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hparams as dict [blocked by 1041] (#1029)

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* hparams as dict

* hparams as dict

* fixing

* fixing

* fixing

* fixing

* typing

* typing

* chnagelog

* update set hparams

* use setter

* simplify

* chnagelog

* imports

* pylint

* typing

* Update training_io.py

* Update training_io.py

* Update lightning.py

* Update test_trainer.py

* Update __init__.py

* Update base.py

* Update utils.py

* Update test_trainer.py

* Update training_io.py

* Update test_trainer.py

* Update test_trainer.py

* Update test_trainer.py

* Update test_trainer.py

* Update callback_config.py

* Update callback_config.py

* Update test_trainer.py

Co-authored-by: William Falcon <waf2107@columbia.edu>
This commit is contained in:
Jirka Borovec
2020-03-04 09:33:39 -05:00
committed by GitHub
co-authored by William Falcon
parent 6a39573267
commit e586ed4767
18 changed files with 168 additions and 87 deletions
Download Patch File Download Diff File Expand all files Collapse all files
pytorch_lightning/callbacks/model_checkpoint.py
+3 -1
View File
@@ -27,9 +27,11 @@ class ModelCheckpoint(Callback):
# save epoch and val_loss in name
ModelCheckpoint(filepath='{epoch:02d}-{val_loss:.2f}.hdf5')
# saves file like: /my/path/here/sample-mnist_epoch=02_val_loss=0.32.ckpt
# if model already exits, the file will be: /my/path/here/sample-mnist-v0_epoch=02_val_loss=0.32.ckpt
monitor: quantity to monitor.
verbose: verbosity mode, False or True.
save_top_k: if `save_top_k == k`,
@@ -135,7 +137,7 @@ class ModelCheckpoint(Callback):
if self.save_function is not None:
self.save_function(filepath)
else:
raise ValueError(".save_function() not set")
raise ValueError("Method `.save_function()` not set")
def check_monitor_top_k(self, current: float) -> bool:
less_than_k_models = len(self.best_k_models) < self.save_top_k
pytorch_lightning/core/lightning.py
+17 -2
View File
@@ -5,7 +5,7 @@ import os
import warnings
from abc import ABC, abstractmethod
from argparse import Namespace
from typing import Optional, Union, Dict, Callable
from typing import Any, Callable, Dict, Optional, Union
import torch
import torch.distributed as dist
@@ -68,6 +68,20 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
#: True if using amp
self.use_amp = False
@property
def hparams(self) -> Namespace:
if not hasattr(self, '_hparams'):
return Namespace()
assert isinstance(self._hparams, dict)
return Namespace(**self._hparams)
@hparams.setter
def hparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
"""Set the model hyper-parameters."""
if isinstance(params, Namespace):
params = vars(params)
self._hparams = params
def print(self, *args, **kwargs):
r"""
Prints only from process 0. Use this in any distributed mode to log only once
@@ -1201,7 +1215,8 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
if cls_takes_hparams:
if ckpt_hparams is not None:
hparams = Namespace(**ckpt_hparams)
is_namespace = checkpoint.get('hparams_type') == 'namespace'
hparams = Namespace(**ckpt_hparams) if is_namespace else ckpt_hparams
else:
warnings.warn(
f"Checkpoint does not contain hyperparameters but {cls.__name__}'s __init__ contains"
pytorch_lightning/core/saving.py
+2 -4
View File
@@ -36,16 +36,14 @@ class ModelIO(object):
"""
def load_hparams_from_tags_csv(tags_csv):
def load_hparams_from_tags_csv(tags_csv) -> Namespace:
if not os.path.isfile(tags_csv):
log.warning(f'Missing Tags: {tags_csv}.')
return Namespace()
tags = {}
with open(tags_csv) as f:
csv_reader = csv.reader(f, delimiter=',')
for row in list(csv_reader)[1:]:
tags[row[0]] = convert(row[1])
tags = {row[0]: convert(row[1]) for row in list(csv_reader)[1:]}
ns = Namespace(**tags)
return ns
pytorch_lightning/loggers/base.py
+17 -10
View File
@@ -1,5 +1,6 @@
import argparse
from abc import ABC, abstractmethod
from argparse import Namespace
from functools import wraps
from typing import Union, Optional, Dict, Iterable, Any, Callable, List
@@ -41,6 +42,12 @@ class LightningLoggerBase(ABC):
"""
pass
def _convert_params(self, params: Union[Dict[str, Any], Namespace]) -> Dict[str, Any]:
# in case converting from namespace
if isinstance(params, Namespace):
params = vars(params)
return params
@abstractmethod
def log_hyperparams(self, params: argparse.Namespace):
"""Record hyperparameters.
@@ -50,11 +57,11 @@ class LightningLoggerBase(ABC):
"""
pass
def save(self):
def save(self) -> None:
"""Save log data."""
pass
def finalize(self, status: str):
def finalize(self, status: str) -> None:
"""Do any processing that is necessary to finalize an experiment.
Args:
@@ -62,7 +69,7 @@ class LightningLoggerBase(ABC):
"""
pass
def close(self):
def close(self) -> None:
"""Do any cleanup that is necessary to close an experiment."""
pass
@@ -72,7 +79,7 @@ class LightningLoggerBase(ABC):
return self._rank
@rank.setter
def rank(self, value: int):
def rank(self, value: int) -> None:
"""Set the process rank."""
self._rank = value
@@ -107,23 +114,23 @@ class LoggerCollection(LightningLoggerBase):
def experiment(self) -> List[Any]:
return [logger.experiment for logger in self._logger_iterable]
def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
[logger.log_metrics(metrics, step) for logger in self._logger_iterable]
def log_hyperparams(self, params: argparse.Namespace):
def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
[logger.log_hyperparams(params) for logger in self._logger_iterable]
def save(self):
def save(self) -> None:
[logger.save() for logger in self._logger_iterable]
def finalize(self, status: str):
def finalize(self, status: str) -> None:
[logger.finalize(status) for logger in self._logger_iterable]
def close(self):
def close(self) -> None:
[logger.close() for logger in self._logger_iterable]
@LightningLoggerBase.rank.setter
def rank(self, value: int):
def rank(self, value: int) -> None:
self._rank = value
for logger in self._logger_iterable:
logger.rank = value
pytorch_lightning/loggers/comet.py
+8 -7
View File
@@ -5,9 +5,9 @@ r"""
CometLogger
-------------
"""
import argparse
from argparse import Namespace
from logging import getLogger
from typing import Optional, Dict, Union
from typing import Optional, Dict, Union, Any
try:
from comet_ml import Experiment as CometExperiment
@@ -162,15 +162,16 @@ class CometLogger(LightningLoggerBase):
return self._experiment
@rank_zero_only
def log_hyperparams(self, params: argparse.Namespace):
self.experiment.log_parameters(vars(params))
def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
params = self._convert_params(params)
self.experiment.log_parameters(params)
@rank_zero_only
def log_metrics(
self,
metrics: Dict[str, Union[torch.Tensor, float]],
step: Optional[int] = None
):
) -> None:
# Comet.ml expects metrics to be a dictionary of detached tensors on CPU
for key, val in metrics.items():
if is_tensor(val):
@@ -182,7 +183,7 @@ class CometLogger(LightningLoggerBase):
self._experiment = None
@rank_zero_only
def finalize(self, status: str):
def finalize(self, status: str) -> None:
r"""
When calling self.experiment.end(), that experiment won't log any more data to Comet. That's why, if you need
to log any more data you need to create an ExistingCometExperiment. For example, to log data when testing your
@@ -199,7 +200,7 @@ class CometLogger(LightningLoggerBase):
return self.experiment.project_name
@name.setter
def name(self, value: str):
def name(self, value: str) -> None:
self.experiment.set_name(value)
@property
pytorch_lightning/loggers/mlflow.py
+7 -6
View File
@@ -23,10 +23,10 @@ Use the logger anywhere in you LightningModule as follows:
self.logger.experiment.whatever_ml_flow_supports(...)
"""
import argparse
from argparse import Namespace
from logging import getLogger
from time import time
from typing import Optional, Dict, Any
from typing import Optional, Dict, Any, Union
try:
import mlflow
@@ -88,12 +88,13 @@ class MLFlowLogger(LightningLoggerBase):
return self._run_id
@rank_zero_only
def log_hyperparams(self, params: argparse.Namespace):
for k, v in vars(params).items():
def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
params = self._convert_params(params)
for k, v in params.items():
self.experiment.log_param(self.run_id, k, v)
@rank_zero_only
def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
timestamp_ms = int(time() * 1000)
for k, v in metrics.items():
if isinstance(v, str):
@@ -105,7 +106,7 @@ class MLFlowLogger(LightningLoggerBase):
pass
@rank_zero_only
def finalize(self, status: str = 'FINISHED'):
def finalize(self, status: str = 'FINISHED') -> None:
if status == 'success':
status = 'FINISHED'
self.experiment.set_terminated(self.run_id, status)
pytorch_lightning/loggers/neptune.py
+12 -11
View File
@@ -6,7 +6,7 @@ Log using `neptune-logger <https://www.neptune.ml>`_
NeptuneLogger
--------------
"""
import argparse
from argparse import Namespace
from logging import getLogger
from typing import Optional, List, Dict, Any, Union, Iterable
@@ -164,8 +164,9 @@ class NeptuneLogger(LightningLoggerBase):
return self._experiment
@rank_zero_only
def log_hyperparams(self, params: argparse.Namespace):
for key, val in vars(params).items():
def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
params = self._convert_params(params)
for key, val in params.items():
self.experiment.set_property(f'param__{key}', val)
@rank_zero_only
@@ -173,7 +174,7 @@ class NeptuneLogger(LightningLoggerBase):
self,
metrics: Dict[str, Union[torch.Tensor, float]],
step: Optional[int] = None
):
) -> None:
"""Log metrics (numeric values) in Neptune experiments
Args:
@@ -184,7 +185,7 @@ class NeptuneLogger(LightningLoggerBase):
self.log_metric(key, val, step=step)
@rank_zero_only
def finalize(self, status: str):
def finalize(self, status: str) -> None:
self.experiment.stop()
@property
@@ -207,7 +208,7 @@ class NeptuneLogger(LightningLoggerBase):
metric_name: str,
metric_value: Union[torch.Tensor, float, str],
step: Optional[int] = None
):
) -> None:
"""Log metrics (numeric values) in Neptune experiments
Args:
@@ -224,7 +225,7 @@ class NeptuneLogger(LightningLoggerBase):
self.experiment.log_metric(metric_name, x=step, y=metric_value)
@rank_zero_only
def log_text(self, log_name: str, text: str, step: Optional[int] = None):
def log_text(self, log_name: str, text: str, step: Optional[int] = None) -> None:
"""Log text data in Neptune experiment
Args:
@@ -235,7 +236,7 @@ class NeptuneLogger(LightningLoggerBase):
self.log_metric(log_name, text, step=step)
@rank_zero_only
def log_image(self, log_name: str, image: Union[str, Any], step: Optional[int] = None):
def log_image(self, log_name: str, image: Union[str, Any], step: Optional[int] = None) -> None:
"""Log image data in Neptune experiment
Args:
@@ -250,7 +251,7 @@ class NeptuneLogger(LightningLoggerBase):
self.experiment.log_image(log_name, x=step, y=image)
@rank_zero_only
def log_artifact(self, artifact: str, destination: Optional[str] = None):
def log_artifact(self, artifact: str, destination: Optional[str] = None) -> None:
"""Save an artifact (file) in Neptune experiment storage.
Args:
@@ -261,7 +262,7 @@ class NeptuneLogger(LightningLoggerBase):
self.experiment.log_artifact(artifact, destination)
@rank_zero_only
def set_property(self, key: str, value: Any):
def set_property(self, key: str, value: Any) -> None:
"""Set key-value pair as Neptune experiment property.
Args:
@@ -271,7 +272,7 @@ class NeptuneLogger(LightningLoggerBase):
self.experiment.set_property(key, value)
@rank_zero_only
def append_tags(self, tags: Union[str, Iterable[str]]):
def append_tags(self, tags: Union[str, Iterable[str]]) -> None:
"""appends tags to neptune experiment
Args:
pytorch_lightning/loggers/tensorboard.py
+6 -13
View File
@@ -1,8 +1,7 @@
import argparse
import csv
import os
from argparse import Namespace
from typing import Optional, Dict, Union
from typing import Optional, Dict, Union, Any
from warnings import warn
import torch
@@ -100,14 +99,8 @@ class TensorBoardLogger(LightningLoggerBase):
return self._experiment
@rank_zero_only
def log_hyperparams(self, params: argparse.Namespace):
if params is None:
return
# in case converting from namespace
if isinstance(params, Namespace):
params = vars(params)
params = dict(params)
def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
params = self._convert_params(params)
if parse_version(torch.__version__) < parse_version("1.3.0"):
warn(
@@ -126,14 +119,14 @@ class TensorBoardLogger(LightningLoggerBase):
self.tags.update(params)
@rank_zero_only
def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
for k, v in metrics.items():
if isinstance(v, torch.Tensor):
v = v.item()
self.experiment.add_scalar(k, v, step)
@rank_zero_only
def save(self):
def save(self) -> None:
try:
self.experiment.flush()
except AttributeError:
@@ -156,7 +149,7 @@ class TensorBoardLogger(LightningLoggerBase):
writer.writerow({'key': k, 'value': v})
@rank_zero_only
def finalize(self, status: str):
def finalize(self, status: str) -> None:
self.save()
@property
pytorch_lightning/loggers/test_tube.py
+10 -9
View File
@@ -1,5 +1,5 @@
import argparse
from typing import Optional, Dict, Any
from argparse import Namespace
from typing import Optional, Dict, Any, Union
try:
from test_tube import Experiment
@@ -92,32 +92,33 @@ class TestTubeLogger(LightningLoggerBase):
return self._experiment
@rank_zero_only
def log_hyperparams(self, params: argparse.Namespace):
def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
# TODO: HACK figure out where this is being set to true
self.experiment.debug = self.debug
self.experiment.argparse(params)
params = self._convert_params(params)
self.experiment.argparse(Namespace(**params))
@rank_zero_only
def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
# TODO: HACK figure out where this is being set to true
self.experiment.debug = self.debug
self.experiment.log(metrics, global_step=step)
@rank_zero_only
def save(self):
def save(self) -> None:
# TODO: HACK figure out where this is being set to true
self.experiment.debug = self.debug
self.experiment.save()
@rank_zero_only
def finalize(self, status: str):
def finalize(self, status: str) -> None:
# TODO: HACK figure out where this is being set to true
self.experiment.debug = self.debug
self.save()
self.close()
@rank_zero_only
def close(self):
def close(self) -> None:
# TODO: HACK figure out where this is being set to true
self.experiment.debug = self.debug
if not self.debug:
@@ -129,7 +130,7 @@ class TestTubeLogger(LightningLoggerBase):
return self._rank
@rank.setter
def rank(self, value: int):
def rank(self, value: int) -> None:
self._rank = value
if self._experiment is not None:
self.experiment.rank = value
pytorch_lightning/loggers/wandb.py
+6 -5
View File
@@ -5,9 +5,9 @@ r"""
WandbLogger
-------------
"""
import argparse
import os
from typing import Optional, List, Dict
from argparse import Namespace
from typing import Optional, List, Dict, Union, Any
import torch.nn as nn
@@ -91,17 +91,18 @@ class WandbLogger(LightningLoggerBase):
wandb.watch(model, log=log, log_freq=log_freq)
@rank_zero_only
def log_hyperparams(self, params: argparse.Namespace):
def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
params = self._convert_params(params)
self.experiment.config.update(params)
@rank_zero_only
def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
if step is not None:
metrics['global_step'] = step
self.experiment.log(metrics)
@rank_zero_only
def finalize(self, status: str = 'success'):
def finalize(self, status: str = 'success') -> None:
try:
exit_code = 0 if status == 'success' else 1
wandb.join(exit_code)
pytorch_lightning/trainer/training_io.py
+6 -4
View File
@@ -4,9 +4,9 @@ import re
import signal
import warnings
from abc import ABC
from argparse import Namespace
from subprocess import call
from typing import Union
from copy import deepcopy
import torch
import torch.distributed as dist
@@ -238,7 +238,9 @@ class TrainerIOMixin(ABC):
checkpoint['state_dict'] = model.state_dict()
if hasattr(model, "hparams"):
checkpoint['hparams'] = vars(model.hparams)
is_namespace = isinstance(model.hparams, Namespace)
checkpoint['hparams'] = vars(model.hparams) if is_namespace else model.hparams
checkpoint['hparams_type'] = 'namespace' if is_namespace else 'dict'
else:
warnings.warn(
"Did not find hyperparameters at model.hparams. Saving checkpoint without"
@@ -322,7 +324,7 @@ class TrainerIOMixin(ABC):
# ----------------------------------
# PRIVATE OPS
# ----------------------------------
def hpc_save(self, folderpath, logger):
def hpc_save(self, folderpath: str, logger):
# make sure the checkpoint folder exists
os.makedirs(folderpath, exist_ok=True)
@@ -333,7 +335,7 @@ class TrainerIOMixin(ABC):
if not os.path.exists(folderpath):
os.makedirs(folderpath, exist_ok=True)
filepath = '{}/hpc_ckpt_{}.ckpt'.format(folderpath, ckpt_number)
filepath = os.path.join(folderpath, f'hpc_ckpt_{ckpt_number}.ckpt')
# give model a chance to do something on hpc_save
model = self.get_model()