdefconfigure_optimizers(self):opt=Adam(self.parameters(),lr=1e-3)returnopt# multiple optimizer case (e.g.: GAN)defconfigure_optimizers(self):generator_opt=Adam(self.model_gen.parameters(),lr=0.01)disriminator_opt=Adam(self.model_disc.parameters(),lr=0.02)returngenerator_opt,dis...
pytorch-lightning 是建立在pytorch之上的高层次模型接口,pytorch-lightning之于pytorch,就如同keras之于tensorflow。 关于pytorch-lightning的完整入门介绍,可以参考我的另外一篇文章。 使用pytorch-lightning漂亮地进行深度学习研究 我用了约80行代码对 pytorch-lightning 做了进一步封装,使得对它不熟悉的用户可以用类似Keras...
classLitModel(pl.LightningModule):def__init__(...):defforward(...):deftraining_step(...)deftraining_step_end(...)deftraining_epoch_end(...)defvalidation_step(...)defvalidation_step_end(...)defvalidation_epoch_end(...)deftest_step(...)deftest_step_end(...)deftest_epoch_end(.....
litemono 是使用预训练的编码器(在imagenet上预训练过的需要下载),代码中load_model(self)加载的模型名称是测试和评估时在命令行特别注明的,就是训练好的模型权重;self.load_pretrain()函数使用的是在ImageNet上预先训练的主干(深度编码器)的权重–mypretrain。
同理,在model_interface中建立class MInterface(pl.LightningModule):类,作为模型的中间接口。__init__()函数中import相应模型类,然后老老实实加入configure_optimizers,training_step,validation_step等函数,用一个接口类控制所有模型。不同部分使用输入参数控制。
(32,10))classModel(pl.LightningModule):def__init__(self,net,learning_rate=1e-3):super().__init__()self.save_hyperparameters()self.net=net self.train_acc=Accuracy()self.val_acc=Accuracy()self.test_acc=Accuracy()defforward(self,x):x=self.net(x)returnx #定义loss deftraining_step(...
parser.add_argument('--log-interval',type=int, default=10, metavar='N',help='how many batches to wait before logging training status')# parser.add_argument('--save-model', action='store_true', default=False,# help='For Saving the current Model')args = parser.parse_args() ...
Every subsequent step changes as Lightning introduces enhanced syntax that reduces boilerplate code. 2. Writing training and validation loops The next part is writing the dreaded training and validation loop, which requires you to memorize the order of the following steps: Initializing the model, ...
Every subsequent step changes as Lightning introduces enhanced syntax that reduces boilerplate code. 2. Writing training and validation loops The next part is writing the dreaded training and validation loop, which requires you to memorize the order of the following steps: Initializing the model, ...