mnn

import MNN.nn as nn import MNN.cv as cv import MNN.numpy as np import MNN.expr as expr import os # Assuming 'mobilenet_v1.mnn' and 'cat.jpg' exist in the current directory # In a real scenario, you'd download these or replace with your model/image. # For demonstration, we'll create dummy files if they don't exist. # Create a dummy MNN model file if it doesn't exist (for runnable example) if not os.path.exists('mobilenet_v1.mnn'): print("Creating dummy mobilenet_v1.mnn for quickstart. This won't run a real model.") with open('mobilenet_v1.mnn', 'w') as f: f.write('dummy_model_content') # Create a dummy image file if it doesn't exist (for runnable example) if not os.path.exists('cat.jpg'): print("Creating dummy cat.jpg for quickstart. This won't process a real image.") # Using PIL to create a simple dummy image try: from PIL import Image img = Image.new('RGB', (224, 224), color = 'red') img.save('cat.jpg') except ImportError: print("Pillow not installed. Skipping dummy image creation.") print("Please install Pillow (pip install Pillow) or provide a 'cat.jpg'.") # Configure runtime (e.g., backend, threads, precision) # Backend 0 typically refers to CPU. 'low' precision might enable FP16 if hardware supports. config = { 'precision': 'low', 'backend': 0, 'numThread': 4 } runtime_manager = nn.create_runtime_manager((config,)) # Load model using the Module API # 'data' and 'prob' are example input/output names, adapt to your model try: net = nn.load_module_from_file('mobilenet_v1.mnn', ['data'], ['prob'], runtime_manager=runtime_manager) except Exception as e: print(f"Could not load dummy model: {e}. Please ensure 'mobilenet_v1.mnn' is a valid MNN model.") exit() # Read and preprocess image using MNN.cv and MNN.numpy # Mean and norm values are typical for MobileNet preprocessing image = cv.imread('cat.jpg') if image is not None: image = cv.resize(image, (224, 224), mean=[103.94, 116.78, 123.68], norm=[0.017, 0.017, 0.017]) input_var = np.expand_dims(image, 0) # Add batch dimension (HWC to NHWC) input_var = expr.convert(input_var, expr.NC4HW4) # NHWC to NC4HW4 for MNN # Perform inference output_var = net.forward(input_var) # Post-process output output_var = expr.convert(output_var, expr.NHWC) # NC4HW4 to NHWC # For a real classification model, you would interpret 'output_var' here print(f"Inference output shape: {output_var.shape}") # Example: print top-1 class if it's a classification model # print(f"Output belongs to class: {np.argmax(output_var)}") else: print("Could not read image. Ensure 'cat.jpg' exists and is a valid image.")