SNN_image_classification_robustness/snn.py at master · BINDS-LAB-UMASS/SNN_image_classification_robustness · GitHub

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import torch
from bindsnet.conversion import ann_to_snn
from torchvision import datasets, transforms
import torch.nn as nn
import torch.nn.functional as F
from bindsnet.network.monitors import Monitor
from time import time as t_
import pandas as pd
import os


percentile = 99.9
random_seed = 0
torch.manual_seed(random_seed)

batch_size = 32
time = 100

ANN_accuracy = 0
SNN_accuracy = 0

if torch.cuda.is_available():
    torch.set_default_tensor_type('torch.cuda.FloatTensor')
    device = torch.device('cuda')
    print("Cuda is available")
else:
    device = torch.device('cpu')
    print("Cuda is not available")

train_dataset = datasets.MNIST('./data',
                               train=True,
                               download=True,
                               transform=transforms.ToTensor())

train_dataset2 = datasets.MNIST('./data',
                               train=False,
                               download=True,
                               transform=transforms.ToTensor())

train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           shuffle=True, batch_size=train_dataset.__len__())

train_loader2 = torch.utils.data.DataLoader(dataset=train_dataset2,
                                           shuffle=True)


for d, target in train_loader:
    data = d.to(device)


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(28 * 28, 1000)
        self.fc2 = nn.Linear(1000, 10)


    def forward(self, x):
            x = x.view(-1, 28*28)
            x = F.relu(self.fc1(x))
            x = self.fc2(x)
            return F.log_softmax(x)

model = torch.load('trained_model.pt')

print()
print('Converting ANN to SNN...')


SNN = ann_to_snn(model, input_shape=[28*28], data=data, percentile=percentile)


SNN.add_monitor(
    Monitor(SNN.layers['2'], state_vars=['s', 'v'], time=time), name='2'
)


correct = 0


def validate():
    global ANN_accuracy
    model.eval()
    val_loss, correct = 0, 0
    for data, target in train_loader2:
        data = data.to(device)
        target = target.to(device)
        output = model(data)
        pred = output.data.max(1)[1]  # get the index of the max log-probability
        correct += pred.eq(target.data).cpu().sum()


    ANN_accuracy = 100. * correct.to(torch.float32) / len(train_loader2.dataset)

    print("ANN accuracy:", ANN_accuracy)

validate()


start = t_()
for index, (data, target) in enumerate(train_loader2):
    print('sample ', index+1, 'elapsed', t_() - start)
    start = t_()

    data = data.to(device)
    data = data.view(-1, 28*28)
    inpts = {'Input': data.repeat(time, 1)}
    SNN.run(inpts=inpts, time=time)
    spikes = {layer: SNN.monitors[layer].get('s') for layer in SNN.monitors}
    voltages = {layer: SNN.monitors[layer].get('v') for layer in SNN.monitors if not layer == 'Input'}
    pred = torch.argmax(voltages['2'].sum(1))
    correct += pred.eq(target.data.to(device)).cpu().sum()
    accuracy = 100. * correct.to(torch.float32) / (index + 1)
    SNN.reset_()

SNN_accuracy = 100. * correct.to(torch.float32) / len(train_loader2.dataset)


print("accuracy:, ", SNN_accuracy)

df = pd.DataFrame({"ANN accuracy":[ANN_accuracy],
                   "SNN accuracy": [SNN_accuracy]})

df.to_csv("accuracy_hidden_1.csv")