snn_mlp_1_non_zero.py

# -*- coding: utf-8 -*-

"""
# File Name : snn_mlp_1.py
# Author: Haowen Fang
# Email: hfang02@syr.edu
# Description: multi-layer snn for MNIST classification. Use dual exponential psp kernel.
"""

import argparse
import pandas as pd
import os
import time
import sys

import torch
import numpy as np
import random
from torch.utils.data import Dataset, DataLoader
from torch.utils.tensorboard import SummaryWriter
from torchvision import datasets
from torchvision import transforms, utils

from snn_lib.snn_layers import *
from snn_lib.optimizers import *
from snn_lib.schedulers import *
from snn_lib.data_loaders import *
import snn_lib.utilities

import omegaconf
from omegaconf import OmegaConf

from pprint import pprint


if torch.cuda.is_available():
    device = torch.device('cuda:0')
else:
    device = torch.device('cpu')

# arg parser
parser = argparse.ArgumentParser(description='mlp snn')
parser.add_argument('--config_file', type=str, default='snn_mlp_1.yaml',
                    help='path to configuration file')
parser.add_argument('--train', action='store_true',
                    help='train model')

parser.add_argument('--test', action='store_true',
                    help='test model')

args = parser.parse_args()

# %% config file
if args.config_file is None:
    print('No config file provided, use default config file')
else:
    print('Config file provided:', args.config_file)

conf = OmegaConf.load(args.config_file)

torch.manual_seed(conf['pytorch_seed'])
np.random.seed(conf['pytorch_seed'])

experiment_name = conf['experiment_name']

# %% checkpoint
save_checkpoint = conf['save_checkpoint']
checkpoint_base_name = conf['checkpoint_base_name']
checkpoint_base_path = conf['checkpoint_base_path']
test_checkpoint_path = conf['test_checkpoint_path']

# %% training parameters
hyperparam_conf = conf['hyperparameters']
length = hyperparam_conf['length']
batch_size = hyperparam_conf['batch_size']
synapse_type = hyperparam_conf['synapse_type']
epoch = hyperparam_conf['epoch']
tau_m = hyperparam_conf['tau_m']
tau_s = hyperparam_conf['tau_s']
filter_tau_m = hyperparam_conf['filter_tau_m']
filter_tau_s = hyperparam_conf['filter_tau_s']

membrane_filter = hyperparam_conf['membrane_filter']

train_bias = hyperparam_conf['train_bias']
train_coefficients = hyperparam_conf['train_coefficients']

# %% mnist config
dataset_config = conf['mnist_config']
max_rate = dataset_config['max_rate']
use_transform = dataset_config['use_transform']

# %% transform config
if use_transform == True:
    rand_transform = get_rand_transform(conf['transform'])
else:
    rand_transform = None

# load mnist training dataset
mnist_trainset = datasets.MNIST(root='./data', train=True, download=True, transform=rand_transform)

# load mnist test dataset
mnist_testset = datasets.MNIST(root='./data', train=False, download=True, transform=None)

# acc file name
acc_file_name = experiment_name + '_' + conf['acc_file_name']

# %% define model
class mysnn(torch.nn.Module):
    def __init__(self):
        super().__init__()

        self.length = length
        self.batch_size = batch_size

        self.train_coefficients = train_coefficients
        self.train_bias = train_bias
        self.membrane_filter = membrane_filter

        self.axon1 = dual_exp_iir_layer((784,), self.length, self.batch_size, tau_m, tau_s, train_coefficients)
        self.snn1 = neuron_layer(784, 500, self.length, self.batch_size, tau_m, self.train_bias, self.membrane_filter)

        self.axon2 = dual_exp_iir_layer((500,), self.length, self.batch_size, tau_m, tau_s, train_coefficients)
        self.snn2 = neuron_layer(500, 500, self.length, self.batch_size, tau_m, self.train_bias, self.membrane_filter)

        self.axon3 = dual_exp_iir_layer((500,), self.length, self.batch_size, tau_m, tau_s, train_coefficients)
        self.snn3 = neuron_layer(500, 10, self.length, self.batch_size, tau_m, self.train_bias, self.membrane_filter)

        self.dropout1 = torch.nn.Dropout(p=0.3, inplace=False)
        self.dropout2 = torch.nn.Dropout(p=0.3, inplace=False)

        # holding the initial states
        self.axon1_states = None
        self.snn1_states = None
        self.axon2_states = None
        self.snn2_states = None
        self.axon3_states = None
        self.snn3_states = None

    def forward(self, inputs):
        """
        :param inputs: [batch, input_size, t]
        :return:
        """

        # holding the initial states
        if self.axon1_states is None:
            self.axon1_states = self.axon1.create_init_states()
        else:
            self.axon1_states = (self.axon1_states[0].detach(), self.axon1_states[1].detach())
        if self.snn1_states is None:
            self.snn1_states = self.snn1.create_init_states()
        else:
            self.snn1_states = (self.snn1_states[0].detach(), self.snn1_states[1].detach())
        if self.axon2_states is None:
            self.axon2_states = self.axon2.create_init_states()
        else:
            self.axon2_states = (self.axon2_states[0].detach(), self.axon2_states[1].detach())
        if self.snn2_states is None:
            self.snn2_states = self.snn2.create_init_states()
        else:
            self.snn2_states = (self.snn2_states[0].detach(), self.snn2_states[1].detach())
        if self.axon3_states is None:
            self.axon3_states = self.axon3.create_init_states()
        else:
            self.axon3_states = (self.axon3_states[0].detach(), self.axon3_states[1].detach())
        if self.snn3_states is None:
            self.snn3_states = self.snn3.create_init_states()
        else:
            self.snn3_states = (self.snn3_states[0].detach(), self.snn3_states[1].detach())

        axon1_out, self.axon1_states = self.axon1(inputs, self.axon1_states)
        spike_l1, self.snn1_states = self.snn1(axon1_out, self.snn1_states)

        drop_1 = self.dropout1(spike_l1)

        axon2_out, self.axon2_states = self.axon2(drop_1, self.axon2_states)
        spike_l2, self.snn2_states = self.snn2(axon2_out, self.snn2_states)

        drop_2 = self.dropout2(spike_l2)

        axon3_out, self.axon3_states = self.axon3(drop_2, self.axon3_states)
        spike_l3, self.snn3_states = self.snn3(axon3_out, self.snn3_states)

        return spike_l3


########################### train function ###################################
def train(model, optimizer, scheduler, train_data_loader, writer=None):
    eval_image_number = 0
    correct_total = 0
    wrong_total = 0

    criterion = torch.nn.CrossEntropyLoss()

    model.train()

    for i_batch, sample_batched in enumerate(train_data_loader):

        x_train = sample_batched[0]
        target = sample_batched[1].to(device)
        x_train = x_train.repeat(length, 1, 1).permute(1, 2, 0).to(device)
        out_spike = model(x_train)

        spike_count = torch.sum(out_spike, dim=2)

        model.zero_grad()
        loss = criterion(spike_count, target.long())
        loss.backward()
        optimizer.step()

        # calculate acc
        _, idx = torch.max(spike_count, dim=1)

        eval_image_number += len(sample_batched[1])
        wrong = len(torch.where(idx != target)[0])

        correct = len(sample_batched[1]) - wrong
        wrong_total += len(torch.where(idx != target)[0])
        correct_total += correct
        acc = correct_total / eval_image_number

        # scheduler step
        if isinstance(scheduler, torch.optim.lr_scheduler.CyclicLR):
            scheduler.step()

    # scheduler step
    if isinstance(scheduler, torch.optim.lr_scheduler.MultiStepLR):
        scheduler.step()

    acc = correct_total / eval_image_number

    return acc, loss


def test(model, test_data_loader, writer=None):
    eval_image_number = 0
    correct_total = 0
    wrong_total = 0

    model.eval()

    criterion = torch.nn.CrossEntropyLoss()

    for i_batch, sample_batched in enumerate(test_data_loader):

        x_test = sample_batched[0]
        target = sample_batched[1].to(device)
        x_test = x_test.repeat(length, 1, 1).permute(1, 2, 0).to(device)    # [batch_size, h * w, length]
        out_spike = model(x_test)

        spike_count = torch.sum(out_spike, dim=2)

        loss = criterion(spike_count, target.long())

        # calculate acc
        _, idx = torch.max(spike_count, dim=1)

        eval_image_number += len(sample_batched[1])
        wrong = len(torch.where(idx != target)[0])

        correct = len(sample_batched[1]) - wrong
        wrong_total += len(torch.where(idx != target)[0])
        correct_total += correct
        acc = correct_total / eval_image_number

    acc = correct_total / eval_image_number

    return acc, loss

if __name__ == "__main__":

    snn = mysnn().to(device)

    writer = SummaryWriter()

    params = list(snn.parameters())

    optimizer = get_optimizer(params, conf)

    scheduler = get_scheduler(optimizer, conf)

    train_data = MNISTDataset(mnist_trainset, max_rate=1, length=length, flatten=True)
    train_dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True, drop_last=True)

    test_data = MNISTDataset(mnist_testset, max_rate=1, length=length, flatten=True)
    test_dataloader = DataLoader(test_data, batch_size=batch_size, shuffle=True, drop_last=True)

    train_acc_list = []
    test_acc_list = []
    checkpoint_list = []

    if args.train == True:
        train_it = 0
        test_it = 0
        for j in range(epoch):

            epoch_time_stamp = time.strftime("%Y%m%d-%H%M%S")

            snn.train()
            train_acc, train_loss = train(snn, optimizer, scheduler, train_dataloader, writer=None)
            train_acc_list.append(train_acc)

            print('Train epoch: {}, acc: {}'.format(j, train_acc))

            # save every checkpoint
            if save_checkpoint == True:
                checkpoint_name = checkpoint_base_name + experiment_name + '_' + str(j) + '_' + epoch_time_stamp
                checkpoint_path = os.path.join(checkpoint_base_path, checkpoint_name)
                checkpoint_list.append(checkpoint_path)

                torch.save({
                    'epoch': j,
                    'snn_state_dict': snn.state_dict(),
                    'optimizer_state_dict': optimizer.state_dict(),
                    'loss': train_loss,
                }, checkpoint_path)

            # test model
            snn.eval()
            test_acc, test_loss = test(snn, test_dataloader, writer=None)

            print('Test epoch: {}, acc: {}'.format(j, test_acc))
            test_acc_list.append(test_acc)

        # save result and get best epoch
        train_acc_list = np.array(train_acc_list)
        test_acc_list = np.array(test_acc_list)

        acc_df = pd.DataFrame(data={'train_acc': train_acc_list, 'test_acc': test_acc_list})

        acc_df.to_csv(acc_file_name)

        best_train_acc = np.max(train_acc_list)
        best_train_epoch = np.argmax(test_acc_list)

        best_test_epoch = np.argmax(test_acc_list)
        best_test_acc = np.max(test_acc_list)

        best_checkpoint = checkpoint_list[best_test_epoch]

        print('Summary:')
        print('Best train acc: {}, epoch: {}'.format(best_train_acc, best_train_epoch))
        print('Best test acc: {}, epoch: {}'.format(best_test_acc, best_test_epoch))
        print('best checkpoint:', best_checkpoint)

    elif args.test == True:

        test_checkpoint = torch.load(test_checkpoint_path)
        snn.load_state_dict(test_checkpoint["snn_state_dict"])

        test_acc, test_loss = test(snn, test_dataloader)

        print('Test checkpoint: {}, acc: {}'.format(test_checkpoint_path, test_acc))