gpu_acceleration.py

import cv2
import tensorflow as tf

# Set the visible devices to the GPU
tf.config.experimental.set_visible_devices([tf.config.experimental.list_physical_devices("GPU")[0]], "GPU")

# Load a deep learning model
model = tf.saved_model.load("model")

# Classify an image using the model on the GPU
def classify_image(image, patch_size=None):
    # Prepare the input tensor
    input_data = tf.constant(image, dtype=tf.float32)

    # Split the input image into patches, if patch_size is specified
    if patch_size is not None:
        patches = cv2.split(image)
    else:
        patches = [image]

    # Classify each patch individually on the GPU
    results = []
    with tf.device("GPU:0"):
        for patch in patches:
            patch_data = tf.constant(patch, dtype=tf.float32)
            patch_result = model(patch_data, training=False).numpy()
            results.append(patch_result.tolist()[0])

    # Merge the classification results for each patch into a single result
    result = sum(results) / len(results)

    # Return the final classification result
    return result