ncnn

import ncnn import numpy as np import os # NCNN requires model files (.param and .bin) # For a runnable quickstart, we'll demonstrate the API flow. # In a real scenario, you'd replace these with actual converted model files. # Create dummy files - these are NOT functional NCNN models # but allow the API calls to proceed without immediate file not found errors. # A real NCNN model conversion would generate proper .param and .bin. with open("dummy_model.param", "w") as f: f.write("7767517\n0\n") # Minimal valid param content for an empty net with open("dummy_model.bin", "wb") as f: f.write(b'') # Empty bin content try: # 1. Initialize NCNN network net = ncnn.Net() print("NCNN Net initialized.") # Optional: Configure options (e.g., enable Vulkan if available) # net.opt.use_vulkan_compute = True # 2. Load model structure (.param) and weights (.bin) # Note: These dummy files will load but won't perform actual inference. # Replace "dummy_model.param" and "dummy_model.bin" with your converted NCNN model paths. ret_param = net.load_param("dummy_model.param") ret_bin = net.load_model("dummy_model.bin") if ret_param == 0 and ret_bin == 0: print("Dummy NCNN model files loaded successfully.") else: print(f"Failed to load dummy model. param_ret={ret_param}, bin_ret={ret_bin}") # A non-zero return code means failure, e.g., malformed model files. # 3. Prepare input data (e.g., from an image or NumPy array) # This step is for demonstration; actual inference won't happen with dummy model. dummy_input_array = np.random.rand(224, 224, 3).astype(np.float32) * 255 mat_in = ncnn.Mat.from_pixels(dummy_input_array, ncnn.PIXEL_RGB, 224, 224) print(f"Dummy input Mat created with shape: {mat_in.w}x{mat_in.h}x{mat_in.c}") # 4. Create an extractor and push input (for a real model) # ex = net.create_extractor() # ex.input("data", mat_in) # "data" is a common input blob name # 5. Run inference and extract output (for a real model) # ret, mat_out = ex.extract("output") # "output" is a common output blob name print("NCNN API usage demonstrated. For real inference, replace dummy files with actual NCNN models.") except Exception as e: print(f"An error occurred: {e}") finally: # Clean up dummy files if os.path.exists("dummy_model.param"): os.remove("dummy_model.param") if os.path.exists("dummy_model.bin"): os.remove("dummy_model.bin")