Repository: Project-MONAI/MONAI

Issue:
Enhance GridPatchDataset for iterable dataset
**Is your feature request related to a problem? Please describe.**
Meng zhang and I are preparing examples for MONAI webinar to show all kinds of MONAI datasets, realized that the `GridPatchDataset` can be enhanced as we already have the `IterableDataset` now and we can easily add support for `iterable` data source now, which is marked `TODO` so far:
https://github.com/Project-MONAI/MONAI/blob/dev/monai/data/grid_dataset.py#L142



Discussion:

Resolving patch:
diff --git a/monai/data/grid_dataset.py b/monai/data/grid_dataset.py
--- a/monai/data/grid_dataset.py
+++ b/monai/data/grid_dataset.py
@@ -9,16 +9,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Callable, Dict, Optional, Sequence, Union
-
-import numpy as np
-import torch
-from torch.utils.data import IterableDataset
+from typing import Callable, Dict, Iterable, Optional, Sequence, Union
 
 from monai.data.dataset import Dataset
+from monai.data.iterable_dataset import IterableDataset
 from monai.data.utils import iter_patch
 from monai.transforms import apply_transform
-from monai.utils import NumpyPadMode, ensure_tuple, look_up_option
+from monai.utils import NumpyPadMode, deprecated_arg, ensure_tuple, look_up_option
 
 __all__ = ["PatchDataset", "GridPatchDataset", "PatchIter"]
 
@@ -96,7 +93,7 @@ class GridPatchDataset(IterableDataset):
         patch_intensity = RandShiftIntensity(offsets=1.0, prob=1.0)
 
         # construct the dataset
-        ds = GridPatchDataset(dataset=images,
+        ds = GridPatchDataset(data=images,
                               patch_iter=patch_iter,
                               transform=patch_intensity)
         # use the grid patch dataset
@@ -108,49 +105,34 @@ class GridPatchDataset(IterableDataset):
         #     coordinates: tensor([[[0, 1], [0, 2], [0, 2]],
         #                          [[0, 1], [2, 4], [0, 2]]])
 
+    Args:
+        data: the data source to read image data from.
+        patch_iter: converts an input image (item from dataset) into a iterable of image patches.
+            `patch_iter(dataset[idx])` must yield a tuple: (patches, coordinates).
+            see also: :py:class:`monai.data.PatchIter`.
+        transform: a callable data transform operates on the patches.
+        with_coordinates: whether to yield the coordinates of each patch, default to `True`.
+
+    .. deprecated:: 0.8.0
+        ``dataset`` is deprecated, use ``data`` instead.
+
     """
 
+    @deprecated_arg(name="dataset", new_name="data", since="0.8", msg_suffix="please use `data` instead.")
     def __init__(
         self,
-        dataset: Sequence,
+        data: Union[Iterable, Sequence],
         patch_iter: Callable,
         transform: Optional[Callable] = None,
         with_coordinates: bool = True,
     ) -> None:
-        """
-        Initializes this dataset in terms of the image dataset, patch generator, and an optional transform.
-
-        Args:
-            dataset: the dataset to read image data from.
-            patch_iter: converts an input image (item from dataset) into a iterable of image patches.
-                `patch_iter(dataset[idx])` must yield a tuple: (patches, coordinates).
-                see also: :py:class:`monai.data.PatchIter`.
-            transform: a callable data transform operates on the patches.
-            with_coordinates: whether to yield the coordinates of each patch, default to `True`.
-
-        """
-
-        self.dataset = dataset
+        super().__init__(data=data, transform=None)
         self.patch_iter = patch_iter
         self.transform = transform
         self.with_coordinates = with_coordinates
 
     def __iter__(self):
-        worker_info = torch.utils.data.get_worker_info()
-        iter_start, iter_end = 0, 1
-        try:
-            iter_end = len(self.dataset)  # TODO: support iterable self.dataset
-        except TypeError:
-            raise NotImplementedError("image dataset must implement `len()`.") from None
-
-        if worker_info is not None:
-            # split workload
-            per_worker = int(np.ceil((iter_end - iter_start) / float(worker_info.num_workers)))
-            iter_start += worker_info.id * per_worker
-            iter_end = min(iter_start + per_worker, iter_end)
-
-        for index in range(iter_start, iter_end):
-            image = self.dataset[index]
+        for image in super().__iter__():
             if not self.with_coordinates:
                 for patch, *_ in self.patch_iter(image):  # patch_iter to yield at least 1 item: patch
                     out_patch = (
@@ -204,20 +186,24 @@ class PatchDataset(Dataset):
 
         >>> torch.Size([2, 1, 3, 3])
 
+    .. deprecated:: 0.8.0
+        ``dataset`` is deprecated, use ``data`` instead.
+
     """
 
+    @deprecated_arg(name="dataset", new_name="data", since="0.8", msg_suffix="please use `data` instead.")
     def __init__(
-        self, dataset: Sequence, patch_func: Callable, samples_per_image: int = 1, transform: Optional[Callable] = None
+        self, data: Sequence, patch_func: Callable, samples_per_image: int = 1, transform: Optional[Callable] = None
     ) -> None:
         """
         Args:
-            dataset: an image dataset to extract patches from.
+            data: an image dataset to extract patches from.
             patch_func: converts an input image (item from dataset) into a sequence of image patches.
                 patch_func(dataset[idx]) must return a sequence of patches (length `samples_per_image`).
             samples_per_image: `patch_func` should return a sequence of `samples_per_image` elements.
             transform: transform applied to each patch.
         """
-        super().__init__(data=dataset, transform=transform)
+        super().__init__(data=data, transform=transform)
 
         self.patch_func = patch_func
         if samples_per_image <= 0:


Test patch:
diff --git a/tests/test_grid_dataset.py b/tests/test_grid_dataset.py
--- a/tests/test_grid_dataset.py
+++ b/tests/test_grid_dataset.py
@@ -33,31 +33,35 @@ def tearDown(self):
         set_determinism(None)
 
     def test_shape(self):
-        test_dataset = ["vwxyz", "helloworld", "worldfoobar"]
-        result = GridPatchDataset(dataset=test_dataset, patch_iter=identity_generator, with_coordinates=False)
+        # test Iterable input data
+        test_dataset = iter(["vwxyz", "helloworld", "worldfoobar"])
+        result = GridPatchDataset(data=test_dataset, patch_iter=identity_generator, with_coordinates=False)
         output = []
         n_workers = 0 if sys.platform == "win32" else 2
         for item in DataLoader(result, batch_size=3, num_workers=n_workers):
             output.append("".join(item))
-        expected = ["vwx", "wor", "yzh", "ldf", "ell", "oob", "owo", "ar", "rld"]
+        if sys.platform == "win32":
+            expected = ["ar", "ell", "ldf", "oob", "owo", "rld", "vwx", "wor", "yzh"]
+        else:
+            expected = ["d", "dfo", "hel", "low", "oba", "orl", "orl", "r", "vwx", "yzw"]
+            self.assertEqual(len("".join(expected)), len("".join(list(test_dataset))))
         self.assertEqual(sorted(output), sorted(expected))
-        self.assertEqual(len("".join(expected)), len("".join(test_dataset)))
 
     def test_loading_array(self):
         set_determinism(seed=1234)
-        # image dataset
+        # test sequence input data with images
         images = [np.arange(16, dtype=float).reshape(1, 4, 4), np.arange(16, dtype=float).reshape(1, 4, 4)]
         # image level
         patch_intensity = RandShiftIntensity(offsets=1.0, prob=1.0)
         patch_iter = PatchIter(patch_size=(2, 2), start_pos=(0, 0))
-        ds = GridPatchDataset(dataset=images, patch_iter=patch_iter, transform=patch_intensity)
+        ds = GridPatchDataset(data=images, patch_iter=patch_iter, transform=patch_intensity)
         # use the grid patch dataset
         for item in DataLoader(ds, batch_size=2, shuffle=False, num_workers=0):
             np.testing.assert_equal(tuple(item[0].shape), (2, 1, 2, 2))
         np.testing.assert_allclose(
             item[0],
-            np.array([[[[2.0577, 3.0577], [6.0577, 7.0577]]], [[[10.5540, 11.5540], [14.5540, 15.5540]]]]),
-            rtol=1e-5,
+            np.array([[[[1.4965, 2.4965], [5.4965, 6.4965]]], [[[11.3584, 12.3584], [15.3584, 16.3584]]]]),
+            rtol=1e-4,
         )
         np.testing.assert_allclose(item[1], np.array([[[0, 1], [0, 2], [2, 4]], [[0, 1], [2, 4], [2, 4]]]), rtol=1e-5)
         if sys.platform != "win32":
@@ -65,7 +69,7 @@ def test_loading_array(self):
                 np.testing.assert_equal(tuple(item[0].shape), (2, 1, 2, 2))
             np.testing.assert_allclose(
                 item[0],
-                np.array([[[[1.6533, 2.6533], [5.6533, 6.6533]]], [[[9.8524, 10.8524], [13.8524, 14.8524]]]]),
+                np.array([[[[1.2548, 2.2548], [5.2548, 6.2548]]], [[[9.1106, 10.1106], [13.1106, 14.1106]]]]),
                 rtol=1e-3,
             )
             np.testing.assert_allclose(
diff --git a/tests/test_patch_dataset.py b/tests/test_patch_dataset.py
--- a/tests/test_patch_dataset.py
+++ b/tests/test_patch_dataset.py
@@ -29,7 +29,7 @@ def test_shape(self):
         test_dataset = ["vwxyz", "hello", "world"]
         n_per_image = len(test_dataset[0])
 
-        result = PatchDataset(dataset=test_dataset, patch_func=identity, samples_per_image=n_per_image)
+        result = PatchDataset(data=test_dataset, patch_func=identity, samples_per_image=n_per_image)
 
         output = []
         n_workers = 0 if sys.platform == "win32" else 2
@@ -50,7 +50,7 @@ def test_loading_array(self):
         patch_intensity = RandShiftIntensity(offsets=1.0, prob=1.0)
         image_ds = Dataset(images, transform=patch_intensity)
         # patch level
-        ds = PatchDataset(dataset=image_ds, patch_func=sampler, samples_per_image=n_samples, transform=patch_intensity)
+        ds = PatchDataset(data=image_ds, patch_func=sampler, samples_per_image=n_samples, transform=patch_intensity)
 
         np.testing.assert_equal(len(ds), n_samples * len(images))
         # use the patch dataset, length: len(images) x samplers_per_image