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lightning/pytorch_lightning/core/lightning.py

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# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""nn.Module with additional great features."""
import collections
import copy
import inspect
import logging
import os
import tempfile
import types
import uuid
from abc import ABC
from argparse import Namespace
from functools import partial
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import torch
from torch import ScriptModule, Tensor
from torch.nn import Module
from torch.optim.optimizer import Optimizer
from pytorch_lightning.core.grads import GradInformation
from pytorch_lightning.core.hooks import CheckpointHooks, DataHooks, ModelHooks
from pytorch_lightning.core.memory import ModelSummary
from pytorch_lightning.core.optimizer import LightningOptimizer
from pytorch_lightning.core.saving import ALLOWED_CONFIG_TYPES, ModelIO, PRIMITIVE_TYPES
from pytorch_lightning.core.step_result import Result
from pytorch_lightning.utilities import rank_zero_warn
from pytorch_lightning.utilities.apply_func import apply_to_collection, convert_to_tensors
from pytorch_lightning.utilities.device_dtype_mixin import DeviceDtypeModuleMixin
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.parsing import AttributeDict, collect_init_args, get_init_args
log = logging.getLogger(__name__)
class LightningModule(
ABC,
DeviceDtypeModuleMixin,
GradInformation,
ModelIO,
ModelHooks,
DataHooks,
CheckpointHooks,
Module,
):
# Below is for property support of JIT in PyTorch 1.7
# since none of them is important when using JIT, we are going to ignore them.
__jit_unused_properties__ = [
"datamodule",
"example_input_array",
"hparams",
"hparams_initial",
"on_gpu",
"current_epoch",
"global_step",
"global_rank",
"local_rank",
"logger",
"model_size",
] + DeviceDtypeModuleMixin.__jit_unused_properties__
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# see (https://github.com/pytorch/pytorch/blob/3e6bb5233f9ca2c5aa55d9cda22a7ee85439aa6e/
# torch/nn/modules/module.py#L227)
torch._C._log_api_usage_once(f"lightning.module.{self.__class__.__name__}")
self.exp_save_path = None
self.loaded_optimizer_states_dict = {}
#: Pointer to the trainer object
self.trainer = None
self._distrib_type = None
self._device_type = None
#: True if using amp
self.use_amp = False
#: The precision used
self.precision = 32
# optionally can be set by user
self._example_input_array = None
self._datamodule = None
self._results: Optional[Result] = None
self._current_fx_name = ''
self._running_manual_backward = False
self._current_hook_fx_name = None
self._current_dataloader_idx = None
self._automatic_optimization: bool = True
self._param_requires_grad_state = dict()
def optimizers(self, use_pl_optimizer: bool = True) -> Union[Optimizer, List[Optimizer], List[LightningOptimizer]]:
if use_pl_optimizer:
opts = list(self.trainer.lightning_optimizers.values())
else:
opts = self.trainer.optimizers
# single optimizer
if isinstance(opts, list) and len(opts) == 1 and isinstance(opts[0], Optimizer):
return opts[0]
# multiple opts
return opts
@property
def example_input_array(self) -> Any:
return self._example_input_array
@property
def current_epoch(self) -> int:
"""The current epoch"""
return self.trainer.current_epoch if self.trainer else 0
@property
def global_step(self) -> int:
"""Total training batches seen across all epochs"""
return self.trainer.global_step if self.trainer else 0
@property
def global_rank(self) -> int:
""" The index of the current process across all nodes and devices. """
return self.trainer.global_rank if self.trainer else 0
@property
def local_rank(self) -> int:
""" The index of the current process within a single node. """
return self.trainer.local_rank if self.trainer else 0
@example_input_array.setter
def example_input_array(self, example: Any) -> None:
self._example_input_array = example
@property
def datamodule(self) -> Any:
return self._datamodule
@datamodule.setter
def datamodule(self, datamodule: Any) -> None:
self._datamodule = datamodule
@property
def on_gpu(self):
"""
True if your model is currently running on GPUs.
Useful to set flags around the LightningModule for different CPU vs GPU behavior.
"""
return self.device.type == "cuda"
@property
def automatic_optimization(self) -> bool:
"""
If False you are responsible for calling .backward, .step, zero_grad.
"""
return self._automatic_optimization
@automatic_optimization.setter
def automatic_optimization(self, automatic_optimization: bool) -> None:
self._automatic_optimization = automatic_optimization
@property
def logger(self):
""" Reference to the logger object in the Trainer. """
return self.trainer.logger if self.trainer else None
def _apply_batch_transfer_handler(self, batch: Any, device: Optional[torch.device] = None, dataloader_idx: int = 0):
batch = self.on_before_batch_transfer(batch, dataloader_idx)
batch = self.transfer_batch_to_device(batch, device)
batch = self.on_after_batch_transfer(batch, dataloader_idx)
return batch
def print(self, *args, **kwargs) -> None:
r"""
Prints only from process 0. Use this in any distributed mode to log only once.
Args:
*args: The thing to print. The same as for Python's built-in print function.
**kwargs: The same as for Python's built-in print function.
Example::
def forward(self, x):
self.print(x, 'in forward')
"""
if self.trainer.is_global_zero:
progress_bar = self.trainer.progress_bar_callback
if progress_bar is not None and progress_bar.is_enabled:
progress_bar.print(*args, **kwargs)
else:
print(*args, **kwargs)
def log(
self,
name: str,
value: Any,
prog_bar: bool = False,
logger: bool = True,
on_step: Optional[bool] = None,
on_epoch: Optional[bool] = None,
reduce_fx: Callable = torch.mean,
tbptt_reduce_fx: Callable = torch.mean,
tbptt_pad_token: int = 0,
enable_graph: bool = False,
sync_dist: bool = False,
sync_dist_op: Union[Any, str] = 'mean',
sync_dist_group: Optional[Any] = None,
add_dataloader_idx: bool = True,
):
"""
Log a key, value
Example::
self.log('train_loss', loss)
The default behavior per hook is as follows
.. csv-table:: ``*`` also applies to the test loop
:header: "LightningMoule Hook", "on_step", "on_epoch", "prog_bar", "logger"
:widths: 20, 10, 10, 10, 10
"training_step", "T", "F", "F", "T"
"training_step_end", "T", "F", "F", "T"
"training_epoch_end", "F", "T", "F", "T"
"validation_step*", "F", "T", "F", "T"
"validation_step_end*", "F", "T", "F", "T"
"validation_epoch_end*", "F", "T", "F", "T"
Args:
name: key name
value: value name
prog_bar: if True logs to the progress bar
logger: if True logs to the logger
on_step: if True logs at this step. None auto-logs at the training_step but not validation/test_step
on_epoch: if True logs epoch accumulated metrics. None auto-logs at the val/test step but not training_step
reduce_fx: reduction function over step values for end of epoch. Torch.mean by default
tbptt_reduce_fx: function to reduce on truncated back prop
tbptt_pad_token: token to use for padding
enable_graph: if True, will not auto detach the graph
sync_dist: if True, reduces the metric across GPUs/TPUs
sync_dist_op: the op to sync across GPUs/TPUs
sync_dist_group: the ddp group to sync across
add_dataloader_idx: if True, appends the index of the current dataloader to
the name (when using multiple). If False, user needs to give unique names for
each dataloader to not mix values
"""
if self._results is not None:
# in any epoch end can't log step metrics (only epoch metric)
if 'epoch_end' in self._current_fx_name and on_step:
m = f'on_step=True cannot be used on {self._current_fx_name} method'
raise MisconfigurationException(m)
if 'epoch_end' in self._current_fx_name and on_epoch is False:
m = f'on_epoch cannot be False when called from the {self._current_fx_name} method'
raise MisconfigurationException(m)
# add log_dict
# TODO: if logged twice fail with crash
# set the default depending on the fx_name
on_step = self.__auto_choose_log_on_step(on_step)
on_epoch = self.__auto_choose_log_on_epoch(on_epoch)
if self._current_hook_fx_name is not None:
self.trainer.logger_connector.check_logging_in_callbacks(
self._current_hook_fx_name, on_step=on_step, on_epoch=on_epoch
)
# make sure user doesn't introduce logic for multi-dataloaders
if "/dataloader_idx_" in name:
raise MisconfigurationException(
f"Logged key: {name} should not contain information about dataloader_idx."
)
training_type_plugin = self.trainer.training_type_plugin
# Determine if dataloader index should be added
dataloader_idx = self._current_dataloader_idx if add_dataloader_idx else None
self._results.log(
name,
value,
prog_bar,
logger,
on_step,
on_epoch,
reduce_fx,
tbptt_reduce_fx,
tbptt_pad_token,
enable_graph,
sync_dist,
sync_dist_op,
sync_dist_group,
training_type_plugin.reduce,
dataloader_idx,
self.device,
)
def log_dict(
self,
dictionary: dict,
prog_bar: bool = False,
logger: bool = True,
on_step: Optional[bool] = None,
on_epoch: Optional[bool] = None,
reduce_fx: Callable = torch.mean,
tbptt_reduce_fx: Callable = torch.mean,
tbptt_pad_token: int = 0,
enable_graph: bool = False,
sync_dist: bool = False,
sync_dist_op: Union[Any, str] = 'mean',
sync_dist_group: Optional[Any] = None,
add_dataloader_idx: bool = True,
):
"""
Log a dictonary of values at once
Example::
values = {'loss': loss, 'acc': acc, ..., 'metric_n': metric_n}
self.log_dict(values)
Args:
dictionary: key value pairs (str, tensors)
prog_bar: if True logs to the progress base
logger: if True logs to the logger
on_step: if True logs at this step. None auto-logs for training_step but not validation/test_step
on_epoch: if True logs epoch accumulated metrics. None auto-logs for val/test step but not training_step
reduce_fx: reduction function over step values for end of epoch. Torch.mean by default
tbptt_reduce_fx: function to reduce on truncated back prop
tbptt_pad_token: token to use for padding
enable_graph: if True, will not auto detach the graph
sync_dist: if True, reduces the metric across GPUs/TPUs
sync_dist_op: the op to sync across GPUs/TPUs
sync_dist_group: the ddp group sync across
add_dataloader_idx: if True, appends the index of the current dataloader to
the name (when using multiple). If False, user needs to give unique names for
each dataloader to not mix values
"""
for k, v in dictionary.items():
self.log(
name=k,
value=v,
prog_bar=prog_bar,
logger=logger,
on_step=on_step,
on_epoch=on_epoch,
reduce_fx=reduce_fx,
enable_graph=enable_graph,
sync_dist=sync_dist,
sync_dist_group=sync_dist_group,
sync_dist_op=sync_dist_op,
tbptt_pad_token=tbptt_pad_token,
tbptt_reduce_fx=tbptt_reduce_fx,
add_dataloader_idx=add_dataloader_idx
)
def write_prediction(
self, name: str, value: Union[torch.Tensor, List[torch.Tensor]], filename: str = 'predictions.pt'
):
"""
Write predictions to disk using ``torch.save``
Example::
self.write_prediction('pred', torch.tensor(...), filename='my_predictions.pt')
Args:
name: a string indicating the name to save the predictions under
value: the predictions, either a single :class:`~torch.Tensor` or a list of them
filename: name of the file to save the predictions to
Note:
when running in distributed mode, calling ``write_prediction`` will create a file for
each device with respective names: ``filename_rank_0.pt``, ``filename_rank_1.pt``, ...
"""
self.trainer.evaluation_loop.predictions._add_prediction(name, value, filename)
def write_prediction_dict(self, predictions_dict: Dict[str, Any], filename: str = 'predictions.pt'):
"""
Write a dictonary of predictions to disk at once using ``torch.save``
Example::
pred_dict = {'pred1': torch.tensor(...), 'pred2': torch.tensor(...)}
self.write_prediction_dict(pred_dict)
Args:
predictions_dict: dict containing predictions, where each prediction should
either be single :class:`~torch.Tensor` or a list of them
Note:
when running in distributed mode, calling ``write_prediction_dict`` will create a file for
each device with respective names: ``filename_rank_0.pt``, ``filename_rank_1.pt``, ...
"""
for k, v in predictions_dict.items():
self.write_prediction(k, v, filename)
def __auto_choose_log_on_step(self, on_step):
if on_step is None:
if self._current_fx_name in {'training_step', 'training_step_end'}:
on_step = True
elif self._current_fx_name in {
'evaluation_step', 'evaluation_step_end', 'evaluation_epoch_end', 'training_epoch_end'
}:
on_step = False
else:
on_step = False
return on_step
def __auto_choose_log_on_epoch(self, on_epoch):
if on_epoch is None:
if self._current_fx_name in {'training_step', 'training_step_end'}:
on_epoch = False
elif self._current_fx_name in {
'evaluation_step', 'evaluation_step_end', 'evaluation_epoch_end', 'training_epoch_end'
}:
on_epoch = True
else:
on_epoch = True
return on_epoch
def all_gather(
self,
data: Union[torch.Tensor, Dict, List, Tuple],
group: Optional[Any] = None,
sync_grads: bool = False,
):
r"""
Allows users to call ``self.all_gather()`` from the LightningModule, thus making
the ```all_gather``` operation accelerator agnostic.
```all_gather``` is a function provided by accelerators to gather a tensor from several
distributed processes
Args:
tensor: int, float, tensor of shape (batch, ...), or a (possibly nested) collection thereof.
group: the process group to gather results from. Defaults to all processes (world)
sync_grads: flag that allows users to synchronize gradients for all_gather op
Return:
A tensor of shape (world_size, batch, ...), or if the input was a collection
the output will also be a collection with tensors of this shape.
"""
group = group if group is not None else torch.distributed.group.WORLD
all_gather = self.trainer.accelerator.all_gather
data = convert_to_tensors(data, device=self.device)
all_gather = partial(all_gather, group=group, sync_grads=sync_grads)
return apply_to_collection(data, torch.Tensor, all_gather)
def forward(self, *args, **kwargs):
r"""
Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to define
the operations you want to use for prediction (i.e.: on a server or as a feature extractor).
Normally you'd call ``self()`` from your :meth:`training_step` method.
This makes it easy to write a complex system for training with the outputs
you'd want in a prediction setting.
You may also find the :func:`~pytorch_lightning.core.decorators.auto_move_data` decorator useful
when using the module outside Lightning in a production setting.
Args:
*args: Whatever you decide to pass into the forward method.
**kwargs: Keyword arguments are also possible.
Return:
Predicted output
Examples::
# example if we were using this model as a feature extractor
def forward(self, x):
feature_maps = self.convnet(x)
return feature_maps
def training_step(self, batch, batch_idx):
x, y = batch
feature_maps = self(x)
logits = self.classifier(feature_maps)
# ...
return loss
# splitting it this way allows model to be used a feature extractor
model = MyModelAbove()
inputs = server.get_request()
results = model(inputs)
server.write_results(results)
# -------------
# This is in stark contrast to torch.nn.Module where normally you would have this:
def forward(self, batch):
x, y = batch
feature_maps = self.convnet(x)
logits = self.classifier(feature_maps)
return logits
"""
return super().forward(*args, **kwargs)
def training_step(self, *args, **kwargs):
r"""
Here you compute and return the training loss and some additional metrics for e.g.
the progress bar or logger.
Args:
batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):
The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.
batch_idx (int): Integer displaying index of this batch
optimizer_idx (int): When using multiple optimizers, this argument will also be present.
hiddens(:class:`~torch.Tensor`): Passed in if
:paramref:`~pytorch_lightning.trainer.trainer.Trainer.truncated_bptt_steps` > 0.
Return:
Any of.
- :class:`~torch.Tensor` - The loss tensor
- ``dict`` - A dictionary. Can include any keys, but must include the key ``'loss'``
- ``None`` - Training will skip to the next batch
Note:
Returning ``None`` is currently not supported for multi-GPU or TPU, or with 16-bit precision enabled.
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
If you define multiple optimizers, this step will be called with an additional
``optimizer_idx`` parameter.
.. code-block:: python
# Multiple optimizers (e.g.: GANs)
def training_step(self, batch, batch_idx, optimizer_idx):
if optimizer_idx == 0:
# do training_step with encoder
if optimizer_idx == 1:
# do training_step with decoder
If you add truncated back propagation through time you will also get an additional
argument with the hidden states of the previous step.
.. code-block:: python
# Truncated back-propagation through time
def training_step(self, batch, batch_idx, hiddens):
# hiddens are the hidden states from the previous truncated backprop step
...
out, hiddens = self.lstm(data, hiddens)
...
return {'loss': loss, 'hiddens': hiddens}
Note:
The loss value shown in the progress bar is smoothed (averaged) over the last values,
so it differs from the actual loss returned in train/validation step.
"""
rank_zero_warn("`training_step` must be implemented to be used with the Lightning Trainer")
def training_step_end(self, *args, **kwargs):
"""
Use this when training with dp or ddp2 because :meth:`training_step`
will operate on only part of the batch. However, this is still optional
and only needed for things like softmax or NCE loss.
Note:
If you later switch to ddp or some other mode, this will still be called
so that you don't have to change your code
.. code-block:: python
# pseudocode
sub_batches = split_batches_for_dp(batch)
batch_parts_outputs = [training_step(sub_batch) for sub_batch in sub_batches]
training_step_end(batch_parts_outputs)
Args:
batch_parts_outputs: What you return in `training_step` for each batch part.
Return:
Anything
When using dp/ddp2 distributed backends, only a portion of the batch is inside the training_step:
.. code-block:: python
def training_step(self, batch, batch_idx):
# batch is 1/num_gpus big
x, y = batch
out = self(x)
# softmax uses only a portion of the batch in the denomintaor
loss = self.softmax(out)
loss = nce_loss(loss)
return loss
If you wish to do something with all the parts of the batch, then use this method to do it:
.. code-block:: python
def training_step(self, batch, batch_idx):
# batch is 1/num_gpus big
x, y = batch
out = self.encoder(x)
return {'pred': out}
def training_step_end(self, training_step_outputs):
gpu_0_pred = training_step_outputs[0]['pred']
gpu_1_pred = training_step_outputs[1]['pred']
gpu_n_pred = training_step_outputs[n]['pred']
# this softmax now uses the full batch
loss = nce_loss([gpu_0_pred, gpu_1_pred, gpu_n_pred])
return loss
See Also:
See the :ref:`advanced/multi_gpu:Multi-GPU training` guide for more details.
"""
def training_epoch_end(self, outputs: List[Any]) -> None:
"""
Called at the end of the training epoch with the outputs of all training steps.
Use this in case you need to do something with all the outputs for every training_step.
.. code-block:: python
# the pseudocode for these calls
train_outs = []
for train_batch in train_data:
out = training_step(train_batch)
train_outs.append(out)
training_epoch_end(train_outs)
Args:
outputs: List of outputs you defined in :meth:`training_step`, or if there are
multiple dataloaders, a list containing a list of outputs for each dataloader.
Return:
None
Note:
If this method is not overridden, this won't be called.
Example::
def training_epoch_end(self, training_step_outputs):
# do something with all training_step outputs
return result
With multiple dataloaders, ``outputs`` will be a list of lists. The outer list contains
one entry per dataloader, while the inner list contains the individual outputs of
each training step for that dataloader.
.. code-block:: python
def training_epoch_end(self, training_step_outputs):
for out in training_step_outputs:
# do something here
"""
def validation_step(self, *args, **kwargs):
r"""
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.
.. code-block:: python
# the pseudocode for these calls
val_outs = []
for val_batch in val_data:
out = validation_step(val_batch)
val_outs.append(out)
validation_epoch_end(val_outs)
Args:
batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):
The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.
batch_idx (int): The index of this batch
dataloader_idx (int): The index of the dataloader that produced this batch
(only if multiple val dataloaders used)
Return:
Any of.
- Any object or value
- ``None`` - Validation will skip to the next batch
.. code-block:: python
# pseudocode of order
out = validation_step()
if defined('validation_step_end'):
out = validation_step_end(out)
out = validation_epoch_end(out)
.. code-block:: python
# 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)
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, :meth:`validation_step` will have an additional argument.
.. code-block:: python
# CASE 2: multiple validation dataloaders
def validation_step(self, batch, batch_idx, dataloader_idx):
# 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 :meth:`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.
"""
def validation_step_end(self, *args, **kwargs):
"""
Use this when validating with dp or ddp2 because :meth:`validation_step`
will operate on only part of the batch. However, this is still optional
and only needed for things like softmax or NCE loss.
Note:
If you later switch to ddp or some other mode, this will still be called
so that you don't have to change your code.
.. code-block:: python
# pseudocode
sub_batches = split_batches_for_dp(batch)
batch_parts_outputs = [validation_step(sub_batch) for sub_batch in sub_batches]
validation_step_end(batch_parts_outputs)
Args:
batch_parts_outputs: What you return in :meth:`validation_step`
for each batch part.
Return:
None or anything
.. code-block:: python
# WITHOUT validation_step_end
# if used in DP or DDP2, this batch is 1/num_gpus large
def validation_step(self, batch, batch_idx):
# batch is 1/num_gpus big
x, y = batch
out = self.encoder(x)
loss = self.softmax(out)
loss = nce_loss(loss)
self.log('val_loss', loss)
# --------------
# with validation_step_end to do softmax over the full batch
def validation_step(self, batch, batch_idx):
# batch is 1/num_gpus big
x, y = batch
out = self(x)
return out
def validation_step_end(self, val_step_outputs):
for out in val_step_outputs:
# do something with these
See Also:
See the :ref:`advanced/multi_gpu:Multi-GPU training` guide for more details.
"""
def validation_epoch_end(self, outputs: List[Any]) -> None:
"""
Called at the end of the validation epoch with the outputs of all validation steps.
.. code-block:: python
# the pseudocode for these calls
val_outs = []
for val_batch in val_data:
out = validation_step(val_batch)
val_outs.append(out)
validation_epoch_end(val_outs)
Args:
outputs: List of outputs you defined in :meth:`validation_step`, or if there
are multiple dataloaders, a list containing a list of outputs for each dataloader.
Return:
None
Note:
If you didn't define a :meth:`validation_step`, this won't be called.
Examples:
With a single dataloader:
.. code-block:: python
def validation_epoch_end(self, val_step_outputs):
for out in val_step_outputs:
# do something
With multiple dataloaders, `outputs` will be a list of lists. The outer list contains
one entry per dataloader, while the inner list contains the individual outputs of
each validation step for that dataloader.
.. code-block:: python
def validation_epoch_end(self, outputs):
for dataloader_output_result in outputs:
dataloader_outs = dataloader_output_result.dataloader_i_outputs
self.log('final_metric', final_value)
"""
def test_step(self, *args, **kwargs):
r"""
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.
.. code-block:: python
# the pseudocode for these calls
test_outs = []
for test_batch in test_data:
out = test_step(test_batch)
test_outs.append(out)
test_epoch_end(test_outs)
Args:
batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):
The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.
batch_idx (int): The index of this batch.
dataloader_idx (int): The index of the dataloader that produced this batch
(only if multiple test dataloaders used).
Return:
Any of.
- Any object or value
- ``None`` - Testing will skip to the next batch
.. code-block:: python
# 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)
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, :meth:`test_step` will have an additional argument.
.. code-block:: python
# CASE 2: multiple test dataloaders
def test_step(self, batch, batch_idx, dataloader_idx):
# 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 :meth:`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.
"""
def test_step_end(self, *args, **kwargs):
"""
Use this when testing with dp or ddp2 because :meth:`test_step` will operate
on only part of the batch. However, this is still optional
and only needed for things like softmax or NCE loss.
Note:
If you later switch to ddp or some other mode, this will still be called
so that you don't have to change your code.
.. code-block:: python
# pseudocode
sub_batches = split_batches_for_dp(batch)
batch_parts_outputs = [test_step(sub_batch) for sub_batch in sub_batches]
test_step_end(batch_parts_outputs)
Args:
batch_parts_outputs: What you return in :meth:`test_step` for each batch part.
Return:
None or anything
.. code-block:: python
# WITHOUT test_step_end
# if used in DP or DDP2, this batch is 1/num_gpus large
def test_step(self, batch, batch_idx):
# batch is 1/num_gpus big
x, y = batch
out = self(x)
loss = self.softmax(out)
self.log('test_loss', loss)
# --------------
# with test_step_end to do softmax over the full batch
def test_step(self, batch, batch_idx):
# batch is 1/num_gpus big
x, y = batch
out = self.encoder(x)
return out
def test_step_end(self, output_results):
# this out is now the full size of the batch
all_test_step_outs = output_results.out
loss = nce_loss(all_test_step_outs)
self.log('test_loss', loss)
See Also:
See the :ref:`advanced/multi_gpu:Multi-GPU training` guide for more details.
"""
def test_epoch_end(self, outputs: List[Any]) -> None:
"""
Called at the end of a test epoch with the output of all test steps.
.. code-block:: python
# the pseudocode for these calls
test_outs = []
for test_batch in test_data:
out = test_step(test_batch)
test_outs.append(out)
test_epoch_end(test_outs)
Args:
outputs: List of outputs you defined in :meth:`test_step_end`, or if there
are multiple dataloaders, a list containing a list of outputs for each dataloader
Return:
None
Note:
If you didn't define a :meth:`test_step`, this won't be called.
Examples:
With a single dataloader:
.. code-block:: python
def test_epoch_end(self, outputs):
# do something with the outputs of all test batches
all_test_preds = test_step_outputs.predictions
some_result = calc_all_results(all_test_preds)
self.log(some_result)
With multiple dataloaders, `outputs` will be a list of lists. The outer list contains
one entry per dataloader, while the inner list contains the individual outputs of
each test step for that dataloader.
.. code-block:: python
def test_epoch_end(self, outputs):
final_value = 0
for dataloader_outputs in outputs:
for test_step_out in dataloader_outputs:
# do something
final_value += test_step_out
self.log('final_metric', final_value)
"""
def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: Optional[int] = None):
"""
Use this function with trainer.predict(...). Override if you need to add any processing logic.
"""
return self(batch)
def configure_callbacks(self):
"""
Configure model-specific callbacks.
When the model gets attached, e.g., when ``.fit()`` or ``.test()`` gets called,
the list returned here will be merged with the list of callbacks passed to the Trainer's ``callbacks`` argument.
If a callback returned here has the same type as one or several callbacks already present in
the Trainer's callbacks list, it will take priority and replace them.
In addition, Lightning will make sure :class:`~pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint`
callbacks run last.
Return:
A list of callbacks which will extend the list of callbacks in the Trainer.
Example::
def configure_callbacks(self):
early_stop = EarlyStopping(monitor"val_acc", mode="max")
checkpoint = ModelCheckpoint(monitor="val_loss")
return [early_stop, checkpoint]
Note:
Certain callback methods like :meth:`~pytorch_lightning.callbacks.base.Callback.on_init_start`
will never be invoked on the new callbacks returned here.
"""
return []
def configure_optimizers(self):
r"""
Choose what optimizers and learning-rate schedulers to use in your optimization.
Normally you'd need one. But in the case of GANs or similar you might have multiple.
Return:
Any of these 6 options.
- Single optimizer.
- List or Tuple - List of optimizers.
- Two lists - The first list has multiple optimizers, the second a list of LR schedulers (or lr_dict).
- Dictionary, with an 'optimizer' key, and (optionally) a 'lr_scheduler'
key whose value is a single LR scheduler or lr_dict.
- Tuple of dictionaries as described, with an optional 'frequency' key.
- None - Fit will run without any optimizer.
Note:
The 'frequency' value is an int corresponding to the number of sequential batches
optimized with the specific optimizer. It should be given to none or to all of the optimizers.
There is a difference between passing multiple optimizers in a list,
and passing multiple optimizers in dictionaries with a frequency of 1:
In the former case, all optimizers will operate on the given batch in each optimization step.
In the latter, only one optimizer will operate on the given batch at every step.
The lr_dict is a dictionary which contains the scheduler and its associated configuration.
The default configuration is shown below.
.. code-block:: python
{
'scheduler': lr_scheduler, # The LR scheduler instance (required)
'interval': 'epoch', # The unit of the scheduler's step size
'frequency': 1, # The frequency of the scheduler
'reduce_on_plateau': False, # For ReduceLROnPlateau scheduler
'monitor': 'val_loss', # Metric for ReduceLROnPlateau to monitor
'strict': True, # Whether to crash the training if `monitor` is not found
'name': None, # Custom name for LearningRateMonitor to use
}
Only the ``scheduler`` key is required, the rest will be set to the defaults above.
Examples::
# most cases
def configure_optimizers(self):
opt = Adam(self.parameters(), lr=1e-3)
return opt
# multiple optimizer case (e.g.: GAN)
def configure_optimizers(self):
generator_opt = Adam(self.model_gen.parameters(), lr=0.01)
disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02)
return generator_opt, disriminator_opt
# example with learning rate schedulers
def configure_optimizers(self):
generator_opt = Adam(self.model_gen.parameters(), lr=0.01)
disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02)
discriminator_sched = CosineAnnealing(discriminator_opt, T_max=10)
return [generator_opt, disriminator_opt], [discriminator_sched]
# example with step-based learning rate schedulers
def configure_optimizers(self):
gen_opt = Adam(self.model_gen.parameters(), lr=0.01)
dis_opt = Adam(self.model_disc.parameters(), lr=0.02)
gen_sched = {'scheduler': ExponentialLR(gen_opt, 0.99),
'interval': 'step'} # called after each training step
dis_sched = CosineAnnealing(discriminator_opt, T_max=10) # called every epoch
return [gen_opt, dis_opt], [gen_sched, dis_sched]
# example with optimizer frequencies
# see training procedure in `Improved Training of Wasserstein GANs`, Algorithm 1
# https://arxiv.org/abs/1704.00028
def configure_optimizers(self):
gen_opt = Adam(self.model_gen.parameters(), lr=0.01)
dis_opt = Adam(self.model_disc.parameters(), lr=0.02)
n_critic = 5
return (
{'optimizer': dis_opt, 'frequency': n_critic},
{'optimizer': gen_opt, 'frequency': 1}
)
Note:
Some things to know:
- Lightning calls ``.backward()`` and ``.step()`` on each optimizer
and learning rate scheduler as needed.
- If you use 16-bit precision (``precision=16``), Lightning will automatically
handle the optimizers for you.
- If you use multiple optimizers, :meth:`training_step` will have an additional
``optimizer_idx`` parameter.
- If you use LBFGS Lightning handles the closure function automatically for you.
- If you use multiple optimizers, gradients will be calculated only
for the parameters of current optimizer at each training step.
- If you need to control how often those optimizers step or override the
default ``.step()`` schedule, override the :meth:`optimizer_step` hook.
- If you only want to call a learning rate scheduler every ``x`` step or epoch,
or want to monitor a custom metric, you can specify these in a lr_dict:
.. code-block:: python
{
'scheduler': lr_scheduler,
'interval': 'step', # or 'epoch'
'monitor': 'val_f1',
'frequency': x,
}
"""
rank_zero_warn("`configure_optimizers` must be implemented to be used with the Lightning Trainer")
def manual_backward(self, loss: Tensor, optimizer: Optional[Optimizer] = None, *args, **kwargs) -> None:
"""
Call this directly from your training_step when doing optimizations manually.
By using this we can ensure that all the proper scaling when using 16-bit etc has been done for you
This function forwards all args to the .backward() call as well.
.. tip:: In manual mode we still automatically clip grads if Trainer(gradient_clip_val=x) is set
.. tip:: In manual mode we still automatically accumulate grad over batches if
Trainer(accumulate_grad_batches=x) is set and you use `optimizer.step()`
Example::
def training_step(...):
opt_a, opt_b = self.optimizers()
loss = ...
# automatically applies scaling, etc...
self.manual_backward(loss)
opt_a.step()
"""
if optimizer is not None:
rank_zero_warn(
"`optimizer` argument to `manual_backward` is deprecated in v1.2 and will be removed in v1.4",
DeprecationWarning
)
# make sure we're using manual opt
self._verify_is_manual_optimization('manual_backward')
# backward
self._running_manual_backward = True
self.trainer.train_loop.backward(loss, optimizer=None, opt_idx=None, *args, **kwargs)
self._running_manual_backward = False
def backward(self, loss: Tensor, optimizer: Optimizer, optimizer_idx: int, *args, **kwargs) -> None:
"""
Override backward with your own implementation if you need to.
Args:
loss: Loss is already scaled by accumulated grads
optimizer: Current optimizer being used
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.
Example::
def backward(self, loss, optimizer, optimizer_idx):
loss.backward()
"""
if self.trainer.train_loop.automatic_optimization or self._running_manual_backward:
loss.backward(*args, **kwargs)
def toggle_optimizer(self, optimizer: Optimizer, optimizer_idx: int):
"""
Makes sure only the gradients of the current optimizer's parameters are calculated
in the training step to prevent dangling gradients in multiple-optimizer setup.
.. note:: Only called when using multiple optimizers
Override for your own behavior
It works with ``untoggle_optimizer`` to make sure param_requires_grad_state is properly reset.
Args:
optimizer: Current optimizer used in training_loop
optimizer_idx: Current optimizer idx in training_loop
"""
# Iterate over all optimizer parameters to preserve their `requires_grad` information
# in case these are pre-defined during `configure_optimizers`
param_requires_grad_state = {}
for opt in self.optimizers(use_pl_optimizer=False):
for group in opt.param_groups:
for param in group['params']:
# If a param already appear in param_requires_grad_state, continue
if param in param_requires_grad_state:
continue
param_requires_grad_state[param] = param.requires_grad
param.requires_grad = False
# Then iterate over the current optimizer's parameters and set its `requires_grad`
# properties accordingly
for group in optimizer.param_groups:
for param in group['params']:
param.requires_grad = param_requires_grad_state[param]
self._param_requires_grad_state = param_requires_grad_state
def untoggle_optimizer(self, optimizer_idx: int):
"""
.. note:: Only called when using multiple optimizers
Override for your own behavior
Args:
optimizer_idx: Current optimizer idx in training_loop
"""
for opt_idx, opt in enumerate(self.optimizers(use_pl_optimizer=False)):
if optimizer_idx != opt_idx:
for group in opt.param_groups:
for param in group['params']:
if param in self._param_requires_grad_state:
param.requires_grad = self._param_requires_grad_state[param]
# save memory
self._param_requires_grad_state = dict()
def optimizer_step(
self,
epoch: int = None,
batch_idx: int = None,
optimizer: Optimizer = None,
optimizer_idx: int = None,
optimizer_closure: Optional[Callable] = None,
on_tpu: bool = None,
using_native_amp: bool = None,
using_lbfgs: bool = None,
) -> None:
r"""
Override this method to adjust the default way the
:class:`~pytorch_lightning.trainer.trainer.Trainer` calls each optimizer.
By default, Lightning calls ``step()`` and ``zero_grad()`` as shown in the example
once per optimizer.
Warning:
If you are overriding this method, make sure that you pass the ``optimizer_closure`` parameter
to ``optimizer.step()`` function as shown in the examples. This ensures that
``train_step_and_backward_closure`` is called within
:meth:`~pytorch_lightning.trainer.training_loop.TrainLoop.run_training_batch`.
Args:
epoch: Current epoch
batch_idx: Index of current batch
optimizer: A PyTorch optimizer
optimizer_idx: If you used multiple optimizers this indexes into that list.
optimizer_closure: closure for all optimizers
on_tpu: true if TPU backward is required
using_native_amp: True if using native amp
using_lbfgs: True if the matching optimizer is lbfgs
Examples::
# DEFAULT
def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx,
optimizer_closure, on_tpu, using_native_amp, using_lbfgs):
optimizer.step(closure=optimizer_closure)
# Alternating schedule for optimizer steps (i.e.: GANs)
def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx,
optimizer_closure, on_tpu, using_native_amp, using_lbfgs):
# update generator opt every 2 steps
if optimizer_idx == 0:
if batch_idx % 2 == 0 :
optimizer.step(closure=optimizer_closure)
optimizer.zero_grad()
# update discriminator opt every 4 steps
if optimizer_idx == 1:
if batch_idx % 4 == 0 :
optimizer.step(closure=optimizer_closure)
optimizer.zero_grad()
# ...
# add as many optimizers as you want
Here's another example showing how to use this for more advanced things such as
learning rate warm-up:
.. code-block:: python
# learning rate warm-up
def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx,
optimizer_closure, on_tpu, using_native_amp, using_lbfgs):
# warm up lr
if self.trainer.global_step < 500:
lr_scale = min(1., float(self.trainer.global_step + 1) / 500.)
for pg in optimizer.param_groups:
pg['lr'] = lr_scale * self.learning_rate
# update params
optimizer.step(closure=optimizer_closure)
optimizer.zero_grad()
"""
if not isinstance(optimizer, LightningOptimizer):
# wraps into LightingOptimizer only for running step
optimizer = LightningOptimizer._to_lightning_optimizer(optimizer, self.trainer, optimizer_idx)
optimizer.step(closure=optimizer_closure)
def optimizer_zero_grad(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int):
optimizer.zero_grad()
def tbptt_split_batch(self, batch: Tensor, split_size: int) -> list:
r"""
When using truncated backpropagation through time, each batch must be split along the
time dimension. Lightning handles this by default, but for custom behavior override
this function.
Args:
batch: Current batch
split_size: The size of the split
Return:
List of batch splits. Each split will be passed to :meth:`training_step` to enable truncated
back propagation through time. The default implementation splits root level Tensors and
Sequences at dim=1 (i.e. time dim). It assumes that each time dim is the same length.
Examples::
def tbptt_split_batch(self, batch, split_size):
splits = []
for t in range(0, time_dims[0], split_size):
batch_split = []
for i, x in enumerate(batch):
if isinstance(x, torch.Tensor):
split_x = x[:, t:t + split_size]
elif isinstance(x, collections.Sequence):
split_x = [None] * len(x)
for batch_idx in range(len(x)):
split_x[batch_idx] = x[batch_idx][t:t + split_size]
batch_split.append(split_x)
splits.append(batch_split)
return splits
Note:
Called in the training loop after
:meth:`~pytorch_lightning.callbacks.base.Callback.on_batch_start`
if :paramref:`~pytorch_lightning.trainer.Trainer.truncated_bptt_steps` > 0.
Each returned batch split is passed separately to :meth:`training_step`.
"""
time_dims = [len(x[0]) for x in batch if isinstance(x, (torch.Tensor, collections.Sequence))]
assert len(time_dims) >= 1, "Unable to determine batch time dimension"
assert all(x == time_dims[0] for x in time_dims), "Batch time dimension length is ambiguous"
splits = []
for t in range(0, time_dims[0], split_size):
batch_split = []
for i, x in enumerate(batch):
if isinstance(x, torch.Tensor):
split_x = x[:, t:t + split_size]
elif isinstance(x, collections.Sequence):
split_x = [None] * len(x)
for batch_idx in range(len(x)):
split_x[batch_idx] = x[batch_idx][t:t + split_size]
batch_split.append(split_x)
splits.append(batch_split)
return splits
def summarize(self, mode: Optional[str] = ModelSummary.MODE_DEFAULT) -> Optional[ModelSummary]:
model_summary = None
if mode in ModelSummary.MODES:
model_summary = ModelSummary(self, mode=mode)
log.info("\n" + str(model_summary))
elif mode is not None:
raise MisconfigurationException(f"`mode` can be None, {', '.join(ModelSummary.MODES)}, got {mode}")
return model_summary
def freeze(self) -> None:
r"""
Freeze all params for inference.
Example::
model = MyLightningModule(...)
model.freeze()
"""
for param in self.parameters():
param.requires_grad = False
self.eval()
def unfreeze(self) -> None:
"""
Unfreeze all parameters for training.
.. code-block:: python
model = MyLightningModule(...)
model.unfreeze()
"""
for param in self.parameters():
param.requires_grad = True
self.train()
def get_progress_bar_dict(self) -> Dict[str, Union[int, str]]:
r"""
Implement this to override the default items displayed in the progress bar.
By default it includes the average loss value, split index of BPTT (if used)
and the version of the experiment when using a logger.
.. code-block::
Epoch 1: 4%|▎ | 40/1095 [00:03<01:37, 10.84it/s, loss=4.501, v_num=10]
Here is an example how to override the defaults:
.. code-block:: python
def get_progress_bar_dict(self):
# don't show the version number
items = super().get_progress_bar_dict()
items.pop("v_num", None)
return items
Return:
Dictionary with the items to be displayed in the progress bar.
"""
# call .item() only once but store elements without graphs
running_train_loss = self.trainer.train_loop.running_loss.mean()
avg_training_loss = None
if running_train_loss is not None:
avg_training_loss = running_train_loss.cpu().item()
elif self.trainer.train_loop.automatic_optimization:
avg_training_loss = float('NaN')
tqdm_dict = {}
if avg_training_loss is not None:
tqdm_dict["loss"] = f"{avg_training_loss:.3g}"
if self.trainer.truncated_bptt_steps is not None:
tqdm_dict["split_idx"] = self.trainer.split_idx
if self.trainer.logger is not None and self.trainer.logger.version is not None:
version = self.trainer.logger.version
# show last 4 places of long version strings
version = version[-4:] if isinstance(version, str) else version
tqdm_dict["v_num"] = version
return tqdm_dict
def _verify_is_manual_optimization(self, fn_name):
if self.trainer.train_loop.automatic_optimization:
raise MisconfigurationException(
f'to use {fn_name}, please disable automatic optimization:'
' set model property `automatic_optimization` as False'
)
@classmethod
def _auto_collect_arguments(cls, frame=None) -> Tuple[Dict, Dict]:
"""
Collect all module arguments in the current constructor and all child constructors.
The child constructors are all the ``__init__`` methods that reach the current class through
(chained) ``super().__init__()`` calls.
Args:
frame: instance frame
Returns:
self_arguments: arguments dictionary of the first instance
parents_arguments: arguments dictionary of the parent's instances
"""
if not frame:
frame = inspect.currentframe()
frame_args = collect_init_args(frame.f_back, [])
self_arguments = frame_args[-1]
# set hyper_parameters in child
self_arguments = self_arguments
parents_arguments = {}
# add all arguments from parents
for args in frame_args[:-1]:
parents_arguments.update(args)
return self_arguments, parents_arguments
def save_hyperparameters(
self,
*args,
ignore: Optional[Union[Sequence[str], str]] = None,
frame: Optional[types.FrameType] = None
) -> None:
"""Save model arguments to ``hparams`` attribute.
Args:
args: single object of `dict`, `NameSpace` or `OmegaConf`
or string names or arguments from class ``__init__``
ignore: an argument name or a list of argument names from
class ``__init__`` to be ignored
frame: a frame object. Default is None
Example::
>>> class ManuallyArgsModel(LightningModule):
... def __init__(self, arg1, arg2, arg3):
... super().__init__()
... # manually assign arguments
... self.save_hyperparameters('arg1', 'arg3')
... def forward(self, *args, **kwargs):
... ...
>>> model = ManuallyArgsModel(1, 'abc', 3.14)
>>> model.hparams
"arg1": 1
"arg3": 3.14
>>> class AutomaticArgsModel(LightningModule):
... def __init__(self, arg1, arg2, arg3):
... super().__init__()
... # equivalent automatic
... self.save_hyperparameters()
... def forward(self, *args, **kwargs):
... ...
>>> model = AutomaticArgsModel(1, 'abc', 3.14)
>>> model.hparams
"arg1": 1
"arg2": abc
"arg3": 3.14
>>> class SingleArgModel(LightningModule):
... def __init__(self, params):
... super().__init__()
... # manually assign single argument
... self.save_hyperparameters(params)
... def forward(self, *args, **kwargs):
... ...
>>> model = SingleArgModel(Namespace(p1=1, p2='abc', p3=3.14))
>>> model.hparams
"p1": 1
"p2": abc
"p3": 3.14
>>> class ManuallyArgsModel(LightningModule):
... def __init__(self, arg1, arg2, arg3):
... super().__init__()
... # pass argument(s) to ignore as a string or in a list
... self.save_hyperparameters(ignore='arg2')
... def forward(self, *args, **kwargs):
... ...
>>> model = ManuallyArgsModel(1, 'abc', 3.14)
>>> model.hparams
"arg1": 1
"arg3": 3.14
"""
if not frame:
frame = inspect.currentframe().f_back
init_args = get_init_args(frame)
assert init_args, "failed to inspect the self init"
if ignore is not None:
if isinstance(ignore, str):
ignore = [ignore]
if isinstance(ignore, (list, tuple)):
ignore = [arg for arg in ignore if isinstance(arg, str)]
init_args = {k: v for k, v in init_args.items() if k not in ignore}
if not args:
# take all arguments
hp = init_args
self._hparams_name = "kwargs" if hp else None
else:
# take only listed arguments in `save_hparams`
isx_non_str = [i for i, arg in enumerate(args) if not isinstance(arg, str)]
if len(isx_non_str) == 1:
hp = args[isx_non_str[0]]
cand_names = [k for k, v in init_args.items() if v == hp]
self._hparams_name = cand_names[0] if cand_names else None
else:
hp = {arg: init_args[arg] for arg in args if isinstance(arg, str)}
self._hparams_name = "kwargs"
# `hparams` are expected here
if hp:
self._set_hparams(hp)
# make deep copy so there is not other runtime changes reflected
self._hparams_initial = copy.deepcopy(self._hparams)
def _set_hparams(self, hp: Union[dict, Namespace, str]) -> None:
if isinstance(hp, Namespace):
hp = vars(hp)
if isinstance(hp, dict):
hp = AttributeDict(hp)
elif isinstance(hp, PRIMITIVE_TYPES):
raise ValueError(f"Primitives {PRIMITIVE_TYPES} are not allowed.")
elif not isinstance(hp, ALLOWED_CONFIG_TYPES):
raise ValueError(f"Unsupported config type of {type(hp)}.")
if isinstance(hp, dict) and isinstance(self.hparams, dict):
self.hparams.update(hp)
else:
self._hparams = hp
@torch.no_grad()
def to_onnx(
self,
file_path: Union[str, Path],
input_sample: Optional[Any] = None,
**kwargs,
):
"""
Saves the model in ONNX format
Args:
file_path: The path of the file the onnx model should be saved to.
input_sample: An input for tracing. Default: None (Use self.example_input_array)
**kwargs: Will be passed to torch.onnx.export function.
Example:
>>> class SimpleModel(LightningModule):
... def __init__(self):
... super().__init__()
... self.l1 = torch.nn.Linear(in_features=64, out_features=4)
...
... def forward(self, x):
... return torch.relu(self.l1(x.view(x.size(0), -1)))
>>> with tempfile.NamedTemporaryFile(suffix='.onnx', delete=False) as tmpfile:
... model = SimpleModel()
... input_sample = torch.randn((1, 64))
... model.to_onnx(tmpfile.name, input_sample, export_params=True)
... os.path.isfile(tmpfile.name)
True
"""
mode = self.training
if input_sample is None:
if self.example_input_array is None:
raise ValueError(
"Could not export to ONNX since neither `input_sample` nor"
" `model.example_input_array` attribute is set."
)
input_sample = self.example_input_array
input_sample = self._apply_batch_transfer_handler(input_sample)
if "example_outputs" not in kwargs:
self.eval()
kwargs["example_outputs"] = self(input_sample)
torch.onnx.export(self, input_sample, file_path, **kwargs)
self.train(mode)
@torch.no_grad()
def to_torchscript(
self,
file_path: Optional[Union[str, Path]] = None,
method: Optional[str] = 'script',
example_inputs: Optional[Any] = None,
**kwargs,
) -> Union[ScriptModule, Dict[str, ScriptModule]]:
"""
By default compiles the whole model to a :class:`~torch.jit.ScriptModule`.
If you want to use tracing, please provided the argument `method='trace'` and make sure that either the
example_inputs argument is provided, or the model has self.example_input_array set.
If you would like to customize the modules that are scripted you should override this method.
In case you want to return multiple modules, we recommend using a dictionary.
Args:
file_path: Path where to save the torchscript. Default: None (no file saved).
method: Whether to use TorchScript's script or trace method. Default: 'script'
example_inputs: An input to be used to do tracing when method is set to 'trace'.
Default: None (Use self.example_input_array)
**kwargs: Additional arguments that will be passed to the :func:`torch.jit.script` or
:func:`torch.jit.trace` function.
Note:
- Requires the implementation of the
:meth:`~pytorch_lightning.core.lightning.LightningModule.forward` method.
- The exported script will be set to evaluation mode.
- It is recommended that you install the latest supported version of PyTorch
to use this feature without limitations. See also the :mod:`torch.jit`
documentation for supported features.
Example:
>>> class SimpleModel(LightningModule):
... def __init__(self):
... super().__init__()
... self.l1 = torch.nn.Linear(in_features=64, out_features=4)
...
... def forward(self, x):
... return torch.relu(self.l1(x.view(x.size(0), -1)))
...
>>> model = SimpleModel()
>>> torch.jit.save(model.to_torchscript(), "model.pt") # doctest: +SKIP
>>> os.path.isfile("model.pt") # doctest: +SKIP
>>> torch.jit.save(model.to_torchscript(file_path="model_trace.pt", method='trace', # doctest: +SKIP
... example_inputs=torch.randn(1, 64))) # doctest: +SKIP
>>> os.path.isfile("model_trace.pt") # doctest: +SKIP
True
Return:
This LightningModule as a torchscript, regardless of whether file_path is
defined or not.
"""
mode = self.training
if method == 'script':
torchscript_module = torch.jit.script(self.eval(), **kwargs)
elif method == 'trace':
# if no example inputs are provided, try to see if model has example_input_array set
if example_inputs is None:
if self.example_input_array is None:
raise ValueError(
'Choosing method=`trace` requires either `example_inputs`'
' or `model.example_input_array` to be defined.'
)
example_inputs = self.example_input_array
# automatically send example inputs to the right device and use trace
example_inputs = self._apply_batch_transfer_handler(example_inputs)
torchscript_module = torch.jit.trace(func=self.eval(), example_inputs=example_inputs, **kwargs)
else:
raise ValueError(f"The 'method' parameter only supports 'script' or 'trace', but value given was: {method}")
self.train(mode)
if file_path is not None:
torch.jit.save(torchscript_module, file_path)
return torchscript_module
@property
def hparams(self) -> Union[AttributeDict, dict, Namespace]:
if not hasattr(self, "_hparams"):
self._hparams = AttributeDict()
return self._hparams
@property
def hparams_initial(self) -> AttributeDict:
if not hasattr(self, "_hparams_initial"):
return AttributeDict()
# prevent any change
return copy.deepcopy(self._hparams_initial)
@property
def model_size(self) -> float:
# todo: think about better way without need to dump model to drive
tmp_name = f"{uuid.uuid4().hex}.pt"
torch.save(self.state_dict(), tmp_name)
size_mb = os.path.getsize(tmp_name) / 1e6
os.remove(tmp_name)
return size_mb
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