torchsig.models.iq_models.xcit.xcit1d.ClassifierMetrics¶

class torchsig.models.iq_models.xcit.xcit1d.ClassifierMetrics[source]¶

Bases: Callback

Methods

`load_state_dict`	Called when loading a checkpoint, implement to reload callback state given callback's `state_dict`.
`on_after_backward`	Called after `loss.backward()` and before optimizers are stepped.
`on_before_backward`	Called before `loss.backward()`.
`on_before_optimizer_step`	Called before `optimizer.step()`.
`on_before_zero_grad`	Called before `optimizer.zero_grad()`.
`on_exception`	Called when any trainer execution is interrupted by an exception.
`on_fit_end`	Called when fit ends.
`on_fit_start`	Called when fit begins.
`on_load_checkpoint`	Called when loading a model checkpoint, use to reload state.
`on_predict_batch_end`	Called when the predict batch ends.
`on_predict_batch_start`	Called when the predict batch begins.
`on_predict_end`	Called when predict ends.
`on_predict_epoch_end`	Called when the predict epoch ends.
`on_predict_epoch_start`	Called when the predict epoch begins.
`on_predict_start`	Called when the predict begins.
`on_sanity_check_end`	Called when the validation sanity check ends.
`on_sanity_check_start`	Called when the validation sanity check starts.
`on_save_checkpoint`	Called when saving a checkpoint to give you a chance to store anything else you might want to save.
`on_test_batch_end`	Called when the test batch ends.
`on_test_batch_start`	Called when the test batch begins.
`on_test_end`	Called when the test ends.
`on_test_epoch_end`	Called when the test epoch ends.
`on_test_epoch_start`	Called when the test epoch begins.
`on_test_start`	Called when the test begins.
`on_train_batch_end`	Called when the train batch ends.
`on_train_batch_start`	Called when the train batch begins.
`on_train_end`	Called when the train ends.
`on_train_epoch_end`	Called when the train epoch ends.
`on_train_epoch_start`	Called when the train epoch begins.
`on_train_start`	Called when the train begins.
`on_validation_batch_end`	Called when the validation batch ends.
`on_validation_batch_start`	Called when the validation batch begins.
`on_validation_end`	Called when the validation loop ends.
`on_validation_epoch_end`	Called when the val epoch ends.
`on_validation_epoch_start`	Called when the val epoch begins.
`on_validation_start`	Called when the validation loop begins.
`setup`	Called when fit, validate, test, predict, or tune begins.
`state_dict`	Called when saving a checkpoint, implement to generate callback's `state_dict`.
`teardown`	Called when fit, validate, test, predict, or tune ends.

Attributes

state_key

Identifier for the state of the callback.

__init__()[source]¶

on_train_epoch_end(trainer, pl_module)[source]¶

Called when the train epoch ends.

To access all batch outputs at the end of the epoch, you can cache step outputs as an attribute of the pytorch_lightning.core.LightningModule and access them in this hook:

class MyLightningModule(L.LightningModule):
    def __init__(self):
        super().__init__()
        self.training_step_outputs = []

    def training_step(self):
        loss = ...
        self.training_step_outputs.append(loss)
        return loss


class MyCallback(L.Callback):
    def on_train_epoch_end(self, trainer, pl_module):
        # do something with all training_step outputs, for example:
        epoch_mean = torch.stack(pl_module.training_step_outputs).mean()
        pl_module.log("training_epoch_mean", epoch_mean)
        # free up the memory
        pl_module.training_step_outputs.clear()

on_validation_epoch_end(trainer, pl_module)[source]¶: Called when the val epoch ends.

load_state_dict(state_dict: dict[str, Any]) → None¶

Called when loading a checkpoint, implement to reload callback state given callback’s state_dict.

Parameters:: state_dict – the callback state returned by state_dict.

on_after_backward(trainer: Trainer, pl_module: LightningModule) → None¶: Called after loss.backward() and before optimizers are stepped.

on_before_backward(trainer: Trainer, pl_module: LightningModule, loss: Tensor) → None¶: Called before loss.backward().

on_before_optimizer_step(trainer: Trainer, pl_module: LightningModule, optimizer: Optimizer) → None¶: Called before optimizer.step().

on_before_zero_grad(trainer: Trainer, pl_module: LightningModule, optimizer: Optimizer) → None¶: Called before optimizer.zero_grad().

on_exception(trainer: Trainer, pl_module: LightningModule, exception: BaseException) → None¶: Called when any trainer execution is interrupted by an exception.

on_fit_end(trainer: Trainer, pl_module: LightningModule) → None¶: Called when fit ends.

on_fit_start(trainer: Trainer, pl_module: LightningModule) → None¶: Called when fit begins.

on_load_checkpoint(trainer: Trainer, pl_module: LightningModule, checkpoint: dict[str, Any]) → None¶

Called when loading a model checkpoint, use to reload state.

Parameters:

trainer – the current Trainer instance.
pl_module – the current LightningModule instance.
checkpoint – the full checkpoint dictionary that got loaded by the Trainer.

on_predict_batch_end(trainer: Trainer, pl_module: LightningModule, outputs: Any, batch: Any, batch_idx: int, dataloader_idx: int = 0) → None¶: Called when the predict batch ends.

on_predict_batch_start(trainer: Trainer, pl_module: LightningModule, batch: Any, batch_idx: int, dataloader_idx: int = 0) → None¶: Called when the predict batch begins.

on_predict_end(trainer: Trainer, pl_module: LightningModule) → None¶: Called when predict ends.

on_predict_epoch_end(trainer: Trainer, pl_module: LightningModule) → None¶: Called when the predict epoch ends.

on_predict_epoch_start(trainer: Trainer, pl_module: LightningModule) → None¶: Called when the predict epoch begins.

on_predict_start(trainer: Trainer, pl_module: LightningModule) → None¶: Called when the predict begins.

on_sanity_check_end(trainer: Trainer, pl_module: LightningModule) → None¶: Called when the validation sanity check ends.

on_sanity_check_start(trainer: Trainer, pl_module: LightningModule) → None¶: Called when the validation sanity check starts.

on_save_checkpoint(trainer: Trainer, pl_module: LightningModule, checkpoint: dict[str, Any]) → None¶

Called when saving a checkpoint to give you a chance to store anything else you might want to save.

Parameters:

trainer – the current Trainer instance.
pl_module – the current LightningModule instance.
checkpoint – the checkpoint dictionary that will be saved.

on_test_batch_end(trainer: Trainer, pl_module: LightningModule, outputs: Tensor | Mapping[str, Any] | None, batch: Any, batch_idx: int, dataloader_idx: int = 0) → None¶: Called when the test batch ends.

on_test_batch_start(trainer: Trainer, pl_module: LightningModule, batch: Any, batch_idx: int, dataloader_idx: int = 0) → None¶: Called when the test batch begins.

on_test_end(trainer: Trainer, pl_module: LightningModule) → None¶: Called when the test ends.

on_test_epoch_end(trainer: Trainer, pl_module: LightningModule) → None¶: Called when the test epoch ends.

on_test_epoch_start(trainer: Trainer, pl_module: LightningModule) → None¶: Called when the test epoch begins.

on_test_start(trainer: Trainer, pl_module: LightningModule) → None¶: Called when the test begins.

on_train_batch_end(trainer: Trainer, pl_module: LightningModule, outputs: Tensor | Mapping[str, Any] | None, batch: Any, batch_idx: int) → None¶: Called when the train batch ends.

Note

The value outputs["loss"] here will be the normalized value w.r.t accumulate_grad_batches of the loss returned from training_step.

on_train_batch_start(trainer: Trainer, pl_module: LightningModule, batch: Any, batch_idx: int) → None¶: Called when the train batch begins.

on_train_end(trainer: Trainer, pl_module: LightningModule) → None¶: Called when the train ends.

on_train_epoch_start(trainer: Trainer, pl_module: LightningModule) → None¶: Called when the train epoch begins.

on_train_start(trainer: Trainer, pl_module: LightningModule) → None¶: Called when the train begins.

on_validation_batch_end(trainer: Trainer, pl_module: LightningModule, outputs: Tensor | Mapping[str, Any] | None, batch: Any, batch_idx: int, dataloader_idx: int = 0) → None¶: Called when the validation batch ends.

on_validation_batch_start(trainer: Trainer, pl_module: LightningModule, batch: Any, batch_idx: int, dataloader_idx: int = 0) → None¶: Called when the validation batch begins.

on_validation_end(trainer: Trainer, pl_module: LightningModule) → None¶: Called when the validation loop ends.

on_validation_epoch_start(trainer: Trainer, pl_module: LightningModule) → None¶: Called when the val epoch begins.

on_validation_start(trainer: Trainer, pl_module: LightningModule) → None¶: Called when the validation loop begins.

setup(trainer: Trainer, pl_module: LightningModule, stage: str) → None¶: Called when fit, validate, test, predict, or tune begins.

state_dict() → dict[str, Any]¶

Called when saving a checkpoint, implement to generate callback’s state_dict.

Returns:: A dictionary containing callback state.

property state_key: str¶

Identifier for the state of the callback.

Used to store and retrieve a callback’s state from the checkpoint dictionary by checkpoint["callbacks"][state_key]. Implementations of a callback need to provide a unique state key if 1) the callback has state and 2) it is desired to maintain the state of multiple instances of that callback.

teardown(trainer: Trainer, pl_module: LightningModule, stage: str) → None¶: Called when fit, validate, test, predict, or tune ends.