hpmpc-fork/MP-SPDZ/Functions/bench/VGG.mpc at master · CASP-Systems-BU/hpmpc-fork · GitHub

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#This program loads a pre-trained model and performs inference on the test set.
#The model is trained in clear by one party which then holds the weights.
#The other party provides the inputs
#It uses the VGG Architecture
program.options_from_args()

from Compiler import ml
import numpy as np
import torch
import torch.nn as nn
sfix.set_precision(7)

batch_size = int(program.args[1])

#Generate the inputs
help=np.ones((batch_size,3,32,32),np.float64)
input=sfix.input_tensor_via(0, help,binary=True)
help=np.ones((batch_size),np.int32)
labels=sint.input_tensor_via(0,help,binary=True,one_hot=True)

#Set the architecture of the model and determine the necessary layers
net = nn.Sequential(
                nn.Conv2d(in_channels=3,
                          out_channels=64,
                          kernel_size=(3, 3),
                          stride=(1, 1),
                          padding=1),
                nn.ReLU(),
                nn.Conv2d(in_channels=64,
                          out_channels=64,
                          kernel_size=(3, 3),
                          stride=(1, 1),
                          padding=1),
                nn.ReLU(),
                nn.MaxPool2d(kernel_size=(2, 2),
                             stride=(2, 2)),
                nn.Conv2d(in_channels=64,
                          out_channels=128,
                          kernel_size=(3, 3),
                          stride=(1, 1),
                          padding=1),
                nn.ReLU(),
                nn.Conv2d(in_channels=128,
                          out_channels=128,
                          kernel_size=(3, 3),
                          stride=(1, 1),
                          padding=1),
                nn.ReLU(),
                nn.MaxPool2d(kernel_size=(2, 2),
                             stride=(2, 2)),
                nn.Conv2d(in_channels=128,
                          out_channels=256,
                          kernel_size=(3, 3),
                          stride=(1, 1),
                          padding=1),
                nn.ReLU(),
                nn.Conv2d(in_channels=256,
                          out_channels=256,
                          kernel_size=(3, 3),
                          stride=(1, 1),
                          padding=1),
                nn.ReLU(),
                nn.Conv2d(in_channels=256,
                          out_channels=256,
                          kernel_size=(3, 3),
                          stride=(1, 1),
                          padding=1),
                nn.ReLU(),
                nn.MaxPool2d(kernel_size=(2, 2),
                             stride=(2, 2)),
                nn.Conv2d(in_channels=256,
                          out_channels=512,
                          kernel_size=(3, 3),
                          stride=(1, 1),
                          padding=1),
                nn.ReLU(),
                nn.Conv2d(in_channels=512,
                          out_channels=512,
                          kernel_size=(3, 3),
                          stride=(1, 1),
                          padding=1),
                nn.ReLU(),
                nn.Conv2d(in_channels=512,
                          out_channels=512,
                          kernel_size=(3, 3),
                          stride=(1, 1),
                          padding=1),
                nn.ReLU(),
                nn.MaxPool2d(kernel_size=(2, 2),
                             stride=(2, 2)),
                nn.Conv2d(in_channels=512,
                          out_channels=512,
                          kernel_size=(3, 3),
                          stride=(1, 1),
                          padding=1),
                nn.ReLU(),
                nn.Conv2d(in_channels=512,
                          out_channels=512,
                          kernel_size=(3, 3),
                          stride=(1, 1),
                          padding=1),
                nn.ReLU(),
                nn.Conv2d(in_channels=512,
                          out_channels=512,
                          kernel_size=(3, 3),
                          stride=(1, 1),
                          padding=1),
                nn.ReLU(),
                nn.MaxPool2d(kernel_size=(2, 2),
                             stride=(2, 2)),
                nn.Linear(512, 4096),
                nn.ReLU(True),
                nn.Linear(4096, 4096),
                nn.ReLU(True),
                nn.Linear(4096, 10),
        )

ml.set_n_threads(1)
ml.Layer.back_batch_size = batch_size

#set the layers of the graph
layers=ml.layers_from_torch(net,[batch_size,32,32,3],batch_size,input_via=0)
graph=ml.Optimizer(layers)

#Run the computation
graph.eval(input,batch_size=batch_size)