1.6 KiB
1.6 KiB
Pytorch-lightning
Seed for ML research
Usage
Add new model
- Create a new model under /models.
- Add model name to trainer_main
AVAILABLE_MODELS = {
'model_1': ExampleModel1
}
Model methods that can be implemented
| Method | Purpose | Input | Output | Required |
|---|---|---|---|---|
| forward() | Forward pass | model_in tuple with your data | model_out tuple to be passed to loss | Y |
| loss() | calculate model loss | model_out tuple from forward() | A scalar | Y |
| check_performance() | run a full loop through val data to check for metrics | dataloader, nb_tests | metrics tuple to be tracked | Y |
| tng_dataloader | Computed option, used to feed tng data | - | Pytorch DataLoader subclass | Y |
| val_dataloader | Computed option, used to feed tng data | - | Pytorch DataLoader subclass | Y |
| test_dataloader | Computed option, used to feed tng data | - | Pytorch DataLoader subclass | Y |
Model lifecycle hooks
Use these hooks to customize functionality
| Method | Purpose | Input | Output | Required |
|---|---|---|---|---|
| on_batch_start() | called right before the batch starts | - | - | N |
| on_batch_end() | called right after the batch ends | - | - | N |
| on_epoch_start() | called right before the epoch starts | - | - | N |
| on_epoch_end() | called right afger the epoch ends | - | - | N |
| on_pre_performance_check() | called right before the performance check starts | - | - | N |
| on_post_performance_check() | called right after the batch starts | - | - | N |