108 lines
3.9 KiB
Python
108 lines
3.9 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, Optional
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from pytorch_lightning.metrics.metric import Metric
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from pytorch_lightning.metrics.functional.psnr import (
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_psnr_update,
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_psnr_compute,
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)
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class PSNR(Metric):
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"""
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Computes peak signal-to-noise ratio
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Args:
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data_range: the range of the data. If None, it is determined from the data (max - min)
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base: a base of a logarithm to use (default: 10)
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reduction: a method to reduce metric score over labels.
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- ``'elementwise_mean'``: takes the mean (default)
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- ``'sum'``: takes the sum
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- ``'none'``: no reduction will be applied
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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 PSNR
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>>> psnr = PSNR()
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>>> preds = torch.tensor([[0.0, 1.0], [2.0, 3.0]])
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>>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]])
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>>> psnr(preds, target)
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tensor(2.5527)
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"""
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def __init__(
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self,
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data_range: Optional[float] = None,
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base: float = 10.0,
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reduction: str = 'elementwise_mean',
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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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if data_range is None:
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self.data_range = None
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self.add_state("min_target", default=torch.tensor(0.0), dist_reduce_fx=torch.min)
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self.add_state("max_target", default=torch.tensor(0.0), dist_reduce_fx=torch.max)
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else:
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self.register_buffer("data_range", torch.tensor(float(data_range)))
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self.base = base
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self.reduction = reduction
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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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if self.data_range is None:
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# keep track of min and max target values
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self.min_target = min(target.min(), self.min_target)
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self.max_target = max(target.max(), self.max_target)
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sum_squared_error, n_obs = _psnr_update(preds, target)
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self.sum_squared_error += sum_squared_error
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self.total += n_obs
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def compute(self):
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"""
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Compute peak signal-to-noise ratio over state.
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"""
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if self.data_range is not None:
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data_range = self.data_range
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else:
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data_range = self.max_target - self.min_target
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return _psnr_compute(self.sum_squared_error, self.total, data_range, self.base, self.reduction)
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