def main():
model_dir = '../hhjung/20180914/Resnet14'
utils.default_model_dir = model_dir
lr = 0.1
start_time = time.time()
train_loader, test_loader = utils.cifar10_loader()
model = ResNet()
if torch.cuda.is_available():
# os.environ["CUDA_VISIBLE_DEVICES"] = '0'
print("USE", torch.cuda.device_count(), "GPUs!")
model = nn.DataParallel(model).cuda()
cudnn.benchmark = True
else:
print("NO GPU -_-;")
optimizer = optim.SGD(model.parameters(),
lr=lr,
momentum=0.9,
weight_decay=1e-4)
criterion = nn.CrossEntropyLoss().cuda()
start_epoch = 0
checkpoint = utils.load_checkpoint(model_dir)
if not checkpoint:
pass
else:
start_epoch = checkpoint['epoch'] + 1
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
for epoch in range(start_epoch, 165):
if epoch < 80:
learning_rate = lr
elif epoch < 120:
learning_rate = lr * 0.1
else:
learning_rate = lr * 0.01
for param_group in optimizer.param_groups:
param_group['lr'] = learning_rate
train(model, optimizer, criterion, train_loader, epoch)
test(model, criterion, test_loader, epoch)
if epoch % 5 == 0:
model_filename = 'checkpoint_%03d.pth.tar' % epoch
utils.save_checkpoint(
{
'epoch': epoch,
'model': model,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, model_filename, model_dir)
utils.conv_weight_L1_printing(model.module)
now = time.gmtime(time.time() - start_time)
print('{} hours {} mins {} secs for training'.format(
now.tm_hour, now.tm_min, now.tm_sec))
def main(model_dir, model, dataset):
utils.default_model_dir = model_dir
utils.c = None
utils.str_w = ''
# model = model
lr = 0.1
start_time = time.time()
if dataset == 'cifar10':
train_loader, test_loader = utils.cifar10_loader()
elif dataset == 'cifar100':
train_loader, test_loader = utils.cifar100_loader()
if torch.cuda.is_available():
# os.environ["CUDA_VISIBLE_DEVICES"] = '0'
print("USE", torch.cuda.device_count(), "GPUs!")
model = nn.DataParallel(model).cuda()
cudnn.benchmark = True
else:
print("NO GPU -_-;")
optimizer = optim.SGD(model.parameters(),
lr=lr,
momentum=0.9,
weight_decay=1e-4)
criterion = nn.CrossEntropyLoss().cuda()
start_epoch = 0
checkpoint = utils.load_checkpoint(model_dir)
if not checkpoint:
pass
else:
start_epoch = checkpoint['epoch'] + 1
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
utils.init_learning(model.module)
weight_extract_test(model, criterion, test_loader)
def train_quantizer(model, train_config, n_epochs=1000):
try:
if VERBOSE == 1:
print(f"Training Config : {train_config}")
model.cuda()
trainloader, testloader = cifar10_loader(4096, "../data")
n_fs = train_config.n_functions
n_bits = train_config.n_bits
n_iter = train_config.n_iter
avoid = []
bit_quantizer = BitQuantizer(model,
n_fs,
n_bits,
verbose=VERBOSE,
avoid=avoid)
with mlflow.start_run(run_name=str(train_config)):
for epoch in tqdm(range(1, n_epochs + 1)):
bit_quantizer.train_hash_functions(n_iter=1)
hashed_model = bit_quantizer.get_hashed_model()
hashed_model.cuda()
# Evaluating hashed model
train_acc_hashed = evaluate(hashed_model,
trainloader,
cuda=True)
test_acc_hashed = evaluate(hashed_model, testloader, cuda=True)
mlflow.log_metric("train_acc", train_acc_hashed, step=epoch)
mlflow.log_metric("test_acc", test_acc_hashed, step=epoch)
return hashed_model, bit_quantizer
except RuntimeError as e:
if "out of memory" in str(e):
print(f"| WARNING: ran out of memory,skipping {train_config}")
torch.cuda.empty_cache()
else:
raise e
def train_model_hash(model, train_config):
try:
if VERBOSE == 1:
print(f"Training Config : {train_config}")
model.cuda()
trainloader, testloader = cifar10_loader(4096, "./data")
n_fs = train_config.n_functions
n_bits = train_config.n_bits
n_iter = train_config.n_iter
avoid = []
bit_quantizer = BitQuantizer(model, n_fs, n_bits, verbose=VERBOSE, avoid=avoid)
bit_quantizer.train_hash_functions(n_iter=n_iter)
hashed_model = bit_quantizer.get_hashed_model()
hashed_model.cuda()
# Evaluating model before hashing
train_acc = evaluate(model, trainloader, cuda=True)
test_acc = evaluate(model, testloader, cuda=True)
# Evaluating hashed model
train_acc_hashed = evaluate(hashed_model, trainloader, cuda=True)
test_acc_hashed = evaluate(hashed_model, testloader, cuda=True)
with mlflow.start_run(run_name=str(train_config)):
mlflow.log_param("train_acc_before_hashing", train_acc)
mlflow.log_param("test_acc_before_hashing", test_acc)
mlflow.log_param("train_acc_after_hashing", train_acc_hashed)
mlflow.log_param("test_acc_after_hashing", test_acc_hashed)
mlflow.log_param("model_name", model_name)
mlflow.log_param("n_bits", n_bits)
mlflow.log_param("n_fs", n_fs)
except RuntimeError as e:
if "out of memory" in str(e):
print(f"| WARNING: ran out of memory,skipping {train_config}")
torch.cuda.empty_cache()
def main():
"""Main pipeline for GAN Visualization."""
args = parse_args()
testloader = cifar10_loader(args.dataroot, args.batch_size_test)
if args.option == "option1":
# Perturb Real Images
perturb_real_images(testloader, args.modelroot, args.batch_size_test,
args.cuda)
print("==> Perturb Real Images done ...")
elif args.option == "option2":
# Synthetic Images Maximizing Classification Output
syn_img(testloader, args.modelroot, args.cuda)
print("==> Synthetic Images Maximizing Classification Output done ...")
elif args.option == "option3":
# Synthetic Features Maximizing Features at Various Layers
syn_features(testloader, args.modelroot, args.batch_size_test,
args.cuda)
print(
"==> Synthetic Features Maximizing Features at Various Layers done ..."
)
else:
print("==> No such option ...")
def main(model_dir, model, dataset, iteration):
utils.default_model_dir = model_dir
utils.c = None
utils.str_w = ''
# model = model
lr = 0.1
start_time = time.time()
if dataset == 'cifar10':
train_loader, test_loader = utils.cifar10_loader()
elif dataset == 'cifar100':
train_loader, test_loader = utils.cifar100_loader()
if torch.cuda.is_available():
# os.environ["CUDA_VISIBLE_DEVICES"] = '0'
print("USE", torch.cuda.device_count(), "GPUs!")
model = nn.DataParallel(model).cuda()
cudnn.benchmark = True
else:
print("NO GPU -_-;")
optimizer = optim.SGD(model.parameters(),
lr=lr,
momentum=0.9,
weight_decay=1e-4)
criterion = nn.CrossEntropyLoss().cuda()
start_epoch = 0
checkpoint = utils.load_checkpoint(model_dir)
if not checkpoint:
pass
else:
start_epoch = checkpoint['epoch'] + 1
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
utils.init_learning(model.module)
for epoch in range(start_epoch, 350):
if epoch < 150:
learning_rate = lr
elif epoch < 250:
learning_rate = lr * 0.1
else:
learning_rate = lr * 0.01
for param_group in optimizer.param_groups:
param_group['lr'] = learning_rate
train(model, optimizer, criterion, train_loader, epoch, True)
test(model, criterion, test_loader, epoch, True)
if epoch % iteration == iteration - 1:
for i in range(iteration):
utils.switching_learning(model.module)
print('switching_learning to Gate')
train(model, optimizer, criterion, train_loader, i, False)
test(model, criterion, test_loader, i, False)
utils.switching_learning(model.module)
print('switching_learning to Gate')
if epoch % 5 == 0:
model_filename = 'checkpoint_%03d.pth.tar' % epoch
utils.save_checkpoint(
{
'epoch': epoch,
'model': model,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, model_filename, model_dir)
now = time.gmtime(time.time() - start_time)
weight_extract(model, optimizer, criterion, train_loader, epoch)
utils.conv_weight_L1_printing(model.module)
print('{} hours {} mins {} secs for training'.format(
now.tm_hour, now.tm_min, now.tm_sec))
def main(model_dir, model, dataset, layer_name, layer_n):
utils.default_model_dir = model_dir
utils.c = None
utils.str_w = ''
# model = model
lr = 0.1
start_time = time.time()
if dataset == 'cifar10':
train_loader, test_loader = utils.cifar10_loader()
elif dataset == 'cifar100':
train_loader, test_loader = utils.cifar100_loader()
if torch.cuda.is_available():
# os.environ["CUDA_VISIBLE_DEVICES"] = '0'
print("USE", torch.cuda.device_count(), "GPUs!")
model = nn.DataParallel(model).cuda()
cudnn.benchmark = True
else:
print("NO GPU -_-;")
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=1e-4)
criterion = nn.CrossEntropyLoss().cuda()
start_epoch = 0
checkpoint = utils.load_checkpoint(model_dir)
if not checkpoint:
pass
else:
start_epoch = checkpoint['epoch'] + 1
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
utils.init_learning(model.module)
for epoch in range(start_epoch, 165): # change 165
if epoch < 80:
learning_rate = lr
elif epoch < 120:
learning_rate = lr * 0.1
else:
learning_rate = lr * 0.01
for param_group in optimizer.param_groups:
param_group['lr'] = learning_rate
train(model, optimizer, criterion, train_loader, epoch, True)
test(model, criterion, test_loader, epoch, True)
utils.switching_learning(model.module)
print('switching_learning to Gate')
train(model, optimizer, criterion, train_loader, epoch, False)
test(model, criterion, test_loader, epoch, False)
utils.switching_learning(model.module)
print('switching_learning to Gate')
model_filename = 'checkpoint_%03d.pth.tar' % epoch
utils.save_checkpoint({
'epoch': epoch,
'model': model,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, model_filename, model_dir)
now = time.gmtime(time.time() - start_time)
weight_extract(model, optimizer, criterion, train_loader, epoch)
class_counter, class_weight_sum, class_average, total_average = utils.load_gate_csv()
_, index = torch.sort(total_average)
layer_name = utils.make_layer_name(layer_n)
for i in index:
# weight delete layer_name[i]
utils.weight_pruning_by_name(model.module, layer_name[i])
test(model, criterion, test_loader, epoch, True)
change index[0] to 'layerN and layerN-M'
# index about (smallest) index[0], index[1], .... (biggest)
# layer name change, layer0 to layer'n-1'
# find 'layer' + str(index[0]) from model.module
# and change self.z to 0
# utils.conv_weight_L1_printing(model.module)
print('{} hours {} mins {} secs for training'.format(now.tm_hour, now.tm_min, now.tm_sec))
def main(model_dir, model, dataset, layer_n, reversed=False):
utils.default_model_dir = model_dir
utils.c = None
utils.str_w = ''
# model = model
lr = 0.1
start_time = time.time()
if dataset == 'cifar10':
train_loader, test_loader = utils.cifar10_loader()
elif dataset == 'cifar100':
train_loader, test_loader = utils.cifar100_loader()
if torch.cuda.is_available():
# os.environ["CUDA_VISIBLE_DEVICES"] = '0'
print("USE", torch.cuda.device_count(), "GPUs!")
model = nn.DataParallel(model).cuda()
cudnn.benchmark = True
else:
print("NO GPU -_-;")
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=1e-4)
criterion = nn.CrossEntropyLoss().cuda()
start_epoch = 0
checkpoint = utils.load_checkpoint(model_dir)
if not checkpoint:
pass
else:
start_epoch = checkpoint['epoch'] + 1
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
utils.init_learning(model.module)
# for epoch in range(start_epoch, 165): # change 165
# if epoch < 80:
# learning_rate = lr
# elif epoch < 120:
# learning_rate = lr * 0.1
# else:
# learning_rate = lr * 0.01
# for param_group in optimizer.param_groups:
# param_group['lr'] = learning_rate
# train(model, optimizer, criterion, train_loader, epoch, True)
# test(model, criterion, test_loader, epoch, True)
# utils.switching_learning(model.module)
# print('switching_learning to Gate')
# train(model, optimizer, criterion, train_loader, epoch, False)
# test(model, criterion, test_loader, epoch, False)
# utils.switching_learning(model.module)
# print('switching_learning to Gate')
# model_filename = 'checkpoint_%03d.pth.tar' % epoch
# utils.save_checkpoint({
# 'epoch': epoch,
# 'model': model,
# 'state_dict': model.state_dict(),
# 'optimizer': optimizer.state_dict(),
# }, model_filename, model_dir)
# now = time.gmtime(time.time() - start_time)
utils.del_csv_weight_for_test()
weight_extract(model, optimizer, criterion, train_loader, 160)
class_counter, class_weight_sum, class_average, total_average = utils.load_gate_csv()
if reversed is True:
_, index = torch.sort(total_average, descending=True)
else:
_, index = torch.sort(total_average)
layer_name = utils.make_layer_name(layer_n)
for i in index:
# weight delete layer_name[i]
utils.weight_pruning_by_name(model.module, layer_name[i])
test(model, criterion, test_loader, 160, True)
import torch
from torch.nn.functional import softmax
from alexnet import AlexNet
from utils import cifar10_loader, device, cifar10_classes
torch.random.manual_seed(128)
batch_size = 1
testloader = cifar10_loader(train=False, batch_size=batch_size)
net = AlexNet()
net.load_state_dict(torch.load("model/model.h5"))
net.eval()
correct = 0
total = 0
def run():
global correct, total
with torch.no_grad():
for data in testloader:
images, labels = data
inputs, labels = images.to(device), labels.to(device)
outputs = net(inputs)
_, predicted = torch.topk(outputs.data, 5)
#print(predicted)
indexes = predicted.numpy()[0].tolist()
#print(indexes)
#print(softmax(outputs).numpy()[0][indexes])
#print([cifar10_classes[i] for i in indexes])
from quantizer import BitQuantizer
from utils import cifar10_loader, evaluate
# model = AlexNet()
# model.load_state_dict(torch.load("./alexnet_pretrained"))
model = resnet32()
checkpoint = torch.load(
"./pytorch_resnet/pretrained_models/resnet32-d509ac18.th")
state = {
k.replace("module.", ""): v
for k, v in checkpoint["state_dict"].items()
}
model.load_state_dict(state)
trainloader, testloader = cifar10_loader(256, "../data")
model.cuda()
n_fs = 32
n_bits = 6
n_iter = 50
avoid = []
bit_quantizer = BitQuantizer(model, n_fs, n_bits, avoid=avoid)
bit_quantizer.train_hash_functions(n_iter=n_iter)
hashed_model = bit_quantizer.get_hashed_model()
hashed_model.cuda()
# Evaluating model before hashing
train_accracy = evaluate(model, trainloader, cuda=True)
def main():
"""Main pipleline implements Generative Adversarial Networks in Pytorch."""
args = parse_args()
# load cifar10 dataset
trainloader, testloader = cifar10_loader(args.dataroot,
args.batch_size_train,
args.batch_size_test)
# Train the Discriminator without the Generator
if args.option == "option1":
print("Train the Discriminator without the Generator ...")
model = Discriminator()
if args.cuda:
model = nn.DataParallel(model).cuda()
cudnn.benchmark = True
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
# train
trainer_d = Trainer_D(model, criterion, optimizer, trainloader,
testloader, args.start_epoch, args.epochs1,
args.cuda, args.batch_size_train, args.lr)
trainer_d.train()
# Train the Discriminator with the Generator
else:
# instantiate discriminator and generator
aD, aG = Discriminator(), Generator()
# resume training from the last time
if args.resume:
# Load checkpoint
print('==> Resuming training from checkpoint ...')
g_ckpt_pth = os.path.join(args.ckptroot, "tempG.model")
d_ckpt_pth = os.path.join(args.ckptroot, "tempD.model")
checkpoint_g = torch.load(g_ckpt_pth)
checkpoint_d = torch.load(d_ckpt_pth)
args.start_epoch = checkpoint_d['epoch']
aG.load_state_dict(checkpoint_g['state_dict'])
aD.load_state_dict(checkpoint_d['state_dict'])
else:
# start over
print("Train the Discriminator with the Generator ...")
if args.cuda:
aD, aG = nn.DataParallel(aD).cuda(), nn.DataParallel(aG).cuda()
cudnn.benchmark = True
optimizer_g = torch.optim.Adam(aG.parameters(),
lr=args.lr,
betas=(args.beta1, args.beta2))
optimizer_d = torch.optim.Adam(aD.parameters(),
lr=args.lr,
betas=(args.beta1, args.beta2))
criterion = nn.CrossEntropyLoss()
# train
trainer_gd = Trainer_GD(aD, aG, criterion, optimizer_d, optimizer_g,
trainloader, testloader, args.batch_size_train,
args.gen_train, args.cuda, args.n_z,
args.start_epoch, args.epochs2)
trainer_gd.train()
import time
import torch
import torch.nn as nn
import torch.optim as optim
from tensorboardX import SummaryWriter
from alexnet import AlexNet
from utils import cifar10_loader, device
trainloader = cifar10_loader(train=True)
testloader = cifar10_loader(train=False)
writer = SummaryWriter("./logs")
epochs = 100
batch_size = 128
log_batch = 200
train_metrics = []
test_metrics = []
net = AlexNet()
net.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
def train():
for epoch in range(epochs):
running_loss = 0.0
correct_classified = 0
total = 0
x = self.ops5(x)
x = x.reshape(-1, 256 * 2 * 2)
x = self.classifier(x)
x = self.softmax(x)
return x
def get_loss(labels, preds):
ce = nn.NLLLoss(reduction="mean")
l1 = ce(torch.log(preds), labels)
return l1
if __name__ == "__main__":
trainloader, testloader = cifar10_loader(batch_size=4096,
data_path="./data")
CUDA = True
model = AlexNet()
if LOAD_CKPT is not None:
LOAD_CKPT = f"./checkpoint/alexnet_{LOAD_CKPT}"
model.load_state_dict(torch.load(LOAD_CKPT))
if CUDA:
model.cuda()
optimizer = torch.optim.Adam(model.parameters())
writer = SummaryWriter()
global_step = 0
for epoch in range(1, N_EPOCHS + 1):
