feat: train fixed-size classifiers and export class probabilities

tests/test_tile_classification.py

@@ -0,0 +1,40 @@
+import pytest
+from PIL import Image
+
+from datasets.tile_classification import YoloTileClassificationDataset, tile_label
+from datasets.yolo import write_data_yaml
+
+
+def test_label_uses_human_class_ids_and_empty_background():
+    assert tile_label("", 9, 4) == 4
+    assert tile_label("1 0.5 0.5 0.2 0.2", 9, 4) == 1
+    assert tile_label("1 0.5 0.5 0.2 0.2\n3 0.5 0.5 0.4 0.4", 9, 4) == 3
+    with pytest.raises(ValueError):
+        tile_label("1 0.5 0.5 nan 0.2", 9, 4)
+
+
+def test_existing_split_is_preserved_and_no_resize(tmp_path):
+    for split in ("train", "val"):
+        (tmp_path / "images" / split).mkdir(parents=True)
+        (tmp_path / "labels" / split).mkdir(parents=True)
+        Image.new("RGB", (16, 16)).save(tmp_path / "images" / split / "sample.png")
+        (tmp_path / "labels" / split / "sample.txt").write_text(
+            "1 0.5 0.5 0.2 0.2" if split == "train" else "")
+    yaml = write_data_yaml(tmp_path / "data.yaml", ["normal", "blackdot"], root=tmp_path)
+    kwargs = {"yaml_path": yaml, "image_size": [16, 16], "transforms": [{"name": "ToTensor"}]}
+    train = YoloTileClassificationDataset(split="train", **kwargs)
+    val = YoloTileClassificationDataset(split="val", **kwargs)
+    assert train.class_counts() == [0, 1]
+    assert val.class_counts() == [1, 0]
+    assert train[0]["image"].shape == (3, 16, 16)
+    Image.new("RGB", (12, 16)).save(train.samples[0][0])
+    with pytest.raises(ValueError, match="resize is forbidden"):
+        train[0]
+
+
+def test_missing_labels_are_not_silent_background(tmp_path):
+    (tmp_path / "images" / "train").mkdir(parents=True)
+    Image.new("RGB", (16, 16)).save(tmp_path / "images" / "train" / "sample.png")
+    yaml = write_data_yaml(tmp_path / "data.yaml", ["normal", "blackdot"], root=tmp_path)
+    with pytest.raises(FileNotFoundError, match="Missing label"):
+        YoloTileClassificationDataset(yaml, "train")