64 lines
2.5 KiB
Python
64 lines
2.5 KiB
Python
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# 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 typing import Any
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import torch
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.trainer.states import RunningStage
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from pytorch_lightning.utilities.warnings import WarningCache
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warning_cache = WarningCache()
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class _LightningModuleWrapperBase(torch.nn.Module):
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def __init__(self, pl_module: LightningModule):
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"""
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Wraps the user's LightningModule and redirects the forward call to the appropriate
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method, either ``training_step``, ``validation_step`` or ``test_step``.
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If the LightningModule is in none of the states `training`, `testing` or `validation`,
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the inputs will be redirected to the
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:meth:`~pytorch_lightning.core.lightning.LightningModule.predict` method.
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Inheriting classes may also modify the inputs or outputs of forward.
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Args:
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pl_module: the model to wrap
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"""
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super().__init__()
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self.module = pl_module
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def forward(self, *inputs, **kwargs):
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running_stage = self.module.running_stage
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if running_stage == RunningStage.TRAINING:
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output = self.module.training_step(*inputs, **kwargs)
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warn_if_output_is_none(output, "training_step")
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elif running_stage == RunningStage.TESTING:
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output = self.module.test_step(*inputs, **kwargs)
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warn_if_output_is_none(output, "test_step")
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elif running_stage == RunningStage.EVALUATING:
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output = self.module.validation_step(*inputs, **kwargs)
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warn_if_output_is_none(output, "validation_step")
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else:
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output = self.module.predict(*inputs, **kwargs)
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return output
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def warn_if_output_is_none(output: Any, method_name: str) -> None:
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""" Warns user about which method returned None. """
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if output is None:
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warning_cache.warn(f'Your {method_name} returned None. Did you forget to return an output?')
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