922 lines
38 KiB
Python
922 lines
38 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Trainer to automate the training."""
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import os
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import warnings
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from pathlib import Path
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from typing import Dict, Iterable, List, Optional, Union
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import torch
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from torch.utils.data import DataLoader
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from pytorch_lightning import _logger as log
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from pytorch_lightning.accelerators.accelerator import Accelerator
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from pytorch_lightning.accelerators.accelerator_connector import AcceleratorConnector
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from pytorch_lightning.trainer.deprecated_api import DeprecatedDistDeviceAttributes
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from pytorch_lightning.callbacks import Callback
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from pytorch_lightning.core.datamodule import LightningDataModule
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.core.step_result import EvalResult, Result
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from pytorch_lightning.loggers import LightningLoggerBase
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from pytorch_lightning.plugins.plugin_connector import PluginConnector
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from pytorch_lightning.profiler import BaseProfiler
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from pytorch_lightning.trainer.callback_hook import TrainerCallbackHookMixin
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from pytorch_lightning.trainer.configuration_validator import ConfigValidator
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from pytorch_lightning.trainer.connectors.callback_connector import CallbackConnector
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from pytorch_lightning.trainer.connectors.checkpoint_connector import CheckpointConnector
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from pytorch_lightning.trainer.connectors.data_connector import DataConnector
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from pytorch_lightning.trainer.connectors.debugging_connector import DebuggingConnector
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from pytorch_lightning.trainer.connectors.env_vars_connector import overwrite_by_env_vars
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from pytorch_lightning.trainer.connectors.logger_connector import LoggerConnector
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from pytorch_lightning.trainer.connectors.model_connector import ModelConnector
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from pytorch_lightning.trainer.connectors.optimizer_connector import OptimizerConnector
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from pytorch_lightning.trainer.connectors.precision_connector import PrecisionConnector
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from pytorch_lightning.trainer.connectors.profiler_connector import ProfilerConnector
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from pytorch_lightning.trainer.connectors.slurm_connector import SLURMConnector
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from pytorch_lightning.trainer.connectors.training_trick_connector import TrainingTricksConnector
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from pytorch_lightning.trainer.data_loading import TrainerDataLoadingMixin
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from pytorch_lightning.trainer.evaluation_loop import EvaluationLoop
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from pytorch_lightning.trainer.logging import TrainerLoggingMixin
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from pytorch_lightning.trainer.model_hooks import TrainerModelHooksMixin
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from pytorch_lightning.trainer.optimizers import TrainerOptimizersMixin
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from pytorch_lightning.trainer.properties import TrainerProperties
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from pytorch_lightning.trainer.states import TrainerState
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from pytorch_lightning.trainer.training_loop import TrainLoop
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from pytorch_lightning.trainer.training_tricks import TrainerTrainingTricksMixin
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from pytorch_lightning.tuner.tuning import Tuner
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from pytorch_lightning.utilities import rank_zero_warn, DeviceType
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from pytorch_lightning.utilities.cloud_io import load as pl_load
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from pytorch_lightning.utilities.debugging import InternalDebugger
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.utilities.memory import recursive_detach
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from pytorch_lightning.utilities.model_helpers import is_overridden
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# warnings to ignore in trainer
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warnings.filterwarnings(
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'ignore', message='torch.distributed.reduce_op is deprecated, ' 'please use torch.distributed.ReduceOp instead'
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)
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class Trainer(
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TrainerProperties,
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TrainerCallbackHookMixin,
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TrainerModelHooksMixin,
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TrainerOptimizersMixin,
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TrainerLoggingMixin,
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TrainerTrainingTricksMixin,
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TrainerDataLoadingMixin,
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DeprecatedDistDeviceAttributes,
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):
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@overwrite_by_env_vars
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def __init__(
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self,
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logger: Union[LightningLoggerBase, Iterable[LightningLoggerBase], bool] = True,
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checkpoint_callback: bool = True,
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callbacks: Optional[Union[List[Callback], Callback]] = None,
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default_root_dir: Optional[str] = None,
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gradient_clip_val: float = 0,
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process_position: int = 0,
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num_nodes: int = 1,
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num_processes: int = 1,
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gpus: Optional[Union[List[int], str, int]] = None,
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auto_select_gpus: bool = False,
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tpu_cores: Optional[Union[List[int], str, int]] = None,
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log_gpu_memory: Optional[str] = None,
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progress_bar_refresh_rate: int = 1,
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overfit_batches: Union[int, float] = 0.0,
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track_grad_norm: Union[int, float, str] = -1,
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check_val_every_n_epoch: int = 1,
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fast_dev_run: Union[int, bool] = False,
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accumulate_grad_batches: Union[int, Dict[int, int], List[list]] = 1,
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max_epochs: int = 1000,
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min_epochs: int = 1,
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max_steps: Optional[int] = None,
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min_steps: Optional[int] = None,
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limit_train_batches: Union[int, float] = 1.0,
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limit_val_batches: Union[int, float] = 1.0,
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limit_test_batches: Union[int, float] = 1.0,
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val_check_interval: Union[int, float] = 1.0,
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flush_logs_every_n_steps: int = 100,
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log_every_n_steps: int = 50,
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accelerator: Optional[Union[str, Accelerator]] = None,
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sync_batchnorm: bool = False,
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precision: int = 32,
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weights_summary: Optional[str] = 'top',
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weights_save_path: Optional[str] = None,
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num_sanity_val_steps: int = 2,
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truncated_bptt_steps: Optional[int] = None,
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resume_from_checkpoint: Optional[Union[Path, str]] = None,
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profiler: Optional[Union[BaseProfiler, bool, str]] = None,
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benchmark: bool = False,
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deterministic: bool = False,
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reload_dataloaders_every_epoch: bool = False,
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auto_lr_find: Union[bool, str] = False,
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replace_sampler_ddp: bool = True,
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terminate_on_nan: bool = False,
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auto_scale_batch_size: Union[str, bool] = False,
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prepare_data_per_node: bool = True,
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plugins: Optional[Union[str, list]] = None,
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amp_backend: str = 'native',
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amp_level: str = 'O2',
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distributed_backend: Optional[str] = None,
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automatic_optimization: Optional[bool] = None,
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move_metrics_to_cpu: bool = False,
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enable_pl_optimizer: bool = False,
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multiple_trainloader_mode: str = 'max_size_cycle',
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):
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r"""
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Customize every aspect of training via flags
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Args:
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accelerator: Previously known as distributed_backend (dp, ddp, ddp2, etc...).
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Can also take in an accelerator object for custom hardware.
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accumulate_grad_batches: Accumulates grads every k batches or as set up in the dict.
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amp_backend: The mixed precision backend to use ("native" or "apex")
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amp_level: The optimization level to use (O1, O2, etc...).
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auto_lr_find: If set to True, will make trainer.tune() run a learning rate finder,
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trying to optimize initial learning for faster convergence. trainer.tune() method will
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set the suggested learning rate in self.lr or self.learning_rate in the LightningModule.
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To use a different key set a string instead of True with the key name.
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auto_scale_batch_size: If set to True, will `initially` run a batch size
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finder trying to find the largest batch size that fits into memory.
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The result will be stored in self.batch_size in the LightningModule.
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Additionally, can be set to either `power` that estimates the batch size through
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a power search or `binsearch` that estimates the batch size through a binary search.
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auto_select_gpus: If enabled and `gpus` is an integer, pick available
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gpus automatically. This is especially useful when
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GPUs are configured to be in "exclusive mode", such
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that only one process at a time can access them.
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benchmark: If true enables cudnn.benchmark.
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callbacks: Add a callback or list of callbacks.
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checkpoint_callback: If ``True``, enable checkpointing.
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It will configure a default ModelCheckpoint callback if there is no user-defined ModelCheckpoint in
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:paramref:`~pytorch_lightning.trainer.trainer.Trainer.callbacks`. Default: ``True``.
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.. warning:: Passing a ModelCheckpoint instance to this argument is deprecated since
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v1.1 and will be unsupported from v1.3. Use `callbacks` argument instead.
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check_val_every_n_epoch: Check val every n train epochs.
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default_root_dir: Default path for logs and weights when no logger/ckpt_callback passed.
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Default: ``os.getcwd()``.
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Can be remote file paths such as `s3://mybucket/path` or 'hdfs://path/'
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deterministic: If true enables cudnn.deterministic.
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distributed_backend: deprecated. Please use 'accelerator'
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fast_dev_run: runs n if set to ``n`` (int) else 1 if set to ``True`` batch(es)
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of train, val and test to find any bugs (ie: a sort of unit test).
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flush_logs_every_n_steps: How often to flush logs to disk (defaults to every 100 steps).
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gpus: number of gpus to train on (int) or which GPUs to train on (list or str) applied per node
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gradient_clip_val: 0 means don't clip.
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limit_train_batches: How much of training dataset to check (floats = percent, int = num_batches)
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limit_val_batches: How much of validation dataset to check (floats = percent, int = num_batches)
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limit_test_batches: How much of test dataset to check (floats = percent, int = num_batches)
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logger: Logger (or iterable collection of loggers) for experiment tracking.
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log_gpu_memory: None, 'min_max', 'all'. Might slow performance
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log_every_n_steps: How often to log within steps (defaults to every 50 steps).
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automatic_optimization: If False you are responsible for calling .backward, .step, zero_grad
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in LightningModule. This argument has been moved to LightningModule. It is deprecated
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here in v1.1 and will be removed in v1.3.
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prepare_data_per_node: If True, each LOCAL_RANK=0 will call prepare data.
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Otherwise only NODE_RANK=0, LOCAL_RANK=0 will prepare data
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process_position: orders the progress bar when running multiple models on same machine.
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progress_bar_refresh_rate: How often to refresh progress bar (in steps). Value ``0`` disables progress bar.
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Ignored when a custom callback is passed to :paramref:`~Trainer.callbacks`.
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profiler: To profile individual steps during training and assist in identifying bottlenecks. Passing bool
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value is deprecated in v1.1 and will be removed in v1.3.
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overfit_batches: Overfit a percent of training data (float) or a set number of batches (int). Default: 0.0
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plugins: Plugins allow modification of core behavior like ddp and amp, and enable custom lightning plugins.
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precision: Full precision (32), half precision (16). Can be used on CPU, GPU or TPUs.
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max_epochs: Stop training once this number of epochs is reached.
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min_epochs: Force training for at least these many epochs
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max_steps: Stop training after this number of steps. Disabled by default (None).
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min_steps: Force training for at least these number of steps. Disabled by default (None).
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num_nodes: number of GPU nodes for distributed training.
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num_processes: number of processes for distributed training with distributed_backend="ddp_cpu"
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num_sanity_val_steps: Sanity check runs n validation batches before starting the training routine.
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Set it to `-1` to run all batches in all validation dataloaders. Default: 2
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reload_dataloaders_every_epoch: Set to True to reload dataloaders every epoch.
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replace_sampler_ddp: Explicitly enables or disables sampler replacement. If not specified this
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will toggled automatically when DDP is used. By default it will add ``shuffle=True`` for
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train sampler and ``shuffle=False`` for val/test sampler. If you want to customize it,
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you can set ``replace_sampler_ddp=False`` and add your own distributed sampler.
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resume_from_checkpoint: Path/URL of the checkpoint from which training is resumed. If there is
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no checkpoint file at the path, start from scratch. If resuming from mid-epoch checkpoint,
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training will start from the beginning of the next epoch.
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sync_batchnorm: Synchronize batch norm layers between process groups/whole world.
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terminate_on_nan: If set to True, will terminate training (by raising a `ValueError`) at the
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end of each training batch, if any of the parameters or the loss are NaN or +/-inf.
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tpu_cores: How many TPU cores to train on (1 or 8) / Single TPU to train on [1]
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track_grad_norm: -1 no tracking. Otherwise tracks that p-norm. May be set to 'inf' infinity-norm.
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truncated_bptt_steps: Truncated back prop breaks performs backprop every k steps of much longer
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sequence.
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val_check_interval: How often to check the validation set. Use float to check within a training epoch,
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use int to check every n steps (batches).
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weights_summary: Prints a summary of the weights when training begins.
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weights_save_path: Where to save weights if specified. Will override default_root_dir
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for checkpoints only. Use this if for whatever reason you need the checkpoints
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stored in a different place than the logs written in `default_root_dir`.
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Can be remote file paths such as `s3://mybucket/path` or 'hdfs://path/'
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Defaults to `default_root_dir`.
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move_metrics_to_cpu: Whether to force internal logged metrics to be moved to cpu.
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This can save some gpu memory, but can make training slower. Use with attention.
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enable_pl_optimizer: If True, each optimizer will be wrapped by
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`pytorch_lightning.core.optimizer.LightningOptimizer`. It allows Lightning to
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handle AMP, TPU, accumulated_gradients, etc..
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multiple_trainloader_mode: How to loop over the datasets when there are multiple train loaders.
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In 'max_size_cycle' mode, the trainer ends one epoch when the largest dataset is traversed,
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and smaller datasets reload when running out of their data. In 'min_size' mode, all the datasets
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reload when reaching the minimum length of datasets.
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"""
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super().__init__()
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self._device_type = DeviceType.CPU
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self._distrib_type = None
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# init connectors
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self.dev_debugger = InternalDebugger(self)
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self.config_validator = ConfigValidator(self)
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self.data_connector = DataConnector(self)
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self.optimizer_connector = OptimizerConnector(self)
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self.accelerator_connector = AcceleratorConnector(self)
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self.logger_connector = LoggerConnector(self)
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self.model_connector = ModelConnector(self)
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self.precision_connector = PrecisionConnector(self)
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self.callback_connector = CallbackConnector(self)
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self.debugging_connector = DebuggingConnector(self)
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self.training_tricks_connector = TrainingTricksConnector(self)
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self.profile_connector = ProfilerConnector(self)
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self.checkpoint_connector = CheckpointConnector(self)
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self.slurm_connector = SLURMConnector(self)
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self.tuner = Tuner(self)
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self.accelerator_backend = None
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self.evaluation_loop = EvaluationLoop(self)
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self.train_loop = TrainLoop(self, multiple_trainloader_mode)
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self.plugin_connector = PluginConnector(self)
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# training state
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self.model = None
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self.shown_warnings = set()
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# init callbacks
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# Declare attributes to be set in callback_connector on_trainer_init
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self.callback_connector.on_trainer_init(
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callbacks,
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checkpoint_callback,
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progress_bar_refresh_rate,
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process_position,
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default_root_dir,
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weights_save_path,
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resume_from_checkpoint,
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)
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# hook
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self.on_init_start()
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# init optimizer + lr scheduler related flags
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self.optimizer_connector.on_trainer_init(enable_pl_optimizer)
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# init data flags
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self.data_connector.on_trainer_init(
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check_val_every_n_epoch, reload_dataloaders_every_epoch, prepare_data_per_node
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)
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# init training tricks
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self.training_tricks_connector.on_trainer_init(
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gradient_clip_val, track_grad_norm, accumulate_grad_batches, truncated_bptt_steps, terminate_on_nan
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)
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# init accelerator related flags
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self.accelerator_connector.on_trainer_init(
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num_processes,
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tpu_cores,
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accelerator,
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distributed_backend,
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auto_select_gpus,
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gpus,
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num_nodes,
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log_gpu_memory,
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sync_batchnorm,
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benchmark,
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replace_sampler_ddp,
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deterministic,
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)
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# init train loop related flags
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# TODO: remove in 1.3.0
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if automatic_optimization is None:
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automatic_optimization = True
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else:
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rank_zero_warn(
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"Disable automatic optimization with the trainer flag is deprecated and will be removed in v1.3.0!"
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"Please use the property on the LightningModule for disabling automatic optimization"
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)
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self.train_loop.on_trainer_init(
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max_epochs,
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min_epochs,
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max_steps,
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min_steps,
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num_sanity_val_steps,
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automatic_optimization,
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weights_summary,
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)
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self.evaluation_loop.on_trainer_init()
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# configure tuner
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self.tuner.on_trainer_init(auto_lr_find, auto_scale_batch_size)
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# configure profiler
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self.profile_connector.on_trainer_init(profiler)
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# init logger flags
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self.logger_connector.on_trainer_init(
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logger,
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flush_logs_every_n_steps,
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log_every_n_steps,
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move_metrics_to_cpu,
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)
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# init debugging flags
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self.debugging_connector.on_init_start(
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limit_train_batches,
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limit_val_batches,
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limit_test_batches,
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val_check_interval,
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overfit_batches,
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fast_dev_run,
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)
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# set precision
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self.precision_connector.on_trainer_init(precision, amp_level, amp_backend)
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# last thing are the plugins which override whatever the trainer used by default
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self.plugin_connector.on_trainer_init(plugins)
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# Callback system
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self.on_init_end()
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def fit(
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self,
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model: LightningModule,
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train_dataloader: Optional[DataLoader] = None,
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val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None,
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datamodule: Optional[LightningDataModule] = None,
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):
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r"""
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Runs the full optimization routine.
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Args:
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datamodule: A instance of :class:`LightningDataModule`.
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model: Model to fit.
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train_dataloader: A Pytorch DataLoader with training samples. If the model has
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a predefined train_dataloader method this will be skipped.
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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)
|
|
|
|
# bookkeeping
|
|
# we reuse fit in .test() but change its behavior using this flag
|
|
self.testing = os.environ.get('PL_TESTING_MODE', self.testing)
|
|
|
|
# ----------------------------
|
|
# SET UP TRAINING
|
|
# ----------------------------
|
|
self.accelerator_backend = self.accelerator_connector.select_accelerator()
|
|
self.call_hook("on_before_accelerator_backend_setup", model)
|
|
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
|
|
log.info(
|
|
'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, test_mode: bool = False, max_batches=None):
|
|
|
|
# used to know if we are logging for val, test + reset cached results
|
|
self.logger_connector.set_stage(test_mode, reset=True)
|
|
|
|
# bookkeeping
|
|
self.evaluation_loop.testing = test_mode
|
|
|
|
# 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(test_mode, 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(test_mode=True)
|
|
|
|
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(test_mode=False, 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
|
|
|
|
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
|
|
os.environ['PL_TESTING_MODE'] = '1'
|
|
self.model = model
|
|
results = self.fit(model)
|
|
self.testing = False
|
|
del os.environ['PL_TESTING_MODE']
|
|
|
|
# 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.fit(model)
|
|
self.testing = False
|
|
|
|
# teardown
|
|
if self.is_function_implemented('teardown'):
|
|
model.teardown('test')
|
|
|
|
return results
|
|
|
|
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
|
|
|
|
@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()
|