openvino-dev

import openvino as ov import numpy as np import os # 1. Create a Core object core = ov.Core() # 2. Define a simple model programmatically (e.g., a single addition operation) # This avoids external files and frameworks for a minimal example. input_a = ov.opset12.parameter([1, 3, 224, 224], ov.Type.f32, name="input_a") input_b = ov.opset12.parameter([1, 3, 224, 224], ov.Type.f32, name="input_b") result_op = ov.opset12.add(input_a, input_b, name="add_result") model = ov.Model([result_op], [input_a, input_b], "simple_add_model") # 3. Compile the model for a specific device (e.g., 'CPU', 'GPU', 'NPU') # Use 'AUTO' to let OpenVINO select the best available device. device = os.environ.get("OPENVINO_DEVICE", "CPU") # Default to CPU try: compiled_model = core.compile_model(model, device) print(f"Model compiled successfully for {device} device.") except RuntimeError as e: print(f"Warning: Could not compile model for device '{device}': {e}") print("Falling back to CPU if available.") device = "CPU" compiled_model = core.compile_model(model, device) # 4. Prepare input data input_data_a = np.random.rand(1, 3, 224, 224).astype(np.float32) input_data_b = np.random.rand(1, 3, 224, 224).astype(np.float32) # 5. Perform inference # Inputs can be passed as a list, dictionary, or single tensor depending on model outputs = compiled_model([input_data_a, input_data_b]) # 6. Process results print(f"Inference successful on {device} device.") # The output is a list of numpy arrays, one for each output of the model print(f"Output shape: {outputs[0].shape}, dtype: {outputs[0].dtype}")