From b13fb0f4e646ed6cae6f401de78c57ea83874ba0 Mon Sep 17 00:00:00 2001 From: alafage Date: Thu, 28 Mar 2024 12:58:59 +0100 Subject: [PATCH] :book: Update documentation --- docs/source/_static/css/custom.css | 4 ++++ .../images/structure_torch_uncertainty.jpg | Bin 0 -> 211358 bytes docs/source/index.rst | 1 - docs/source/introduction_uncertainty.rst | 2 ++ docs/source/quickstart.rst | 6 ++++++ torch_uncertainty/metrics/regression/log10.py | 3 ++- .../metrics/regression/mse_log.py | 5 +++-- .../metrics/regression/relative_error.py | 10 ++++++---- torch_uncertainty/metrics/regression/silog.py | 3 ++- .../metrics/regression/threshold_accuracy.py | 3 ++- 10 files changed, 27 insertions(+), 10 deletions(-) create mode 100644 docs/source/_static/images/structure_torch_uncertainty.jpg diff --git a/docs/source/_static/css/custom.css b/docs/source/_static/css/custom.css index e414831b..a6b1a1d5 100644 --- a/docs/source/_static/css/custom.css +++ b/docs/source/_static/css/custom.css @@ -87,3 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b/docs/source/index.rst index 09a9d53e..fb9c4805 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -43,7 +43,6 @@ the following paper: installation quickstart - introduction_uncertainty auto_tutorials/index cli_guide api diff --git a/docs/source/introduction_uncertainty.rst b/docs/source/introduction_uncertainty.rst index 4e92c4fe..215444a0 100644 --- a/docs/source/introduction_uncertainty.rst +++ b/docs/source/introduction_uncertainty.rst @@ -1,3 +1,5 @@ +:orphan: + Introduction to Classification Uncertainty ========================================== diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index 18960abb..1b713077 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -15,6 +15,12 @@ Yet, we take account that their will be as many different uses of TorchUncertain This page provides ideas on how to benefit from TorchUncertainty at all levels: from ready-to-train lightning-based models to using only specific PyTorch layers. +.. figure:: _static/images/structure_torch_uncertainty.jpg + :alt: TorchUncertainty structure + :figclass: figure-caption + + **TorchUncertainty structure** + Training with TorchUncertainty's Uncertainty-aware Routines ----------------------------------------------------------- diff --git a/torch_uncertainty/metrics/regression/log10.py b/torch_uncertainty/metrics/regression/log10.py index acd9a0e1..e93ce571 100644 --- a/torch_uncertainty/metrics/regression/log10.py +++ b/torch_uncertainty/metrics/regression/log10.py @@ -17,7 +17,8 @@ def __init__(self, **kwargs) -> None: - :attr:`target`: :math:`(N)` Args: - kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. + kwargs: Additional keyword arguments, see `Advanced metric settings + `_. """ super().__init__(**kwargs) self.add_state( diff --git a/torch_uncertainty/metrics/regression/mse_log.py b/torch_uncertainty/metrics/regression/mse_log.py index caae3186..6aca1208 100644 --- a/torch_uncertainty/metrics/regression/mse_log.py +++ b/torch_uncertainty/metrics/regression/mse_log.py @@ -4,7 +4,7 @@ class MeanSquaredLogError(MeanSquaredError): def __init__(self, squared: bool = True, **kwargs) -> None: - r"""`Compute MeanSquaredLogError`_ (MSELog). + r"""Compute MeanSquaredLogError (MSELog). .. math:: \text{MSELog} = \frac{1}{N}\sum_i^N (\log \hat{y_i} - \log y_i)^2 @@ -22,7 +22,8 @@ def __init__(self, squared: bool = True, **kwargs) -> None: Args: squared: If True returns MSELog value, if False returns EMSELog value. - kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. + kwargs: Additional keyword arguments, see `Advanced metric settings + `_. Reference: As in e.g. From big to small: Multi-scale local planar guidance for monocular depth estimation diff --git a/torch_uncertainty/metrics/regression/relative_error.py b/torch_uncertainty/metrics/regression/relative_error.py index 27ac1eb4..af428450 100644 --- a/torch_uncertainty/metrics/regression/relative_error.py +++ b/torch_uncertainty/metrics/regression/relative_error.py @@ -5,7 +5,7 @@ class MeanGTRelativeAbsoluteError(MeanAbsoluteError): def __init__(self, **kwargs) -> None: - r"""`Compute Mean Absolute Error relative to the Ground Truth`_ (MAErel or ARE). + r"""Compute Mean Absolute Error relative to the Ground Truth (MAErel or ARE). .. math:: \text{MAErel} = \frac{1}{N}\sum_i^N \frac{| y_i - \hat{y_i} |}{y_i} @@ -22,7 +22,8 @@ def __init__(self, **kwargs) -> None: relative mean absolute error over the state Args: - kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. + kwargs: Additional keyword arguments, see `Advanced metric settings + `_. Reference: As in e.g. From big to small: Multi-scale local planar guidance for monocular depth estimation @@ -38,7 +39,7 @@ class MeanGTRelativeSquaredError(MeanSquaredError): def __init__( self, squared: bool = True, num_outputs: int = 1, **kwargs ) -> None: - r"""Compute `mean squared error relative to the Ground Truth`_ (MSErel or SRE). + r"""Compute mean squared error relative to the Ground Truth (MSErel or SRE). .. math:: \text{MSErel} = \frac{1}{N}\sum_i^N \frac{(y_i - \hat{y_i})^2}{y_i} @@ -56,7 +57,8 @@ def __init__( Args: squared: If True returns MSErel value, if False returns RMSErel value. num_outputs: Number of outputs in multioutput setting - kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. + kwargs: Additional keyword arguments, see `Advanced metric settings + `_. Reference: As in e.g. From big to small: Multi-scale local planar guidance for monocular depth estimation diff --git a/torch_uncertainty/metrics/regression/silog.py b/torch_uncertainty/metrics/regression/silog.py index 370b7036..f8e71000 100644 --- a/torch_uncertainty/metrics/regression/silog.py +++ b/torch_uncertainty/metrics/regression/silog.py @@ -20,7 +20,8 @@ def __init__(self, lmbda: float = 1, **kwargs: Any) -> None: Args: lmbda: The regularization parameter on the variance of error (default 1). - kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. + kwargs: Additional keyword arguments, see `Advanced metric settings + `_. Reference: Depth Map Prediction from a Single Image using a Multi-Scale Deep Network. diff --git a/torch_uncertainty/metrics/regression/threshold_accuracy.py b/torch_uncertainty/metrics/regression/threshold_accuracy.py index 68068ad8..29d5ce5b 100644 --- a/torch_uncertainty/metrics/regression/threshold_accuracy.py +++ b/torch_uncertainty/metrics/regression/threshold_accuracy.py @@ -12,7 +12,8 @@ def __init__(self, power: int, lmbda: float = 1.25, **kwargs) -> None: power: The power to raise the threshold to. Often in [1, 2, 3]. lmbda: The threshold to compare the max of ratio of predictions to targets and its inverse to. Defaults to 1.25. - kwargs: Additional arguments to pass to the metric class. + kwargs: Additional keyword arguments, see `Advanced metric settings + `_. """ super().__init__(**kwargs) if power < 0: