Merge pull request #171 from furiosamg/minor-modifications

docs/examples/ssd_mobilenet_onnx.py

@@ -15,6 +15,12 @@
 quantized_onnx = quantize(onnx_model, calib_range)
 
 with create_runner(quantized_onnx, compiler_config=compiler_config) as runner:
+    # Models in the Model Zoo have built-in optimizations that, by default,
+    # bypass normalization, quantization, and type conversion. If you compile
+    # and utilize these models without employing these optimizations, it's
+    # necessary to set up preprocessing steps to incorporate normalization and
+    # type casting. To accomplish this, you should introduce an extra parameter,
+    # `with_scaling=True`.
     inputs, contexts = mobilenet.preprocess(image, with_scaling=True)
     outputs = runner.run(inputs)
     mobilenet.postprocess(outputs, contexts[0])

furiosa/models/client/api.py

@@ -3,6 +3,8 @@
 
 from tqdm import tqdm
 
+from furiosa.runtime.sync import create_runner
+
 from .. import vision
 from ..types import Model, PythonPostProcessor
 
@@ -102,8 +104,6 @@ def decorate_result(
 
 
 def run_inferences(model_cls: Type[Model], input_paths: Sequence[str], postprocess: Optional[str]):
-    from furiosa.runtime.sync import create_runner
-
     warning = """WARN: the benchmark results may depend on the number of input samples,
 sizes of the images, and a machine where this benchmark is running."""
     if postprocess:

pyproject.toml

@@ -64,8 +64,20 @@ Documentation = "https://furiosa-ai.github.io/furiosa-models/latest/"
 [tool.flit.module]
 name = "furiosa.models"
 
-[tool.flit.external-data]
-directory = "furiosa/models/data"
+[tool.flit.sdist]
+exclude = [
+    '.dvc',
+    '.dvcignore',
+    '.github',
+    '.gitignore',
+    'Makefile',
+    'ci-constraints.txt',
+    'docker',
+    'docs',
+    'mkdocs.yml',
+    'tekton',
+    'tests',
+]
 
 [tool.pytest.ini_options]
 addopts = "--benchmark-autosave"