pyaupro 0.1.11

Efficient per-region overlap (PRO) calculation implemented using torchmetrics.

pyaupro: Efficient Per-Region Overlap Computation

This package is intended to compute the per-region overlap metric using an efficient torchmetrics implementation.

If you are used to torchmetrics, for example to BinaryROC, you will find yourself at home using pyaupro.

We export a single metric called PerRegionOverlap, which is described in the paper referenced below.

Bergmann, Paul, Kilian Batzner, Michael Fauser, David Sattlegger, and Carsten Steger. “The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection.” International Journal of Computer Vision 129, no. 4 (April 1, 2021): 1038–59. https://doi.org/10.1007/s11263-020-01400-4.

Usage Example

from pyaupro import PerRegionOverlap, auc_compute, generate_random_data

# generate random data for testing
preds, target = generate_random_data(batch_size=1, seed=42)

# initialize an approximate PRO-metric with 100 thresholds
pro_curve = PerRegionOverlap(thresholds=100)

# update the metric with the random preds and target
pro_curve.update(preds, target)

# compute the fpr and pro values for the curve
fpr, pro = pro_curve.compute()

# calculate the area under the curve
score = auc_compute(fpr, pro, reorder=True)

# plot the curve
pro_curve.plot(score=True)

Usage Details

The arguments to instantiate pyaupro.PerRegionOverlap are as follows.

thresholds:
    Can be one of:
    - If set to `None`, will use a non-binned reference approach provided by the authors of MVTecAD, where
        no thresholds are explicitly calculated. Most accurate but also most memory consuming approach.
    - If set to an `int` (larger than 1), will use that number of thresholds linearly spaced from
        0 to 1 as bins for the calculation.
    - If set to an `list` of floats, will use the indicated thresholds in the list as bins for the calculation
    - If set to an 1d `tensor` of floats, will use the indicated thresholds in the tensor as
        bins for the calculation.
ignore_index:
    Specifies a target value that is ignored and does not contribute to the metric calculation
validate_args: bool indicating if input arguments and tensors should be validated for correctness.
    Set to ``False`` for faster computations.
changepoints_only:
    Modify the exact curve to retain the relevant points only.
reference_implementation:
    Fall back to the official MVTecAD implementation for the exact computation.
kwargs:
    Additional keyword arguments, see :ref:`Metric kwargs` for more info.

An update of the metric expects a three-dimensional preds tensor where the first dimension is the batch dimension (floats between zero and one; otherwise, the values are considered logits) and an equally shaped target tensor containing binary ground truth labels ({0,1} values).

If thresholds is None, the metric computes an exact Per-Region Overlap (PRO) curve over all possible values. In this case, each update step appends the given tensors, and the calculation happens in compute. We use the official implementation provided in MVTecAD for exact calculation.

If thresholds are given, the computation is approximate and happens at each update step. In the approximate case, compute returns a mean of the batched computations during update.

We provide an auc_compute utility for area under the curve computation, which is also used in PerRegionOverlap.plot if score=True. The arguments for pyaupro.auc_compute are as follows.

x:
    Ascending (or descending if ``descending=True``) sorted vector if, 
    otherwise ``reorder`` must be used.
y:
    Vector of the same size as ``x``.
limit:
    Integration limit chosen for ``x`` such that only the values until
    the limit are used for computation.
descending:
    Input vector ``x`` is descending or ``reorder`` sorts descending.
check:
    Check if the given vector is monotonically increasing or decreasing.
return_curve:
    Return the final tensors used to compute the area under the curve.

How to develop

• Use uv sync to install dependencies from the lock file.
• Use uv lock to update the lock file if necessary given the pinned dependencies.
• Use uv lock --upgrade to upgrade the lock file the latest valid dependencies.
• Use uv pip install --editable . to install the local package.
• Use uv run pytest tests to test the local package.

It might happen that the host github.com is not trusted, in this case use uv sync --allow-insecure-host https://github.com if you trust github.com.