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import torch |
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from torch import Tensor |
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from typing_extensions import Literal |
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from torchmetrics.utilities.checks import _check_same_shape |
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from torchmetrics.utilities.distributed import reduce |
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def _ergas_update(preds: Tensor, target: Tensor) -> tuple[Tensor, Tensor]: |
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"""Update and returns variables required to compute Erreur Relative Globale Adimensionnelle de Synthèse. |
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Args: |
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preds: Predicted tensor |
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target: Ground truth tensor |
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""" |
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if preds.dtype != target.dtype: |
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raise TypeError( |
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"Expected `preds` and `target` to have the same data type." |
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f" Got preds: {preds.dtype} and target: {target.dtype}." |
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) |
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_check_same_shape(preds, target) |
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if len(preds.shape) != 4: |
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raise ValueError( |
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f"Expected `preds` and `target` to have BxCxHxW shape. Got preds: {preds.shape} and target: {target.shape}." |
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) |
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return preds, target |
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def _ergas_compute( |
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preds: Tensor, |
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target: Tensor, |
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ratio: float = 4, |
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reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean", |
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) -> Tensor: |
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"""Erreur Relative Globale Adimensionnelle de Synthèse. |
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Args: |
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preds: estimated image |
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target: ground truth image |
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ratio: ratio of high resolution to low resolution |
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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'`` or ``None``: no reduction will be applied |
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Example: |
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>>> from torch import rand |
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>>> preds = rand([16, 1, 16, 16]) |
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>>> target = preds * 0.75 |
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>>> preds, target = _ergas_update(preds, target) |
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>>> torch.round(_ergas_compute(preds, target)) |
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tensor(10.) |
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""" |
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b, c, h, w = preds.shape |
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preds = preds.reshape(b, c, h * w) |
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target = target.reshape(b, c, h * w) |
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diff = preds - target |
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sum_squared_error = torch.sum(diff * diff, dim=2) |
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rmse_per_band = torch.sqrt(sum_squared_error / (h * w)) |
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mean_target = torch.mean(target, dim=2) |
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ergas_score = 100 / ratio * torch.sqrt(torch.sum((rmse_per_band / mean_target) ** 2, dim=1) / c) |
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return reduce(ergas_score, reduction) |
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def error_relative_global_dimensionless_synthesis( |
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preds: Tensor, |
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target: Tensor, |
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ratio: float = 4, |
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reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean", |
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) -> Tensor: |
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"""Calculates `Error relative global dimensionless synthesis`_ (ERGAS) metric. |
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Args: |
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preds: estimated image |
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target: ground truth image |
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ratio: ratio of high resolution to low resolution |
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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'`` or ``None``: no reduction will be applied |
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Return: |
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Tensor with RelativeG score |
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Raises: |
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TypeError: |
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If ``preds`` and ``target`` don't have the same data type. |
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ValueError: |
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If ``preds`` and ``target`` don't have ``BxCxHxW shape``. |
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Example: |
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>>> from torch import rand |
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>>> from torchmetrics.functional.image import error_relative_global_dimensionless_synthesis |
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>>> preds = rand([16, 1, 16, 16]) |
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>>> target = preds * 0.75 |
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>>> error_relative_global_dimensionless_synthesis(preds, target) |
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tensor(9.6193) |
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""" |
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preds, target = _ergas_update(preds, target) |
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return _ergas_compute(preds, target, ratio, reduction) |
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