Lightning-AI / pytorch-lightning Public Notifications Fork 3.4k Star 28.6k New issue Jump to bottom CombinedLoader: NoneType object is not iterable #16912 Closed awaelchli opened this issue Mar 1, 2023· 4 comments · Fixed by #17007 Closed CombinedLoader: NoneType object is not ...
from lightning.fabric.utilities import LightningEnum # noqa: F401 from lightning.fabric.utilities import move_data_to_device # noqa: F401 from lightning.fabric.utilities import suggested_max_num_workers # noqa: F401 from lightning.pytorch.utilities.combined_loader import CombinedLoader # noqa: F401 ...
self.val_iter = iter(self.val_loader) 1. 2. 3. 4. 5. 6. 7. 如何修改?scv3可适当参考,另外发现一篇博客讲解如何使用LightningDataModule的,LightningDataModule的使用scv3首先进行初始化并保存了超参数,通过get_training_size获得training_size,指定图像的尺寸,并获取伪真值(为了设置伪真值自定义了一个训练...
# 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...
train_loader = dataset.get_train_loader() val_loader = dataset.get_val_loader() 在每个训练周期结束时重置数据加载器: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 dataset.reset() 或者,可以在模型验证之前在 GPU 上重新创建验证管道: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 dataset...
Added state_dict and load_state_dict utilities for CombinedLoader + utilities for dataloader (#8364) Added rank_zero_only to LightningModule.log function (#7966) Added metric_attribute to LightningModule.log function (#7966) Added a warning if Trainer(log_every_n_steps) is a value too high...
trainer.test(model, test_loader) ``` 上述代码中,首先定义了一个继承自`pl.LightningModule`的模型类`MyModel`,其中实现了模型的结构、前向传播逻辑、训练、验证和测试步骤逻辑以及优化器的配置方法。然后,创建了训练、验证和测试数据加载器。接下来,创建了一个`pl.Trainer`对象,用于配置训练器的参数,如使用的...
第(2)步由前一节中使用的create_combined_model函数执行。 第(3)步通过使用torch.quantization.prepare_qat来实现,该函数插入了伪量化模块。 作为第(4)步,您可以开始“微调”模型,然后将其转换为完全量化的版本(第 5 步)。 要将微调后的模型转换为量化模型,您可以调用torch.quantization.convert函数(在我们的情况...
The model uses PyTorch Lightning implementation of distributed data parallelism at the module level which can run across multiple machines. Mixed precision training Mixed precision is the combined use of different numerical precisions in a computational method. Mixed precision training offers significant co...
combined=F.relu(self.fc_combined1(combined))combined=self.fc_combined2(combined)returncombined# ...