138 lines
4.7 KiB
Python
Executable File
138 lines
4.7 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 torch
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#import utils
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import pickle
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import metrics
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import argparse
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import numpy as np
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import torch.nn as nn
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import gpu_sample_draw
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from datetime import datetime
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import config_frequentist as cfg
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from torch.optim import Adam, lr_scheduler
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from models.NonBayesianModels.LeNet import LeNet
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from models.NonBayesianModels.AlexNet import AlexNet
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from models.NonBayesianModels.ThreeConvThreeFC import ThreeConvThreeFC
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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 getModel(net_type, inputs, outputs,wide=cfg.wide):
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if (net_type == 'lenet'):
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return LeNet(outputs, inputs,wide)
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elif (net_type == 'alexnet'):
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return AlexNet(outputs, inputs)
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elif (net_type == '3conv3fc'):
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return ThreeConvThreeFC(outputs, inputs)
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else:
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raise ValueError('Network should be either [LeNet / AlexNet / 3Conv3FC')
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def train_model(net, optimizer, criterion, train_loader):
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train_loss = 0.0
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net.train()
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accs = []
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for data, target in train_loader:
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data, target = data.to(device), target.to(device)
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optimizer.zero_grad()
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output = net(data)
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loss = criterion(output, target)
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loss.backward()
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optimizer.step()
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train_loss += loss.item()*data.size(0)
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accs.append(metrics.acc(output.detach(), target))
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return train_loss, np.mean(accs)
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def validate_model(net, criterion, valid_loader):
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valid_loss = 0.0
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net.eval()
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accs = []
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for data, target in valid_loader:
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data, target = data.to(device), target.to(device)
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output = net(data)
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loss = criterion(output, target)
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valid_loss += loss.item()*data.size(0)
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accs.append(metrics.acc(output.detach(), target))
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return valid_loss, np.mean(accs)
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def run(dataset, net_type):
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# Hyper Parameter settings
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n_epochs = cfg.n_epochs
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lr = cfg.lr
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num_workers = cfg.num_workers
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valid_size = cfg.valid_size
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batch_size = cfg.batch_size
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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).to(device)
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ckpt_dir = f'checkpoints/{dataset}/frequentist'
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ckpt_name = f'checkpoints/{dataset}/frequentist/model_{net_type}_{cfg.wide}.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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criterion = nn.CrossEntropyLoss()
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optimizer = Adam(net.parameters(), lr=lr)
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lr_sched = lr_scheduler.ReduceLROnPlateau(optimizer, patience=6, verbose=True)
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#valid_loss_min = np.Inf
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#early_stop = []
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#thrs=1e-9
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train_data = []
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for epoch in range(1, n_epochs+1):
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train_loss, train_acc = train_model(net, optimizer, criterion, train_loader)
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valid_loss, valid_acc = validate_model(net, criterion, valid_loader)
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lr_sched.step(valid_loss)
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train_loss = train_loss/len(train_loader.dataset)
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valid_loss = valid_loss/len(valid_loader.dataset)
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train_data.append([epoch,train_loss,train_acc,valid_loss,valid_acc])
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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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#early_stop.append(valid_acc)
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#if epoch % 4 == 0 and epoch > 0:
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# print("Value 1: {} >= {}, Value 2: {} >= {}, Value 2: {} >= {}".format(early_stop[0],valid_acc-thrs,early_stop[1],valid_acc-thrs,early_stop[2],valid_acc-thrs))
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# if abs(early_stop[0]) >= valid_acc-thrs and abs(early_stop[1]) >= valid_acc-thrs and abs(early_stop[2]) >= valid_acc-thrs:
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# break
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# early_stop = []
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#if train_acc >= 0.99:
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# break
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#if gpu_sample_draw.total_watt_consumed() > 100000:
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# break
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# save model when finished
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#if epoch == n_epochs:
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#torch.save(net.state_dict(), ckpt_name)
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with open("freq_exp_data_"+str(cfg.wide)+".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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parser = argparse.ArgumentParser(description = "PyTorch Frequentist Model Training")
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parser.add_argument('--net_type', default='lenet', type=str, help='model')
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parser.add_argument('--dataset', default='MNIST', type=str, help='dataset = [MNIST/CIFAR10/CIFAR100]')
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args = parser.parse_args()
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run(args.dataset, args.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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