def predict()

in jetson_inference/artifacts/aws.greengrass.JetsonDLRImageClassification/1.0.0/inference.py [0:0]

• 31 lines of code
• 5 McCabe index (conditional complexity)

def predict(image_data):
    r"""
    Predict image with DLR.
    :param image: numpy array of the Image inference with.
    """
    try:
        # Run DLR to perform inference with DLC optimized model
        model_output = dlr_model.run(image_data)
        max_score_id = np.argmax(model_output)
        max_score = np.max(model_output)
        print("max score id:",max_score_id)
        print("class:",labels[max_score_id])
        print("max score",str(max_score))
        probabilities = model_output[0][0]
        sort_classes_by_probability = np.argsort(probabilities)[::-1]
        results_file = "{}/{}.log".format(results_directory,os.path.basename(os.path.realpath(model_path)))
        message = '{"class":"' + labels[max_score_id] + '"' + ',"confidence":"' + str(max_score) +'"}'
        payload = {
            "message": message,
            "timestamp": datetime.now().strftime('%Y-%m-%dT%H:%M:%S')
        }
        topic = "demo/topic"
        if enableSendMessages:
           ipc_client.new_publish_to_iot_core().activate(
               request=PublishToIoTCoreRequest(topic_name=topic, qos='0',
                                            payload=json.dumps(payload).encode()))

        with open(results_file, 'a') as f:
            print("{}: Top {} predictions with score {} or above ".format(str(
                datetime.now()), max_no_of_results, score_threshold), file=f)
            for i in sort_classes_by_probability[:max_no_of_results]:
                if probabilities[i] >= score_threshold:
                    print("[ Class: {}, Score: {} ]".format(
                        labels[i], probabilities[i]), file=f)

    except Exception as e:
        print("Exception occurred during prediction: %s", e)