78 lines
2.6 KiB
Python
78 lines
2.6 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 torch
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from typing import Any, Callable, Optional, Union
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from pytorch_lightning.metrics.metric import Metric
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class MeanSquaredError(Metric):
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"""
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Computes mean squared error.
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Args:
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compute_on_step:
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Forward only calls ``update()`` and return None if this is set to False. default: True
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dist_sync_on_step:
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Synchronize metric state across processes at each ``forward()``
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before returning the value at the step. default: False
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process_group:
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Specify the process group on which synchronization is called. default: None (which selects the entire world)
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Example:
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>>> from pytorch_lightning.metrics import MeanSquaredError
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>>> target = torch.tensor([2.5, 5.0, 4.0, 8.0])
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>>> preds = torch.tensor([3.0, 5.0, 2.5, 7.0])
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>>> mean_squared_error = MeanSquaredError()
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>>> mean_squared_error(preds, target)
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tensor(0.8750)
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"""
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def __init__(
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self,
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compute_on_step: bool = True,
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dist_sync_on_step: bool = False,
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process_group: Optional[Any] = None,
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):
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super().__init__(
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compute_on_step=compute_on_step,
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dist_sync_on_step=dist_sync_on_step,
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process_group=process_group,
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)
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self.add_state("sum_squared_error", default=torch.tensor(0.0), dist_reduce_fx="sum")
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self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
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def update(self, preds: torch.Tensor, target: torch.Tensor):
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"""
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Update state with predictions and targets.
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Args:
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preds: Predictions from model
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target: Ground truth values
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"""
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self._check_same_shape(preds, target)
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squared_error = torch.pow(preds - target, 2)
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self.sum_squared_error += torch.sum(squared_error)
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self.total += target.numel()
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def compute(self):
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"""
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Computes mean squared error over state.
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"""
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return self.sum_squared_error / self.total
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