Resolve merge conflicts in batched transforms integration

Author: srivarra

examples/transforms/batched_transforms.ipynb

@@ -16,7 +16,7 @@ classifiers = [
     "Programming Language :: Python :: 3.13",
 ]
 dependencies = [
-    "iohub[tensorstore]>=0.2.2rc0",
+    "iohub[tensorstore]>=0.3.0a2",
     "kornia",
     "torch>=2.4.1",
     "timm>=0.9.5",

pyproject.toml

@@ -53,6 +53,7 @@ def test_datamodule_setup_fit(
             assert fov_name not in exclude_fovs
     assert len(all_tracks) == len_total
     for batch in dm.train_dataloader():
+        dm.on_after_batch_transfer(batch, 0)
         assert batch["anchor"].shape == (
             batch_size,
             len(channel_names),
@@ -94,6 +95,7 @@ def test_datamodule_z_window_size(
     else:
         expected_z_shape = z_window_size
     for batch in dm.train_dataloader():
+        dm.on_after_batch_transfer(batch, 0)
         assert batch["anchor"].shape == (
             batch_size,
             len(channel_names),

tests/data/test_triplet.py

@@ -0,0 +1,119 @@
+import pytest
+import torch
+from monai.transforms import AdjustContrast, Compose
+
+from viscy.transforms import BatchedRandAdjustContrast, BatchedRandAdjustContrastd
+
+
+@pytest.mark.parametrize("ndim", [4, 5])
+@pytest.mark.parametrize("prob", [0.0, 0.5, 1.0])
+@pytest.mark.parametrize(
+    "device", ["cpu", "cuda"] if torch.cuda.is_available() else ["cpu"]
+)
+@pytest.mark.parametrize("compose", [True, False])
+def test_batched_adjust_contrast(device, ndim, prob, compose):
+    img = torch.rand([16] + [2] * (ndim - 1), device=device) + 0.1
+    transform = BatchedRandAdjustContrast(prob=prob, gamma=(0.5, 2.0))
+    if compose:
+        transform = Compose([transform])
+    result = transform(img)
+    assert result.shape == img.shape
+    changed = ~torch.isclose(result, img, atol=1e-6).all(
+        dim=list(range(1, result.ndim))
+    )
+    if prob == 1.0:
+        assert changed.all()
+    elif prob == 0.5:
+        assert changed.any()
+        assert not changed.all()
+    elif prob == 0.0:
+        assert not changed.any()
+    assert result.device == img.device
+    if not compose:
+        repeat = transform(img, randomize=False)
+        assert torch.equal(result, repeat)
+
+
+@pytest.mark.parametrize("gamma", [0.8, 1.5, (0.5, 2.0)])
+@pytest.mark.parametrize("retain_stats", [True, False])
+@pytest.mark.parametrize("invert_image", [True, False])
+def test_batched_adjust_contrast_options(gamma, retain_stats, invert_image):
+    img = torch.rand(8, 1, 8, 8, 8) + 0.1
+    original_mean = img.mean()
+    original_std = img.std()
+
+    transform = BatchedRandAdjustContrast(
+        prob=1.0, gamma=gamma, retain_stats=retain_stats, invert_image=invert_image
+    )
+    result = transform(img)
+
+    assert result.shape == img.shape
+
+    if retain_stats:
+        assert torch.isclose(result.mean(), original_mean, atol=1e-5)
+        assert torch.isclose(result.std(), original_std, atol=1e-5)
+
+    if not (isinstance(gamma, (int, float)) and gamma == 1.0):
+        assert not torch.equal(result, img)
+
+
+def test_batched_adjust_contrast_dict():
+    img = torch.rand([16, 1, 4, 8, 8]) + 0.1
+    data = {"a": img, "b": img.clone()}
+    transform = BatchedRandAdjustContrastd(keys=["a", "b"], prob=1.0, gamma=(0.5, 2.0))
+    result = transform(data)
+    assert not torch.equal(result["a"], img)
+    assert torch.equal(result["a"], result["b"])
+
+
+def test_batched_adjust_contrast_gamma_validation():
+    with pytest.raises(ValueError):
+        BatchedRandAdjustContrast(gamma=0.0)
+
+    with pytest.raises(ValueError):
+        BatchedRandAdjustContrast(gamma=-0.5)
+
+    with pytest.raises(ValueError):
+        BatchedRandAdjustContrast(gamma=(0.5, 2.0, 1.0))
+
+    with pytest.raises(ValueError):
+        BatchedRandAdjustContrast(gamma=(-0.1, 2.0))
+
+    BatchedRandAdjustContrast(gamma=0.1)
+    BatchedRandAdjustContrast(gamma=0.3)
+    BatchedRandAdjustContrast(gamma=1.5)
+    BatchedRandAdjustContrast(gamma=(0.2, 0.8))
+    BatchedRandAdjustContrast(gamma=(0.5, 2.0))
+
+
+@pytest.mark.parametrize("gamma_value", [0.2, 0.5, 0.8, 1.2, 2.0])
+@pytest.mark.parametrize("retain_stats", [True, False])
+@pytest.mark.parametrize("invert_image", [True, False])
+def test_batched_adjust_contrast_vs_monai(gamma_value, retain_stats, invert_image):
+    torch.manual_seed(42)
+
+    batch_size = 4
+    img_batch = torch.rand(batch_size, 1, 8, 8, 8) + 0.1
+
+    batched_transform = BatchedRandAdjustContrast(
+        prob=1.0,
+        gamma=(gamma_value, gamma_value),
+        retain_stats=retain_stats,
+        invert_image=invert_image,
+    )
+
+    torch.manual_seed(42)
+    batched_result = batched_transform(img_batch)
+
+    monai_transform = AdjustContrast(
+        gamma=gamma_value, retain_stats=retain_stats, invert_image=invert_image
+    )
+
+    monai_results = []
+    for i in range(batch_size):
+        individual_result = monai_transform(img_batch[i])
+        monai_results.append(individual_result)
+
+    monai_batch_result = torch.stack(monai_results)
+
+    assert torch.allclose(batched_result, monai_batch_result, atol=1e-6, rtol=1e-5)