mirror of
https://github.com/wassname/pytorch-lightning.git
synced 2026-09-22 13:30:11 +08:00
Jirka Borovec
d71556e7a1
* upgrade req. * move MkDocs * create Sphinx * init Sphinx * move md from MkDocs to Sphinx * CI: build docs * build Sphinx formatting move docs from MD to docstring in particular package/modules formatting add Sphinx ext. rename root_module to core drop implicit name "_logger" drop duplicate name "overwrite" fix imports use pytorch theme add sample link mapping try fix RTD build use forked template fix some docs warnings fix paths add deprecation warnings fix flake8 fix paths revert refactor revert MLFlowLogger * revert example import * update link * Update lightning_module_template.py
138 lines
4.1 KiB
Python
138 lines
4.1 KiB
Python
"""
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# Hooks
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There are cases when you might want to do something different at different parts of the training/validation loop.
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To enable a hook, simply override the method in your LightningModule and the trainer will call it at the correct time.
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**Contributing** If there's a hook you'd like to add, simply:
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1. Fork PyTorchLightning.
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2. Add the hook :py:mod:`pytorch_lightning.base_module.hooks.py`.
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3. Add the correct place in the :py:mod:`pytorch_lightning.models.trainer` where it should be called.
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"""
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import torch
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try:
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from apex import amp
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APEX_AVAILABLE = True
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except ImportError:
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APEX_AVAILABLE = False
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class ModelHooks(torch.nn.Module):
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def on_sanity_check_start(self):
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"""
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Called before starting evaluate
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:return:
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"""
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pass
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def on_batch_start(self, batch):
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"""Called in the training loop before anything happens for that batch.
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:param batch:
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:return:
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"""
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# do something when the batch starts
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pass
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def on_batch_end(self):
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"""Called in the training loop after the batch."""
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# do something when the batch ends
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pass
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def on_epoch_start(self):
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"""Called in the training loop at the very beginning of the epoch."""
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# do something when the epoch starts
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pass
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def on_epoch_end(self):
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"""Called in the training loop at the very end of the epoch."""
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# do something when the epoch ends
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pass
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def on_pre_performance_check(self):
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"""Called at the very beginning of the validation loop."""
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# do something before validation starts
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pass
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def on_post_performance_check(self):
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"""Called at the very end of the validation loop."""
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# do something before validation end
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pass
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def on_before_zero_grad(self, optimizer):
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"""Called after optimizer.step() and before optimizer.zero_grad()
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Called in the training loop after taking an optimizer step and before zeroing grads.
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Good place to inspect weight information with weights updated.
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for optimizer in optimizers::
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optimizer.step()
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model.on_before_zero_grad(optimizer) # < ---- called here
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optimizer.zero_grad
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:param optimizer:
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:return:
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"""
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# do something with the optimizer or inspect it.
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pass
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def on_after_backward(self):
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"""Called after loss.backward() and before optimizers do anything.
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:return:
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Called in the training loop after model.backward()
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This is the ideal place to inspect or log gradient information
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.. code-block:: python
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def on_after_backward(self):
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# example to inspect gradient information in tensorboard
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if self.trainer.global_step % 25 == 0: # don't make the tf file huge
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params = self.state_dict()
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for k, v in params.items():
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grads = v
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name = k
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self.logger.experiment.add_histogram(tag=name, values=grads,
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global_step=self.trainer.global_step)
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"""
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pass
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def backward(self, use_amp, loss, optimizer):
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"""Override backward with your own implementation if you need to
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:param use_amp: Whether amp was requested or not
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:param loss: Loss is already scaled by accumulated grads
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:param optimizer: Current optimizer being used
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:return:
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Called to perform backward step.
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Feel free to override as needed.
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The loss passed in has already been scaled for accumulated gradients if requested.
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.. code-block:: python
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def backward(self, use_amp, loss, optimizer):
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if use_amp:
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with amp.scale_loss(loss, optimizer) as scaled_loss:
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scaled_loss.backward()
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else:
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loss.backward()
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"""
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if use_amp:
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with amp.scale_loss(loss, optimizer) as scaled_loss:
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scaled_loss.backward()
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else:
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loss.backward()
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