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(.....
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...
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。 main.py函数...
# load images and bbox img_path = os.path.join(self.root_dir, "PNGImages", self.imgs[idx]) mask_path = os.path.join(self.root_dir, "PedMasks", self.masks[idx]) img = Image.open(img_path).convert("RGB") mask = Image.open(mask_path) ...
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, ...
BERT 和 GPT 等超大模型正在成为 NLP 领域应用中的趋势。然而训练这种大模型面临内存限制的问题，为了解决这个难题，研究者使用 Megatron-LM 和 PyTorch-Lightning 模型并行性扩大训练。其中，Megatron-LM 只专注于大规模训练语言模型，而 PyTorch-Lightning 仅基于 sharded 优化器状态和梯度，如 DeepSpeed。在计算机视觉...
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, ...
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) 1. 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') ...
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...