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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_deprecation, 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__)


[docs]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 def lr_schedulers(self) -> Optional[Union[Any, List[Any]]]: if not self.trainer.lr_schedulers: return None # ignore other keys "interval", "frequency", etc. lr_schedulers = [s["scheduler"] for s in self.trainer.lr_schedulers] # single scheduler if len(lr_schedulers) == 1: return lr_schedulers[0] # multiple schedulers return lr_schedulers @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
[docs] 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)
[docs] 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, )
[docs] 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 )
[docs] 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)
[docs] 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
[docs] 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)
[docs] 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)
[docs] 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")
[docs] 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. """
[docs] 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 """
[docs] 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 val_outs = [] for val_batch in val_data: out = validation_step(val_batch) if defined('validation_step_end'): out = validation_step_end(out) val_outs.append(out) val_outs = validation_epoch_end(val_outs) .. 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. """
[docs] 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. """
[docs] 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) """
[docs] 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. """
[docs] 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. """
[docs] 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) """
[docs] 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)
[docs] 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 []
[docs] 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")
[docs] 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_deprecation( "`optimizer` argument to `manual_backward` is deprecated in v1.2 and will be removed in v1.4" ) # 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
[docs] 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)
[docs] 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
[docs] 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()
[docs] 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)
[docs] def optimizer_zero_grad(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int): optimizer.zero_grad()
[docs] 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
[docs] 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()
[docs] 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()
[docs] 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
[docs] 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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