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multilayer.py
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"""A simple example of how to train a 2-layer tPC without any comparison to HNs"""
import os
import argparse
import json
import time
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
import matplotlib.pyplot as plt
plt.style.use('ggplot')
from src.models import MultilayertPC
from src.utils import *
from src.get_data import *
path = 'multilayer'
result_path = os.path.join('./results/', path)
if not os.path.exists(result_path):
os.makedirs(result_path)
num_path = os.path.join('./results/', path, 'numerical')
if not os.path.exists(num_path):
os.makedirs(num_path)
fig_path = os.path.join('./results/', path, 'fig')
if not os.path.exists(fig_path):
os.makedirs(fig_path)
model_path = os.path.join('./results/', path, 'models')
if not os.path.exists(model_path):
os.makedirs(model_path)
# add parser as varaible of the main class
parser = argparse.ArgumentParser(description='Sequential memories')
parser.add_argument('--seq-len-max', type=int, default=11,
help='max power of length of input for training (default: 11),\
specify multiple values separated by whitespace')
parser.add_argument('--seed', type=int, default=[1], nargs='+',
help='seed for model init (default: 1); can be multiple, separated by space')
parser.add_argument('--latent-size', type=int, default=480,
help='hidden size for training 480 for mnist; 1900 for cifar10')
parser.add_argument('--lr', type=float, default=1e-4,
help='learning rate for PC')
parser.add_argument('--epochs', type=int, default=200,
help='number of epochs to train (default: 100)')
parser.add_argument('--nonlinearity', type=str, default='tanh',
help='nonlinear function used in the model')
parser.add_argument('--mode', type=str, default='train', choices=['train', 'recall'],
help='mode of the script: train or recall (just to save time)')
parser.add_argument('--query', type=str, default='online', choices=['online', 'offline'],
help='how you query the recall; online means query with true memory at each time, \
offline means query with the predictions')
parser.add_argument('--data', type=str, default='mnist', choices=['mnist', 'cifar'],
help='which dataset to memorize')
parser.add_argument('--repeat', type=float, default=0,
help='percentage of repeating digits')
args = parser.parse_args()
def _plot_recalls(recall, args):
seq_len = recall.shape[0]
fig, ax = plt.subplots(1, seq_len, figsize=(seq_len, 1))
for j in range(seq_len):
if args.data == 'mnist':
img = to_np(recall[j].reshape((28, 28)))
else:
img = to_np(recall[j].reshape((3, 32, 32)).permute(1, 2, 0))
ax[j].imshow(img, cmap='gray_r')
ax[j].axis('off')
plt.tight_layout()
plt.savefig(fig_path + f'/mtPC_len{seq_len}_query{args.query}_{args.data}_repeat{int(args.repeat*100)}percen', dpi=150)
def _plot_memory(x, args):
seq_len = x.shape[0]
fig, ax = plt.subplots(1, seq_len, figsize=(seq_len, 1))
for j in range(seq_len):
if args.data == 'mnist':
img = to_np(x[j].reshape((28, 28)))
else:
img = to_np(x[j].reshape((3, 32, 32)).permute(1, 2, 0))
ax[j].imshow(img, cmap='gray_r')
ax[j].axis('off')
plt.tight_layout()
plt.savefig(fig_path + f'/memory_len{seq_len}_{args.data}_repeat{int(args.repeat*100)}percen', dpi=150)
def _plot_PC_loss(loss, seq_len, learn_iters, dataset):
# plotting loss for tunning; temporary
plt.figure()
plt.plot(loss, label='squared error sum')
plt.legend()
plt.savefig(fig_path + f'/losses_len{seq_len}_iters{learn_iters}_{dataset}')
def main(args):
device = "cuda" if torch.cuda.is_available() else "cpu"
print(device)
# variables for data and model
seq_len_max = args.seq_len_max
learn_iters = args.epochs
learn_lr = args.lr
latent_size = args.latent_size
seed = args.seed
mode = args.mode
dataset = args.data
input_size = 784 if dataset == 'mnist' else 3072
# inference variables: no need to tune too much
inf_iters = 100
inf_lr = 1e-2
MSEs = []
seq_lens = [2 ** pow for pow in range(1, seq_len_max)]
for seq_len in seq_lens:
# varying lr for different datasets
if dataset == 'cifar':
if seq_len == 16:
learn_lr /= 2
if seq_len == 32:
learn_lr /= 2
if seq_len == 128:
learn_lr /= 2
if seq_len == 512:
learn_lr /= 2
elif dataset == 'mnist':
if seq_len == 64:
learn_lr /= 2
if seq_len == 256:
learn_lr /= 2
if seq_len == 512:
learn_lr /= 2
print(f'Training variables: seq_len:{seq_len}; seed:{seed}; lr:{learn_lr}')
# load data
if dataset == 'mnist':
seq = load_sequence_mnist(seed, seq_len, order=False, binary=False).to(device)
elif dataset == 'cifar':
seq = load_sequence_cifar(seed, seq_len).to(device)
# ...or any other custom dataset
# if we want to have repeating digits
if args.repeat > 0:
seq = replace_images(seq, seed=seed, p=args.repeat)
seq = seq.reshape((seq_len, input_size))
# multilayer PC
model = MultilayertPC(latent_size, input_size, nonlin='tanh').to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=learn_lr)
# path to save model to/load model from
PATH = os.path.join(model_path, f'PC_len{seq_len}_seed{seed}_{dataset}_repeat{int(args.repeat*100)}percen.pt')
if mode == 'train':
# train PC
PC_losses = train_multilayer_tPC(model, optimizer, seq, learn_iters, inf_iters, inf_lr, device)
# save the current model and plot the loss for tunning
torch.save(model.state_dict(), PATH)
_plot_PC_loss(PC_losses, seq_len, learn_iters, dataset)
elif mode == 'recall':
# recall mode, no training need, fast
model.load_state_dict(torch.load(PATH, map_location=torch.device(device)))
model.eval()
# slighlt increase the inferenc iters during retrieval
inf_iters = 200
with torch.no_grad():
recalls = multilayer_recall(model, seq, inf_iters, inf_lr, args, device)
if seq_len <= 16:
_plot_recalls(recalls, args)
_plot_memory(seq, args)
MSEs.append(float(to_np(torch.mean((seq - recalls) ** 2))))
if mode == 'recall':
results = {}
results["PC"] = MSEs
json.dump(results, open(num_path + f"/MSEs_seed{seed}_query{args.query}_{args.data}_repeat{int(args.repeat*100)}percen.json", 'w'))
if __name__ == "__main__":
for s in args.seed:
start_time = time.time()
args.seed = s
main(args)
print(f'Seed complete, total time: {time.time() - start_time}')