async def main(): await mpc.start() #load test data print(f'loading data...') data = load_data('Face', 'test') #data = np.array(data) data = np.reshape(data, (-1, 100)) hor_filter = np.array([[-1, -1, -1], [ 0, 0, 0], [ 1, 1, 1]]) ver_filter = np.array([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]]) start = timer() result = sharpen_img(data, (hor_filter, ver_filter)).tolist() print(type(result), len(result), type(result[0])) print("$$$\n") print(await mpc.output(result)) print("$$$") end = timer() running_time = end - start print(f'MPC total time: {running_time}')
async def main(): await mpc.start() image = load_data('Image', 'test')[0] kernel = load_data('Kernel', 'model')[0] image = np.reshape(image, (int(math.sqrt(len(image))), int(math.sqrt(len(image))))) kernel = np.reshape(kernel, (int(math.sqrt(len(kernel))), int(math.sqrt(len(kernel))))) start = timer() result = convolution(image, kernel) end = timer() running_time = end - start print(f'Run time: {running_time}') print("$$$\n") print(running_time) print("$$$")
async def main(): await mpc.start() image = load_data('Image', 'test')[0] image = np.reshape( image, (int(math.sqrt(len(image))), int(math.sqrt(len(image))))) start = timer() result = relu(image) end = timer() running_time = end - start print(f'Run time: {running_time}') print("$$$\n") print(running_time) print("$$$")
async def main(): await mpc.start() image = load_data('Image', 'test')[0] #image = np.reshape(image, (int(math.sqrt(len(image))), int(math.sqrt(len(image))))) #start = timer() #result = relu(image) #result = list(np.asarray(result).flatten()) #end = timer() #running_time = end - start #print(f'Run time: {running_time}') start = timer() print(await mpc.output(image)) end = timer() running_time = end - start print("$$$\n") print(running_time) print("$$$")
async def main(): print("Starting MPC...") await mpc.start() print("MPC started") print("Loading data...") images = load_data("Face", "test") image = images[0] with open("parameters/weights.pkl", "rb") as f: weights = pickle.load(f) with open("parameters/biases.pkl", "rb") as f: biases = pickle.load(f) print("Data loaded") image = np.reshape(image, (int(math.sqrt(len(image))), int(math.sqrt(len(image))))) print("Data reshaped") start = timer() print("---------- LAYER 0 ----------") inputs_1 = layer(image, weights[0], biases[0]) await mpc.barrier() print("---------- LAYER 1 ----------") inputs_2 = layer(inputs_1, weights[1], biases[1]) await mpc.barrier() print("---------- LAYER 2 ----------") result = layer(inputs_2, weights[2], biases[2]) await mpc.barrier() print("---------- FLATTENING ----------") result = list(np.asarray(result).flatten()) end = timer() compute_time = end - start print(f"Compute time: {compute_time}") print("$$$\n") print(await mpc.output(result)) print("$$$")