71 lines
2.8 KiB
Python
71 lines
2.8 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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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from typing import Union
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from pytorch_lightning.utilities import rank_zero_warn, rank_zero_info
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class DebuggingConnector:
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def __init__(self, trainer):
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self.trainer = trainer
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def on_init_start(
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self,
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overfit_pct,
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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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self.trainer.fast_dev_run = fast_dev_run
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if self.trainer.fast_dev_run:
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limit_train_batches = 1
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limit_val_batches = 1
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limit_test_batches = 1
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self.trainer.num_sanity_val_steps = 0
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self.trainer.max_epochs = 1
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rank_zero_info(
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'Running in fast_dev_run mode: will run a full train,' ' val and test loop using a single batch'
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)
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self.trainer.limit_train_batches = _determine_batch_limits(limit_train_batches, 'limit_train_batches')
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self.trainer.limit_val_batches = _determine_batch_limits(limit_val_batches, 'limit_val_batches')
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self.trainer.limit_test_batches = _determine_batch_limits(limit_test_batches, 'limit_test_batches')
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self.trainer.val_check_interval = _determine_batch_limits(val_check_interval, 'val_check_interval')
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self.trainer.overfit_batches = _determine_batch_limits(overfit_batches, 'overfit_batches')
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self.determine_data_use_amount(self.trainer.overfit_batches)
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def determine_data_use_amount(self, overfit_batches: float) -> None:
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"""Use less data for debugging purposes"""
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if overfit_batches > 0:
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self.trainer.limit_train_batches = overfit_batches
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self.trainer.limit_val_batches = overfit_batches
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self.trainer.limit_test_batches = overfit_batches
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def _determine_batch_limits(batches: Union[int, float], name: str) -> Union[int, float]:
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if 0 <= batches <= 1:
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return batches
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elif batches > 1 and batches % 1.0 == 0:
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return int(batches)
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else:
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raise MisconfigurationException(
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f'You have passed invalid value {batches} for {name}, it has to be in [0.0, 1.0] or an int.'
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)
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