DOC: Use upstream/downstream instead of source/destination

For monai.deploy.core.Operator, instead of sometimes using "upstream"
and "downstream" and sometimes using "source" and "destination",
consistently use former.

Signed-off-by: Lee Newberg <lee.newberg@kitware.com>

Author: Lee Newberg

docs/source/developing_with_sdk/creating_application_class.md

@@ -54,7 +54,7 @@ In `compose()` method, operators are instantiated and connected through <a href=
 
 > add_flow(upstream_op, downstream_op, io_map=None)
 
-`io_map` is a dictionary of mapping from the source operator's label to the destination operator's label(s) and its type is `Dict[str, str|Set[str]]`.
+`io_map` is a dictionary of mapping from the upstream operator's label to the downstream operator's label(s) and its type is `Dict[str, str|Set[str]]`.
 
 We can skip specifying `io_map` if both the number of `upstream_op`'s outputs and the number of `downstream_op`'s inputs are one.
 For example, if Operators named `task1` and `task2` has only one input and output (with the label `image`), `self.add_flow(task1, task2)` is same with `self.add_flow(task1, task2, {"image": "image"})` or `self.add_flow(task1, task2, {"image": {"image"}})`.

examples/apps/ai_livertumor_seg_app/app.py

@@ -68,7 +68,7 @@ def compose(self):
         # Add the publishing operator to save the input and seg images for Render Server.
         # Note the PublisherOperator has temp impl till a proper rendering module is created.
         self.add_flow(unetr_seg_op, publisher_op, {"saved_images_folder": "saved_images_folder"})
-        # Note below the dicom_seg_writer requires two inputs, each coming from a upstream operator.
+        # Note below the dicom_seg_writer requires two inputs, each coming from an upstream operator.
         self.add_flow(
             series_selector_op, dicom_seg_writer, {"study_selected_series_list": "study_selected_series_list"}
         )

examples/apps/ai_spleen_seg_app/app.py

@@ -71,7 +71,7 @@ def compose(self):
             series_selector_op, series_to_vol_op, {"study_selected_series_list": "study_selected_series_list"}
         )
         self.add_flow(series_to_vol_op, bundle_spleen_seg_op, {"image": "image"})
-        # Note below the dicom_seg_writer requires two inputs, each coming from a upstream operator.
+        # Note below the dicom_seg_writer requires two inputs, each coming from an upstream operator.
         self.add_flow(
             series_selector_op, dicom_seg_writer, {"study_selected_series_list": "study_selected_series_list"}
         )

monai/deploy/core/application.py

@@ -192,8 +192,8 @@ def add_flow(
         Args:
             upstream_op (Operator): An instance of the upstream operator of type Operator.
             downstream_op (Operator): An instance of the downstream operator of type Operator.
-            io_map (Optional[Dict[str, Union[str, Set[str]]]]): A dictionary of mapping from the source operator's label
-                                                                 to the destination operator's label(s).
+            io_map (Optional[Dict[str, Union[str, Set[str]]]]): A dictionary of mapping from the upstream operator's label
+                                                                 to the downstream operator's label(s).
         """
 
         # Ensure that the upstream and downstream operators are valid

monai/deploy/core/graphs/graph.py

@@ -28,21 +28,21 @@ def add_flow(self, op_u: Operator, op_v: Operator, io_map: Dict[str, Set[str]]):
         """Add an edge to the graph.
 
         Args:
-            op_u (Operator): A source operator.
-            op_v (Operator): A destination operator.
-            io_map (Dict[str, Set[str]]): A dictionary of mapping from the source operator's label to the destination
+            op_u (Operator): An upstream operator.
+            op_v (Operator): A downstream operator.
+            io_map (Dict[str, Set[str]]): A dictionary of mapping from the upstream operator's label to the downstream
                                           operator's label(s).
         """
         pass
 
     @abstractmethod
     def get_io_map(self, op_u: Operator, op_v) -> Dict[str, Set[str]]:
-        """Get a mapping from the source operator's output label to the destination operator's input label.
+        """Get a mapping from the upstream operator's output label to the downstream operator's input label.
         Args:
-            op_u (Operator): A source operator.
-            op_v (Operator): A destination operator.
+            op_u (Operator): An upstream operator.
+            op_v (Operator): A downstream operator.
         Returns:
-            A dictionary of mapping from the source operator's output label to the destination operator's
+            A dictionary of mapping from the upstream operator's output label to the downstream operator's
             input label(s).
         """
         pass

notebooks/tutorials/01_simple_app.ipynb

@@ -343,7 +343,7 @@
     "\n",
     "> add_flow(upstream_op, downstream_op, io_map=None)\n",
     "\n",
-    "`io_map` is a dictionary of mapping from the source operator's label to the destination operator's label(s) and its type is `Dict[str, str|Set[str]]`. \n",
+    "`io_map` is a dictionary of mapping from the upstream operator's label to the downstream operator's label(s) and its type is `Dict[str, str|Set[str]]`. \n",
     "\n",
     "We can skip specifying `io_map` if both the number of `upstream_op`'s outputs and the number of `downstream_op`'s inputs are one so `self.add_flow(sobel_op, median_op)` is same with `self.add_flow(sobel_op, median_op, {\"image\": \"image\"})` or `self.add_flow(sobel_op, median_op, {\"image\": {\"image\"}})`.\n"
    ]