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("$$$\n")
print(await mpc.output(result))
print("$$$")