train_BootrapEnsemble_MNIST.py

from __future__ import division, print_function
import time
import torch.utils.data
from torchvision import transforms, datasets
import argparse
import matplotlib
from src.Bootstrap_Ensemble.model import *
import copy

matplotlib.use('Agg')
import matplotlib.pyplot as plt

os.environ["CUDA_VISIBLE_DEVICES"] = "1"

parser = argparse.ArgumentParser(description='Train Ensemble of MAP nets using bootstrapping')

parser.add_argument('--weight_decay', type=float, nargs='?', action='store', default=0,
                    help='Specify the precision of an isotropic Gaussian prior. Default: 0.')
parser.add_argument('--subsample', type=float, nargs='?', action='store', default=0.8,
                    help='Rate at which to subsample the dataset to train each net in the ensemble. Default: 0.8.')
parser.add_argument('--n_nets', type=int, nargs='?', action='store', default=100,
                    help='Number of nets in ensemble. Default: 100.')
parser.add_argument('--epochs', type=int, nargs='?', action='store', default=10,
                    help='How many epochs to train each net. Default: 10.')
parser.add_argument('--lr', type=float, nargs='?', action='store', default=1e-3,
                    help='learning rate. Default: 1e-3.')
parser.add_argument('--models_dir', type=str, nargs='?', action='store', default='Ensemble_models',
                    help='Where to save learnt weights and train vectors. Default: \'Ensemble_models\'.')
parser.add_argument('--results_dir', type=str, nargs='?', action='store', default='Ensemble_results',
                    help='Where to save learnt training plots. Default: \'Ensemble_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 = 60000
batch_size = 128
nb_epochs = args.epochs
log_interval = 1

# ------------------------------------------------------------------------------------------------------
# dataset
cprint('c', '\nData:')

# 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()


trainset = datasets.MNIST(root='../data', train=True, download=True, transform=transform_train)
valset = datasets.MNIST(root='../data', train=False, download=True, transform=transform_test)

## ---------------------------------------------------------------------------------------------------------------------
# net dims
cprint('c', '\nNetwork:')

lr = args.lr
weight_decay = args.weight_decay
########################################################################################
# This is The Bootstrapy part

Nruns = args.n_nets

weight_set_samples = []

p_subsample = args.subsample


############    Nruns:ensemble 数量
for iii in range(Nruns):
    keep_idx = []
    for idx in range(len(trainset)):

        if np.random.binomial(1, p_subsample, size=1) == 1:
            keep_idx.append(idx)

    keep_idx = np.array(keep_idx)

    from torch.utils.data.sampler import SubsetRandomSampler

    sampler = SubsetRandomSampler(keep_idx)

    if use_cuda:
        trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=False, pin_memory=True,
                                                  num_workers=3, sampler=sampler)
        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=False, pin_memory=False,
                                                  num_workers=3, sampler=sampler)
        valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,
                                                num_workers=3)

    ###############################################################
    net = Bootstrap_Net(lr=lr, channels_in=1, side_in=28, cuda=use_cuda, classes=10, batch_size=batch_size,
                        weight_decay=weight_decay, n_hid=1200)

    epoch = 0

    ## ---------------------------------------------------------------------------------------------------------------------
    # train
    cprint('c', '\nTrain:')

    print('  init cost variables:')
    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_cost = np.inf
    best_err = np.inf

    nb_its_dev = 1

    tic0 = time.time()
    for i in range(epoch, nb_epochs):

        net.set_mode_train(True)

        tic = time.time()
        nb_samples = 0

        for x, y in trainloader:
            cost_pred, err = net.fit(x, y)

            err_train[i] += err
            pred_cost_train[i] += cost_pred
            nb_samples += len(x)

        pred_cost_train[i] /= nb_samples
        err_train[i] /= nb_samples

        toc = time.time()
        net.epoch = i
        # ---- print
        print("it %d/%d, Jtr_pred = %f, err = %f, " % (i, nb_epochs, 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)

                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]

    toc0 = time.time()
    runtime_per_it = (toc0 - tic0) / float(nb_epochs)
    cprint('r', '   average time: %f seconds\n' % runtime_per_it)

    ## ---------------------------------------------------------------------------------------------------------------------
    # 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))

    ########
    weight_set_samples.append(copy.deepcopy(net.model.state_dict()))


    ## ---------------------------------------------------------------------------------------------------------------------
    # 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 + '/cost%d.png' % iii, 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%d.png' % iii, bbox_extra_artists=(lgd,), box_inches='tight')


save_object(weight_set_samples, models_dir+'/state_dicts.pkl')