Features: Store

deeptrack/features.py

@@ -222,7 +222,7 @@ def propagate_data_to_dependencies(
     "ChannelFirst2d",
     "Upscale",  # TODO ***AL***
     "NonOverlapping",  # TODO ***AL***
-    "Store",  # TODO ***JH***
+    "Store",
     "Squeeze",
     "Unsqueeze",
     "ExpandDims",
@@ -8804,9 +8804,9 @@ def _resample_volume_position(
 class Store(Feature):
     """Store the output of a feature for reuse.
 
-    The `Store` feature evaluates a given feature and stores its output in an 
-    internal dictionary. Subsequent calls with the same key will return the 
-    stored value unless the `replace` parameter is set to `True`. This enables 
+    The `Store` feature evaluates a given feature and stores its output in an
+    internal dictionary. Subsequent calls with the same key will return the
+    stored value unless the `replace` parameter is set to `True`. This enables
     caching and reuse of computed feature outputs.
 
     Parameters
@@ -8815,10 +8815,10 @@ class Store(Feature):
         The feature to evaluate and store.
     key: Any
         The key used to identify the stored output.
-    replace: bool, optional
-        If `True`, replaces the stored value with a new computation. It defaults 
-        to `False`.
-    **kwargs:: dict of str to Any
+    replace: PropertyLike[bool], optional
+        If `True`, replaces the stored value with the current computation. It
+        defaults to `False`.
+    **kwargs: dict of str to Any
         Additional keyword arguments passed to the parent `Feature` class.
 
     Attributes
@@ -8852,22 +8852,26 @@ class Store(Feature):
     >>> cached_output = store_feature(None, key="example", replace=False)
     >>> print(cached_output == output)
     True
+    >>> print(cached_output == value_feature())
+    False
 
     Retrieve the stored value recomputing:
     >>> value_feature.update()
     >>> cached_output = store_feature(None, key="example", replace=True)
     >>> print(cached_output == output)
     False
+    >>> print(cached_output == value_feature())
+    True
 
     """
 
     __distributed__: bool = False
 
     def __init__(
-        self: Store,
+        self: Feature,
         feature: Feature,
         key: Any,
-        replace: bool = False,
+        replace: PropertyLike[bool] = False,
         **kwargs: Any,
     ):
         """Initialize the Store feature.
@@ -8878,8 +8882,8 @@ def __init__(
             The feature to evaluate and store.
         key: Any
             The key used to identify the stored output.
-        replace: bool, optional
-            If `True`, replaces the stored value with a new computation. 
+        replace: PropertyLike[bool], optional
+            If `True`, replaces the stored value with a new computation.
             It defaults to `False`.
         **kwargs:: dict of str to Any
             Additional keyword arguments passed to the parent `Feature` class.
@@ -8891,7 +8895,7 @@ def __init__(
         self._store: dict[Any, Image] = {}
 
     def get(
-        self: Store,
+        self: Feature,
         _: Any,
         key: Any,
         replace: bool,

deeptrack/tests/test_features.py

@@ -2408,10 +2408,35 @@ def test_Store(self):
         value_feature.update()
         cached_output = store_feature(None, key="example", replace=False)
         self.assertEqual(cached_output, output)
+        self.assertNotEqual(cached_output, value_feature())
 
         value_feature.update()
         cached_output = store_feature(None, key="example", replace=True)
         self.assertNotEqual(cached_output, output)
+        self.assertEqual(cached_output, value_feature())
+
+        if TORCH_AVAILABLE:
+
+            value_feature = features.Value(lambda: torch.rand(1))
+
+            store_feature = features.Store(
+                feature=value_feature, key="example"
+            )
+
+            output = store_feature(None, key="example", replace=False)
+
+            value_feature.update()
+            cached_output = store_feature(None, key="example", replace=False)
+            torch.testing.assert_close(cached_output, output)
+            with self.assertRaises(AssertionError):
+                torch.testing.assert_close(cached_output, value_feature())
+
+            value_feature.update()
+            cached_output = store_feature(None, key="example", replace=True)
+            with self.assertRaises(AssertionError):
+                torch.testing.assert_close(cached_output, output)
+            torch.testing.assert_close(cached_output, value_feature())
+
 
 
     def test_Squeeze(self):

tutorials/3-advanced-topics/DTAT391B_sources.folder.ipynb

@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -111,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -174,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -430,6 +430,13 @@
     "List all image paths in train:"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Prepare an example path to one of the files:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 15,