Source code for pytorch_lightning.trainer.trainer

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

"""Trainer to automate the training."""

import logging
import os
import warnings
from pathlib import Path
from typing import Dict, Iterable, List, Optional, Union

import torch
from import DataLoader

from pytorch_lightning.accelerators.accelerator import Accelerator
from pytorch_lightning.accelerators.accelerator_connector import AcceleratorConnector
from pytorch_lightning.callbacks import Callback
from pytorch_lightning.core.datamodule import LightningDataModule
from pytorch_lightning.core.lightning import LightningModule
from pytorch_lightning.core.step_result import EvalResult, Result
from pytorch_lightning.loggers import LightningLoggerBase
from pytorch_lightning.plugins.plugin_connector import PluginConnector
from pytorch_lightning.profiler import BaseProfiler
from pytorch_lightning.trainer.callback_hook import TrainerCallbackHookMixin
from pytorch_lightning.trainer.configuration_validator import ConfigValidator
from pytorch_lightning.trainer.connectors.callback_connector import CallbackConnector
from pytorch_lightning.trainer.connectors.checkpoint_connector import CheckpointConnector
from pytorch_lightning.trainer.connectors.data_connector import DataConnector
from pytorch_lightning.trainer.connectors.debugging_connector import DebuggingConnector
from pytorch_lightning.trainer.connectors.env_vars_connector import overwrite_by_env_vars
from pytorch_lightning.trainer.connectors.logger_connector import LoggerConnector
from pytorch_lightning.trainer.connectors.model_connector import ModelConnector
from pytorch_lightning.trainer.connectors.optimizer_connector import OptimizerConnector
from pytorch_lightning.trainer.connectors.precision_connector import PrecisionConnector
from pytorch_lightning.trainer.connectors.profiler_connector import ProfilerConnector
from pytorch_lightning.trainer.connectors.slurm_connector import SLURMConnector
from pytorch_lightning.trainer.connectors.training_trick_connector import TrainingTricksConnector
from pytorch_lightning.trainer.data_loading import TrainerDataLoadingMixin
from pytorch_lightning.trainer.deprecated_api import DeprecatedDistDeviceAttributes
from pytorch_lightning.trainer.evaluation_loop import EvaluationLoop
from pytorch_lightning.trainer.logging import TrainerLoggingMixin
from pytorch_lightning.trainer.model_hooks import TrainerModelHooksMixin
from pytorch_lightning.trainer.optimizers import TrainerOptimizersMixin
from import TrainerProperties
from pytorch_lightning.trainer.states import TrainerState
from pytorch_lightning.trainer.training_loop import TrainLoop
from pytorch_lightning.trainer.training_tricks import TrainerTrainingTricksMixin
from pytorch_lightning.tuner.tuning import Tuner
from pytorch_lightning.utilities import AMPType, DeviceType, rank_zero_warn
from pytorch_lightning.utilities.cloud_io import load as pl_load
from pytorch_lightning.utilities.debugging import InternalDebugger
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.memory import recursive_detach
from pytorch_lightning.utilities.model_utils import is_overridden

log = logging.getLogger(__name__)
# warnings to ignore in trainer
    'ignore', message='torch.distributed.reduce_op is deprecated, ' 'please use torch.distributed.ReduceOp instead'

[docs]class Trainer( TrainerProperties, TrainerCallbackHookMixin, TrainerModelHooksMixin, TrainerOptimizersMixin, TrainerLoggingMixin, TrainerTrainingTricksMixin, TrainerDataLoadingMixin, DeprecatedDistDeviceAttributes, ):
[docs] @overwrite_by_env_vars def __init__( self, logger: Union[LightningLoggerBase, Iterable[LightningLoggerBase], bool] = True, checkpoint_callback: bool = True, callbacks: Optional[List[Callback]] = None, default_root_dir: Optional[str] = None, gradient_clip_val: float = 0, process_position: int = 0, num_nodes: int = 1, num_processes: int = 1, gpus: Optional[Union[List[int], str, int]] = None, auto_select_gpus: bool = False, tpu_cores: Optional[Union[List[int], str, int]] = None, log_gpu_memory: Optional[str] = None, progress_bar_refresh_rate: int = 1, overfit_batches: Union[int, float] = 0.0, track_grad_norm: Union[int, float, str] = -1, check_val_every_n_epoch: int = 1, fast_dev_run: Union[int, bool] = False, accumulate_grad_batches: Union[int, Dict[int, int], List[list]] = 1, max_epochs: int = 1000, min_epochs: int = 1, max_steps: Optional[int] = None, min_steps: Optional[int] = None, limit_train_batches: Union[int, float] = 1.0, limit_val_batches: Union[int, float] = 1.0, limit_test_batches: Union[int, float] = 1.0, val_check_interval: Union[int, float] = 1.0, flush_logs_every_n_steps: int = 100, log_every_n_steps: int = 50, accelerator: Optional[Union[str, Accelerator]] = None, sync_batchnorm: bool = False, precision: int = 32, weights_summary: Optional[str] = 'top', weights_save_path: Optional[str] = None, num_sanity_val_steps: int = 2, truncated_bptt_steps: Optional[int] = None, resume_from_checkpoint: Optional[Union[Path, str]] = None, profiler: Optional[Union[BaseProfiler, bool, str]] = None, benchmark: bool = False, deterministic: bool = False, reload_dataloaders_every_epoch: bool = False, auto_lr_find: Union[bool, str] = False, replace_sampler_ddp: bool = True, terminate_on_nan: bool = False, auto_scale_batch_size: Union[str, bool] = False, prepare_data_per_node: bool = True, plugins: Optional[Union[str, list]] = None, amp_backend: str = 'native', amp_level: str = 'O2', distributed_backend: Optional[str] = None, automatic_optimization: Optional[bool] = None, move_metrics_to_cpu: bool = False, enable_pl_optimizer: bool = None, # todo: remove in v1.3 ): r""" Customize every aspect of training via flags Args: accelerator: Previously known as distributed_backend (dp, ddp, ddp2, etc...). Can also take in an accelerator object for custom hardware. accumulate_grad_batches: Accumulates grads every k batches or as set up in the dict. amp_backend: The mixed precision backend to use ("native" or "apex") amp_level: The optimization level to use (O1, O2, etc...). auto_lr_find: If set to True, will make trainer.tune() run a learning rate finder, trying to optimize initial learning for faster convergence. trainer.tune() method will set the suggested learning rate in or self.learning_rate in the LightningModule. To use a different key set a string instead of True with the key name. auto_scale_batch_size: If set to True, will `initially` run a batch size finder trying to find the largest batch size that fits into memory. The result will be stored in self.batch_size in the LightningModule. Additionally, can be set to either `power` that estimates the batch size through a power search or `binsearch` that estimates the batch size through a binary search. auto_select_gpus: If enabled and `gpus` is an integer, pick available gpus automatically. This is especially useful when GPUs are configured to be in "exclusive mode", such that only one process at a time can access them. benchmark: If true enables cudnn.benchmark. callbacks: Add a list of callbacks. checkpoint_callback: If ``True``, enable checkpointing. It will configure a default ModelCheckpoint callback if there is no user-defined ModelCheckpoint in :paramref:`~pytorch_lightning.trainer.trainer.Trainer.callbacks`. Default: ``True``. .. warning:: Passing a ModelCheckpoint instance to this argument is deprecated since v1.1 and will be unsupported from v1.3. Use `callbacks` argument instead. check_val_every_n_epoch: Check val every n train epochs. default_root_dir: Default path for logs and weights when no logger/ckpt_callback passed. Default: ``os.getcwd()``. Can be remote file paths such as `s3://mybucket/path` or 'hdfs://path/' deterministic: If true enables cudnn.deterministic. distributed_backend: deprecated. Please use 'accelerator' fast_dev_run: runs n if set to ``n`` (int) else 1 if set to ``True`` batch(es) of train, val and test to find any bugs (ie: a sort of unit test). flush_logs_every_n_steps: How often to flush logs to disk (defaults to every 100 steps). gpus: number of gpus to train on (int) or which GPUs to train on (list or str) applied per node gradient_clip_val: 0 means don't clip. limit_train_batches: How much of training dataset to check (floats = percent, int = num_batches) limit_val_batches: How much of validation dataset to check (floats = percent, int = num_batches) limit_test_batches: How much of test dataset to check (floats = percent, int = num_batches) logger: Logger (or iterable collection of loggers) for experiment tracking. log_gpu_memory: None, 'min_max', 'all'. Might slow performance log_every_n_steps: How often to log within steps (defaults to every 50 steps). automatic_optimization: If False you are responsible for calling .backward, .step, zero_grad in LightningModule. This argument has been moved to LightningModule. It is deprecated here in v1.1 and will be removed in v1.3. prepare_data_per_node: If True, each LOCAL_RANK=0 will call prepare data. Otherwise only NODE_RANK=0, LOCAL_RANK=0 will prepare data process_position: orders the progress bar when running multiple models on same machine. progress_bar_refresh_rate: How often to refresh progress bar (in steps). Value ``0`` disables progress bar. Ignored when a custom callback is passed to :paramref:`~Trainer.callbacks`. profiler: To profile individual steps during training and assist in identifying bottlenecks. Passing bool value is deprecated in v1.1 and will be removed in v1.3. overfit_batches: Overfit a percent of training data (float) or a set number of batches (int). Default: 0.0 plugins: Plugins allow modification of core behavior like ddp and amp, and enable custom lightning plugins. precision: Full precision (32), half precision (16). Can be used on CPU, GPU or TPUs. max_epochs: Stop training once this number of epochs is reached. min_epochs: Force training for at least these many epochs max_steps: Stop training after this number of steps. Disabled by default (None). min_steps: Force training for at least these number of steps. Disabled by default (None). num_nodes: number of GPU nodes for distributed training. num_processes: number of processes for distributed training with distributed_backend="ddp_cpu" num_sanity_val_steps: Sanity check runs n validation batches before starting the training routine. Set it to `-1` to run all batches in all validation dataloaders. Default: 2 reload_dataloaders_every_epoch: Set to True to reload dataloaders every epoch. replace_sampler_ddp: Explicitly enables or disables sampler replacement. If not specified this will toggled automatically when DDP is used. By default it will add ``shuffle=True`` for train sampler and ``shuffle=False`` for val/test sampler. If you want to customize it, you can set ``replace_sampler_ddp=False`` and add your own distributed sampler. resume_from_checkpoint: Path/URL of the checkpoint from which training is resumed. If there is no checkpoint file at the path, start from scratch. If resuming from mid-epoch checkpoint, training will start from the beginning of the next epoch. sync_batchnorm: Synchronize batch norm layers between process groups/whole world. terminate_on_nan: If set to True, will terminate training (by raising a `ValueError`) at the end of each training batch, if any of the parameters or the loss are NaN or +/-inf. tpu_cores: How many TPU cores to train on (1 or 8) / Single TPU to train on [1] track_grad_norm: -1 no tracking. Otherwise tracks that p-norm. May be set to 'inf' infinity-norm. truncated_bptt_steps: Truncated back prop breaks performs backprop every k steps of much longer sequence. val_check_interval: How often to check the validation set. Use float to check within a training epoch, use int to check every n steps (batches). weights_summary: Prints a summary of the weights when training begins. weights_save_path: Where to save weights if specified. Will override default_root_dir for checkpoints only. Use this if for whatever reason you need the checkpoints stored in a different place than the logs written in `default_root_dir`. Can be remote file paths such as `s3://mybucket/path` or 'hdfs://path/' Defaults to `default_root_dir`. move_metrics_to_cpu: Whether to force internal logged metrics to be moved to cpu. This can save some gpu memory, but can make training slower. Use with attention. enable_pl_optimizer: If True, each optimizer will be wrapped by `pytorch_lightning.core.optimizer.LightningOptimizer`. It allows Lightning to handle AMP, TPU, accumulated_gradients, etc. .. warning:: Currently deprecated and it will be removed in v1.3 """ super().__init__() self._device_type = DeviceType.CPU self._distrib_type = None # init connectors self.dev_debugger = InternalDebugger(self) self.config_validator = ConfigValidator(self) self.data_connector = DataConnector(self) self.optimizer_connector = OptimizerConnector(self) self.accelerator_connector = AcceleratorConnector(self) self.logger_connector = LoggerConnector(self) self.model_connector = ModelConnector(self) self.precision_connector = PrecisionConnector(self) self.callback_connector = CallbackConnector(self) self.debugging_connector = DebuggingConnector(self) self.training_tricks_connector = TrainingTricksConnector(self) self.profile_connector = ProfilerConnector(self) self.checkpoint_connector = CheckpointConnector(self) self.slurm_connector = SLURMConnector(self) self.tuner = Tuner(self) self.accelerator_backend = None self.evaluation_loop = EvaluationLoop(self) self.train_loop = TrainLoop(self) self.plugin_connector = PluginConnector(self) # training state self.model = None self.shown_warnings = set() # init callbacks # Declare attributes to be set in callback_connector on_trainer_init self.callback_connector.on_trainer_init( callbacks, checkpoint_callback, progress_bar_refresh_rate, process_position, default_root_dir, weights_save_path, resume_from_checkpoint, ) # hook self.on_init_start() # init optimizer + lr scheduler related flags self.optimizer_connector.on_trainer_init(enable_pl_optimizer) # init data flags self.data_connector.on_trainer_init( check_val_every_n_epoch, reload_dataloaders_every_epoch, prepare_data_per_node ) # init training tricks self.training_tricks_connector.on_trainer_init( gradient_clip_val, track_grad_norm, accumulate_grad_batches, truncated_bptt_steps, terminate_on_nan ) # init accelerator related flags self.accelerator_connector.on_trainer_init( num_processes, tpu_cores, accelerator, distributed_backend, auto_select_gpus, gpus, num_nodes, log_gpu_memory, sync_batchnorm, benchmark, replace_sampler_ddp, deterministic, ) # init train loop related flags # TODO: remove in 1.3.0 if automatic_optimization is None: automatic_optimization = True else: rank_zero_warn( "Disable automatic optimization with the trainer flag is deprecated and will be removed in v1.3.0!" "Please use the property on the LightningModule for disabling automatic optimization" ) self.train_loop.on_trainer_init( max_epochs, min_epochs, max_steps, min_steps, num_sanity_val_steps, automatic_optimization, weights_summary, ) self.evaluation_loop.on_trainer_init() # configure tuner self.tuner.on_trainer_init(auto_lr_find, auto_scale_batch_size) # configure profiler self.profile_connector.on_trainer_init(profiler) # init logger flags self.logger_connector.on_trainer_init( logger, flush_logs_every_n_steps, log_every_n_steps, move_metrics_to_cpu, ) # init debugging flags self.debugging_connector.on_init_start( limit_train_batches, limit_val_batches, limit_test_batches, val_check_interval, overfit_batches, fast_dev_run, ) # set precision self.precision_connector.on_trainer_init(precision, amp_level, amp_backend) # last thing are the plugins which override whatever the trainer used by default self.plugin_connector.on_trainer_init(plugins) # Callback system self.on_init_end()
[docs] def setup_trainer(self, model: LightningModule): """ Sanity check a few things before starting actual training or testing. Args: model: The model to run sanity test on. """ # -------------------------- # Setup?? # -------------------------- ref_model = self.get_model() # set the ranks and devices self.accelerator_backend.dist.rank = self.global_rank self.accelerator_backend.dist.device = ref_model.device # set local properties on the model self.model_connector.copy_trainer_model_properties(model) # init amp. Must be done here instead of __init__ to allow ddp to work if self.amp_backend == AMPType.NATIVE and self.precision == 16 and not self.use_tpu: self.scaler = self.precision_connector.backend.scaler # log hyper-parameters if self.logger is not None: # save exp to get started (this is where the first experiment logs are written) self.logger.log_hyperparams(ref_model.hparams_initial) self.logger.log_graph(ref_model) # wait for all to join if on distributed self.accelerator_backend.barrier("setup_trainer") # register auto-resubmit when on SLURM self.slurm_connector.register_slurm_signal_handlers() # track model now. # if cluster resets state, the model will update with the saved weights self.model = model
[docs] def fit( self, model: LightningModule, train_dataloader: Optional[DataLoader] = None, val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None, datamodule: Optional[LightningDataModule] = None, ): r""" Runs the full optimization routine. Args: datamodule: A instance of :class:`LightningDataModule`. model: Model to fit. train_dataloader: A Pytorch DataLoader with training samples. If the model has a predefined train_dataloader method this will be skipped. val_dataloaders: Either a single Pytorch Dataloader or a list of them, specifying validation samples. If the model has a predefined val_dataloaders method this will be skipped """ # bookkeeping self._state = TrainerState.RUNNING # ---------------------------- # LINK DATA # ---------------------------- # setup data, etc... self.train_loop.setup_fit(model, train_dataloader, val_dataloaders, datamodule) # hook self.data_connector.prepare_data(model) # ---------------------------- # SET UP TRAINING # ---------------------------- self.accelerator_backend = self.accelerator_connector.select_accelerator() self.accelerator_backend.setup(model) # ---------------------------- # INSPECT THESE FOR MAIN LOOPS # ---------------------------- # assign training and eval functions... inspect these to see the train and eval loops :) self.accelerator_backend.train_loop = self.train self.accelerator_backend.validation_loop = self.run_evaluation self.accelerator_backend.test_loop = self.run_evaluation # ---------------------------- # TRAIN # ---------------------------- # hook self.call_hook('on_fit_start') results = self.accelerator_backend.train() self.accelerator_backend.teardown() # ---------------------------- # POST-Training CLEAN UP # ---------------------------- # hook self.call_hook('on_fit_end') # hook self.teardown('fit') if self.is_function_implemented('teardown'): model.teardown('fit') # return 1 when finished # used for testing or when we need to know that training succeeded if self._state != TrainerState.INTERRUPTED: self._state = TrainerState.FINISHED return results or 1
def train(self): self.run_sanity_check(self.get_model()) # set stage for logging self.logger_connector.set_stage("train") self.checkpoint_connector.has_trained = False # enable train mode model = self.get_model() model.train() torch.set_grad_enabled(True) # reload data when needed self.train_loop.reset_train_val_dataloaders(model) # hook self.train_loop.on_train_start() try: if self.train_loop.should_skip_training(): return # run all epochs for epoch in range(self.current_epoch, self.max_epochs): # hook self.train_loop.on_train_epoch_start(epoch) with self.profiler.profile("run_training_epoch"): # run train epoch self.train_loop.run_training_epoch() if self.max_steps and self.max_steps <= self.global_step: return # update LR schedulers self.optimizer_connector.update_learning_rates(interval='epoch') # early stopping met_min_epochs = epoch >= self.min_epochs - 1 met_min_steps = self.global_step >= self.min_steps if self.min_steps else True if self.should_stop: if met_min_epochs and met_min_steps: return 'Trainer was signaled to stop but required minimum epochs' f' ({self.min_epochs}) or minimum steps ({self.min_steps}) has' ' not been met. Training will continue...' ) except KeyboardInterrupt: rank_zero_warn('Detected KeyboardInterrupt, attempting graceful shutdown...') # user could press ctrl+c many times... only shutdown once if not self.interrupted: self.interrupted = True self._state = TrainerState.INTERRUPTED self.on_keyboard_interrupt() finally: # hook self.train_loop.on_train_end() def run_evaluation(self, max_batches=None): # used to know if we are logging for val, test + reset cached results self.logger_connector.set_stage(self.testing, reset=True) # bookkeeping self.evaluation_loop.testing = self.testing # prepare dataloaders dataloaders, max_batches = self.evaluation_loop.get_evaluation_dataloaders(max_batches) # check if we want to skip this evaluation if self.evaluation_loop.should_skip_evaluation(dataloaders, max_batches): return [], [] # ref model model = self.get_model() # enable eval mode + no grads self.evaluation_loop.on_evaluation_model_eval() model.zero_grad() torch.set_grad_enabled(False) # hook self.evaluation_loop.on_evaluation_start() # set up the eval loop self.evaluation_loop.setup(model, max_batches, dataloaders) # hook self.evaluation_loop.on_evaluation_epoch_start() # run validation/testing for dataloader_idx, dataloader in enumerate(dataloaders): # bookkeeping dl_outputs = [] dataloader = self.accelerator_backend.process_dataloader(dataloader) dl_max_batches = self.evaluation_loop.max_batches[dataloader_idx] for batch_idx, batch in enumerate(dataloader): if batch is None: continue # stop short when running on limited batches if batch_idx >= dl_max_batches: break # hook self.evaluation_loop.on_evaluation_batch_start(batch, batch_idx, dataloader_idx) # lightning module methods with self.profiler.profile("evaluation_step_and_end"): output = self.evaluation_loop.evaluation_step(batch, batch_idx, dataloader_idx) output = self.evaluation_loop.evaluation_step_end(output) # hook + store predictions self.evaluation_loop.on_evaluation_batch_end(output, batch, batch_idx, dataloader_idx) # log batch metrics self.evaluation_loop.log_evaluation_step_metrics(output, batch_idx) # track epoch level outputs dl_outputs = self.track_output_for_epoch_end(dl_outputs, output) # store batch level output per dataloader self.evaluation_loop.outputs.append(dl_outputs) # lightning module method deprecated_eval_results = self.evaluation_loop.evaluation_epoch_end() # hook self.evaluation_loop.on_evaluation_epoch_end() # hook self.evaluation_loop.on_evaluation_end() # log epoch metrics eval_loop_results = self.evaluation_loop.log_epoch_metrics_on_evaluation_end() # save predictions to disk self.evaluation_loop.predictions.to_disk() # enable train mode again self.evaluation_loop.on_evaluation_model_train() torch.set_grad_enabled(True) return eval_loop_results, deprecated_eval_results def track_output_for_epoch_end(self, outputs, output): if output is not None: if isinstance(output, Result): output.detach() if self.move_metrics_to_cpu: output.cpu() elif isinstance(output, dict): output = recursive_detach(output, to_cpu=self.move_metrics_to_cpu) elif isinstance(output, torch.Tensor) and output.is_cuda and self.move_metrics_to_cpu: output = output.cpu() outputs.append(output) return outputs def run_test(self): # only load test dataloader for testing # self.reset_test_dataloader(ref_model) with self.profiler.profile("run_test_evaluation"): eval_loop_results, _ = self.run_evaluation() if len(eval_loop_results) == 0: return 1 # remove the tensors from the eval results for i, result in enumerate(eval_loop_results): if isinstance(result, dict): for k, v in result.items(): if isinstance(v, torch.Tensor): result[k] = v.cpu().item() return eval_loop_results def run_sanity_check(self, ref_model): using_val_step = ref_model.val_dataloader is not None and is_overridden('validation_step', ref_model) should_sanity_check = using_val_step and self.num_sanity_val_steps > 0 and self.limit_val_batches > 0 # run tiny validation (if validation defined) # to make sure program won't crash during val if should_sanity_check: self.reset_val_dataloader(ref_model) self.num_sanity_val_batches = [ min(self.num_sanity_val_steps, val_batches) for val_batches in self.num_val_batches ] # hook and callback self.running_sanity_check = True self.on_sanity_check_start() # run eval step _, eval_results = self.run_evaluation(max_batches=self.num_sanity_val_batches) # allow no returns from eval if eval_results is not None and len(eval_results) > 0: # when we get a list back, used only the last item if isinstance(eval_results, list): eval_results = eval_results[-1] if isinstance(eval_results, EvalResult): callback_metrics = eval_results.callback_metrics else: _, _, _, callback_metrics, _ = self.process_dict_result(eval_results) self.logger_connector.callback_metrics = callback_metrics self.on_sanity_check_end() self.running_sanity_check = False
[docs] def test( self, model: Optional[LightningModule] = None, test_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None, ckpt_path: Optional[str] = 'best', verbose: bool = True, datamodule: Optional[LightningDataModule] = None, ): r""" Separates from fit to make sure you never run on your test set until you want to. Args: ckpt_path: Either ``best`` or path to the checkpoint you wish to test. If ``None``, use the weights from the last epoch to test. Default to ``best``. datamodule: A instance of :class:`LightningDataModule`. model: The model to test. test_dataloaders: Either a single Pytorch Dataloader or a list of them, specifying validation samples. verbose: If True, prints the test results Returns: The final test result dictionary. If no test_epoch_end is defined returns a list of dictionaries """ # -------------------- # SETUP HOOK # -------------------- self.verbose_test = verbose self.logger_connector.set_stage("test") # If you supply a datamodule you can't supply train_dataloader or val_dataloaders if test_dataloaders and datamodule: raise MisconfigurationException( 'You cannot pass test_dataloaders to trainer.test if you supply a datamodule' ) # Attach datamodule to get setup/prepare_data added to model before the call to it below self.data_connector.attach_datamodule(model or self.get_model(), datamodule, 'test') if model is not None: results = self.__test_given_model(model, test_dataloaders) else: results = self.__test_using_best_weights(ckpt_path, test_dataloaders) self.teardown('test') return results
def __test_using_best_weights(self, ckpt_path, test_dataloaders): model = self.get_model() # if user requests the best checkpoint but we don't have it, error if ckpt_path == 'best' and not self.checkpoint_callback.best_model_path: raise MisconfigurationException( 'ckpt_path is "best", but ModelCheckpoint is not configured to save the best model.' ) # load best weights if ckpt_path is not None: # ckpt_path is 'best' so load the best model if ckpt_path == 'best': ckpt_path = self.checkpoint_callback.best_model_path if len(ckpt_path) == 0: rank_zero_warn( f'.test() found no path for the best weights, {ckpt_path}. Please ' f'specify a path for a checkpoint .test(ckpt_path=PATH)' ) return {} if self.accelerator_backend is not None and not self.use_tpu: self.accelerator_backend.barrier() ckpt = pl_load(ckpt_path, map_location=lambda storage, loc: storage) model.load_state_dict(ckpt['state_dict']) # attach dataloaders if test_dataloaders is not None: self.data_connector.attach_dataloaders(model, test_dataloaders=test_dataloaders) # run tests self.tested_ckpt_path = ckpt_path self.testing = True self.model = model results = self.testing = False # teardown if self.is_function_implemented('teardown'): model_ref = self.get_model() model_ref.teardown('test') return results def __test_given_model(self, model, test_dataloaders): # attach data if test_dataloaders is not None: self.data_connector.attach_dataloaders(model, test_dataloaders=test_dataloaders) # run test # sets up testing so we short circuit to eval self.testing = True self.model = model results = self.testing = False # teardown if self.is_function_implemented('teardown'): model.teardown('test') return results
[docs] def tune( self, model: LightningModule, train_dataloader: Optional[DataLoader] = None, val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None, datamodule: Optional[LightningDataModule] = None, ): r""" Runs routines to tune hyperparameters before training. Args: datamodule: A instance of :class:`LightningDataModule`. model: Model to tune. train_dataloader: A Pytorch DataLoader with training samples. If the model has a predefined train_dataloader method this will be skipped. val_dataloaders: Either a single Pytorch Dataloader or a list of them, specifying validation samples. If the model has a predefined val_dataloaders method this will be skipped """ self.tuner.tune(model, train_dataloader, val_dataloaders, datamodule)
def call_setup_hook(self, model): # call setup after the ddp process has connected stage_name = 'test' if self.testing else 'fit' if self.datamodule is not None: called = self.datamodule.has_setup_test if self.testing else self.datamodule.has_setup_fit if not called: self.datamodule.setup(stage_name) self.setup(model, stage_name) model.setup(stage_name) def _reset_result_and_set_hook_fx_name(self, hook_name): # on_before_zero_grad is called within training_step if "batch_start" in hook_name or "on_before_zero_grad" in hook_name: return True model_ref = self.get_model() if model_ref is not None: # used to track current hook name called model_ref._results = Result() model_ref._current_hook_fx_name = hook_name return False def _cache_logged_metrics(self): model_ref = self.get_model() if model_ref is not None: # capture logging for this hook self.logger_connector.cache_logged_metrics() def call_hook(self, hook_name, *args, **kwargs): # set hook_name to model + reset Result obj skip = self._reset_result_and_set_hook_fx_name(hook_name) # always profile hooks with self.profiler.profile(hook_name): # first call trainer hook if hasattr(self, hook_name): trainer_hook = getattr(self, hook_name) trainer_hook(*args, **kwargs) # next call hook in lightningModule output = None model_ref = self.get_model() if is_overridden(hook_name, model_ref): hook_fx = getattr(model_ref, hook_name) output = hook_fx(*args, **kwargs) # if the PL module doesn't have the hook then call the accelator # used to auto-reduce things for the user with Results obj elif hasattr(self.accelerator_backend, hook_name): accelerator_hook = getattr(self.accelerator_backend, hook_name) output = accelerator_hook(*args, **kwargs) if not skip: self._cache_logged_metrics() return output
[docs] @staticmethod def available_plugins(): """ List of all available plugins that can be string arguments to the trainer. Returns: List of all available plugins that are supported as string arguments. """ return PluginConnector.available_plugins()

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