Repository: Project-MONAI/MONAI

Issue:
decollate needs to support more types
**Is your feature request related to a problem? Please describe.**
Currently, the `decollate_batch` API only supports `dict` data, need to support at least `list` type.


Discussion:

Resolving patch:
diff --git a/monai/data/utils.py b/monai/data/utils.py
--- a/monai/data/utils.py
+++ b/monai/data/utils.py
@@ -288,10 +288,10 @@ def list_data_collate(batch: Sequence):
         raise TypeError(re_str)
 
 
-def decollate_batch(data: dict, batch_size: Optional[int] = None) -> List[dict]:
+def decollate_batch(data: Union[dict, list, torch.Tensor], batch_size: Optional[int] = None) -> List[dict]:
     """De-collate a batch of data (for example, as produced by a `DataLoader`).
 
-    Returns a list of dictionaries. Each dictionary will only contain the data for a given batch.
+    Returns a list of dictionaries, list or Tensor, mapping to a given batch.
 
     Images originally stored as (B,C,H,W,[D]) will be returned as (C,H,W,[D]). Other information,
     such as metadata, may have been stored in a list (or a list inside nested dictionaries). In
@@ -315,19 +315,20 @@ def decollate_batch(data: dict, batch_size: Optional[int] = None) -> List[dict]:
         print(out[0])
         >>> {'image': tensor([[[4.3549e-01...43e-01]]]), 'image_meta_dict': {'scl_slope': 0.0}}
 
+        batch_data = [torch.rand((2,1,10,10), torch.rand((2,3,5,5)]
+        out = decollate_batch(batch_data)
+        print(out[0])
+        >>> [tensor([[[4.3549e-01...43e-01]]], tensor([[[5.3435e-01...45e-01]]])]
+
+        batch_data = torch.rand((2,1,10,10)
+        out = decollate_batch(batch_data)
+        print(out[0])
+        >>> tensor([[[4.3549e-01...43e-01]]])
+
     Args:
         data: data to be de-collated.
         batch_size: number of batches in data. If `None` is passed, try to figure out batch size.
     """
-    if not isinstance(data, dict):
-        raise RuntimeError("Only currently implemented for dictionary data (might be trivial to adapt).")
-    if batch_size is None:
-        for v in data.values():
-            if isinstance(v, torch.Tensor):
-                batch_size = v.shape[0]
-                break
-    if batch_size is None:
-        raise RuntimeError("Couldn't determine batch size, please specify as argument.")
 
     def torch_to_single(d: torch.Tensor):
         """If input is a torch.Tensor with only 1 element, return just the element."""
@@ -350,7 +351,27 @@ def decollate(data: Any, idx: int):
             return data[idx]
         raise TypeError(f"Not sure how to de-collate type: {type(data)}")
 
-    return [{key: decollate(data[key], idx) for key in data.keys()} for idx in range(batch_size)]
+    def _detect_batch_size(batch_data):
+        for v in batch_data:
+            if isinstance(v, torch.Tensor):
+                return v.shape[0]
+        warnings.warn("batch_data is not a sequence of tensors in decollate, use `len(batch_data[0])` directly.")
+        return len(batch_data[0])
+
+    result: List[Any]
+    if isinstance(data, dict):
+        batch_size = _detect_batch_size(batch_data=data.values()) if batch_size is None else batch_size
+        result = [{key: decollate(data[key], idx) for key in data.keys()} for idx in range(batch_size)]
+    elif isinstance(data, list):
+        batch_size = _detect_batch_size(batch_data=data) if batch_size is None else batch_size
+        result = [[decollate(d, idx) for d in data] for idx in range(batch_size)]
+    elif isinstance(data, torch.Tensor):
+        batch_size = data.shape[0]
+        result = [data[idx] for idx in range(batch_size)]
+    else:
+        raise NotImplementedError("Only currently implemented for dictionary, list or Tensor data.")
+
+    return result
 
 
 def pad_list_data_collate(


Test patch:
diff --git a/tests/test_decollate.py b/tests/test_decollate.py
--- a/tests/test_decollate.py
+++ b/tests/test_decollate.py
@@ -18,9 +18,23 @@
 import torch
 from parameterized import parameterized
 
-from monai.data import CacheDataset, DataLoader, create_test_image_2d
+from monai.data import CacheDataset, DataLoader, Dataset, create_test_image_2d
 from monai.data.utils import decollate_batch
-from monai.transforms import AddChanneld, Compose, LoadImaged, RandFlipd, SpatialPadd, ToTensord
+from monai.transforms import (
+    AddChannel,
+    AddChanneld,
+    Compose,
+    LoadImage,
+    LoadImaged,
+    RandAffine,
+    RandFlip,
+    RandFlipd,
+    RandRotate90,
+    SpatialPad,
+    SpatialPadd,
+    ToTensor,
+    ToTensord,
+)
 from monai.transforms.post.dictionary import Decollated
 from monai.transforms.spatial.dictionary import RandAffined, RandRotate90d
 from monai.utils import optional_import, set_determinism
@@ -31,10 +45,23 @@
 
 KEYS = ["image"]
 
-TESTS: List[Tuple] = []
-TESTS.append((SpatialPadd(KEYS, 150), RandFlipd(KEYS, prob=1.0, spatial_axis=1)))
-TESTS.append((RandRotate90d(KEYS, prob=0.0, max_k=1),))
-TESTS.append((RandAffined(KEYS, prob=0.0, translate_range=10),))
+TESTS_DICT: List[Tuple] = []
+TESTS_DICT.append((SpatialPadd(KEYS, 150), RandFlipd(KEYS, prob=1.0, spatial_axis=1)))
+TESTS_DICT.append((RandRotate90d(KEYS, prob=0.0, max_k=1),))
+TESTS_DICT.append((RandAffined(KEYS, prob=0.0, translate_range=10),))
+
+TESTS_LIST: List[Tuple] = []
+TESTS_LIST.append((SpatialPad(150), RandFlip(prob=1.0, spatial_axis=1)))
+TESTS_LIST.append((RandRotate90(prob=0.0, max_k=1),))
+TESTS_LIST.append((RandAffine(prob=0.0, translate_range=10),))
+
+
+class _ListCompose(Compose):
+    def __call__(self, input_):
+        img, metadata = self.transforms[0](input_)
+        for t in self.transforms[1:]:
+            img = t(img)
+        return img, metadata
 
 
 class TestDeCollate(unittest.TestCase):
@@ -42,7 +69,8 @@ def setUp(self) -> None:
         set_determinism(seed=0)
 
         im = create_test_image_2d(100, 101)[0]
-        self.data = [{"image": make_nifti_image(im) if has_nib else im} for _ in range(6)]
+        self.data_dict = [{"image": make_nifti_image(im) if has_nib else im} for _ in range(6)]
+        self.data_list = [make_nifti_image(im) if has_nib else im for _ in range(6)]
 
     def tearDown(self) -> None:
         set_determinism(None)
@@ -68,18 +96,10 @@ def check_match(self, in1, in2):
         else:
             raise RuntimeError(f"Not sure how to compare types. type(in1): {type(in1)}, type(in2): {type(in2)}")
 
-    @parameterized.expand(TESTS)
-    def test_decollation(self, *transforms):
-
+    def check_decollate(self, dataset):
         batch_size = 2
         num_workers = 2
 
-        t_compose = Compose([AddChanneld(KEYS), Compose(transforms), ToTensord(KEYS)])
-        # If nibabel present, read from disk
-        if has_nib:
-            t_compose = Compose([LoadImaged("image"), t_compose])
-
-        dataset = CacheDataset(self.data, t_compose, progress=False)
         loader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
 
         for b, batch_data in enumerate(loader):
@@ -90,6 +110,36 @@ def test_decollation(self, *transforms):
                 for i, d in enumerate(decollated):
                     self.check_match(dataset[b * batch_size + i], d)
 
+    @parameterized.expand(TESTS_DICT)
+    def test_decollation_dict(self, *transforms):
+        t_compose = Compose([AddChanneld(KEYS), Compose(transforms), ToTensord(KEYS)])
+        # If nibabel present, read from disk
+        if has_nib:
+            t_compose = Compose([LoadImaged("image"), t_compose])
+
+        dataset = CacheDataset(self.data_dict, t_compose, progress=False)
+        self.check_decollate(dataset=dataset)
+
+    @parameterized.expand(TESTS_LIST)
+    def test_decollation_tensor(self, *transforms):
+        t_compose = Compose([AddChannel(), Compose(transforms), ToTensor()])
+        # If nibabel present, read from disk
+        if has_nib:
+            t_compose = Compose([LoadImage(image_only=True), t_compose])
+
+        dataset = Dataset(self.data_list, t_compose)
+        self.check_decollate(dataset=dataset)
+
+    @parameterized.expand(TESTS_LIST)
+    def test_decollation_list(self, *transforms):
+        t_compose = Compose([AddChannel(), Compose(transforms), ToTensor()])
+        # If nibabel present, read from disk
+        if has_nib:
+            t_compose = _ListCompose([LoadImage(image_only=False), t_compose])
+
+        dataset = Dataset(self.data_list, t_compose)
+        self.check_decollate(dataset=dataset)
+
 
 if __name__ == "__main__":
     unittest.main()