black

Author: FynnBe

example/model_usage.ipynb

@@ -39,6 +39,7 @@
     "\n",
     "from numpy.typing import NDArray\n",
     "\n",
+    "\n",
     "def show_images(images: Dict[str, Union[Tensor, NDArray[Any], Path]]):\n",
     "    v = napari.Viewer()\n",
     "    for name, im in images.items():\n",
@@ -194,7 +195,9 @@
     "input_paths = {ipt.id: download(ipt.test_tensor).path for ipt in model.inputs}\n",
     "print(f\"input paths: {input_paths}\")\n",
     "assert isinstance(model, v0_5.ModelDescr)\n",
-    "sample = create_sample_for_model(model=model, inputs=input_paths, sample_id=\"my_demo_sample\")\n",
+    "sample = create_sample_for_model(\n",
+    "    model=model, inputs=input_paths, sample_id=\"my_demo_sample\"\n",
+    ")\n",
     "sample"
    ]
   },
@@ -268,7 +271,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from bioimageio.core import predict#, predict_many\n",
+    "from bioimageio.core import predict  # , predict_many\n",
     "\n",
     "predict(model=model, inputs=sample)\n",
     "# predict_many(model=model, inputs=[sample])"
@@ -295,7 +298,11 @@
     "# Do demonstrate this we load the sample image.\n",
     "from pprint import pprint\n",
     "\n",
-    "large_input_sample = create_sample_for_model(model=model, inputs={ipt.id: ipt.sample_tensor.download().path for ipt in model.inputs}, sample_id=\"sample input\")\n",
+    "large_input_sample = create_sample_for_model(\n",
+    "    model=model,\n",
+    "    inputs={ipt.id: ipt.sample_tensor.download().path for ipt in model.inputs},\n",
+    "    sample_id=\"sample input\",\n",
+    ")\n",
     "pprint({m: t.tagged_shape for m, t in large_input_sample.members.items()})"
    ]
   },
@@ -319,12 +326,12 @@
    "outputs": [],
    "source": [
     "# Instead, we can use the method `predict_sample_with_blocking`, which will block/pad the image to a shape that fits the model.\n",
-    "large_output_sample = prediction_pipeline.predict_sample_with_blocking(large_input_sample)\n",
+    "large_output_sample = prediction_pipeline.predict_sample_with_blocking(\n",
+    "    large_input_sample\n",
+    ")\n",
     "\n",
     "# show the prediction result\n",
-    "show_images(\n",
-    "    {**large_input_sample.members, **large_output_sample.members}\n",
-    ")"
+    "show_images({**large_input_sample.members, **large_output_sample.members})"
    ]
   }
  ],