277 lines
8.1 KiB
Python
277 lines
8.1 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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import pickle
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from argparse import Namespace
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from typing import Optional
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from unittest.mock import MagicMock
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import numpy as np
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from pytorch_lightning import Trainer
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from pytorch_lightning.loggers import LightningLoggerBase, LoggerCollection, TensorBoardLogger
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from pytorch_lightning.loggers.base import DummyExperiment, DummyLogger
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from pytorch_lightning.trainer.states import TrainerState
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from pytorch_lightning.utilities import rank_zero_only
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from tests.base import BoringModel
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def test_logger_collection():
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mock1 = MagicMock()
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mock2 = MagicMock()
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logger = LoggerCollection([mock1, mock2])
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assert logger[0] == mock1
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assert logger[1] == mock2
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assert logger.experiment[0] == mock1.experiment
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assert logger.experiment[1] == mock2.experiment
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assert logger.save_dir is None
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logger.update_agg_funcs({'test': np.mean}, np.sum)
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mock1.update_agg_funcs.assert_called_once_with({'test': np.mean}, np.sum)
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mock2.update_agg_funcs.assert_called_once_with({'test': np.mean}, np.sum)
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logger.agg_and_log_metrics({'test': 2.0}, 4)
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mock1.agg_and_log_metrics.assert_called_once_with({'test': 2.0}, 4)
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mock2.agg_and_log_metrics.assert_called_once_with({'test': 2.0}, 4)
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logger.close()
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mock1.close.assert_called_once()
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mock2.close.assert_called_once()
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class CustomLogger(LightningLoggerBase):
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def __init__(self):
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super().__init__()
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self.hparams_logged = None
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self.metrics_logged = {}
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self.finalized = False
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@property
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def experiment(self):
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return 'test'
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@rank_zero_only
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def log_hyperparams(self, params):
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self.hparams_logged = params
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@rank_zero_only
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def log_metrics(self, metrics, step):
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self.metrics_logged = metrics
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@rank_zero_only
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def finalize(self, status):
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self.finalized_status = status
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@property
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def save_dir(self) -> Optional[str]:
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"""
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Return the root directory where experiment logs get saved, or `None` if the logger does not
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save data locally.
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"""
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return None
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@property
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def name(self):
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return "name"
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@property
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def version(self):
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return "1"
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def test_custom_logger(tmpdir):
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class CustomModel(BoringModel):
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def training_step(self, batch, batch_idx):
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output = self.layer(batch)
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loss = self.loss(batch, output)
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self.log('train_loss', loss)
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return {"loss": loss}
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logger = CustomLogger()
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model = CustomModel()
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trainer = Trainer(
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max_steps=2,
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log_every_n_steps=1,
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logger=logger,
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default_root_dir=tmpdir,
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)
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trainer.fit(model)
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assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
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assert logger.hparams_logged == model.hparams
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assert logger.metrics_logged != {}
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assert logger.finalized_status == "success"
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def test_multiple_loggers(tmpdir):
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class CustomModel(BoringModel):
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def training_step(self, batch, batch_idx):
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output = self.layer(batch)
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loss = self.loss(batch, output)
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self.log('train_loss', loss)
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return {"loss": loss}
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model = CustomModel()
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logger1 = CustomLogger()
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logger2 = CustomLogger()
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trainer = Trainer(
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max_steps=2,
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log_every_n_steps=1,
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logger=[logger1, logger2],
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default_root_dir=tmpdir,
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)
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trainer.fit(model)
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assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
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assert logger1.hparams_logged == model.hparams
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assert logger1.metrics_logged != {}
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assert logger1.finalized_status == "success"
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assert logger2.hparams_logged == model.hparams
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assert logger2.metrics_logged != {}
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assert logger2.finalized_status == "success"
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def test_multiple_loggers_pickle(tmpdir):
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"""Verify that pickling trainer with multiple loggers works."""
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logger1 = CustomLogger()
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logger2 = CustomLogger()
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trainer = Trainer(
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logger=[logger1, logger2],
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)
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pkl_bytes = pickle.dumps(trainer)
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trainer2 = pickle.loads(pkl_bytes)
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trainer2.logger.log_metrics({"acc": 1.0}, 0)
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assert trainer2.logger[0].metrics_logged == {"acc": 1.0}
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assert trainer2.logger[1].metrics_logged == {"acc": 1.0}
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def test_adding_step_key(tmpdir):
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class CustomTensorBoardLogger(TensorBoardLogger):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.logged_step = 0
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def log_metrics(self, metrics, step):
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if "val_acc" in metrics:
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assert step == self.logged_step
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super().log_metrics(metrics, step)
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class CustomModel(BoringModel):
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def training_epoch_end(self, outputs):
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self.logger.logged_step += 1
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self.log_dict({
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"step": self.logger.logged_step,
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"train_acc": self.logger.logged_step / 10
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})
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def validation_epoch_end(self, outputs):
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self.logger.logged_step += 1
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self.log_dict({
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"step": self.logger.logged_step,
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"val_acc": self.logger.logged_step / 10
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})
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model = CustomModel()
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trainer = Trainer(
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max_epochs=3,
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logger=CustomTensorBoardLogger(save_dir=tmpdir),
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default_root_dir=tmpdir,
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limit_train_batches=0.1,
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limit_val_batches=0.1,
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num_sanity_val_steps=0,
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)
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trainer.fit(model)
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def test_with_accumulate_grad_batches():
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"""Checks if the logging is performed once for `accumulate_grad_batches` steps."""
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class StoreHistoryLogger(CustomLogger):
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def __init__(self):
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super().__init__()
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self.history = {}
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@rank_zero_only
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def log_metrics(self, metrics, step):
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if step not in self.history:
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self.history[step] = {}
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self.history[step].update(metrics)
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logger = StoreHistoryLogger()
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np.random.seed(42)
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for i, loss in enumerate(np.random.random(10)):
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logger.agg_and_log_metrics({'loss': loss}, step=int(i / 5))
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assert logger.history == {0: {'loss': 0.5623850983416314}}
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logger.close()
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assert logger.history == {0: {'loss': 0.5623850983416314}, 1: {'loss': 0.4778883735637184}}
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def test_dummyexperiment_support_indexing():
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experiment = DummyExperiment()
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assert experiment[0] == experiment
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def test_dummylogger_support_indexing():
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logger = DummyLogger()
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assert logger[0] == logger
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def test_np_sanitization():
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class CustomParamsLogger(CustomLogger):
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def __init__(self):
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super().__init__()
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self.logged_params = None
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@rank_zero_only
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def log_hyperparams(self, params):
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params = self._convert_params(params)
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params = self._sanitize_params(params)
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self.logged_params = params
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logger = CustomParamsLogger()
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np_params = {
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"np.bool_": np.bool_(1),
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"np.byte": np.byte(2),
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"np.intc": np.intc(3),
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"np.int_": np.int_(4),
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"np.longlong": np.longlong(5),
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"np.single": np.single(6.0),
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"np.double": np.double(8.9),
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"np.csingle": np.csingle(7 + 2j),
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"np.cdouble": np.cdouble(9 + 4j),
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}
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sanitized_params = {
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"np.bool_": True,
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"np.byte": 2,
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"np.intc": 3,
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"np.int_": 4,
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"np.longlong": 5,
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"np.single": 6.0,
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"np.double": 8.9,
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"np.csingle": "(7+2j)",
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"np.cdouble": "(9+4j)",
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}
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logger.log_hyperparams(Namespace(**np_params))
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assert logger.logged_params == sanitized_params
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