Rename "Defer errors" to "Stop on first error" (#2449)

* Rename "Defer Errors" to "Stop on first error"

* throw_early -> fail_fast

backend/src/api/api.py

@@ -607,19 +607,19 @@ def add_package(
 class Iterator(Generic[I]):
     iter_supplier: Callable[[], Iterable[I | Exception]]
     expected_length: int
-    defer_errors: bool = False
+    fail_fast: bool = True
 
     @staticmethod
     def from_iter(
         iter_supplier: Callable[[], Iterable[I | Exception]],
         expected_length: int,
-        defer_errors: bool = False,
+        fail_fast: bool = True,
     ) -> Iterator[I]:
-        return Iterator(iter_supplier, expected_length, defer_errors=defer_errors)
+        return Iterator(iter_supplier, expected_length, fail_fast=fail_fast)
 
     @staticmethod
     def from_list(
-        l: list[L], map_fn: Callable[[L, int], I], defer_errors: bool = False
+        l: list[L], map_fn: Callable[[L, int], I], fail_fast: bool = True
     ) -> Iterator[I]:
         """
         Creates a new iterator from a list that is mapped using the given
@@ -633,11 +633,11 @@ def supplier():
                 except Exception as e:
                     yield e
 
-        return Iterator(supplier, len(l), defer_errors=defer_errors)
+        return Iterator(supplier, len(l), fail_fast=fail_fast)
 
     @staticmethod
     def from_range(
-        count: int, map_fn: Callable[[int], I], defer_errors: bool = False
+        count: int, map_fn: Callable[[int], I], fail_fast: bool = True
     ) -> Iterator[I]:
         """
         Creates a new iterator the given number of items where each item is
@@ -652,7 +652,7 @@ def supplier():
                 except Exception as e:
                     yield e
 
-        return Iterator(supplier, count, defer_errors=defer_errors)
+        return Iterator(supplier, count, fail_fast=fail_fast)
 
 
 N = TypeVar("N")

backend/src/packages/chaiNNer_ncnn/ncnn/batch_processing/load_models.py

@@ -30,8 +30,8 @@
     icon="MdLoop",
     inputs=[
         DirectoryInput(),
-        BoolInput("Defer errors", default=True).with_docs(
-            "Ignore errors that occur during iteration and throw them after processing. Use this if you want to make sure one bad model doesn't interrupt your batch.",
+        BoolInput("Stop on first error", default=False).with_docs(
+            "Instead of collecting errors and throwing them at the end of processing, stop iteration and throw an error as soon as one occurs.",
             hint=True,
         ),
     ],
@@ -49,7 +49,7 @@
 )
 def load_models_node(
     directory: str,
-    defer_errors: bool,
+    fail_fast: bool,
 ) -> tuple[Iterator[tuple[NcnnModelWrapper, str, str, int]], str]:
     logger.debug(f"Iterating over models in directory: {directory}")
 
@@ -81,4 +81,4 @@ def load_model(filepath_pairs: tuple[str, str], index: int):
 
     model_files = list(zip(param_files, bin_files))
 
-    return Iterator.from_list(model_files, load_model, defer_errors), directory
+    return Iterator.from_list(model_files, load_model, fail_fast), directory

backend/src/packages/chaiNNer_onnx/onnx/batch_processing/load_models.py

@@ -30,8 +30,8 @@
     icon="MdLoop",
     inputs=[
         DirectoryInput(),
-        BoolInput("Defer errors", default=True).with_docs(
-            "Ignore errors that occur during iteration and throw them after processing. Use this if you want to make sure one bad model doesn't interrupt your batch.",
+        BoolInput("Stop on first error", default=False).with_docs(
+            "Instead of collecting errors and throwing them at the end of processing, stop iteration and throw an error as soon as one occurs.",
             hint=True,
         ),
     ],
@@ -49,7 +49,7 @@
 )
 def load_models_node(
     directory: str,
-    defer_errors: bool,
+    fail_fast: bool,
 ) -> tuple[Iterator[tuple[OnnxModel, str, str, int]], str]:
     logger.debug(f"Iterating over models in directory: {directory}")
 
@@ -62,4 +62,4 @@ def load_model(path: str, index: int):
     supported_filetypes = [".onnx"]
     model_files = list_all_files_sorted(directory, supported_filetypes)
 
-    return Iterator.from_list(model_files, load_model, defer_errors), directory
+    return Iterator.from_list(model_files, load_model, fail_fast), directory

backend/src/packages/chaiNNer_pytorch/pytorch/iteration/load_models.py

@@ -27,8 +27,8 @@
     icon="MdLoop",
     inputs=[
         DirectoryInput(),
-        BoolInput("Defer errors", default=True).with_docs(
-            "Ignore errors that occur during iteration and throw them after processing. Use this if you want to make sure one bad model doesn't interrupt your batch.",
+        BoolInput("Stop on first error", default=False).with_docs(
+            "Instead of collecting errors and throwing them at the end of processing, stop iteration and throw an error as soon as one occurs.",
             hint=True,
         ),
     ],
@@ -46,7 +46,7 @@
 )
 def load_models_node(
     directory: str,
-    defer_errors: bool,
+    fail_fast: bool,
 ) -> tuple[Iterator[tuple[ModelDescriptor, str, str, int]], str]:
     logger.debug(f"Iterating over models in directory: {directory}")
 
@@ -59,4 +59,4 @@ def load_model(path: str, index: int):
     supported_filetypes = [".pt", ".pth", ".ckpt", ".safetensors"]
     model_files: list[str] = list_all_files_sorted(directory, supported_filetypes)
 
-    return Iterator.from_list(model_files, load_model, defer_errors), directory
+    return Iterator.from_list(model_files, load_model, fail_fast), directory

backend/src/packages/chaiNNer_standard/image/batch_processing/load_image_pairs.py

@@ -34,8 +34,8 @@
                 "Limit the number of images to iterate over. This can be useful for testing the iterator without having to iterate over all images."
             )
         ),
-        BoolInput("Defer errors", default=True).with_docs(
-            "Ignore errors that occur during iteration and throw them after processing. Use this if you want to make sure one bad image doesn't interrupt your batch.",
+        BoolInput("Stop on first error", default=False).with_docs(
+            "Instead of collecting errors and throwing them at the end of processing, stop iteration and throw an error as soon as one occurs.",
             hint=True,
         ),
     ],
@@ -60,7 +60,7 @@ def load_image_pairs_node(
     directory_b: str,
     use_limit: bool,
     limit: int,
-    defer_errors: bool,
+    fail_fast: bool,
 ) -> tuple[Iterator[tuple[np.ndarray, np.ndarray, str, str, str, str, int]], str, str]:
     def load_images(filepaths: tuple[str, str], index: int):
         path_a, path_b = filepaths
@@ -90,7 +90,7 @@ def load_images(filepaths: tuple[str, str], index: int):
     image_files = list(zip(image_files_a, image_files_b))
 
     return (
-        Iterator.from_list(image_files, load_images, defer_errors),
+        Iterator.from_list(image_files, load_images, fail_fast),
         directory_a,
         directory_b,
     )

backend/src/packages/chaiNNer_standard/image/batch_processing/load_images.py

@@ -70,8 +70,8 @@ def list_glob(directory: str, globexpr: str, ext_filter: list[str]) -> list[str]
                 "Limit the number of images to iterate over. This can be useful for testing the iterator without having to iterate over all images."
             )
         ),
-        BoolInput("Defer errors", default=True).with_docs(
-            "Ignore errors that occur during iteration and throw them after processing. Use this if you want to make sure one bad image doesn't interrupt your batch.",
+        BoolInput("Stop on first error", default=False).with_docs(
+            "Instead of collecting errors and throwing them at the end of processing, stop iteration and throw an error as soon as one occurs.",
             hint=True,
         ),
     ],
@@ -95,7 +95,7 @@ def load_images_node(
     glob_str: str,
     use_limit: bool,
     limit: int,
-    defer_errors: bool,
+    fail_fast: bool,
 ) -> tuple[Iterator[tuple[np.ndarray, str, str, int]], str]:
     def load_image(path: str, index: int):
         img, img_dir, basename = load_image_node(path)
@@ -115,4 +115,4 @@ def load_image(path: str, index: int):
     if use_limit:
         just_image_files = just_image_files[:limit]
 
-    return Iterator.from_list(just_image_files, load_image, defer_errors), directory
+    return Iterator.from_list(just_image_files, load_image, fail_fast), directory

backend/src/process.py

@@ -606,10 +606,10 @@ async def update_progress():
             except Aborted:
                 raise
             except Exception as e:
-                if iterator_output.iterator.defer_errors:
-                    deferred_errors.append(str(e))
-                else:
+                if iterator_output.iterator.fail_fast:
                     raise e
+                else:
+                    deferred_errors.append(str(e))
 
         # reset cached value
         self.cache.delete_many(all_iterated_nodes)