84 lines
3.7 KiB
Python
Executable File
84 lines
3.7 KiB
Python
Executable File
import sys
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sys.path.append('..')
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import os
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import argparse
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import torch
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from torch import nn
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import numpy as np
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from torch.optim import Adam
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import utils
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import metrics
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import config_mixtures as cfg
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import utils_mixture as mix_utils
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from main_bayesian import train_model as train_bayesian, validate_model as validate_bayesian
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from main_frequentist import train_model as train_frequentist, validate_model as validate_frequentist
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# CUDA settings
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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def train_splitted(num_tasks, bayesian=True, net_type='lenet'):
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assert 10 % num_tasks == 0
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# Hyper Parameter settings
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train_ens = cfg.train_ens
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valid_ens = cfg.valid_ens
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n_epochs = cfg.n_epochs
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lr_start = cfg.lr_start
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if bayesian:
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ckpt_dir = f"checkpoints/MNIST/bayesian/splitted/{num_tasks}-tasks/"
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else:
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ckpt_dir = f"checkpoints/MNIST/frequentist/splitted/{num_tasks}-tasks/"
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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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loaders, datasets = mix_utils.get_splitmnist_dataloaders(num_tasks, return_datasets=True)
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models = mix_utils.get_splitmnist_models(num_tasks, bayesian=bayesian, pretrained=False, net_type=net_type)
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for task in range(1, num_tasks + 1):
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print(f"Training task-{task}..")
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trainset, testset, _, _ = datasets[task-1]
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train_loader, valid_loader, _ = loaders[task-1]
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net = models[task-1]
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net = net.to(device)
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ckpt_name = ckpt_dir + f"model_{net_type}_{num_tasks}.{task}.pt"
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criterion = (metrics.ELBO(len(trainset)) if bayesian else nn.CrossEntropyLoss()).to(device)
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optimizer = Adam(net.parameters(), lr=lr_start)
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valid_loss_max = np.Inf
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for epoch in range(n_epochs): # loop over the dataset multiple times
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utils.adjust_learning_rate(optimizer, metrics.lr_linear(epoch, 0, n_epochs, lr_start))
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if bayesian:
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train_loss, train_acc, train_kl = train_bayesian(net, optimizer, criterion, train_loader, num_ens=train_ens)
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valid_loss, valid_acc = validate_bayesian(net, criterion, valid_loader, num_ens=valid_ens)
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print('Epoch: {} \tTraining Loss: {:.4f} \tTraining Accuracy: {:.4f} \tValidation Loss: {:.4f} \tValidation Accuracy: {:.4f} \ttrain_kl_div: {:.4f}'.format(
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epoch, train_loss, train_acc, valid_loss, valid_acc, train_kl))
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else:
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train_loss, train_acc = train_frequentist(net, optimizer, criterion, train_loader)
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valid_loss, valid_acc = validate_frequentist(net, criterion, valid_loader)
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print('Epoch: {} \tTraining Loss: {:.4f} \tTraining Accuracy: {:.4f} \tValidation Loss: {:.4f} \tValidation Accuracy: {:.4f}'.format(
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epoch, train_loss, train_acc, valid_loss, valid_acc))
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# save model if validation accuracy has increased
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if valid_loss <= valid_loss_max:
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print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format(
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valid_loss_max, valid_loss))
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torch.save(net.state_dict(), ckpt_name)
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valid_loss_max = valid_loss
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print(f"Done training task-{task}")
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description = "PyTorch Bayesian Split Model Training")
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parser.add_argument('--num_tasks', default=2, type=int, help='number of tasks')
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parser.add_argument('--net_type', default='lenet', type=str, help='model')
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parser.add_argument('--bayesian', default=1, type=int, help='is_bayesian_model(0/1)')
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args = parser.parse_args()
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train_splitted(args.num_tasks, bool(args.bayesian), args.net_type)
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