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train_BayesByBackprop_UCI.py
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train_BayesByBackprop_UCI.py
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from __future__ import division, print_function
import time
import torch
import torch.utils.data
from torchvision import transforms, datasets
# torchvision包由流行的数据集（torchvision.datasets）、模型架构(torchvision.models)和用于计算机视觉的常见图像转换组成(torchvision.transforms)
import argparse
import matplotlib
from src.Bayes_By_Backprop.UCI.model import *
from src.Bayes_By_Backprop_Local_Reparametrization.UCI.model import *
from utils import readmts_uci_har,transform_labels
import os
matplotlib.use('Agg')
import matplotlib.pyplot as plt
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
parser = argparse.ArgumentParser(description='Train Bayesian Neural Net on MNIST with Variational Inference')
parser.add_argument('--model', type=str, nargs='?', action='store', default='Gaussian_prior',
help='Model to run. Options are \'Gaussian_prior\', \'Laplace_prior\', \'GMM_prior\','
' \'Local_Reparam\'. Default: \'Local_Reparam\'.')
parser.add_argument('--prior_sig', type=float, nargs='?', action='store', default=0.1,
help='Standard deviation of prior. Default: 0.1.')
parser.add_argument('--epochs', type=int, nargs='?', action='store', default=100,
help='How many epochs to train. Default: 200.')
parser.add_argument('--lr', type=float, nargs='?', action='store', default=1e-4,
help='learning rate. Default: 1e-3.')
parser.add_argument('--n_samples', type=float, nargs='?', action='store', default=3,
help='How many MC samples to take when approximating the ELBO. Default: 3.')
parser.add_argument('--models_dir', type=str, nargs='?', action='store', default='UCI_BBP_models',
help='Where to save learnt weights and train vectors. Default: \'BBP_models\'.')
parser.add_argument('--results_dir', type=str, nargs='?', action='store', default='UCI_BBP_results',
help='Where to save learnt training plots. Default: \'BBP_results\'.')
args = parser.parse_args()
# Where to save models weights
models_dir = args.models_dir
# Where to save plots and error, accuracy vectors
results_dir = args.results_dir
mkdir(models_dir)
mkdir(results_dir)
# ------------------------------------------------------------------------------------------------------
# train config
NTrainPointsMNIST = 9269
batch_size = 100
nb_epochs = args.epochs
log_interval = 1
# ------------------------------------------------------------------------------------------------------
# dataset
cprint('c', '\nData:')
file_name = '/data/weiyuhua/data/mts_data/UCI_HAR_Dataset'
x_train, y_train, x_test, y_test = readmts_uci_har(file_name)
data = np.concatenate((x_train, x_test),axis=0)
label = np.concatenate((y_train, y_test),axis=0)
N = data.shape[0]
ind = int(N*0.9)
x_train = data[:ind]
y_train = label[:ind]
x_test = data[ind:]
y_test = label[ind:]
y_train, y_test = transform_labels(y_train, y_test)
NUM_CLASSES = 6
x_train = x_train.swapaxes(1,2)
x_test = x_test.swapaxes(1,2)
x_train = torch.from_numpy(x_train).float()
y_train = torch.from_numpy(y_train)
print(x_train.size(), y_train.size())
trainset = torch.utils.data.TensorDataset(x_train, y_train)
x_test = torch.from_numpy(x_test).float()
y_test = torch.from_numpy(y_test)
print(x_test.size(), y_test.size())
valset = torch.utils.data.TensorDataset(x_test, y_test)
# load data
# data augmentation
# transform_train = transforms.Compose([
# transforms.ToTensor(),
# transforms.Normalize(mean=(0.1307,), std=(0.3081,))
# ])
#
# transform_test = transforms.Compose([
# transforms.ToTensor(),
# transforms.Normalize(mean=(0.1307,), std=(0.3081,))
# ])
use_cuda = torch.cuda.is_available()
if use_cuda:
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True,
num_workers=3)
#pin_memory就是锁页内存，创建DataLoader时，设置pin_memory=True，
#则意味着生成的Tensor数据最开始是属于内存中的锁页内存，这样将内存的Tensor转义到GPU的显存就会更快一些。
#当计算机的内存充足的时候，可以设置pin_memory=True。当系统卡住，或者交换内存使用过多的时候，设置pin_memory=False。
valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True,
num_workers=3)
else:
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=False,
num_workers=3)
valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,
num_workers=3)
## ---------------------------------------------------------------------------------------------------------------------
# net dims
cprint('c', '\nNetwork:')
lr = args.lr
nsamples = int(args.n_samples) # How many samples to estimate ELBO with at each iteration
########################################################################################
if args.model == 'Local_Reparam':
net = BBP_Bayes_Net_LR(lr=lr, channels_in=9, side_in=128, cuda=use_cuda, classes=6, batch_size=batch_size,
Nbatches=(NTrainPointsMNIST / batch_size), nhid=1200, prior_sig=args.prior_sig)
elif args.model == 'Laplace_prior':
net = BBP_Bayes_Net(lr=lr, channels_in=9, side_in=128, cuda=use_cuda, classes=6, batch_size=batch_size,
Nbatches=(NTrainPointsMNIST / batch_size), nhid=1200,
prior_instance=laplace_prior(mu=0, b=args.prior_sig))
elif args.model == 'Gaussian_prior':
net = BBP_Bayes_Net(lr=lr, channels_in=9, side_in=128, cuda=use_cuda, classes=6, batch_size=batch_size,
Nbatches=(NTrainPointsMNIST / batch_size), nhid=1200,
prior_instance=isotropic_gauss_prior(mu=0, sigma=args.prior_sig))
elif args.model == 'GMM_prior': #混合高斯模型 spike-and-slab
net = BBP_Bayes_Net(lr=lr, channels_in=9, side_in=128, cuda=use_cuda, classes=6, batch_size=batch_size,
Nbatches=(NTrainPointsMNIST / batch_size), nhid=1200,
prior_instance=spike_slab_2GMM(mu1=0, mu2=0, sigma1=args.prior_sig, sigma2=0.0005, pi=0.75))
else:
print('Invalid model type')
exit(1)
## ---------------------------------------------------------------------------------------------------------------------
# train
epoch = 0
cprint('c', '\nTrain:')
print(' init cost variables:')
kl_cost_train = np.zeros(nb_epochs)
pred_cost_train = np.zeros(nb_epochs)
err_train = np.zeros(nb_epochs)
cost_dev = np.zeros(nb_epochs)
err_dev = np.zeros(nb_epochs)
best_err = np.inf
nb_its_dev = 1
tic0 = time.time()
for i in range(epoch, nb_epochs):
# We draw more samples on the first epoch in order to ensure convergence
if i == 0:
ELBO_samples = 10
else:
ELBO_samples = nsamples
net.set_mode_train(True)
tic = time.time()
nb_samples = 0
for x, y in trainloader:
cost_dkl, cost_pred, err = net.fit(x, y, samples=ELBO_samples)
err_train[i] += err
kl_cost_train[i] += cost_dkl
pred_cost_train[i] += cost_pred
nb_samples += len(x)
kl_cost_train[i] /= nb_samples # Normalise by number of samples in order to get comparable number to the -log like
pred_cost_train[i] /= nb_samples
err_train[i] /= nb_samples
toc = time.time()
net.epoch = i
# ---- print
print("it %d/%d, Jtr_KL = %f, Jtr_pred = %f, err = %f, " % (
i, nb_epochs, kl_cost_train[i], pred_cost_train[i], err_train[i]), end="")
cprint('r', ' time: %f seconds\n' % (toc - tic))
# ---- dev
if i % nb_its_dev == 0:
net.set_mode_train(False)
nb_samples = 0
for j, (x, y) in enumerate(valloader):
cost, err, probs = net.eval(x, y) # This takes the expected weights to save time, not proper inference
cost_dev[i] += cost
err_dev[i] += err
nb_samples += len(x)
cost_dev[i] /= nb_samples
err_dev[i] /= nb_samples
cprint('g', ' Jdev = %f, err = %f\n' % (cost_dev[i], err_dev[i]))
if err_dev[i] < best_err:
best_err = err_dev[i]
cprint('b', 'best test error')
net.save(models_dir + '/theta_best.dat')
toc0 = time.time()
runtime_per_it = (toc0 - tic0) / float(nb_epochs)
cprint('r', ' average time: %f seconds\n' % runtime_per_it)
net.save(models_dir + '/theta_last.dat')
## ---------------------------------------------------------------------------------------------------------------------
# results
cprint('c', '\nRESULTS:')
nb_parameters = net.get_nb_parameters()
best_cost_dev = np.min(cost_dev)
best_cost_train = np.min(pred_cost_train)
err_dev_min = err_dev[::nb_its_dev].min()
print(' cost_dev: %f (cost_train %f)' % (best_cost_dev, best_cost_train))
print(' err_dev: %f' % (err_dev_min))
print(' nb_parameters: %d (%s)' % (nb_parameters, humansize(nb_parameters)))
print(' time_per_it: %fs\n' % (runtime_per_it))
## Save results for plots
# np.save('results/test_predictions.npy', test_predictions)
np.save(results_dir + '/KL_cost_train.npy', kl_cost_train)
np.save(results_dir + '/pred_cost_train.npy', pred_cost_train)
np.save(results_dir + '/cost_dev.npy', cost_dev)
np.save(results_dir + '/err_train.npy', err_train)
np.save(results_dir + '/err_dev.npy', err_dev)
## ---------------------------------------------------------------------------------------------------------------------
# fig cost vs its
textsize = 15
marker = 5
plt.figure(dpi=100)
fig, ax1 = plt.subplots()
ax1.plot(pred_cost_train, 'r--')
ax1.plot(range(0, nb_epochs, nb_its_dev), cost_dev[::nb_its_dev], 'b-')
ax1.set_ylabel('Cross Entropy')
plt.xlabel('epoch')
plt.grid(b=True, which='major', color='k', linestyle='-')
plt.grid(b=True, which='minor', color='k', linestyle='--')
lgd = plt.legend(['train error', 'test error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})
ax = plt.gca()
plt.title('classification costs')
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(textsize)
item.set_weight('normal')
plt.savefig(results_dir + '/pred_cost.png', bbox_extra_artists=(lgd,), bbox_inches='tight')
plt.figure()
fig, ax1 = plt.subplots()
ax1.plot(kl_cost_train, 'r')
ax1.set_ylabel('nats?')
plt.xlabel('epoch')
plt.grid(b=True, which='major', color='k', linestyle='-')
plt.grid(b=True, which='minor', color='k', linestyle='--')
ax = plt.gca()
plt.title('DKL (per sample)')
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(textsize)
item.set_weight('normal')
plt.savefig(results_dir + '/KL_cost.png', bbox_extra_artists=(lgd,), bbox_inches='tight')
plt.figure(dpi=100)
fig2, ax2 = plt.subplots()
ax2.set_ylabel('% error')
ax2.semilogy(range(0, nb_epochs, nb_its_dev), 100 * err_dev[::nb_its_dev], 'b-')
ax2.semilogy(100 * err_train, 'r--')
plt.xlabel('epoch')
plt.grid(b=True, which='major', color='k', linestyle='-')
plt.grid(b=True, which='minor', color='k', linestyle='--')
ax2.get_yaxis().set_minor_formatter(matplotlib.ticker.ScalarFormatter())
ax2.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})
ax = plt.gca()
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(textsize)
item.set_weight('normal')
plt.savefig(results_dir + '/err.png', bbox_extra_artists=(lgd,), box_inches='tight')