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associative_memory.py
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associative_memory.py
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# -*- coding: utf-8 -*-
"""
# File Name : associative_memory.py
# Author: Haowen Fang
# Email: hfang02@syr.edu
# Description: Train snn to reproduce the input pattern at output layer.
"""
# %%
import torch
import numpy as np
import random
import matplotlib.pyplot as plt
import importlib
import matplotlib
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets
import sys
import os
import snn_lib.utilities as utilities
from snn_lib.snn_layers import *
import argparse
# %% configurations
dataset_path = './dataset/associative_target.npy'
checkpoint_base_name = "associative_memory_checkpoint_"
checkpoint_base_path = './associative_memory_checkpoint/'
torch.manual_seed(2)
np.random.seed(0)
length = 300
input_size = 300
batch_size = 64
synapse_type = 'dual_exp'
epoch = 20
tau_m = 8
tau_s = 2
filter_tau_m = tau_m
filter_tau_s = tau_s
dropout_rate = 0.3
modify_input_prob = 0.6
remove_prob = 0.5
remove_row_prob = 0.2
remove_col_prob = 0.2
remove_block_prob = 0.2
mutate_prob = 0.7
noise_prob = 0.005
remove_block_h = 30
remove_block_w = 40
remove_block_h = 40
remove_col_w = 30
remove_row_h = 30
optimizer_choice = 1
scheduler_choice = 1
optimizer_config = {0: (torch.optim.Adam, 0.001),
1: (torch.optim.AdamW, 0.001),
2: (torch.optim.SGD, 0.0001)}
scheduler_config = {0: None,
1: (torch.optim.lr_scheduler.MultiStepLR, [50,100, 150], 0.1),
# order: milestones, gamma=0.1
2: (torch.optim.lr_scheduler.CosineAnnealingWarmRestarts, 1000),
# T_0
3: (torch.optim.lr_scheduler.CyclicLR, 0.001, 0.01, 200)
}
# %% utility functions
def add_noise_spike(spike_array,probability = 0.003):
'''
:param spike_array: 2d array [spike train num, length]
:param probability:
:return:
'''
noise_mat = np.random.rand(*spike_array.shape).astype(np.float32)
noise_mat[np.where(noise_mat > 1-probability)] = 1
noise_mat[np.where(noise_mat <= 1 - probability)] = 0
new_arr = spike_array + noise_mat
new_arr[np.where(new_arr > 1)] = 1
return new_arr
def remove_row(spike_array,remove_width, position = None):
'''
remove a few rows in the spike mat (set rows to 0)
:param spike_array: 2d array [spike train num, length]
:param remove_width: How many rows to remove
:param position: spike will be removed from row position to position+remove_width
:return:
'''
h,w = spike_array.shape
upper_row = np.random.randint(0,h-remove_width)
if position != None:
upper_row = position
new_arr = spike_array
new_arr[upper_row:upper_row+remove_width,:] = 0
return new_arr
def remove_col(spike_array,remove_width):
'''
remove a few columns in spike mat (set columns to 0)
:param spike_array: 2d array [spike train num, length]
:param width:
:return:
'''
h,w = spike_array.shape
left_col = np.random.randint(0,w-remove_width)
new_arr = spike_array
new_arr[:,left_col:left_col+remove_width] = 0
return new_arr
def remove_block(spike_array,remove_hight, remove_width):
'''
set a block region in spike mat to 0
:param spike_array: 2d array [spike train num, length]
:param width:
:return:
'''
h,w = spike_array.shape
top_left_row = np.random.randint(0,w-remove_hight)
top_left_col = np.random.randint(0,w-remove_width)
new_arr = spike_array
new_arr[top_left_row:top_left_row+remove_hight,top_left_col:top_left_col+remove_width] = 0
return new_arr
class PatternDataset(Dataset):
"""random pattern dataset"""
def __init__(self, input_pattern, target, filtered_target,length):
'''
:param input_pattern: [number, hight, width/time]
:param label_cat:
:param target:
:param length:
'''
self.input_pattern = input_pattern
self.target = target
self.filtered_target = filtered_target
self.length = length
self.modify_input_prob = modify_input_prob
self.remove_prob = remove_prob
self.remove_row_prob = remove_row_prob
self.remove_col_prob = remove_col_prob
self.noise_prob = noise_prob
self.mutate_prob = mutate_prob
self.remove_block_h = remove_block_h
self.remove_block_w = remove_block_w
self.remove_block_h =remove_block_h
self.remove_col_w = remove_col_w
self.remove_row_h = remove_row_h
def __len__(self):
#input only has 10 samples, so increase length by 100
#each sample is a class
return len(self.input_pattern)*100
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
#input data only has 10 sample
#by mod get the actual idx, which is also the class
real_idx = idx % 10
label = real_idx
input_pattern = self.input_pattern[real_idx].copy()
output_target = self.target[real_idx]
filtered_target = self.filtered_target[real_idx]
if np.random.rand() > self.modify_input_prob:
#modify input data, remove, delete, add noise, mutate
if np.random.rand() < self.mutate_prob:
input_pattern = utilities.mutate_spike_pattern(input_pattern, 0, 1)
#random select remove row, col, or block
choice = np.random.randint(2)
if np.random.rand() < self.remove_prob:
if choice == 0:
input_pattern = remove_block(input_pattern,remove_block_w, remove_block_h)
elif choice == 1:
input_pattern = remove_row(input_pattern, remove_block_h)
input_pattern = add_noise_spike(input_pattern, self.noise_prob)
return input_pattern, label, output_target, filtered_target
class mysnn(torch.nn.Module):
def __init__(self):
super().__init__()
self.length = length
self.batch_size = batch_size
self.train_tau_m = True
self.train_tau_s = True
self.train_coefficients = True
self.train_decay_v = False
self.train_v0 = False
self.train_reset_v = False
self.train_reset_decay = False
self.train_threshold = False
self.train_bias = True
self.membrane_filter = False
self.axon1 = dual_exp_iir_layer((input_size,), self.length,self.batch_size, tau_m, tau_s, self.train_coefficients)
self.dense1 = neuron_layer(input_size, 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, self.train_coefficients)
self.dense2 = neuron_layer(500, 200, self.length, self.batch_size, tau_m, self.train_bias, self.membrane_filter)
self.axon3 = dual_exp_iir_layer((200,), self.length, self.batch_size, tau_m, tau_s, self.train_coefficients)
self.dense3 = neuron_layer(200, 500, self.length, self.batch_size, tau_m, self.train_bias, self.membrane_filter)
self.axon4 = dual_exp_iir_layer((500,), self.length, self.batch_size, tau_m, tau_s, self.train_coefficients)
self.dense4 = neuron_layer(500, 300, self.length, self.batch_size, tau_m, self.train_bias, self.membrane_filter)
self.output_filter = filter_layer(300, self.length, self.batch_size, filter_tau_m, filter_tau_s)
def forward(self, inputs):
"""
:param inputs: [batch, input_size, t]
:return:
"""
axon1_states = self.axon1.create_init_states()
dense1_states = self.dense1.create_init_states()
axon2_states = self.axon2.create_init_states()
dense2_states = self.dense2.create_init_states()
axon3_states = self.axon3.create_init_states()
dense3_states = self.dense3.create_init_states()
axon4_states = self.axon4.create_init_states()
dense4_states = self.dense4.create_init_states()
axon1_out, axon1_states = self.axon1(inputs, axon1_states)
spike_dense1, dense1_states = self.dense1(axon1_out, dense1_states)
axon2_out, axon2_states = self.axon2(spike_dense1, axon2_states)
spike_dense2, dense2_states = self.dense2(axon2_out, dense2_states)
axon3_out, axon3_states = self.axon3(spike_dense2, axon3_states)
spike_dense3, dense3_states = self.dense3(axon3_out, dense3_states)
axon4_out, axon4_states = self.axon4(spike_dense3, axon4_states)
spike_dense4, dense4_states = self.dense4(axon4_out, dense4_states)
filtered_output = self.output_filter(spike_dense4)
return spike_dense4, filtered_output
# %%
def train(model, optimizer, scheduler, data_loader):
'''
'''
model.train()
eval_image_number = 0
correct_total = 0
wrong_total = 0
criterion = torch.nn.MSELoss()
for i_batch, sample_batched in enumerate(data_loader):
x_train = sample_batched[0].to(device)
label = sample_batched[1].to(device)
target_pattern = sample_batched[2].to(device)
filtered_target_pattern = sample_batched[3].to(device)
out_spike, filtered_out_spike = model(x_train)
model.zero_grad()
loss = criterion(filtered_out_spike, filtered_target_pattern)
print('train loss: {}'.format(loss))
loss.backward()
optimizer.step()
if isinstance(scheduler, torch.optim.lr_scheduler.CosineAnnealingWarmRestarts) or \
isinstance(scheduler, torch.optim.lr_scheduler.CyclicLR):
scheduler.step()
if isinstance(scheduler, torch.optim.lr_scheduler.MultiStepLR):
scheduler.step()
return loss
def test(model, data_loader):
model.eval()
loss_list = []
criterion = torch.nn.MSELoss()
for i_batch, sample_batched in enumerate(data_loader):
criterion = torch.nn.MSELoss()
x_test = sample_batched[0].to(device)
label = sample_batched[1].to(device)
target_pattern = sample_batched[2].to(device)
filtered_target_pattern = sample_batched[3].to(device)
out_spike, filtered_out_spike = model(x_test)
loss = criterion(filtered_out_spike, filtered_target_pattern)
print('Test loss: {}'.format(loss))
loss_list.append(loss.cpu().detach().numpy())
#calculate loss of this epoch
loss_array = np.stack(loss_list)
average_loss = loss_array.mean()
return average_loss
if __name__ == "__main__":
train_model = True
if torch.cuda.is_available():
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
original_pattern = np.load(dataset_path).astype(np.float32)
delayed_target = np.zeros(original_pattern.shape, dtype=np.float32)
delayed_target[:, :, 4:] = original_pattern[:, :, :-4]
filtered_target = []
for target_idx, delayed_target_pattern in enumerate(delayed_target):
filtered_target.append(utilities.filter_spike_multiple(delayed_target_pattern, filter_type='dual_exp',
tau_m=filter_tau_m, tau_s=filter_tau_s))
# shape [pattern idx, spike train idx, time]
filtered_target = np.array(filtered_target)
snn = mysnn().to(device)
params = list(snn.parameters())
optimizer_class = optimizer_config[optimizer_choice][0]
learning_rate = optimizer_config[optimizer_choice][1]
optimizer = optimizer_class(params, learning_rate)
scheduler = None
if scheduler_choice == 0:
scheduler = None
elif scheduler_choice == 1:
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, scheduler_config[scheduler_choice][1],
scheduler_config[scheduler_choice][2])
elif scheduler_choice == 2:
scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer,
scheduler_config[scheduler_choice][1])
elif scheduler_choice == 3:
scheduler = torch.optim.lr_scheduler.CyclicLR(optimizer, scheduler_config[scheduler_choice][1],
scheduler_config[scheduler_choice][2],
scheduler_config[scheduler_choice][3])
elif scheduler_choice == 4:
scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, scheduler_config[scheduler_choice][1],
scheduler_config[scheduler_choice][2],
scheduler_config[scheduler_choice][3])
# %% train
train_data = PatternDataset(original_pattern, delayed_target, filtered_target, length)
train_dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True, drop_last=True)
if train_model:
for j in range(epoch):
print('train epoch: {}'.format(j))
snn.train()
train_loss = train(snn, optimizer, scheduler, train_dataloader)
save_checkpoint = True
if save_checkpoint:
checkpoint_path = os.path.join(checkpoint_base_path, checkpoint_base_name + str(j))
torch.save({
'epoch': j,
'snn_state_dict': snn.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': train_loss,
}, checkpoint_path)
# %% test
test_data = PatternDataset(original_pattern, delayed_target, filtered_target, length)
test_dataloader = DataLoader(test_data, batch_size=batch_size, shuffle=True, drop_last=True)
best_epoch = 0
lowest_loss = 100
# test
for j in range(epoch):
checkpoint_path = os.path.join(checkpoint_base_path, checkpoint_base_name + str(j))
checkpoint = torch.load(checkpoint_path)
snn.load_state_dict(checkpoint["snn_state_dict"])
snn.eval()
average_loss = test(snn, test_dataloader)
if lowest_loss > average_loss:
lowest_loss = average_loss
best_epoch = j
print('Test epoch: {}, loss: {}'.format(j, average_loss))
print('Best epoch: {}'.format(best_epoch))
input_list = []
output_list = []
checkpoint_path = os.path.join(checkpoint_base_path, checkpoint_base_name + str(best_epoch))
checkpoint = torch.load(checkpoint_path)
snn.load_state_dict(checkpoint["snn_state_dict"])
snn.eval()
for i_batch, sample_batched in enumerate(test_dataloader):
criterion = torch.nn.MSELoss()
x_test = sample_batched[0].to(device)
label = sample_batched[1].to(device)
target_pattern = sample_batched[2].to(device)
filtered_target_pattern = sample_batched[3].to(device)
out_spike, filtered_out_spike = snn(x_test)
loss = criterion(filtered_out_spike, filtered_target_pattern)
print(i_batch, loss)
input_list.append(x_test.cpu().detach().numpy())
output_list.append(out_spike.cpu().detach().numpy())