simlearner3d.models
Model
- class simlearner3d.models.generic_model.Model(**kwargs)[source]
Model training, validation, test.
- Read the Pytorch Lightning docs:
https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html
- configure_optimizers()[source]
Choose what optimizers and learning-rate schedulers to use in your optimization.
- Returns
An optimizer, or a config of a scheduler and an optimizer.
- test_step(batch, batch_idx: int)[source]
Operates on a single batch of data from the test set. In this step you’d normally generate examples or calculate anything of interest such as accuracy.
- Parameters
- Returns
Tensor- The loss tensordict- A dictionary. Can include any keys, but must include the key'loss'.None- Skip to the next batch.
# if you have one test dataloader: def test_step(self, batch, batch_idx): ... # if you have multiple test dataloaders: def test_step(self, batch, batch_idx, dataloader_idx=0): ...
Examples:
# CASE 1: A single test dataset def test_step(self, batch, batch_idx): x, y = batch # implement your own out = self(x) loss = self.loss(out, y) # log 6 example images # or generated text... or whatever sample_imgs = x[:6] grid = torchvision.utils.make_grid(sample_imgs) self.logger.experiment.add_image('example_images', grid, 0) # calculate acc labels_hat = torch.argmax(out, dim=1) test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0) # log the outputs! self.log_dict({'test_loss': loss, 'test_acc': test_acc})
If you pass in multiple test dataloaders,
test_step()will have an additional argument. We recommend setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.# CASE 2: multiple test dataloaders def test_step(self, batch, batch_idx, dataloader_idx=0): # dataloader_idx tells you which dataset this is. ...
Note
If you don’t need to test you don’t need to implement this method.
Note
When the
test_step()is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of the test epoch, the model goes back to training mode and gradients are enabled.
- training_step(batch, batch_idx: int)[source]
Here you compute and return the training loss and some additional metrics for e.g. the progress bar or logger.
- Parameters
- Returns
Tensor- The loss tensordict- A dictionary which can include any keys, but must include the key'loss'in the case of automatic optimization.None- In automatic optimization, this will skip to the next batch (but is not supported for multi-GPU, TPU, or DeepSpeed). For manual optimization, this has no special meaning, as returning the loss is not required.
In this step you’d normally do the forward pass and calculate the loss for a batch. You can also do fancier things like multiple forward passes or something model specific.
Example:
def training_step(self, batch, batch_idx): x, y, z = batch out = self.encoder(x) loss = self.loss(out, x) return loss
To use multiple optimizers, you can switch to ‘manual optimization’ and control their stepping:
def __init__(self): super().__init__() self.automatic_optimization = False # Multiple optimizers (e.g.: GANs) def training_step(self, batch, batch_idx): opt1, opt2 = self.optimizers() # do training_step with encoder ... opt1.step() # do training_step with decoder ... opt2.step()
Note
When
accumulate_grad_batches> 1, the loss returned here will be automatically normalized byaccumulate_grad_batchesinternally.
- validation_step(batch, batch_idx: int)[source]
Operates on a single batch of data from the validation set. In this step you’d might generate examples or calculate anything of interest like accuracy.
- Parameters
- Returns
Tensor- The loss tensordict- A dictionary. Can include any keys, but must include the key'loss'.None- Skip to the next batch.
# if you have one val dataloader: def validation_step(self, batch, batch_idx): ... # if you have multiple val dataloaders: def validation_step(self, batch, batch_idx, dataloader_idx=0): ...
Examples:
# CASE 1: A single validation dataset def validation_step(self, batch, batch_idx): x, y = batch # implement your own out = self(x) loss = self.loss(out, y) # log 6 example images # or generated text... or whatever sample_imgs = x[:6] grid = torchvision.utils.make_grid(sample_imgs) self.logger.experiment.add_image('example_images', grid, 0) # calculate acc labels_hat = torch.argmax(out, dim=1) val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0) # log the outputs! self.log_dict({'val_loss': loss, 'val_acc': val_acc})
If you pass in multiple val dataloaders,
validation_step()will have an additional argument. We recommend setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.# CASE 2: multiple validation dataloaders def validation_step(self, batch, batch_idx, dataloader_idx=0): # dataloader_idx tells you which dataset this is. ...
Note
If you don’t need to validate you don’t need to implement this method.
Note
When the
validation_step()is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of validation, the model goes back to training mode and gradients are enabled.