from __future__ import print_function import os import data import utils import torch import pickle import metrics import argparse import numpy as np import config_bayesian as cfg from datetime import datetime from torch.nn import functional as F from torch.optim import Adam, lr_scheduler import amd_sample_draw from models.BayesianModels.BayesianLeNet import BBBLeNet from models.BayesianModels.BayesianAlexNet import BBBAlexNet from models.BayesianModels.Bayesian3Conv3FC import BBB3Conv3FC from stopping_crit import earlyStopping, energyBound, accuracyBound # CUDA settings device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu") def getModel(net_type, inputs, outputs, priors, layer_type, activation_type): if (net_type == 'lenet'): return BBBLeNet(outputs, inputs, priors, layer_type, activation_type,wide=cfg.wide) elif (net_type == 'alexnet'): return BBBAlexNet(outputs, inputs, priors, layer_type, activation_type) elif (net_type == '3conv3fc'): return BBB3Conv3FC(outputs, inputs, priors, layer_type, activation_type) else: raise ValueError('Network should be either [LeNet / AlexNet / 3Conv3FC') def train_model(net, optimizer, criterion, trainloader, num_ens=1, beta_type=0.1, epoch=None, num_epochs=None): net.train() training_loss = 0.0 accs = [] kl_list = [] for i, (inputs, labels) in enumerate(trainloader, 1): optimizer.zero_grad() inputs, labels = inputs.to(device), labels.to(device) outputs = torch.zeros(inputs.shape[0], net.num_classes, num_ens).to(device) kl = 0.0 for j in range(num_ens): net_out, _kl = net(inputs) kl += _kl outputs[:, :, j] = F.log_softmax(net_out, dim=1) kl = kl / num_ens kl_list.append(kl.item()) log_outputs = utils.logmeanexp(outputs, dim=2) beta = metrics.get_beta(i-1, len(trainloader), beta_type, epoch, num_epochs) loss = criterion(log_outputs, labels, kl, beta) loss.backward() optimizer.step() accs.append(metrics.acc(log_outputs.data, labels)) training_loss += loss.cpu().data.numpy() return training_loss/len(trainloader), np.mean(accs), np.mean(kl_list) def validate_model(net, criterion, validloader, num_ens=1, beta_type=0.1, epoch=None, num_epochs=None): """Calculate ensemble accuracy and NLL Loss""" net.train() valid_loss = 0.0 accs = [] for i, (inputs, labels) in enumerate(validloader): inputs, labels = inputs.to(device), labels.to(device) outputs = torch.zeros(inputs.shape[0], net.num_classes, num_ens).to(device) kl = 0.0 for j in range(num_ens): net_out, _kl = net(inputs) kl += _kl outputs[:, :, j] = F.log_softmax(net_out, dim=1).data log_outputs = utils.logmeanexp(outputs, dim=2) beta = metrics.get_beta(i-1, len(validloader), beta_type, epoch, num_epochs) valid_loss += criterion(log_outputs, labels, kl, beta).item() accs.append(metrics.acc(log_outputs, labels)) return valid_loss/len(validloader), np.mean(accs) def run(dataset, net_type): # Hyper Parameter settings layer_type = cfg.layer_type activation_type = cfg.activation_type priors = cfg.priors train_ens = cfg.train_ens valid_ens = cfg.valid_ens n_epochs = cfg.n_epochs lr_start = cfg.lr_start num_workers = cfg.num_workers valid_size = cfg.valid_size batch_size = cfg.batch_size beta_type = cfg.beta_type trainset, testset, inputs, outputs = data.getDataset(dataset) train_loader, valid_loader, test_loader = data.getDataloader( trainset, testset, valid_size, batch_size, num_workers) net = getModel(net_type, inputs, outputs, priors, layer_type, activation_type).to(device) ckpt_dir = f'checkpoints/{dataset}/bayesian' ckpt_name = f'checkpoints/{dataset}/bayesian/model_{net_type}_{layer_type}_{activation_type}_{cfg.wide}.pt' if not os.path.exists(ckpt_dir): os.makedirs(ckpt_dir, exist_ok=True) with open("stp", "r") as file: stp = int(file.read()) with open("sav", "r") as file: sav = int(file.read()) criterion = metrics.ELBO(len(trainset)).to(device) optimizer = Adam(net.parameters(), lr=lr_start) lr_sched = lr_scheduler.ReduceLROnPlateau(optimizer, patience=6, verbose=True) #valid_loss_max = np.Inf if stp == 2: early_stop = [] train_data = [] for epoch in range(n_epochs): # loop over the dataset multiple times train_loss, train_acc, train_kl = train_model(net, optimizer, criterion, train_loader, num_ens=train_ens, beta_type=beta_type, epoch=epoch, num_epochs=n_epochs) valid_loss, valid_acc = validate_model(net, criterion, valid_loader, num_ens=valid_ens, beta_type=beta_type, epoch=epoch, num_epochs=n_epochs) lr_sched.step(valid_loss) train_data.append([epoch,train_loss,train_acc,valid_loss,valid_acc]) print('Epoch: {} \tTraining Loss: {:.4f} \tTraining Accuracy: {:.4f} \tValidation Loss: {:.4f} \tValidation Accuracy: {:.4f} \ttrain_kl_div: {:.4f}'.format( epoch, train_loss, train_acc, valid_loss, valid_acc, train_kl)) if stp == 2: print('Using early stopping') if earlyStopping(early_stop,train_acc,cfg.sens) == None: break elif stp == 3: print('Using energy bound') if energyBound(cfg.energy_thrs) == None: break elif stp == 4: print('Using accuracy bound') if accuracyBound(cfg.acc_thrs) == None: break else: print('Training for {} epochs'.format(cfg.n_epochs)) if sav == 1: # save model when finished if epoch == n_epochs: torch.save(net.state_dict(), ckpt_name) with open("bayes_exp_data_"+str(cfg.wide)+".pkl", 'wb') as f: pickle.dump(train_data, f) if __name__ == '__main__': now = datetime.now() current_time = now.strftime("%H:%M:%S") print("Initial Time =", current_time) parser = argparse.ArgumentParser(description = "PyTorch Bayesian Model Training") parser.add_argument('--net_type', default='lenet', type=str, help='model') parser.add_argument('--dataset', default='CIFAR10', type=str, help='dataset = [MNIST/CIFAR10/CIFAR100]') args = parser.parse_args() run(args.dataset, args.net_type) now = datetime.now() current_time = now.strftime("%H:%M:%S") print("Final Time =", current_time)