Source code for catalyst.dl.experiment.config

# flake8: noqa
# @TODO: code formatting issue for 20.07 release
from typing import Any, Callable, Dict, List, Mapping, Union
from collections import OrderedDict
from copy import deepcopy

import torch
from torch import nn
from import DataLoader

from catalyst.core import IExperiment
from import Augmentor, AugmentorCompose
from catalyst.dl import (
from catalyst.dl.utils import check_callback_isinstance
from catalyst.registry import (
from import Criterion, Model, Optimizer, Scheduler

[docs]class ConfigExperiment(IExperiment): """ Experiment created from a configuration file. """ STAGE_KEYWORDS = [ # noqa: WPS115 "criterion_params", "optimizer_params", "scheduler_params", "data_params", "transform_params", "stage_params", "callbacks_params", ]
[docs] def __init__(self, config: Dict): """ Args: config (dict): dictionary with parameters """ self._config: Dict = deepcopy(config) self._initial_seed: int = self._config.get("args", {}).get("seed", 42) self._verbose: bool = self._config.get("args", {}).get( "verbose", False ) self._check_time: bool = self._config.get("args", {}).get( "timeit", False ) self._check_run: bool = self._config.get("args", {}).get( "check", False ) self._overfit: bool = self._config.get("args", {}).get( "overfit", False ) self.__prepare_logdir() self._config["stages"]["stage_params"] = utils.merge_dicts( deepcopy( self._config["stages"].get("state_params", {}) ), # saved for backward compatibility deepcopy(self._config["stages"].get("stage_params", {})), deepcopy(self._config.get("args", {})), {"logdir": self._logdir}, ) self.stages_config: Dict = self._get_stages_config( self._config["stages"] )
def __prepare_logdir(self): # noqa: WPS112 exclude_tag = "none" logdir = self._config.get("args", {}).get("logdir", None) baselogdir = self._config.get("args", {}).get("baselogdir", None) if logdir is not None and logdir.lower() != exclude_tag: self._logdir = logdir elif baselogdir is not None and baselogdir.lower() != exclude_tag: logdir_postfix = self._get_logdir(self._config) self._logdir = f"{baselogdir}/{logdir_postfix}" else: self._logdir = None @property def hparams(self) -> OrderedDict: """Returns hyperparameters""" return OrderedDict(self._config) def _get_stages_config(self, stages_config: Dict): stages_defaults = {} stages_config_out = OrderedDict() for key in self.STAGE_KEYWORDS: if key == "stage_params": # backward compatibility stages_defaults[key] = utils.merge_dicts( deepcopy(stages_config.get("state_params", {})), deepcopy(stages_config.get(key, {})), ) else: stages_defaults[key] = deepcopy(stages_config.get(key, {})) for stage in stages_config: if ( stage in self.STAGE_KEYWORDS or stage == "state_params" or stages_config.get(stage) is None ): continue stages_config_out[stage] = {} for key2 in self.STAGE_KEYWORDS: if key2 == "stage_params": # backward compatibility stages_config_out[stage][key2] = utils.merge_dicts( deepcopy(stages_defaults.get("state_params", {})), deepcopy(stages_defaults.get(key2, {})), deepcopy(stages_config[stage].get("state_params", {})), deepcopy(stages_config[stage].get(key2, {})), ) else: stages_config_out[stage][key2] = utils.merge_dicts( deepcopy(stages_defaults.get(key2, {})), deepcopy(stages_config[stage].get(key2, {})), ) return stages_config_out def _get_logdir(self, config: Dict) -> str: timestamp = utils.get_utcnow_time() config_hash = utils.get_short_hash(config) logdir = f"{timestamp}.{config_hash}" return logdir @property def initial_seed(self) -> int: """Experiment's initial seed value.""" return self._initial_seed @property def logdir(self): """Path to the directory where the experiment logs.""" return self._logdir @property def stages(self) -> List[str]: """Experiment's stage names.""" stages_keys = list(self.stages_config.keys()) return stages_keys @property def distributed_params(self) -> Dict: """Dict with the parameters for distributed and FP16 methond.""" return self._config.get("distributed_params", {})
[docs] def get_stage_params(self, stage: str) -> Mapping[str, Any]: """Returns the state parameters for a given stage.""" return self.stages_config[stage].get("stage_params", {})
@staticmethod def _get_model(**params): key_value_flag = params.pop("_key_value", False) if key_value_flag: model = {} for model_key, model_params in params.items(): model[model_key] = ConfigExperiment._get_model( # noqa: WPS437 **model_params ) model = nn.ModuleDict(model) else: model = MODELS.get_from_params(**params) return model
[docs] def get_model(self, stage: str): """Returns the model for a given stage.""" model_params = self._config["model_params"] model = self._get_model(**model_params) return model
@staticmethod def _get_criterion(**params): key_value_flag = params.pop("_key_value", False) if key_value_flag: criterion = {} for key, key_params in params.items(): criterion[ key ] = ConfigExperiment._get_criterion( # noqa: WPS437 **key_params ) else: criterion = CRITERIONS.get_from_params(**params) if criterion is not None and torch.cuda.is_available(): criterion = criterion.cuda() return criterion
[docs] def get_criterion(self, stage: str) -> Criterion: """Returns the criterion for a given stage.""" criterion_params = self.stages_config[stage].get( "criterion_params", {} ) criterion = self._get_criterion(**criterion_params) return criterion
def _get_optimizer( self, stage: str, model: Union[Model, Dict[str, Model]], **params ) -> Optimizer: # @TODO 1: refactoring; this method is too long # @TODO 2: load state dicts for schedulers & criterion layerwise_params = params.pop("layerwise_params", OrderedDict()) no_bias_weight_decay = params.pop("no_bias_weight_decay", True) # linear scaling rule from lr_scaling_params = params.pop("lr_linear_scaling", None) if lr_scaling_params: data_params = dict(self.stages_config[stage]["data_params"]) batch_size = data_params.get("batch_size") per_gpu_scaling = data_params.get("per_gpu_scaling", False) distributed_rank = utils.get_rank() distributed = distributed_rank > -1 if per_gpu_scaling and not distributed: num_gpus = max(1, torch.cuda.device_count()) batch_size *= num_gpus base_lr = lr_scaling_params.get("lr") base_batch_size = lr_scaling_params.get("base_batch_size", 256) lr_scaling = batch_size / base_batch_size params["lr"] = base_lr * lr_scaling # scale default lr else: lr_scaling = 1.0 # getting model parameters model_key = params.pop("_model", None) if model_key is None: assert isinstance( model, nn.Module ), "model is key-value, but optimizer has no specified model" model_params = utils.process_model_params( model, layerwise_params, no_bias_weight_decay, lr_scaling ) elif isinstance(model_key, str): model_params = utils.process_model_params( model[model_key], layerwise_params, no_bias_weight_decay, lr_scaling, ) elif isinstance(model_key, (list, tuple)): model_params = [] for model_key_el in model_key: model_params_el = utils.process_model_params( model[model_key_el], layerwise_params, no_bias_weight_decay, lr_scaling, ) model_params.extend(model_params_el) else: raise ValueError("unknown type of model_params") load_from_previous_stage = params.pop( "load_from_previous_stage", False ) optimizer_key = params.pop("optimizer_key", None) optimizer = OPTIMIZERS.get_from_params(**params, params=model_params) if load_from_previous_stage and self.stages.index(stage) != 0: checkpoint_path = f"{self.logdir}/checkpoints/best_full.pth" checkpoint = utils.load_checkpoint(checkpoint_path) dict2load = optimizer if optimizer_key is not None: dict2load = {optimizer_key: optimizer} utils.unpack_checkpoint(checkpoint, optimizer=dict2load) # move optimizer to device device = utils.get_device() for param in model_params: param = param["params"][0] optimizer_state = optimizer.state[param] for state_key, state_value in optimizer_state.items(): optimizer_state[state_key] = utils.any2device( state_value, device ) # update optimizer params for key, value in params.items(): for optimizer_param_group in optimizer.param_groups: optimizer_param_group[key] = value return optimizer
[docs] def get_optimizer( self, stage: str, model: Union[Model, Dict[str, Model]] ) -> Union[Optimizer, Dict[str, Optimizer]]: """ Returns the optimizer for a given stage. Args: stage (str): stage name model (Union[Model, Dict[str, Model]]): model or a dict of models Returns: optimizer for selected stage """ optimizer_params = self.stages_config[stage].get( "optimizer_params", {} ) key_value_flag = optimizer_params.pop("_key_value", False) if key_value_flag: optimizer = {} for key, params in optimizer_params.items(): # load specified optimizer from checkpoint optimizer_key = "optimizer_key" assert optimizer_key not in params, "keyword reserved" params[optimizer_key] = key optimizer[key] = self._get_optimizer(stage, model, **params) else: optimizer = self._get_optimizer(stage, model, **optimizer_params) return optimizer
@staticmethod def _get_scheduler(*, optimizer, **params): key_value_flag = params.pop("_key_value", False) if key_value_flag: scheduler = {} for scheduler_key, scheduler_params in params.items(): scheduler[ scheduler_key ] = ConfigExperiment._get_scheduler( # noqa: WPS437 optimizer=optimizer, **scheduler_params ) else: scheduler = SCHEDULERS.get_from_params( **params, optimizer=optimizer ) return scheduler
[docs] def get_scheduler(self, stage: str, optimizer: Optimizer) -> Scheduler: """Returns the scheduler for a given stage.""" scheduler_params = self.stages_config[stage].get( "scheduler_params", {} ) scheduler = self._get_scheduler( optimizer=optimizer, **scheduler_params ) return scheduler
@staticmethod def _get_transform(**params) -> Callable: key_value_flag = params.pop("_key_value", False) if key_value_flag: transforms_composition = { transform_key: ConfigExperiment._get_transform( # noqa: WPS437 **transform_params ) for transform_key, transform_params in params.items() } transform = AugmentorCompose( { key: Augmentor( dict_key=key, augment_fn=transform, input_key=key, output_key=key, ) for key, transform in transforms_composition.items() } ) else: if "transforms" in params: transforms_composition = [ ConfigExperiment._get_transform( # noqa: WPS437 **transform_params ) for transform_params in params["transforms"] ] params.update(transforms=transforms_composition) transform = TRANSFORMS.get_from_params(**params) return transform
[docs] def get_transforms( self, stage: str = None, dataset: str = None ) -> Callable: """ Returns transform for a given stage and dataset. Args: stage (str): stage name dataset (str): dataset name (e.g. "train", "valid"), will be used only if the value of `_key_value`` is ``True`` Returns: Callable: transform function """ transform_params = deepcopy( self.stages_config[stage].get("transform_params", {}) ) key_value_flag = transform_params.pop("_key_value", False) if key_value_flag: transform_params = transform_params.get(dataset, {}) transform_fn = self._get_transform(**transform_params) if transform_fn is None: def transform_fn(dict_): # noqa: WPS440 return dict_ elif not isinstance(transform_fn, AugmentorCompose): transform_fn_origin = transform_fn def transform_fn(dict_): # noqa: WPS440 return transform_fn_origin(**dict_) return transform_fn
[docs] def get_loaders( self, stage: str, epoch: int = None, ) -> "OrderedDict[str, DataLoader]": """Returns the loaders for a given stage.""" data_params = dict(self.stages_config[stage]["data_params"]) loaders = utils.get_loaders_from_params( get_datasets_fn=self.get_datasets, initial_seed=self.initial_seed, stage=stage, **data_params, ) return loaders
@staticmethod def _get_callback(**params): wrapper_params = params.pop("_wrapper", None) callback = CALLBACKS.get_from_params(**params) if wrapper_params is not None: wrapper_params["base_callback"] = callback callback = ConfigExperiment._get_callback( # noqa: WPS437 **wrapper_params ) return callback @staticmethod def _process_callbacks(callbacks: OrderedDict) -> None: """ Iterate over each of the callbacks and update approptiate parameters required for success run of config experiment. Arguments: callbacks (OrderedDict): finalized order of callbacks. """ for callback in callbacks.values(): if isinstance(callback, CheckpointCallback): if callback.load_on_stage_start is None: callback.load_on_stage_start = "best" if ( isinstance(callback.load_on_stage_start, dict) and "model" not in callback.load_on_stage_start ): callback.load_on_stage_start["model"] = "best"
[docs] def get_callbacks(self, stage: str) -> "OrderedDict[Callback]": """Returns the callbacks for a given stage.""" callbacks_params = self.stages_config[stage].get( "callbacks_params", {} ) callbacks = OrderedDict() for key, callback_params in callbacks_params.items(): callback = self._get_callback(**callback_params) callbacks[key] = callback default_callbacks = [] if self._verbose: default_callbacks.append(("_verbose", VerboseLogger)) if self._check_time: default_callbacks.append(("_timer", TimerCallback)) if self._check_run: default_callbacks.append(("_check", CheckRunCallback)) if self._overfit: default_callbacks.append(("_overfit", BatchOverfitCallback)) if not stage.startswith("infer"): default_callbacks.append(("_metrics", MetricManagerCallback)) default_callbacks.append( ("_validation", ValidationManagerCallback) ) default_callbacks.append(("_console", ConsoleLogger)) if self.logdir is not None: default_callbacks.append(("_saver", CheckpointCallback)) default_callbacks.append(("_tensorboard", TensorboardLogger)) if self.stages_config[stage].get("criterion_params", {}): default_callbacks.append(("_criterion", CriterionCallback)) if self.stages_config[stage].get("optimizer_params", {}): default_callbacks.append(("_optimizer", OptimizerCallback)) if self.stages_config[stage].get("scheduler_params", {}): default_callbacks.append(("_scheduler", SchedulerCallback)) default_callbacks.append(("_exception", ExceptionCallback)) for callback_name, callback_fn in default_callbacks: is_already_present = False for x in callbacks.values(): if check_callback_isinstance(x, callback_fn): is_already_present = True break if not is_already_present: callbacks[callback_name] = callback_fn() self._process_callbacks(callbacks) return callbacks
__all__ = ["ConfigExperiment"]