pytorch-lightning/pytorch_lightning/core/hooks.py

"""
Model Hooks
===========

There are cases when you might want to do something different at different parts of the training/validation loop.
 To enable a hook, simply override the method in your LightningModule and the trainer will call it at the correct time.

**Contributing** If there's a hook you'd like to add, simply:

1. Fork PyTorchLightning.

2. Add the hook :py:mod:`pytorch_lightning.base_module.hooks.py`.

3. Add the correct place in the :py:mod:`pytorch_lightning.models.trainer` where it should be called.

"""
from typing import Any

import torch
from torch import Tensor
from torch.optim.optimizer import Optimizer

try:
    from apex import amp
except ImportError:
    APEX_AVAILABLE = False
else:
    APEX_AVAILABLE = True


class ModelHooks(torch.nn.Module):

    def on_sanity_check_start(self):
        """
        Called before starting evaluate
        .. warning:: will be deprecated.
        :return:
        """

    def on_train_start(self) -> None:
        """Called at the beginning of training before sanity check
        """
        # do something at the start of training

    def on_train_end(self) -> None:
        """
        Called at the end of training before logger experiment is closed
        """
        # do something at the end of training

    def on_batch_start(self, batch: Any) -> None:
        """Called in the training loop before anything happens for that batch.

        :param batch:
        """
        # do something when the batch starts

    def on_batch_end(self) -> None:
        """Called in the training loop after the batch."""
        # do something when the batch ends

    def on_epoch_start(self) -> None:
        """Called in the training loop at the very beginning of the epoch."""
        # do something when the epoch starts

    def on_epoch_end(self) -> None:
        """Called in the training loop at the very end of the epoch."""
        # do something when the epoch ends

    def on_pre_performance_check(self) -> None:
        """Called at the very beginning of the validation loop."""
        # do something before validation starts

    def on_post_performance_check(self) -> None:
        """Called at the very end of the validation loop."""
        # do something before validation end

    def on_before_zero_grad(self, optimizer: Optimizer) -> None:
        """Called after optimizer.step() and before optimizer.zero_grad()

        Called in the training loop after taking an optimizer step and before zeroing grads.
        Good place to inspect weight information with weights updated.

        for optimizer in optimizers::

            optimizer.step()
            model.on_before_zero_grad(optimizer) # < ---- called here
            optimizer.zero_grad

        :param optimizer: The optimizer for which grads should be zeroed.
        """
        # do something with the optimizer or inspect it.

    def on_after_backward(self) -> None:
        """Called in the training loop after loss.backward() and before optimizers do anything.

        This is the ideal place to inspect or log gradient information

        .. code-block:: python

            def on_after_backward(self):
                # example to inspect gradient information in tensorboard
                if self.trainer.global_step % 25 == 0:  # don't make the tf file huge
                    params = self.state_dict()
                    for k, v in params.items():
                        grads = v
                        name = k
                        self.logger.experiment.add_histogram(tag=name, values=grads,
                                                             global_step=self.trainer.global_step)

        """

    def backward(self, trainer, loss: Tensor, optimizer: Optimizer, optimizer_idx: int) -> None:
        """Override backward with your own implementation if you need to

        :param trainer: Pointer to the trainer
        :param loss: Loss is already scaled by accumulated grads
        :param optimizer: Current optimizer being used
        :param optimizer_idx: Index of the current optimizer being used

        Called to perform backward step.
        Feel free to override as needed.

        The loss passed in has already been scaled for accumulated gradients if requested.

        .. code-block:: python

            def backward(self, use_amp, loss, optimizer):
                if use_amp:
                    with amp.scale_loss(loss, optimizer) as scaled_loss:
                        scaled_loss.backward()
                else:
                    loss.backward()

        """
        if trainer.precision == 16:

            # .backward is not special on 16-bit with TPUs
            if not trainer.on_tpu:
                with amp.scale_loss(loss, optimizer) as scaled_loss:
                    scaled_loss.backward()
        else:
            loss.backward()