Did some Python formatting
This commit is contained in:
Eddie Cueto
parent
0f8ee842a3
commit
3c928cc350
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@ -9,32 +9,15 @@ with (open("configuration.pkl", "rb")) as file:
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except EOFError:
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break
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#with open("frq", "r") as file:
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# frq = int(file.read())
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#with open("bay", "r") as file:
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# bay = int(file.read())
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# pickle_name = "{}_wattdata_{}.pkl".format(model_t,size)
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# print("GPU energy file config: {}".format(pickle_name))
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#if frq == 1:
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# model_t = "freq"
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# with open("tmp", "r") as file:
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# size = float(file.read())
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#if bay == 1:
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# model_t = "bayes"
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# with open("tmp", "r") as file:
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# size = int(file.read())
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#pickle_name = "{}_wattdata_{}.pkl".format(model_t,size)
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#print("GPU energy file config: {}".format(pickle_name))
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#print(cfg)
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# print(cfg)
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if __name__ == '__main__':
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dataDump = []
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#var = True
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#pickling_on = open("wattdata.pickle","wb")
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while True:
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try:
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dataDump.append(get_sample_of_gpu())
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@ -42,17 +25,5 @@ if __name__ == '__main__':
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pickle.dump(dataDump, f)
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except EOFError:
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warn('Pickle ran out of space')
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size += 0.01
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finally:
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f.close()
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#if retcode == 0:
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#break
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#pickle.dump(dataDump, pickling_on)
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#pickling_on.close()
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15
arguments.py
15
arguments.py
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@ -7,16 +7,21 @@ all_args = argparse.ArgumentParser()
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def makeArguments(arguments: ArgumentParser) -> dict:
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all_args.add_argument("-b", "--Bayesian", action="store", dest="b",
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type=int, choices=range(1,8), help="Bayesian model of size x")
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type=int, choices=range(1, 8),
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help="Bayesian model of size x")
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all_args.add_argument("-f", "--Frequentist", action="store", dest="f",
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type=int, choices=range(1,8), help="Frequentist model of size x")
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type=int, choices=range(1, 8),
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help="Frequentist model of size x")
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all_args.add_argument("-E", "--EarlyStopping", action="store_true",
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help="Early Stopping criteria")
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all_args.add_argument("-e", "--EnergyBound", action="store_true",
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help="Energy Bound criteria")
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all_args.add_argument("-a", "--AccuracyBound", action="store_true",
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help="Accuracy Bound criteria")
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all_args.add_argument("-s", "--Save", action="store_true", help="Save model")
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all_args.add_argument('--net_type', default='lenet', type=str, help='model = [lenet/AlexNet/3Conv3FC]')
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all_args.add_argument('--dataset', default='CIFAR10', type=str, help='dataset = [MNIST/CIFAR10/CIFAR100]')
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all_args.add_argument("-s", "--Save", action="store_true",
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help="Save model")
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all_args.add_argument('--net_type', default='lenet', type=str,
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help='model = [lenet/AlexNet/3Conv3FC]')
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all_args.add_argument('--dataset', default='CIFAR10', type=str,
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help='dataset = [MNIST/CIFAR10/CIFAR100]')
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return vars(all_args.parse_args())
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@ -10,7 +10,6 @@ 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 gpu_power_func import total_watt_consumed
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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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@ -27,18 +26,23 @@ with (open("configuration.pkl", "rb")) as file:
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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,wide=cfg["model"]["size"])
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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, activation_type)
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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 / 3Conv3FC')
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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, beta_type=0.1, epoch=None, num_epochs=None):
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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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@ -48,7 +52,8 @@ def train_model(net, optimizer, criterion, trainloader, num_ens=1, beta_type=0.1
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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, num_ens).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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@ -60,7 +65,8 @@ def train_model(net, optimizer, criterion, trainloader, num_ens=1, beta_type=0.1
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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, epoch, num_epochs)
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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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@ -70,7 +76,8 @@ def train_model(net, optimizer, criterion, trainloader, num_ens=1, beta_type=0.1
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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, epoch=None, num_epochs=None):
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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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@ -78,7 +85,8 @@ def validate_model(net, criterion, validloader, num_ens=1, beta_type=0.1, epoch=
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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, num_ens).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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@ -87,7 +95,8 @@ def validate_model(net, criterion, validloader, num_ens=1, beta_type=0.1, epoch=
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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, epoch, num_epochs)
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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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@ -113,10 +122,12 @@ def run(dataset, net_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, activation_type).to(device)
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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}_{activation_type}_{cfg["model"]["size"]}.pt'
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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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@ -126,25 +137,40 @@ def run(dataset, net_type):
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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, verbose=True)
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#valid_loss_max = np.Inf
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#if stp == 2:
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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, criterion, train_loader, num_ens=train_ens, beta_type=beta_type, epoch=epoch, num_epochs=n_epochs)
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valid_loss, valid_acc = validate_model(net, criterion, valid_loader, num_ens=valid_ens, beta_type=beta_type, epoch=epoch, num_epochs=n_epochs)
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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,valid_acc])
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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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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,cfg["model"]["sens"]) == 1:
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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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@ -152,7 +178,7 @@ def run(dataset, net_type):
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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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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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@ -162,10 +188,10 @@ def run(dataset, net_type):
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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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@ -174,4 +200,3 @@ 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("Final Time =", current_time)
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@ -2,15 +2,12 @@ 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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from datetime import datetime
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from torch.optim import Adam, lr_scheduler
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from gpu_power_func import total_watt_consumed
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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 stopping_crit import earlyStopping, energyBound, accuracyBound
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@ -27,23 +24,24 @@ with (open("configuration.pkl", "rb")) as file:
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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,wide=cfg["model"]["size"]):
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def getModel(net_type, inputs, outputs, wide=cfg["model"]["size"]):
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if (net_type == 'lenet'):
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return LeNet(outputs, inputs,wide)
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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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raise ValueError('Network should be either [LeNet / AlexNet / \
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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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for datas, target in train_loader:
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data, target = datas.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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@ -58,8 +56,8 @@ 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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for datas, target in valid_loader:
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data, target = datas.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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@ -82,7 +80,8 @@ def run(dataset, net_type):
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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["model"]["size"]}.pt'
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ckpt_name = f'checkpoints/{dataset}/frequentist/model\
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_{net_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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@ -92,35 +91,41 @@ def run(dataset, net_type):
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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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#if stp == 2:
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lr_sched = lr_scheduler.ReduceLROnPlateau(optimizer, patience=6,
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verbose=True)
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# valid_loss_min = 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(1, n_epochs+1):
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train_loss, train_acc = train_model(net, optimizer, criterion, train_loader)
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train_loss, train_acc = train_model(net, optimizer, criterion,
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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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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: {: .4f}\
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\tValidation Loss: {: .4f} \tValidation Accuracy: {: .4f}\
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'.format(epoch, train_loss, train_acc, valid_loss, valid_acc))
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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,cfg["model"]["sens"]) == 1:
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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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# 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["model"]["acc_thrs"]) == 1:
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# print('Using accuracy bound')
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if accuracyBound(train_acc,
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cfg["model"]["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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|
|
@ -142,4 +147,3 @@ if __name__ == '__main__':
|
|||
now = datetime.now()
|
||||
current_time = now.strftime("%H:%M:%S")
|
||||
print("Final Time =", current_time)
|
||||
|
||||
|
|
|
|||
|
|
@ -12,15 +12,16 @@ def kill(proc_pid):
|
|||
proc.kill()
|
||||
process.kill()
|
||||
|
||||
|
||||
cfg = {
|
||||
"model": {"net_type": None, "type": None, "size": None, "layer_type": "lrt",
|
||||
"activation_type": "softplus", "priors": {
|
||||
"model": {"net_type": None, "type": None, "size": None, "layer_type":
|
||||
"lrt", "activation_type": "softplus", "priors": {
|
||||
'prior_mu': 0,
|
||||
'prior_sigma': 0.1,
|
||||
'posterior_mu_initial': (0, 0.1), # (mean, std) normal_
|
||||
'posterior_rho_initial': (-5, 0.1), # (mean, std) normal_
|
||||
'posterior_mu_initial': (0, 0.1), # (mean,std) normal_
|
||||
'posterior_rho_initial': (-5, 0.1), # (mean,std) normal_
|
||||
},
|
||||
"n_epochs": 3,
|
||||
"n_epochs": 100,
|
||||
"sens": 1e-9,
|
||||
"energy_thrs": 10000,
|
||||
"acc_thrs": 0.99,
|
||||
|
|
@ -30,7 +31,8 @@ cfg = {
|
|||
"batch_size": 256,
|
||||
"train_ens": 1,
|
||||
"valid_ens": 1,
|
||||
"beta_type": 0.1, # 'Blundell', 'Standard', etc. Use float for const value
|
||||
"beta_type": 0.1, # 'Blundell','Standard',etc.
|
||||
# Use float for const value
|
||||
},
|
||||
"data": None,
|
||||
"stopping_crit": None,
|
||||
|
|
@ -46,10 +48,10 @@ if all(v is None for v in check):
|
|||
elif None in check:
|
||||
if args['f'] is not None:
|
||||
cmd = ["python", "main_frequentist.py"]
|
||||
cfg["model"]["type"] = "frequentist"
|
||||
cfg["model"]["type"] = "freq"
|
||||
elif args['b'] is not None:
|
||||
cmd = ["python", "main_bayesian.py"]
|
||||
cfg["model"]["type"] = "bayesian"
|
||||
cfg["model"]["type"] = "bayes"
|
||||
else:
|
||||
raise Exception("Only one argument allowed")
|
||||
|
||||
|
|
@ -76,13 +78,14 @@ else:
|
|||
cfg["save"] = 0
|
||||
|
||||
|
||||
cfg["pickle_path"] = "{}_wattdata_{}.pkl".format(cfg["model"]["type"],cfg["model"]["size"])
|
||||
cfg["pickle_path"] = "{}_wattdata_{}.pkl".format(cfg["model"]["type"],
|
||||
cfg["model"]["size"])
|
||||
|
||||
with open("configuration.pkl", "wb") as f:
|
||||
pickle.dump(cfg, f)
|
||||
|
||||
#print(args)
|
||||
#print(cfg)
|
||||
# print(args)
|
||||
# print(cfg)
|
||||
|
||||
sleep(3)
|
||||
|
||||
|
|
@ -104,12 +107,12 @@ path = path.replace('\n', '')
|
|||
startWattCounter = 'python ' + path + '/amd_sample_draw.py'
|
||||
|
||||
|
||||
|
||||
p1 = sub.Popen(cmd)
|
||||
p2 = sub.Popen(startWattCounter.split(),stdin=sub.PIPE,stdout=sub.PIPE, stderr=sub.PIPE)
|
||||
p3 = sub.Popen(cmd2,stdin=sub.PIPE,stdout=sub.PIPE, stderr=sub.PIPE)
|
||||
p4 = sub.Popen(cmd3,stdin=sub.PIPE,stdout=sub.PIPE, stderr=sub.PIPE)
|
||||
p5 = sub.Popen(cmd4,stdin=sub.PIPE,stdout=sub.PIPE, stderr=sub.PIPE)
|
||||
p2 = sub.Popen(startWattCounter.split(), stdin=sub.PIPE, stdout=sub.PIPE,
|
||||
stderr=sub.PIPE)
|
||||
p3 = sub.Popen(cmd2, stdin=sub.PIPE, stdout=sub.PIPE, stderr=sub.PIPE)
|
||||
p4 = sub.Popen(cmd3, stdin=sub.PIPE, stdout=sub.PIPE, stderr=sub.PIPE)
|
||||
p5 = sub.Popen(cmd4, stdin=sub.PIPE, stdout=sub.PIPE, stderr=sub.PIPE)
|
||||
|
||||
retcode = p1.wait()
|
||||
print("Return code: {}".format(retcode))
|
||||
|
|
@ -119,4 +122,3 @@ kill(p2.pid)
|
|||
kill(p3.pid)
|
||||
kill(p4.pid)
|
||||
kill(p5.pid)
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue