update for tests

README.md

@@ -5,7 +5,7 @@
 A collection of metrics to evaluate layout generation that can be easily used in 🤗 huggingface [evaluate](https://huggingface.co/docs/evaluate/index).
 
 | 📊 Metric | 🤗 Space | 📝 Paper |
-|:---------|:---------|----------|
+|:---------:|:---------|:----------|
 | [![FID](https://github.com/creative-graphic-design/huggingface-evaluate_layout-metrics/actions/workflows/layout_generative_model_scores.yaml/badge.svg)](https://github.com/creative-graphic-design/huggingface-evaluate_layout-metrics/actions/workflows/layout_generative_model_scores.yaml) | [`creative-graphic-design/layout-generative-model-scores`](https://huggingface.co/spaces/creative-graphic-design/layout-generative-model-scores) | [[Heusel+ NeurIPS'17](https://arxiv.org/abs/1706.08500)], [[Naeem+ ICML'20](https://arxiv.org/abs/2002.09797)] |
 | [![Max. IoU](https://github.com/creative-graphic-design/huggingface-evaluate_layout-metrics/actions/workflows/layout_maximum_iou.yaml/badge.svg)](https://github.com/creative-graphic-design/huggingface-evaluate_layout-metrics/actions/workflows/layout_maximum_iou.yaml) | [`creative-graphic-design/layout-maximum-iou`](https://huggingface.co/spaces/creative-graphic-design/layout-maximum-iou) | [[Kikuchi+ ACMMM'21](https://arxiv.org/abs/2108.00871)] |
 | [![Avg. IoU](https://github.com/creative-graphic-design/huggingface-evaluate_layout-metrics/actions/workflows/layout_average_iou.yaml/badge.svg)](https://github.com/creative-graphic-design/huggingface-evaluate_layout-metrics/actions/workflows/layout_average_iou.yaml) | [`creative-graphic-design/layout-average-iou`](https://huggingface.co/spaces/creative-graphic-design/layout-average-iou) | [[Arroyo+ CVPR'21](https://arxiv.org/abs/2104.02416)], [[Kong+ ECCV'22](https://arxiv.org/abs/2112.05112)] |

metrics/layout_occlusion/layout-occlusion.py

@@ -12,7 +12,18 @@
 """
 
 _KWARGS_DESCRIPTION = """\
-FIXME
+Args:
+    predictions (`list` of `lists` of `float`): A list of lists of floats representing bounding boxes.
+    gold_labels (`list` of `lists` of `int`): A list of lists of integers representing class labels.
+    saliency_maps_1 (`list` of `str`): A list of strings representing path to saliency maps 1.
+    saliency_maps_2 (`list` of `str`): A list of strings representing path to saliency maps 2.
+
+Returns:
+    float: Average saliency of areas covered by elements. Lower values are generally better (in 0.0 - 1.0 range).
+
+Examples:
+    Examples 1: Single processing
+        >>> metric = evaluate.load("creative-graphic-design/layout-occlusion")
 """
 
 _CITATION = """\

tests/conftest.py

@@ -26,6 +26,26 @@ def poster_height() -> int:
     return 750
 
 
+@pytest.fixture
+def batch_size() -> int:
+    return 512
+
+
+@pytest.fixture
+def max_layout_elements() -> int:
+    return 25
+
+
+@pytest.fixture
+def num_coordinates() -> int:
+    return 4
+
+
+@pytest.fixture
+def num_class_labels() -> int:
+    return 10
+
+
 @pytest.fixture
 def is_CI() -> bool:
     return bool(os.environ.get("CI", False))

tests/layout_occulusion_test.py

@@ -1,10 +1,13 @@
+import io
 import os
 import pathlib
 from typing import List
 
 import evaluate
+import numpy as np
 import pytest
 import torch
+from PIL import Image
 
 
 @pytest.fixture
@@ -23,6 +26,91 @@ def expected_score(is_CI: bool) -> float:
     return 0.15746160746433283 if is_CI else 0.20880194364379892
 
 
+def create_in_memory_saliency_maps(
+    batch_size: int, poster_width: int, poster_height: int
+):
+    def _create_random_gaussian_image(w, h):
+        """Create a random black image with a white Gaussian."""
+        # Generate random parameters for Gaussian
+        x, y = np.meshgrid(np.linspace(-1, 1, w), np.linspace(-1, 1, h))
+
+        # Generate random center for Gaussian
+        mu_x = np.random.rand() - 0.5
+        mu_y = np.random.rand() - 0.5
+        d = np.sqrt((x - mu_x) ** 2 + (y - mu_y) ** 2)
+
+        # Generate random sigma for Gaussian
+        sigma = 0.2 + np.random.rand() * 0.4
+
+        g = np.exp(-((d) ** 2 / (2.0 * sigma**2)))
+
+        # Create a new image with black background
+        image = Image.fromarray(g * 255).convert("L")
+
+        return image
+
+    def _create_in_memory_saliency_maps(batch_size: int):
+        images = [
+            _create_random_gaussian_image(w=poster_width, h=poster_height)
+            for _ in range(batch_size)
+        ]
+
+        image_filepaths = []
+        for image in images:
+            image_io = io.BytesIO()
+            image.save(image_io, format="PNG")
+            image_io.seek(0)
+            image_filepaths.append(image_io)
+        return image_filepaths
+
+    return _create_in_memory_saliency_maps(batch_size)
+
+
+def test_metric_random(
+    metric_path: str,
+    batch_size: int,
+    poster_width: int,
+    poster_height: int,
+    max_layout_elements: int,
+    num_coordinates: int,
+    num_class_labels: int,
+):
+    metric = evaluate.load(
+        path=metric_path,
+        canvas_width=poster_width,
+        canvas_height=poster_height,
+    )
+    batch_predictions = np.random.rand(
+        batch_size,
+        max_layout_elements,
+        num_coordinates,
+    )
+    batch_gold_labels = np.random.randint(
+        num_class_labels,
+        size=(
+            batch_size,
+            max_layout_elements,
+            1,
+        ),
+    )
+    metric.add_batch(
+        predictions=batch_predictions,
+        gold_labels=batch_gold_labels,
+        saliency_maps_1=create_in_memory_saliency_maps(
+            batch_size=batch_size,
+            poster_width=poster_width,
+            poster_height=poster_height,
+        ),
+        saliency_maps_2=create_in_memory_saliency_maps(
+            batch_size=batch_size,
+            poster_width=poster_width,
+            poster_height=poster_height,
+        ),
+    )
+    score = metric.compute()
+    assert score is not None
+
+
 def test_metric(
     metric_path: str,
     poster_predictions: torch.Tensor,