opt = Adam(self.parameters(), lr=1e-3) return opt # multiple optimizer case (e.g.: GAN) def configure_optimizers(self): generator_opt = Adam(self.model_gen.parameters(), lr=0.01) disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02) return generator_opt, disriminator_opt...
LightningModule 允许通过调用 self.save_hyperparameters() 自动保存传递给 init 的所有超参数: class MyLightningModule(LightningModule): def __init__(self, learning_rate, another_parameter, *args, **kwargs): super().__init__() # 直接下面这一行保存所有超参数 self.save_hyperparameters() 超参数...
完全版模板可以在GitHub:https://github.com/miracleyoo/pytorch-lightning-template找到。 04 Lightning Module 简介 主页:https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html 三个核心组件: 模型 优化器 Train/Val/Test步骤 数据流伪代码: outs...
defconfigure_optimizers(self):opt=Adam(self.parameters(),lr=1e-3)returnopt # multiple optimizercase(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,disriminator_opt #...
在data_interface中建立一个class DInterface(pl.LightningDataModule):用作所有数据集文件的接口。__init__()函数中import相应Dataset类,setup()进行实例化,并老老实实加入所需要的的train_dataloader, val_dataloader, ...
params = [p for p in model.parameters() if p.requires_grad] optimizer = torch.optim.SGD(params, lr=0.005, momentum=0.9, weight_decay=0.0005) lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1) num_epochs = 8 ...
LightningModule其实是一个torch.nn.Module,但增加了一些功能:net = Net.load_from_checkpoint(PATH) net.freeze() out = net(x)示例:利用Lightning 构建网络训练网络1. 构建模型import lightning.pytorch as pl import torch.nn as nn import torch.nn.functional as F class LitModel(pl.LightningModule): ...
class LitModel(LightningModule): def __init__(self, in_dim, out_dim): super().__init__() self.save_hyperparameters() self.l1 = nn.Linear(self.hparams.in_dim, self.hparams.out_dim) # if you train and save the model like this it will use these values when loading# the weights....
The params argument in TracerPythonScript.run no longer prepends -- automatically to parameters (#15518) Only check versions / env when not in the cloud (#15504) Periodically sync database to the drive (#15441) Slightly safer multi node (#15538) Reuse existing commands when running connect ...
# Train and check accuracy after each epoch for nepoch in range(8): train_one_epoch(qat_model, criterion, optimizer, data_loader, torch.device('cpu'), num_train_batches) if nepoch > 3: # Freeze quantizer parameters qat_model.apply(torch.ao.quantization.disable_observer) if nepoch > 2...