fix channel dim selection on segmentation target (#1509)

Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Jirka <jirka.borovec@seznam.cz>

src/flash/image/segmentation/input.py

@@ -94,7 +94,7 @@ def load_data(
 
     def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
         if DataKeys.TARGET in sample:
-            sample[DataKeys.TARGET] = np.array(load_image(sample[DataKeys.TARGET])).transpose((2, 0, 1))[:, :, 0]
+            sample[DataKeys.TARGET] = np.array(load_image(sample[DataKeys.TARGET])).transpose((2, 0, 1))[0, :, :]
         return super().load_sample(sample)
 
 

tests/image/semantic_segm/test_data.py

@@ -1,13 +1,15 @@
 import os
 from pathlib import Path
-from typing import Dict, List, Tuple
+from typing import Callable, Dict, List, Tuple
 
 import numpy as np
 import pytest
 import torch
 
 from flash import Trainer
 from flash.core.data.io.input import DataKeys
+from flash.core.data.io.input_transform import InputTransform
+from flash.core.data.transforms import ApplyToKeys
 from flash.core.utilities.imports import (
     _FIFTYONE_AVAILABLE,
     _MATPLOTLIB_AVAILABLE,
@@ -43,12 +45,22 @@ def _rand_labels(size: Tuple[int, int], num_classes: int):
     return Image.fromarray(data.astype(np.uint8))
 
 
-def create_random_data(image_files: List[str], label_files: List[str], size: Tuple[int, int], num_classes: int):
+def create_random_data(
+    image_files: List[str], label_files: List[str], size: Tuple[int, int], num_classes: int
+) -> Tuple[List[Image.Image], List[Image.Image]]:
+    imgs = []
     for img_file in image_files:
-        _rand_image(size).save(img_file)
+        img = _rand_image(size)
+        img.save(img_file)
+        imgs.append(img)
 
+    labels = []
     for label_file in label_files:
-        _rand_labels(size, num_classes).save(label_file)
+        label = _rand_labels(size, num_classes)
+        label.save(label_file)
+        labels.append(label)
+
+    return imgs, labels
 
 
 @pytest.mark.skipif(not _TOPIC_IMAGE_AVAILABLE, reason="image libraries aren't installed.")
@@ -58,6 +70,56 @@ def test_smoke():
         dm = SemanticSegmentationData(batch_size=1)
         assert dm is not None
 
+    @staticmethod
+    @pytest.mark.skipif(not _TOPIC_IMAGE_AVAILABLE, reason="image libraries aren't installed.")
+    def test_identity(tmpdir):
+        class IdentityTransform(InputTransform):
+            def per_sample_transform(self) -> Callable:
+                return ApplyToKeys(
+                    DataKeys.INPUT,
+                    np.array,
+                )
+
+            def per_batch_transform(self) -> Callable:
+                return lambda x: x
+
+        tmp_dir = Path(tmpdir)
+
+        # create random dummy data
+
+        os.makedirs(str(tmp_dir / "images"))
+        os.makedirs(str(tmp_dir / "targets"))
+
+        images = [str(tmp_dir / "images" / "img1.png")]
+
+        targets = [str(tmp_dir / "targets" / "img1.png")]
+
+        num_classes: int = 2
+        img_size: Tuple[int, int] = (128, 128)
+        images_data, targets_data = create_random_data(images, targets, img_size, num_classes)
+
+        # instantiate the data module
+
+        dm = SemanticSegmentationData.from_files(
+            test_files=images,
+            test_targets=targets,
+            batch_size=1,
+            num_workers=0,
+            num_classes=num_classes,
+            transform=IdentityTransform(),
+        )
+
+        assert dm is not None
+        assert dm.test_dataloader() is not None
+
+        # check test data
+        data = next(iter(dm.test_dataloader()))
+        imgs, labels = data[DataKeys.INPUT], data[DataKeys.TARGET]
+        assert imgs.shape == (1, 128, 128, 3)
+        assert labels.shape == (1, 128, 128)
+        assert torch.allclose(imgs, torch.from_numpy(np.array(images_data[0])))
+        assert torch.allclose(labels, torch.from_numpy(np.array(targets_data[0]))[:, :, 0])
+
     @staticmethod
     def test_from_folders(tmpdir):
         tmp_dir = Path(tmpdir)