# 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 torch.utils.data import DataLoader
from catalyst.core import IExperiment
from catalyst.data import Augmentor, AugmentorCompose
from catalyst.dl import (
AMPOptimizerCallback,
BatchOverfitCallback,
Callback,
CheckpointCallback,
CheckRunCallback,
ConsoleLogger,
CriterionCallback,
ExceptionCallback,
IOptimizerCallback,
ISchedulerCallback,
MetricManagerCallback,
OptimizerCallback,
SchedulerCallback,
TensorboardLogger,
TimerCallback,
utils,
ValidationManagerCallback,
VerboseLogger,
)
from catalyst.dl.utils import check_amp_available, check_callback_isinstance
from catalyst.registry import (
CALLBACKS,
CRITERIONS,
MODELS,
OPTIMIZERS,
SCHEDULERS,
TRANSFORMS,
)
from catalyst.tools.typing 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 https://arxiv.org/pdf/1706.02677.pdf
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: Union[Optimizer, Dict[str, Optimizer]], **params: Any
) -> Union[Scheduler, Dict[str, Scheduler]]:
optimizer_key = params.pop("_optimizer", None)
optimizer_ = optimizer[optimizer_key] if optimizer_key else optimizer
scheduler = SCHEDULERS.get_from_params(**params, optimizer=optimizer_)
return scheduler
[docs] def get_scheduler(
self, stage: str, optimizer: Union[Optimizer, Dict[str, Optimizer]]
) -> Union[Scheduler, Dict[str, Scheduler]]:
"""Returns the scheduler for a given stage."""
params = self.stages_config[stage].get("scheduler_params", {})
key_value_flag = params.pop("_key_value", False)
if key_value_flag:
scheduler: Dict[str, Scheduler] = {}
for key, scheduler_params in params.items():
scheduler[key] = self._get_scheduler(
optimizer=optimizer, **scheduler_params
)
else:
scheduler = self._get_scheduler(optimizer=optimizer, **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_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, stage_index: int = None
) -> None:
"""
Iterate over each of the callbacks and update
appropriate parameters required for success
run of config experiment.
Arguments:
callbacks (OrderedDict): finalized order of callbacks.
stage_index (int): number of a current stage
"""
if stage_index is None:
stage_index = -float("inf")
for callback in callbacks.values():
# NOTE: in experiments with multiple stages need to omit
# loading of a best model state for the first stage
# but for the other stages by default should
# load best state of a model
# @TODO: move this logic to ``CheckpointCallback``
if isinstance(callback, CheckpointCallback) and stage_index > 0:
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 = [(Name, InterfaceClass, InstanceFactory)]
default_callbacks = []
is_amp_enabled = (
self.distributed_params.get("amp", False) and check_amp_available()
)
optimizer_cls = (
AMPOptimizerCallback if is_amp_enabled else OptimizerCallback
)
if self._verbose:
default_callbacks.append(("_verbose", None, VerboseLogger))
if self._check_time:
default_callbacks.append(("_timer", None, TimerCallback))
if self._check_run:
default_callbacks.append(("_check", None, CheckRunCallback))
if self._overfit:
default_callbacks.append(("_overfit", None, BatchOverfitCallback))
if not stage.startswith("infer"):
default_callbacks.append(("_metrics", None, MetricManagerCallback))
default_callbacks.append(
("_validation", None, ValidationManagerCallback)
)
default_callbacks.append(("_console", None, ConsoleLogger))
if self.logdir is not None:
default_callbacks.append(("_saver", None, CheckpointCallback))
default_callbacks.append(
("_tensorboard", None, TensorboardLogger)
)
if self.stages_config[stage].get("criterion_params", {}):
default_callbacks.append(
("_criterion", None, CriterionCallback)
)
if self.stages_config[stage].get("optimizer_params", {}):
default_callbacks.append(
("_optimizer", IOptimizerCallback, optimizer_cls)
)
if self.stages_config[stage].get("scheduler_params", {}):
default_callbacks.append(
("_scheduler", ISchedulerCallback, SchedulerCallback)
)
default_callbacks.append(("_exception", None, ExceptionCallback))
for (
callback_name,
callback_interface,
callback_fn,
) in default_callbacks:
callback_interface = callback_interface or callback_fn
is_already_present = any(
check_callback_isinstance(x, callback_interface)
for x in callbacks.values()
)
if not is_already_present:
callbacks[callback_name] = callback_fn()
# NOTE: stage should be in self.stages_config
# othervise will be raised ValueError
stage_index = list(self.stages_config.keys()).index(stage)
self._process_callbacks(callbacks, stage_index)
return callbacks
__all__ = ["ConfigExperiment"]