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