pip test pass

.gitignore

@@ -9,6 +9,7 @@ MODULE*
 
 # py
 __pycache__
+*.egg-info
 
 # model
 *.gynn

python/CMakeLists.txt

@@ -1,5 +1,5 @@
 file(GLOB BIND_SRC "*.cc")
-pybind11_add_module(mininn MODULE ${BIND_SRC})
-target_link_libraries(mininn PRIVATE runtime kernel operator parser graph utils)
+pybind11_add_module(mininn_capi MODULE ${BIND_SRC})
+target_link_libraries(mininn_capi PRIVATE runtime kernel operator parser graph utils)
 
-message(STATUS "Source files for mininn library: ${BIND_SRC}")
+message(STATUS "Source files for mininn_capi library: ${BIND_SRC}")

python/mininn/predictor.py

@@ -1,10 +1,10 @@
-import mininn
+from mininn import mininn_capi
 
 class Predictor():
     def __init__(self, filename):
-        graph = mininn.Graph()
-        mininn.load_model(filename, graph)
-        self.Predictor = mininn.Predictor(graph)
+        graph = mininn_capi.Graph()
+        mininn_capi.load_model(filename, graph)
+        self.Predictor = mininn_capi.Predictor(graph)
 
     def get_input(self):
         inputs = self.Predictor.get_input_tensors()

python/pybind11.cc

@@ -54,7 +54,7 @@ void bind_class(py::module_ &m) {
         .def("get_output_tensors", &Predictor::get_output_tensors);
 }
 
-PYBIND11_MODULE(mininn, m) {
+PYBIND11_MODULE(mininn_capi, m) {
     bind_function(m);
     bind_enum(m);
     bind_class(m);

python/setup.py

@@ -1,25 +1,37 @@
-from setuptools import setup, find_packages, Extension
+from setuptools import setup, find_packages
+from setuptools.command.build_py import build_py
+
+import os
+import shutil
 
 VERSION = '0.0.1' 
-DESCRIPTION = 'My first Python package'
-LONG_DESCRIPTION = 'My first Python package with a slightly longer description'
+DESCRIPTION = 'python interface of MiniNN'
+LONG_DESCRIPTION = 'Build a deep learning inference framework from scratch'
 
-ext_modules = [
-    Extension(
-        'mininn',
-        ["mininn.so"],
-    ),
-]
+class CustomBuildCommand(build_py):
+    """Custom build step to copy CMake-built .so file into the package."""
+    def run(self):
+        cmake_output = os.path.abspath("../build/python/mininn_capi.cpython-311-x86_64-linux-gnu.so")
+        target_dir = os.path.join(self.build_lib, "mininn")
+
+        if not os.path.exists(target_dir):
+            os.makedirs(target_dir)
+
+        if os.path.exists(cmake_output):
+            shutil.copyfile(cmake_output, os.path.join(target_dir, "mininn_capi.so"))
+        else:
+            raise FileNotFoundError(f"{cmake_output} does not exist. Build with CMake first.")
+
+        super().run()
 
 
-# 配置
 setup(
-    name="mininn", 
-    version=VERSION,
-    author="masteryi-0018",
-    author_email="<[email protected]>",
-    description=DESCRIPTION,
-    long_description=LONG_DESCRIPTION,
-    packages=find_packages(),
-    install_requires=[],
+    name = "mininn", 
+    version = VERSION,
+    author = "masteryi-0018",
+    author_email = "<[email protected]>",
+    description = DESCRIPTION,
+    long_description = LONG_DESCRIPTION,
+    packages = find_packages(),
+    cmdclass={"build_py": CustomBuildCommand},
 )

python/test/pip_test.py

@@ -15,13 +15,13 @@ def main():
 
     outputs = my_predictor.get_output()
     c = outputs[0]
-    print(c[0], c.get_shape())
+    print(c.get_data()[0], c.get_shape())
 
     output_size = c.get_size()
     golden = [3.0] * output_size
-    assert c == golden
+    assert c.get_data() == golden
     return
 
 
 if __name__ == "__main__":
-    main()
+    main()

python/test/pybind_test.py

@@ -1,8 +1,8 @@
-import mininn
+import mininn_capi
 
 def main():
     # Tensor
-    tensor = mininn.Tensor()
+    tensor = mininn_capi.Tensor()
     tensor.set_shape([1, 3, 224, 224])
     print(tensor.get_shape())
     print(tensor.get_size())
@@ -11,8 +11,8 @@ def main():
 
     # parser
     filename = "../../convertor/mininn_test.gynn"
-    graph = mininn.Graph()
-    mininn.load_model(filename, graph)
+    graph = mininn_capi.Graph()
+    mininn_capi.load_model(filename, graph)
 
     # graph
     node_num = len(graph.get_nodes())
@@ -26,7 +26,7 @@ def main():
     print(graph.get_outputs())
 
     # predictor
-    predictor = mininn.Predictor(graph)
+    predictor = mininn_capi.Predictor(graph)
     input = predictor.get_input_tensors()
     output = predictor.get_output_tensors()
     for i in range(len(input)):