105 lines
3.5 KiB
Python
105 lines
3.5 KiB
Python
# Copyright The PyTorch Lightning team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import torch
|
|
from typing import Any, Optional, Sequence
|
|
|
|
from pytorch_lightning.metrics.metric import Metric
|
|
from pytorch_lightning.utilities import rank_zero_warn
|
|
from pytorch_lightning.metrics.functional.ssim import _ssim_update, _ssim_compute
|
|
|
|
|
|
class SSIM(Metric):
|
|
"""
|
|
Computes `Structual Similarity Index Measure
|
|
<https://en.wikipedia.org/wiki/Structural_similarity>`_ (SSIM).
|
|
|
|
Args:
|
|
kernel_size: size of the gaussian kernel (default: (11, 11))
|
|
sigma: Standard deviation of the gaussian kernel (default: (1.5, 1.5))
|
|
reduction: a method to reduce metric score over labels.
|
|
|
|
- ``'elementwise_mean'``: takes the mean (default)
|
|
- ``'sum'``: takes the sum
|
|
- ``'none'``: no reduction will be applied
|
|
|
|
data_range: Range of the image. If ``None``, it is determined from the image (max - min)
|
|
k1: Parameter of SSIM. Default: 0.01
|
|
k2: Parameter of SSIM. Default: 0.03
|
|
|
|
Return:
|
|
Tensor with SSIM score
|
|
|
|
Example:
|
|
>>> from pytorch_lightning.metrics import SSIM
|
|
>>> preds = torch.rand([16, 1, 16, 16])
|
|
>>> target = preds * 0.75
|
|
>>> ssim = SSIM()
|
|
>>> ssim(preds, target)
|
|
tensor(0.9219)
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
kernel_size: Sequence[int] = (11, 11),
|
|
sigma: Sequence[float] = (1.5, 1.5),
|
|
reduction: str = "elementwise_mean",
|
|
data_range: Optional[float] = None,
|
|
k1: float = 0.01,
|
|
k2: float = 0.03,
|
|
compute_on_step: bool = True,
|
|
dist_sync_on_step: bool = False,
|
|
process_group: Optional[Any] = None,
|
|
):
|
|
super().__init__(
|
|
compute_on_step=compute_on_step,
|
|
dist_sync_on_step=dist_sync_on_step,
|
|
process_group=process_group,
|
|
)
|
|
rank_zero_warn(
|
|
'Metric `SSIM` will save all targets and'
|
|
' predictions in buffer. For large datasets this may lead'
|
|
' to large memory footprint.'
|
|
)
|
|
|
|
self.add_state("y", default=[], dist_reduce_fx=None)
|
|
self.add_state("y_pred", default=[], dist_reduce_fx=None)
|
|
self.kernel_size = kernel_size
|
|
self.sigma = sigma
|
|
self.data_range = data_range
|
|
self.k1 = k1
|
|
self.k2 = k2
|
|
self.reduction = reduction
|
|
|
|
def update(self, preds: torch.Tensor, target: torch.Tensor):
|
|
"""
|
|
Update state with predictions and targets.
|
|
|
|
Args:
|
|
preds: Predictions from model
|
|
target: Ground truth values
|
|
"""
|
|
preds, target = _ssim_update(preds, target)
|
|
self.y_pred.append(preds)
|
|
self.y.append(target)
|
|
|
|
def compute(self):
|
|
"""
|
|
Computes explained variance over state.
|
|
"""
|
|
preds = torch.cat(self.y_pred, dim=0)
|
|
target = torch.cat(self.y, dim=0)
|
|
return _ssim_compute(
|
|
preds, target, self.kernel_size, self.sigma, self.reduction, self.data_range, self.k1, self.k2
|
|
)
|