def train_model(args):
if os.path.isdir(args.save) == False:
os.makedirs(args.save)
save_dir = '{}eval-{}-{}'.format(args.save, args.note,
time.strftime("%Y%m%d-%H%M%S"))
utils.create_exp_dir(save_dir, scripts_to_save=glob.glob('*.py'))
log_format = '%(asctime)s %(message)s'
logging.basicConfig(stream=sys.stdout,
level=logging.INFO,
format=log_format,
datefmt='%m/%d %I:%M:%S %p')
fh = logging.FileHandler(os.path.join(save_dir, 'log.txt'))
fh.setFormatter(logging.Formatter(log_format))
logging.getLogger().addHandler(fh)
if args.cifar100:
CIFAR_CLASSES = 100
data_folder = 'cifar-100-python'
else:
CIFAR_CLASSES = 10
data_folder = 'cifar-10-batches-py'
if not torch.cuda.is_available():
logging.info('No GPU device available')
sys.exit(1)
np.random.seed(args.seed)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled = True
torch.cuda.manual_seed(args.seed)
logging.info("args = %s", args)
logging.info("unparsed args = %s", unparsed)
num_gpus = torch.cuda.device_count()
if args.arch in genotypes.__dict__.keys():
genotype = eval("genotypes.%s" % args.arch)
else:
genotype = eval(args.arch)
print('---------Genotype---------')
logging.info(genotype)
print('--------------------------')
model = Network(args.init_channels, CIFAR_CLASSES, args.layers,
args.auxiliary, genotype)
model = torch.nn.DataParallel(model)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
optimizer = torch.optim.SGD(model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.cifar100:
train_transform, valid_transform = utils._data_transforms_cifar100(
args)
else:
train_transform, valid_transform = utils._data_transforms_cifar10(args)
if args.cifar100:
train_data = dset.CIFAR100(root=args.tmp_data_dir,
train=True,
download=True,
transform=train_transform)
valid_data = dset.CIFAR100(root=args.tmp_data_dir,
train=False,
download=True,
transform=valid_transform)
else:
train_data = dset.CIFAR10(root=args.tmp_data_dir,
train=True,
download=True,
transform=train_transform)
valid_data = dset.CIFAR10(root=args.tmp_data_dir,
train=False,
download=True,
transform=valid_transform)
train_queue = torch.utils.data.DataLoader(train_data,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True,
num_workers=args.workers)
valid_queue = torch.utils.data.DataLoader(valid_data,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=args.workers)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs))
best_acc = 0.0
for epoch in range(args.epochs):
scheduler.step()
logging.info('Epoch: %d lr %e', epoch, scheduler.get_lr()[0])
model.module.drop_path_prob = args.drop_path_prob * epoch / args.epochs
model.drop_path_prob = args.drop_path_prob * epoch / args.epochs
start_time = time.time()
train_acc, train_obj = train(train_queue, model, criterion, optimizer)
logging.info('Train_acc: %f', train_acc)
valid_acc, valid_obj = infer(valid_queue, model, criterion)
if valid_acc > best_acc:
best_acc = valid_acc
logging.info('Valid_acc: %f', valid_acc)
logging.info('Best_acc: %f', best_acc)
end_time = time.time()
duration = end_time - start_time
print('Epoch time: %ds.' % duration)
utils.save(model.module, os.path.join(save_dir, 'weights.pt'))
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
np.random.seed(args.seed)
torch.cuda.set_device(args.gpu)
device = torch.device("cuda:{}".format(args.gpu))
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled = True
torch.cuda.manual_seed(args.seed)
cudnn.deterministic = True
logging.info('gpu device = %d' % args.gpu)
logging.info("args = %s", args)
if args.arch is not None:
genotype = eval("genotypes.%s" % args.arch)
if args.dir is not None:
with open(os.path.join(args.dir, "genotype.pickle"), 'rb') as f:
genotype = pickle.load(f)
print("Unpickling genotype.pickle")
logging.info(genotype)
model = Network(args.init_channels, CIFAR_CLASSES, args.layers,
args.auxiliary, genotype)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
optimizer = torch.optim.SGD(model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
train_transform, valid_transform = utils._data_transforms_cifar100(args)
train_data = dset.CIFAR100(root=args.data,
train=True,
download=True,
transform=train_transform)
valid_data = dset.CIFAR100(root=args.data,
train=False,
download=True,
transform=valid_transform)
logging.info("[INFO] len(train_data): {}, len(valid_data): {}".format(
len(train_data), len(valid_data)))
train_queue = torch.utils.data.DataLoader(train_data,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True,
num_workers=2)
valid_queue = torch.utils.data.DataLoader(valid_data,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=2)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs))
test_error = []
best_acc = 0.0
for epoch in range(args.epochs):
logging.info('[INFO] epoch %d lr %e', epoch + 1, scheduler.get_lr()[0])
model.drop_path_prob = args.drop_path_prob * epoch / args.epochs
train_acc, train_obj = train(train_queue, model, criterion, optimizer)
logging.info('[INFO] train_acc %f', train_acc)
writer.add_scalar("train_acc", train_acc, epoch + 1)
writer.add_scalar("train_obj", train_obj, epoch + 1)
scheduler.step()
valid_acc, valid_obj = infer(valid_queue, model, criterion)
if valid_acc > best_acc:
best_acc = valid_acc
utils.save(model, os.path.join(args.save, 'best_weights.pt'))
logging.info('[INFO] valid_acc %f', valid_acc)
writer.add_scalar("valid_acc", valid_acc, epoch + 1)
writer.add_scalar("valid_obj", valid_obj, epoch + 1)
writer.add_scalar("test_error", 100 - valid_acc, epoch + 1)
utils.save(model, os.path.join(args.save, 'weights.pt'))
test_error.append(100 - valid_acc)
logging.info('[INFO] best_acc %f', best_acc)
with open("{}/test_error.pickle".format(args.save), 'wb') as f:
pickle.dump(test_error, f)
def main():
if not torch.cuda.is_available():
logging.info('No GPU device available')
sys.exit(1)
np.random.seed(args.seed)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled = True
torch.cuda.manual_seed(args.seed)
logging.info("args = %s", args)
# prepare dataset
if args.cifar100:
train_transform, valid_transform = utils._data_transforms_cifar100(
args)
else:
train_transform, valid_transform = utils._data_transforms_cifar10(args)
if args.cifar100:
train_data = dset.CIFAR100(root=args.tmp_data_dir,
train=True,
download=True,
transform=train_transform)
else:
train_data = dset.CIFAR10(root=args.tmp_data_dir,
train=True,
download=True,
transform=train_transform)
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True,
num_workers=args.workers)
valid_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[split:num_train]),
pin_memory=True,
num_workers=args.workers)
# build Network
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
switches = []
for i in range(14):
switches.append([True for j in range(len(PRIMITIVES))])
switches_normal = copy.deepcopy(switches)
switches_reduce = copy.deepcopy(switches)
# eps_no_archs = [10, 10, 10]
eps_no_archs = [2, 2, 2]
for sp in range(len(num_to_keep)):
# if sp < 1:
# continue
model = Network(args.init_channels + int(add_width[sp]),
CIFAR_CLASSES,
args.layers + int(add_layers[sp]),
criterion,
switches_normal=switches_normal,
switches_reduce=switches_reduce,
p=float(drop_rate[sp]))
model = nn.DataParallel(model)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
network_params = []
for k, v in model.named_parameters():
if not (k.endswith('alphas_normal')
or k.endswith('alphas_reduce')):
network_params.append(v)
optimizer = torch.optim.SGD(network_params,
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optimizer_a = torch.optim.Adam(model.module.arch_parameters(),
# lr=args.arch_learning_rate, betas=(0.5, 0.999), weight_decay=args.arch_weight_decay)
optimizer_a = torch.optim.Adam(model.module.arch_parameters(),
lr=args.arch_learning_rate,
betas=(0, 0.999),
weight_decay=args.arch_weight_decay)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs), eta_min=args.learning_rate_min)
sm_dim = -1
epochs = args.epochs
eps_no_arch = eps_no_archs[sp]
scale_factor = 0.2
# cur_sub_model = get_cur_model(model,switches_normal,switches_reduce,num_to_keep,num_to_drop,sp)
for epoch in range(epochs):
scheduler.step()
lr = scheduler.get_lr()[0]
logging.info('Epoch: %d lr: %e', epoch, lr)
epoch_start = time.time()
# training
if epoch < eps_no_arch:
# if 0:
model.module.p = float(
drop_rate[sp]) * (epochs - epoch - 1) / epochs
model.module.update_p()
train_acc, train_obj = train(train_queue,
valid_queue,
model,
network_params,
criterion,
optimizer,
optimizer_a,
lr,
train_arch=False)
else:
model.module.p = float(drop_rate[sp]) * np.exp(
-(epoch - eps_no_arch) * scale_factor)
model.module.update_p()
train_acc, train_obj = train(train_queue,
valid_queue,
model,
network_params,
criterion,
optimizer,
optimizer_a,
lr,
train_arch=True)
logging.info('Train_acc %f', train_acc)
epoch_duration = time.time() - epoch_start
logging.info('Epoch time: %ds', epoch_duration)
# validation
if epochs - epoch < 5:
valid_acc, valid_obj = infer(valid_queue, model, criterion)
logging.info('Valid_acc %f', valid_acc)
utils.save(model, os.path.join(args.save, 'weights.pt'))
print('------Dropping %d paths------' % num_to_drop[sp])
# Save switches info for s-c refinement.
if sp == len(num_to_keep) - 1:
switches_normal_2 = copy.deepcopy(switches_normal)
switches_reduce_2 = copy.deepcopy(switches_reduce)
# drop operations with low architecture weights
arch_param = model.module.arch_parameters()
normal_prob = F.softmax(arch_param[0], dim=sm_dim).data.cpu().numpy()
for i in range(14):
idxs = []
for j in range(len(PRIMITIVES)):
if switches_normal[i][j]:
idxs.append(j)
if sp == len(num_to_keep) - 1:
# for the last stage, drop all Zero operations
drop = get_min_k_no_zero(normal_prob[i, :], idxs,
num_to_drop[sp])
else:
drop = get_min_k(normal_prob[i, :], num_to_drop[sp])
for idx in drop:
switches_normal[i][idxs[idx]] = False
reduce_prob = F.softmax(arch_param[1], dim=-1).data.cpu().numpy()
for i in range(14):
idxs = []
for j in range(len(PRIMITIVES)):
if switches_reduce[i][j]:
idxs.append(j)
if sp == len(num_to_keep) - 1:
drop = get_min_k_no_zero(reduce_prob[i, :], idxs,
num_to_drop[sp])
else:
drop = get_min_k(reduce_prob[i, :], num_to_drop[sp])
for idx in drop:
switches_reduce[i][idxs[idx]] = False
logging.info('switches_normal = %s', switches_normal)
logging_switches(switches_normal)
logging.info('switches_reduce = %s', switches_reduce)
logging_switches(switches_reduce)
if sp == len(num_to_keep) - 1:
arch_param = model.module.arch_parameters()
normal_prob = F.softmax(arch_param[0],
dim=sm_dim).data.cpu().numpy()
reduce_prob = F.softmax(arch_param[1],
dim=sm_dim).data.cpu().numpy()
normal_final = [0 for idx in range(14)]
reduce_final = [0 for idx in range(14)]
# remove all Zero operations
for i in range(14):
if switches_normal_2[i][0] == True:
normal_prob[i][0] = 0
normal_final[i] = max(normal_prob[i])
if switches_reduce_2[i][0] == True:
reduce_prob[i][0] = 0
reduce_final[i] = max(reduce_prob[i])
# Generate Architecture, similar to DARTS
keep_normal = [0, 1]
keep_reduce = [0, 1]
n = 3
start = 2
for i in range(3): # 选出最大的两个前序节点
end = start + n
tbsn = normal_final[start:end]
tbsr = reduce_final[start:end]
edge_n = sorted(range(n), key=lambda x: tbsn[x])
keep_normal.append(edge_n[-1] + start)
keep_normal.append(edge_n[-2] + start)
edge_r = sorted(range(n), key=lambda x: tbsr[x])
keep_reduce.append(edge_r[-1] + start)
keep_reduce.append(edge_r[-2] + start)
start = end
n = n + 1
# set switches according the ranking of arch parameters
for i in range(14):
if not i in keep_normal:
for j in range(len(PRIMITIVES)):
switches_normal[i][j] = False
if not i in keep_reduce:
for j in range(len(PRIMITIVES)):
switches_reduce[i][j] = False
# translate switches into genotype
genotype = parse_network(switches_normal, switches_reduce)
logging.info(genotype)
## restrict skipconnect (normal cell only)
logging.info('Restricting skipconnect...')
# generating genotypes with different numbers of skip-connect operations
for sks in range(0, 9):
max_sk = 8 - sks
num_sk = check_sk_number(switches_normal)
if not num_sk > max_sk:
continue
while num_sk > max_sk:
normal_prob = delete_min_sk_prob(switches_normal,
switches_normal_2,
normal_prob)
switches_normal = keep_1_on(switches_normal_2, normal_prob)
switches_normal = keep_2_branches(switches_normal,
normal_prob)
num_sk = check_sk_number(switches_normal)
logging.info('Number of skip-connect: %d', max_sk)
genotype = parse_network(switches_normal, switches_reduce)
logging.info(genotype)
def __init__(self,
args: Namespace,
genotype: Genotype,
my_dataset: MyDataset,
choose_cell=False):
self.__args = args
self.__dataset = my_dataset
self.__previous_epochs = 0
if args.seed is None:
raise Exception('designate seed.')
elif args.epochs is None:
raise Exception('designate epochs.')
if not (args.arch or args.arch_path):
raise Exception('need to designate arch.')
log_format = '%(asctime)s %(message)s'
logging.basicConfig(stream=sys.stdout,
level=logging.INFO,
format=log_format,
datefmt='%m/%d %I:%M:%S %p')
fh = logging.FileHandler(os.path.join(args.save, 'log.txt'))
fh.setFormatter(logging.Formatter(log_format))
logging.getLogger().addHandler(fh)
np.random.seed(args.seed)
cudnn.benchmark = True
cudnn.enabled = True
torch.manual_seed(args.seed)
logging.info(f'gpu device = {args.gpu}')
logging.info(f'args = {args}')
logging.info(f'Train genotype: {genotype}')
if my_dataset == MyDataset.CIFAR10:
self.model = NetworkCIFAR(args.init_ch, 10, args.layers,
args.auxiliary, genotype)
train_transform, valid_transform = utils._data_transforms_cifar10(
args)
train_data = dset.CIFAR10(root=args.data,
train=True,
download=True,
transform=train_transform)
valid_data = dset.CIFAR10(root=args.data,
train=False,
download=True,
transform=valid_transform)
elif my_dataset == MyDataset.CIFAR100:
self.model = NetworkCIFAR(args.init_ch, 100, args.layers,
args.auxiliary, genotype)
train_transform, valid_transform = utils._data_transforms_cifar100(
args)
train_data = dset.CIFAR100(root=args.data,
train=True,
download=True,
transform=train_transform)
valid_data = dset.CIFAR100(root=args.data,
train=False,
download=True,
transform=valid_transform)
elif my_dataset == MyDataset.ImageNet:
self.model = NetworkImageNet(args.init_ch, 1000, args.layers,
args.auxiliary, genotype)
self.__criterion_smooth = CrossEntropyLabelSmooth(
1000, args.label_smooth).to(device)
traindir = os.path.join(args.data, 'train')
validdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_data = dset.ImageFolder(
traindir,
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=0.4,
contrast=0.4,
saturation=0.4,
hue=0.2),
transforms.ToTensor(),
normalize,
]))
valid_data = dset.ImageFolder(
validdir,
transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
]))
else:
raise Exception('No match Dataset')
checkpoint = None
if use_DataParallel:
print('use Data Parallel')
if args.checkpoint_path:
checkpoint = torch.load(args.checkpoint_path)
utils.load(self.model, checkpoint['state_dict'],
args.to_parallel)
self.__previous_epochs = checkpoint['epoch']
args.epochs -= self.__previous_epochs
if args.epochs <= 0:
raise Exception('args.epochs is too small.')
self.model = nn.DataParallel(self.model)
self.__module = self.model.module
torch.cuda.manual_seed_all(args.seed)
else:
if args.checkpoint_path:
checkpoint = torch.load(args.checkpoint_path)
utils.load(self.model, checkpoint['state_dict'],
args.to_parallel)
args.epochs -= checkpoint['epoch']
if args.epochs <= 0:
raise Exception('args.epochs is too small.')
torch.cuda.manual_seed(args.seed)
self.__module = self.model
self.model.to(device)
param_size = utils.count_parameters_in_MB(self.model)
logging.info(f'param size = {param_size}MB')
self.__criterion = nn.CrossEntropyLoss().to(device)
self.__optimizer = torch.optim.SGD(self.__module.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.wd)
if checkpoint:
self.__optimizer.load_state_dict(checkpoint['optimizer'])
num_workers = torch.cuda.device_count() * 4
if choose_cell:
num_train = len(train_data) # 50000
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train)) # 25000
self.__train_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batchsz,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[:split]),
pin_memory=True,
num_workers=num_workers)
self.__valid_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batchsz,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[split:]),
pin_memory=True,
num_workers=num_workers)
else:
self.__train_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batchsz,
shuffle=True,
pin_memory=True,
num_workers=num_workers)
self.__valid_queue = torch.utils.data.DataLoader(
valid_data,
batch_size=args.batchsz,
shuffle=False,
pin_memory=True,
num_workers=num_workers)
if my_dataset == MyDataset.CIFAR10 or MyDataset.CIFAR100:
self.__scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
self.__optimizer, args.epochs)
elif my_dataset == MyDataset.ImageNet:
self.__scheduler = torch.optim.lr_scheduler.StepLR(
self.__optimizer, args.decay_period, gamma=args.gamma)
else:
raise Exception('No match Dataset')
if checkpoint:
self.__scheduler.load_state_dict(checkpoint['scheduler'])
def main():
# if not torch.cuda.is_available():
# logging.info('no gpu device available')
# sys.exit(1)
np.random.seed(args.seed)
# torch.cuda.set_device(args.gpu)
# cudnn.benchmark = True
# torch.manual_seed(args.seed)
# cudnn.enabled = True
# torch.cuda.manual_seed(args.seed)
# logging.info('gpu device = %d' % args.gpu)
# logging.info("args = %s", args)
torch.manual_seed(args.seed)
logging.info('use cpu')
logging.info("args = %s", args)
criterion = nn.CrossEntropyLoss()
# criterion = criterion.cuda()
criterion.to(device)
model = Network(args.init_channels,
CIFAR_CLASSES,
args.layers,
criterion,
learnable_bn=args.learnable_bn)
# model = model.cuda()
model.to(device)
a = list(model.parameters())
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
# optimizer = torch.optim.SGD(
# model.parameters(),
# args.learning_rate,
# momentum=args.momentum,
# weight_decay=args.weight_decay)
################################################################################
# AdaS: optimizer and scheduler
optimizer = SGDVec(params=model.parameters(),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = AdaS(
parameters=list(model.parameters()),
init_lr=args.learning_rate,
# min_lr=kwargs['min_lr'],
# zeta=kwargs['zeta'],
p=args.scheduler_p,
beta=args.scheduler_beta)
################################################################################
# train_transform, valid_transform = utils._data_transforms_cifar100(args)
# train_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=train_transform)
"""From https://github.com/chenxin061/pdarts/"""
if args.cifar100:
train_transform, valid_transform = utils._data_transforms_cifar100(
args)
else:
train_transform, valid_transform = utils._data_transforms_cifar10(args)
if args.cifar100:
train_data = dset.CIFAR100(root=args.data,
train=True,
download=True,
transform=train_transform)
else:
train_data = dset.CIFAR10(root=args.data,
train=True,
download=True,
transform=train_transform)
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True,
num_workers=2)
valid_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[split:num_train]),
pin_memory=True,
num_workers=2)
# scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
# optimizer, float(args.epochs), eta_min=args.learning_rate_min)
architect = Architect(model, args)
"""Hessian"""
analyser = Analyzer(model, args)
"""adaptive stopping"""
stop_checker = StopChecker()
METRICS = Metrics(list(model.parameters()), p=1)
PERFORMANCE_STATISTICS = {}
ARCH_STATISTICS = {}
GENOTYPE_STATISTICS = {}
metrics_path = './metrics_stat_test_adas.xlsx'
weights_path = './weights_stat_test_adas.xlsx'
genotypes_path = './genotypes_stat_test_adas.xlsx'
for epoch in range(args.epochs):
# scheduler.step()
# lr = scheduler.get_lr()[0]
# logging.info
genotype = model.genotype()
logging.info('genotype = %s', genotype)
if epoch % 5 == 0 or epoch == args.epochs - 1:
GENOTYPE_STATISTICS[f'epoch_{epoch}'] = [genotype]
genotypes_df = pd.DataFrame(data=GENOTYPE_STATISTICS)
genotypes_df.to_excel(genotypes_path)
print(F.softmax(model.alphas_normal, dim=-1))
print(F.softmax(model.alphas_reduce, dim=-1))
# training
train_acc, train_obj = train(epoch, train_queue, valid_queue, model,
architect, criterion, optimizer, METRICS,
scheduler, analyser)
logging.info('train_acc %f', train_acc)
# validation
valid_acc, valid_obj = infer(valid_queue, model, criterion)
logging.info('valid_acc %f', valid_acc)
# metrics
io_metrics = METRICS.evaluate(epoch)
PERFORMANCE_STATISTICS[
f'in_S_epoch_{epoch}'] = io_metrics.input_channel_S
PERFORMANCE_STATISTICS[
f'out_S_epoch_{epoch}'] = io_metrics.output_channel_S
PERFORMANCE_STATISTICS[f'fc_S_epoch_{epoch}'] = io_metrics.fc_S
PERFORMANCE_STATISTICS[
f'in_rank_epoch_{epoch}'] = io_metrics.input_channel_rank
PERFORMANCE_STATISTICS[
f'out_rank_epoch_{epoch}'] = io_metrics.output_channel_rank
PERFORMANCE_STATISTICS[f'fc_rank_epoch_{epoch}'] = io_metrics.fc_rank
PERFORMANCE_STATISTICS[
f'in_condition_epoch_{epoch}'] = io_metrics.input_channel_condition
PERFORMANCE_STATISTICS[
f'out_condition_epoch_{epoch}'] = io_metrics.output_channel_condition
################################################################################
# AdaS: update learning rates
lr_metrics = scheduler.step(epoch, METRICS)
PERFORMANCE_STATISTICS[
f'rank_velocity_epoch_{epoch}'] = lr_metrics.rank_velocity
PERFORMANCE_STATISTICS[
f'learning_rate_epoch_{epoch}'] = lr_metrics.r_conv
################################################################################
# write metrics data to xls file
metrics_df = pd.DataFrame(data=PERFORMANCE_STATISTICS)
metrics_df.to_excel(metrics_path)
# weights
weights_normal = F.softmax(model.alphas_normal,
dim=-1).detach().cpu().numpy()
weights_reduce = F.softmax(model.alphas_reduce,
dim=-1).detach().cpu().numpy()
# normal
ARCH_STATISTICS[f'normal_none_epoch{epoch}'] = weights_normal[:, 0]
ARCH_STATISTICS[f'normal_max_epoch{epoch}'] = weights_normal[:, 1]
ARCH_STATISTICS[f'normal_avg_epoch{epoch}'] = weights_normal[:, 2]
ARCH_STATISTICS[f'normal_skip_epoch{epoch}'] = weights_normal[:, 3]
ARCH_STATISTICS[f'normal_sep_3_epoch{epoch}'] = weights_normal[:, 4]
ARCH_STATISTICS[f'normal_sep_5_epoch{epoch}'] = weights_normal[:, 5]
ARCH_STATISTICS[f'normal_dil_3_epoch{epoch}'] = weights_normal[:, 6]
ARCH_STATISTICS[f'normal_dil_5_epoch{epoch}'] = weights_normal[:, 7]
# reduce
ARCH_STATISTICS[f'reduce_none_epoch{epoch}'] = weights_reduce[:, 0]
ARCH_STATISTICS[f'reduce_max_epoch{epoch}'] = weights_reduce[:, 1]
ARCH_STATISTICS[f'reduce_avg_epoch{epoch}'] = weights_reduce[:, 2]
ARCH_STATISTICS[f'reduce_skip_epoch{epoch}'] = weights_reduce[:, 3]
ARCH_STATISTICS[f'reduce_sep_3_epoch{epoch}'] = weights_reduce[:, 4]
ARCH_STATISTICS[f'reduce_sep_5_epoch{epoch}'] = weights_reduce[:, 5]
ARCH_STATISTICS[f'reduce_dil_3_epoch{epoch}'] = weights_reduce[:, 6]
ARCH_STATISTICS[f'reduce_dil_5_epoch{epoch}'] = weights_reduce[:, 7]
# write weights data to xls file
weights_df = pd.DataFrame(data=ARCH_STATISTICS)
weights_df.to_excel(weights_path)
# adaptive stopping criterion
if args.adaptive_stop and epoch >= 10:
# apply local stopping criterion
stop_checker.local_stop(METRICS, epoch)
# freeze some edges based on their knowledge gains
iteration_p = 0
for p in model.parameters():
if ~METRICS.layers_index_todo[iteration_p]:
p.requires_grad = False
p.grad = None
iteration_p += 1
utils.save(model, os.path.join(args.save, 'weights.pt'))
def main():
torch.set_num_threads(3)
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
np.random.seed(args.seed)
torch.cuda.set_device(args.gpu)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled = True
torch.cuda.manual_seed(args.seed)
logging.info('gpu device = %d' % args.gpu)
logging.info("args = %s", args)
if not 'debug' in args.save:
api = API('pth file path')
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
if args.method == 'snas':
# Create the decrease step for the gumbel softmax temperature
args.epochs = 100
tau_step = (args.tau_min - args.tau_max) / args.epochs
tau_epoch = args.tau_max
model = TinyNetwork(C=args.init_channels, N=5, max_nodes=4, num_classes=n_classes,
criterion=criterion, search_space=NAS_BENCH_201, k=args.k, species='gumbel')
elif args.method == 'dirichlet':
model = TinyNetwork(C=args.init_channels, N=5, max_nodes=4, num_classes=n_classes,
criterion=criterion, search_space=NAS_BENCH_201, k=args.k, species='dirichlet')
elif args.method == 'darts':
model = TinyNetwork(C=args.init_channels, N=5, max_nodes=4, num_classes=n_classes,
criterion=criterion, search_space=NAS_BENCH_201, k=args.k, species='softmax')
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
optimizer = torch.optim.SGD(
model.get_weights(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.dataset == 'cifar10':
train_transform, valid_transform = utils._data_transforms_cifar10(args)
train_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=train_transform)
elif args.dataset == 'cifar100':
train_transform, valid_transform = utils._data_transforms_cifar100(args)
train_data = dset.CIFAR100(root=args.data, train=True, download=True, transform=train_transform)
elif args.dataset == 'svhn':
train_transform, valid_transform = utils._data_transforms_svhn(args)
train_data = dset.SVHN(root=args.data, split='train', download=True, transform=train_transform)
elif args.dataset == 'imagenet16-120':
import torchvision.transforms as transforms
from nasbench201.DownsampledImageNet import ImageNet16
mean = [x / 255 for x in [122.68, 116.66, 104.01]]
std = [x / 255 for x in [63.22, 61.26, 65.09]]
lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(16, padding=2), transforms.ToTensor(), transforms.Normalize(mean, std)]
train_transform = transforms.Compose(lists)
train_data = ImageNet16(root=os.path.join(args.data,'imagenet16'), train=True, transform=train_transform, use_num_of_class_only=120)
assert len(train_data) == 151700
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True)
valid_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:num_train]),
pin_memory=True)
architect = Architect(model, args)
# configure progressive parameter
epoch = 0
ks = [4, 2]
num_keeps = [5, 3]
train_epochs = [2, 2] if 'debug' in args.save else [50, 50]
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(sum(train_epochs)), eta_min=args.learning_rate_min)
for i, current_epochs in enumerate(train_epochs):
for e in range(current_epochs):
lr = scheduler.get_lr()[0]
logging.info('epoch %d lr %e', epoch, lr)
genotype = model.genotype()
logging.info('genotype = %s', genotype)
model.show_arch_parameters()
# training
train_acc, train_obj = train(train_queue, valid_queue, model, architect, criterion, optimizer, lr, e)
logging.info('train_acc %f', train_acc)
# validation
valid_acc, valid_obj = infer(valid_queue, model, criterion)
logging.info('valid_acc %f', valid_acc)
if not 'debug' in args.save:
# nasbench201
result = api.query_by_arch(model.genotype())
logging.info('{:}'.format(result))
cifar10_train, cifar10_test, cifar100_train, cifar100_valid, \
cifar100_test, imagenet16_train, imagenet16_valid, imagenet16_test = distill(result)
logging.info('cifar10 train %f test %f', cifar10_train, cifar10_test)
logging.info('cifar100 train %f valid %f test %f', cifar100_train, cifar100_valid, cifar100_test)
logging.info('imagenet16 train %f valid %f test %f', imagenet16_train, imagenet16_valid, imagenet16_test)
# tensorboard
writer.add_scalars('accuracy', {'train':train_acc,'valid':valid_acc}, epoch)
writer.add_scalars('loss', {'train':train_obj,'valid':valid_obj}, epoch)
writer.add_scalars('nasbench201/cifar10', {'train':cifar10_train,'test':cifar10_test}, epoch)
writer.add_scalars('nasbench201/cifar100', {'train':cifar100_train,'valid':cifar100_valid, 'test':cifar100_test}, epoch)
writer.add_scalars('nasbench201/imagenet16', {'train':imagenet16_train,'valid':imagenet16_valid, 'test':imagenet16_test}, epoch)
utils.save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'alpha': model.arch_parameters()
}, False, args.save)
epoch += 1
scheduler.step()
if args.method == 'snas':
# Decrease the temperature for the gumbel softmax linearly
tau_epoch += tau_step
logging.info('tau %f', tau_epoch)
model.set_tau(tau_epoch)
if not i == len(train_epochs) - 1:
model.pruning(num_keeps[i+1])
# architect.pruning([model._mask])
model.wider(ks[i+1])
optimizer = configure_optimizer(optimizer, torch.optim.SGD(
model.get_weights(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay))
scheduler = configure_scheduler(scheduler, torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(sum(train_epochs)), eta_min=args.learning_rate_min))
logging.info('pruning finish, %d ops left per edge', num_keeps[i+1])
logging.info('network wider finish, current pc parameter %d', ks[i+1])
genotype = model.genotype()
logging.info('genotype = %s', genotype)
model.show_arch_parameters()
writer.close()
def main():
if not torch.cuda.is_available():
logging.info('No GPU device available')
sys.exit(1)
np.random.seed(args.seed)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled=True
torch.cuda.manual_seed(args.seed)
logging.info("args = %s", args)
logging.info("unparsed args = %s", unparsed)
num_gpus = torch.cuda.device_count()
logging.info('Training with %d GPU(s)', num_gpus)
model = eval("se_resnet%s(num_classes=CIFAR_CLASSES)" % args.resnet_type)
if num_gpus > 1:
model = torch.nn.DataParallel(model)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
optimizer = torch.optim.SGD(
model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay
)
if args.cifar100:
train_transform, valid_transform = utils._data_transforms_cifar100(args)
else:
train_transform, valid_transform = utils._data_transforms_cifar10(args)
if args.cifar100:
train_data = dset.CIFAR100(root=args.tmp_data_dir, train=True, download=True, transform=train_transform)
valid_data = dset.CIFAR100(root=args.tmp_data_dir, train=False, download=True, transform=valid_transform)
else:
train_data = dset.CIFAR10(root=args.tmp_data_dir, train=True, download=True, transform=train_transform)
valid_data = dset.CIFAR10(root=args.tmp_data_dir, train=False, download=True, transform=valid_transform)
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size, shuffle=True, pin_memory=True, num_workers=args.workers)
valid_queue = torch.utils.data.DataLoader(
valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=args.workers)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[90, 135], gamma=0.1)
best_acc = 0.0
results ={'tr_acc': [], 'tr_loss': [], 'val_acc': [], 'val_loss': []}
for epoch in range(args.epochs):
scheduler.step()
logging.info('Epoch: %d lr %e', epoch, scheduler.get_lr()[0])
start_time = time.time()
train_acc, train_obj = train(train_queue, model, criterion, optimizer)
logging.info('Train_acc: %f', train_acc)
results['tr_acc'].append(train_acc)
results['tr_loss'].append(train_obj)
valid_acc, valid_obj = infer(valid_queue, model, criterion)
if valid_acc > best_acc:
best_acc = valid_acc
utils.save(model, os.path.join(args.save, 'best_weights.pt'))
logging.info('Valid_acc: %f', valid_acc)
results['val_acc'].append(valid_acc)
results['val_loss'].append(valid_obj)
end_time = time.time()
duration = end_time - start_time
print('Epoch time: %ds.' % duration )
utils.save(model, os.path.join(args.save, 'final_weights.pt'))
with open('{}/train_loss.txt'.format(args.save), 'w') as file:
for item in results['tr_loss']:
file.write(str(item) + '\n')
with open('{}/train_acc.txt'.format(args.save), 'w') as file:
for item in results['tr_acc']:
file.write(str(item) + '\n')
logging.info('Best testing accuracy is: %f\n___________________________________END_____________________________', best_acc)
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
np.random.seed(args.seed)
torch.cuda.set_device(args.gpu)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled=True
torch.cuda.manual_seed(args.seed)
logging.info('gpu device = %d' % args.gpu)
logging.info("args = %s", args)
genotype = eval("genotypes.%s" % args.arch)
model = Network(args.init_channels, CIFAR_CLASSES, args.layers, args.auxiliary, genotype)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
optimizer = torch.optim.SGD(
model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay
)
if args.set=='cifar100':
train_transform, valid_transform = utils._data_transforms_cifar100(args)
train_data = dset.CIFAR100(root=args.data, train=True, download=True, transform=train_transform)
valid_data = dset.CIFAR100(root=args.data, train=False, download=True, transform=valid_transform)
else:
train_transform, valid_transform = utils._data_transforms_cifar10(args)
train_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=train_transform)
valid_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=valid_transform)
#train_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=train_transform)
#valid_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=valid_transform)
# train_queue = torch.utils.data.DataLoader(
# train_data, batch_size=args.batch_size, shuffle=True, pin_memory=True, num_workers=2)
# valid_queue = torch.utils.data.DataLoader(
# valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=2)
valid_queue = torch.utils.data.DataLoader(
valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=4)
num_train = len(train_data)
indices = list(range(num_train))
split = 45000
# int(np.floor(args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True, num_workers=4)
train_valid_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:]),
pin_memory=True, num_workers=4)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs))
best_acc = 0.0
for epoch in range(args.epochs):
logging.info('epoch %d lr %e', epoch, scheduler.get_lr()[0])
model.drop_path_prob = args.drop_path_prob * epoch / args.epochs
train_acc, train_obj = train(train_queue, model, criterion, optimizer)
scheduler.step()
logging.info('train_acc %f', train_acc)
##
valid_acc, valid_obj = infer(train_valid_queue, model, criterion)
if valid_acc > best_acc:
best_acc = valid_acc
test_acc, _ = infer(valid_queue, model, criterion)
logging.info('Test_acc: %f', test_acc)
logging.info('Valid_acc: %f', valid_acc)
logging.info('Best_acc: %f', best_acc)
####
# valid_acc, valid_obj = infer(valid_queue, model, criterion)
# if valid_acc > best_acc:
# best_acc = valid_acc
# logging.info('valid_acc %f, best_acc %f', valid_acc, best_acc)
utils.save(model, os.path.join(args.save, 'weights.pt'))
def main():
if not torch.cuda.is_available():
logging.info('No GPU device available')
sys.exit(1)
np.random.seed(args.seed)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled=True
torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
os.environ['PYTHONHASHSEED'] = str(args.seed)
logging.info("args = %s", args)
logging.info("unparsed args = %s", unparsed)
num_gpus = torch.cuda.device_count()
genotype = eval("genotypes.%s" % args.arch)
print('---------Genotype---------')
logging.info(genotype)
print('--------------------------')
resnet_types = {'resnet20': 3, 'resnet32': 5, 'resnet44': 7, 'resnet56': 9, 'resnet110': 18}
n_sizes = resnet_types[args.net_type]
logging.info('Number of attentional residual block(s): %s', n_sizes * 3)
model = att_resnet_cifar(genotype, n_size=n_sizes, num_classes=CIFAR_CLASSES)
if num_gpus > 1:
model = torch.nn.DataParallel(model)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
optimizer = torch.optim.SGD(
model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay
)
if args.cifar100:
train_transform, valid_transform = utils._data_transforms_cifar100(args)
else:
train_transform, valid_transform = utils._data_transforms_cifar10(args)
if args.cifar100:
train_data = dset.CIFAR100(root=args.tmp_data_dir, train=True, download=True, transform=train_transform)
valid_data = dset.CIFAR100(root=args.tmp_data_dir, train=False, download=True, transform=valid_transform)
else:
train_data = dset.CIFAR10(root=args.tmp_data_dir, train=True, download=True, transform=train_transform)
valid_data = dset.CIFAR10(root=args.tmp_data_dir, train=False, download=True, transform=valid_transform)
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size, shuffle=True, pin_memory=True, num_workers=args.workers)
valid_queue = torch.utils.data.DataLoader(
valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=args.workers)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs))
best_acc = 0.0
for epoch in range(args.epochs):
scheduler.step()
logging.info('Epoch: %d lr %e', epoch, scheduler.get_lr()[0])
if num_gpus > 1:
model.module._block.drop_path_prob = args.drop_path_prob * epoch / args.epochs
else:
model._block.drop_path_prob = args.drop_path_prob * epoch / args.epochs
start_time = time.time()
train_acc, train_obj = train(train_queue, model, criterion, optimizer)
logging.info('Train_acc: %f', train_acc)
valid_acc, valid_obj = infer(valid_queue, model, criterion)
if valid_acc > best_acc:
best_acc = valid_acc
logging.info('Valid_acc: %f', valid_acc)
end_time = time.time()
duration = end_time - start_time
print('Epoch time: %ds.' % duration )
utils.save(model, os.path.join(args.save, 'weights.pt'))
logging.info("_____________________________________\nBest Valid Accuracy is: %f\n______________________END_____________________", best_acc)
def main():
if not torch.cuda.is_available():
logging.info('No GPU device available')
sys.exit(1)
np.random.seed(args.seed)
cudnn.benchmark = True #找寻特定卷积算法
torch.manual_seed(args.seed)
cudnn.enabled = True
torch.cuda.manual_seed(args.seed)
logging.info("args = %s", args)
# prepare dataset
if args.cifar100:
train_transform, valid_transform = utils._data_transforms_cifar100(
args)
else:
train_transform, valid_transform = utils._data_transforms_cifar10(args)
if args.cifar100:
train_data = dset.CIFAR100(root=args.tmp_data_dir,
train=True,
download=False,
transform=train_transform)
else:
train_data = dset.CIFAR10(root=args.tmp_data_dir,
train=True,
download=False,
transform=train_transform)
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True,
num_workers=args.workers)
valid_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[split:num_train]),
pin_memory=True,
num_workers=args.workers) #pinmemary 锁存固定，高端设备玩主
#************************************************************************************************#
#第二阶段：配置网络逻辑层
# criterion = nn.CrossEntropyLoss() #可以选择特定的train阶段损失函数
# criterion = criterion.cuda()
#L0-1损失函数
criterion_train = ConvSeparateLoss(
weight=args.aux_loss_weight
) if args.sep_loss == 'l2' else TriSeparateLoss(
weight=args.aux_loss_weight)
criterion_val = nn.CrossEntropyLoss()
criterion_train = criterion_train.cuda()
criterion_val = nn.CrossEntropyLoss().cuda()
switches = [] #操作淘汰标志
for i in range(14):
switches.append([True for j in range(len(PRIMITIVES))])
switches_normal = copy.deepcopy(switches)
switches_reduce = copy.deepcopy(switches)
# To be moved to args
num_to_keep = [5, 3, 1]
num_to_drop = [2, 2, 2] #操作的淘汰数
if len(args.add_width) == args.stages:
add_width = args.add_width
else:
add_width = [[0, 16], [0, 8, 16]][args.stages - 2] #add_width初始
if len(args.add_layers) == args.stages:
add_layers = args.add_layers
else:
add_layers = [[0, 7], [0, 6, 12]][args.stages - 2] #add_layers初始
if len(args.dropout_rate) == args.stages:
drop_rate = args.dropout_rate
else:
drop_rate = [0.0] * args.stages #dropout逻辑
eps_no_archs = [args.noarc] * args.stages #前n个epoch只更新逻辑参数
if len(args.sample) == args.stages:
sample = args.sample
else:
sample = [[4, 8], [4, 4, 4]][args.stages - 2]
epochs = [25, 25, 25]
#***************************************************************************************#
#第三阶段：训练逻辑实现层#
for sp in range(len(num_to_keep)):
model = Network(args.init_channels + int(add_width[sp]),
CIFAR_CLASSES,
args.layers + int(add_layers[sp]),
criterion_val,
switches_normal=switches_normal,
switches_reduce=switches_reduce,
p=float(drop_rate[sp]),
K=int(sample[sp]),
use_baidu=args.use_baidu,
use_EN=args.use_EN)
model = nn.DataParallel(model) #多GPU并行
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
logging.info("layers=%d", args.layers + int(add_layers[sp]))
logging.info("channels=%d", args.init_channels + int(add_width[sp]))
logging.info("K=%d", int(sample[sp]))
network_params = []
for k, v in model.named_parameters():
if not (k.endswith('alphas_normal') or k.endswith('alphas_reduce')
or k.endswith('betas_reduce')
or k.endswith('betas_normal')): #具体是啥
network_params.append(v)
optimizer = torch.optim.SGD(network_params,
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer_a = torch.optim.Adam(model.module.arch_parameters(),
lr=args.arch_learning_rate,
betas=(0.5, 0.999),
weight_decay=args.arch_weight_decay)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs), eta_min=args.learning_rate_min)
sm_dim = -1
# epochs = args.epochs
eps_no_arch = eps_no_archs[sp]
scale_factor = 0.2
for epoch in range(epochs[sp]):
scheduler.step()
lr = scheduler.get_lr()[0]
logging.info('Epoch: %d lr: %e', epoch, lr)
epoch_start = time.time()
# training
if epoch < eps_no_arch:
model.module.p = float(drop_rate[sp]) * (epochs[sp] - epoch -
1) / epochs[sp] #！！！
model.module.update_p()
train_acc, train_obj = train(train_queue,
valid_queue,
model,
network_params,
criterion_train,
optimizer,
optimizer_a,
lr,
train_arch=False)
else:
model.module.p = float(drop_rate[sp]) * np.exp(
-(epoch - eps_no_arch) * scale_factor)
model.module.update_p()
train_acc, train_obj = train(train_queue,
valid_queue,
model,
network_params,
criterion_train,
optimizer,
optimizer_a,
lr,
train_arch=True)
logging.info('Train_acc %f', train_acc)
epoch_duration = time.time() - epoch_start
logging.info('Epoch time: %ds', epoch_duration)
# print("beats",model.module.arch_parameters()[1])
# validation
if epochs[sp] - epoch < 5:
valid_acc, valid_obj = infer(valid_queue, model, criterion_val)
logging.info('Valid_acc %f', valid_acc)
# print("epoch=",epoch,'weights_normal=',model.module.weights_normal,'weights_reduce=',model.module.weights_reduce)
# print('weights2_normal=',model.module.weights2_normal,'\n','weights2_reduce=',model.module.weights2_reduce)
#/************************************************************/
arch_normal = model.module.arch_parameters()[0]
arch_reduce = model.module.arch_parameters()[1]
betas_nor = model.module.weights2_normal
betas_redu = model.module.weights2_reduce
shengcheng(arch_normal, arch_reduce, switches_normal,
switches_reduce, betas_nor, betas_redu)
#/***********************************************************/
utils.save(model, os.path.join(args.save, 'weights.pt'))
print('------Dropping %d paths------' % num_to_drop[sp])
#************************************************************************************8
# Save switches info for s-c refinement.
if sp == len(num_to_keep) - 1:
switches_normal_2 = copy.deepcopy(switches_normal)
switches_reduce_2 = copy.deepcopy(switches_reduce)
# drop operations with low architecture weights
arch_param = model.module.arch_parameters()
normal_prob = F.sigmoid(arch_param[0]).data.cpu().numpy() ##化概率
for i in range(14):
idxs = []
for j in range(len(PRIMITIVES)):
if switches_normal[i][j]:
idxs.append(j)
# for the last stage, drop all Zero operations
# drop1 = get_min_k_no_zero(normal_prob[i, :], idxs, num_to_drop[sp]) ###看函数？？？看理论
drop2 = get_min_k(normal_prob[i, :], num_to_drop[sp])
# if sp == len(num_to_keep) - 1:
# for idx in drop1:
# switches_normal[i][idxs[idx]] = False
# else:
for idx in drop2:
switches_normal[i][idxs[idx]] = False #不断地关掉无效操作，正则化方法
logging.info('switches_normal = %s', switches_normal)
logging_switches(switches_normal)
if args.use_baidu == False:
reduce_prob = F.sigmoid(arch_param[1]).data.cpu().numpy()
#reduce_prob = F.softmax(arch_param[1], dim=-1).data.cpu().numpy()
for i in range(14):
idxs = []
for j in range(len(PRIMITIVES)):
if switches_reduce[i][j]:
idxs.append(j)
if sp == len(num_to_keep) - 1:
drop = get_min_k_no_zero(reduce_prob[i, :], idxs,
num_to_drop[sp])
else:
drop = get_min_k(reduce_prob[i, :], num_to_drop[sp])
for idx in drop:
switches_reduce[i][idxs[idx]] = False
logging.info('switches_reduce = %s', switches_reduce)
logging_switches(switches_reduce)
train_data = CIFAR10_bad(root=args.data,
train=True,
download=True,
transform=train_transform,
bad_rate=args.bad_rate)
else:
train_data = CIFAR10(root=args.data,
train=True,
download=True,
transform=train_transform)
test_data = CIFAR10(root=args.data,
train=False,
download=True,
transform=valid_transform)
else:
train_transform, valid_transform = _data_transforms_cifar100(args)
if args.bad_rate is not None:
train_data = CIFAR100_bad(root=args.data,
train=True,
download=True,
transform=train_transform,
bad_rate=args.bad_rate)
else:
train_data = CIFAR100(root=args.data,
train=True,
download=True,
transform=train_transform)
test_data = CIFAR100(root=args.data,
train=False,
download=True,
transform=valid_transform)
def main():
if not torch.cuda.is_available():
logging.info('No GPU device available')
sys.exit(1)
np.random.seed(args.seed)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled=True
torch.cuda.manual_seed(args.seed)
logging.info("args = %s", args)
# prepare dataset
if args.cifar100:
train_transform, valid_transform = utils._data_transforms_cifar100(args)
else:
# train_transform, valid_transform = utils._data_transforms_cifar10(args)
train_transform = transforms.Compose([transforms.Resize(32),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
valid_transform = transforms.Compose([transforms.Resize(32),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
if args.cifar100:
train_data = dset.CIFAR100(root=args.tmp_data_dir, train=True, download=True, transform=train_transform)
else:
train_data = dset.CIFAR10(root=args.tmp_data_dir, train=True, download=True, transform=train_transform)
label_dim = 10
image_size = 32
# label preprocess
onehot = torch.zeros(label_dim, label_dim)
onehot = onehot.scatter_(1, torch.LongTensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]).view(label_dim, 1), 1).view(label_dim, label_dim, 1, 1)
fill = torch.zeros([label_dim, label_dim, image_size, image_size])
for i in range(label_dim):
fill[i, i, :, :] = 1
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True, num_workers=args.workers)
valid_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:num_train]),
pin_memory=True, num_workers=args.workers)
adversarial_loss = nn.MSELoss()
adversarial_loss.cuda()
# build Network
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
switches = []
for i in range(14):
switches.append([True for j in range(len(PRIMITIVES))])
switches_normal = copy.deepcopy(switches)
switches_reduce = copy.deepcopy(switches)
# To be moved to args
num_to_keep = [5, 3, 1]
num_to_drop = [3, 2, 2]
if len(args.add_width) == 3:
add_width = args.add_width
else:
add_width = [0, 0, 0]
if len(args.add_layers) == 3:
add_layers = args.add_layers
else:
add_layers = [0, 6, 12]
if len(args.dropout_rate) ==3:
drop_rate = args.dropout_rate
else:
drop_rate = [0.0, 0.0, 0.0]
eps_no_archs = [10, 10, 10]
# gen = Generator(100)
# gen.cuda()
# gen.apply(weights_init)
# logging.info("param size gen= %fMB", utils.count_parameters_in_MB(gen))
# optimizer_gen = torch.optim.Adam(gen.parameters(), lr=args.lr,
# betas=(args.b1, args.b2))
# sp = 0
# disc = Network(args.init_channels + int(add_width[sp]), CIFAR_CLASSES, args.layers + int(add_layers[sp]), criterion, switches_normal=switches_normal, switches_reduce=switches_reduce, p=float(drop_rate[sp]))
# disc = nn.DataParallel(disc)
# disc = disc.cuda()
# logging.info("param size disc= %fMB", utils.count_parameters_in_MB(disc))
# network_params = []
# for k, v in disc.named_parameters():
# if not (k.endswith('alphas_normal') or k.endswith('alphas_reduce')):
# network_params.append(v)
# optimizer_disc = torch.optim.SGD(
# network_params,
# args.learning_rate,
# momentum=args.momentum,
# weight_decay=args.weight_decay)
# scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
# optimizer_disc, float(args.epochs), eta_min=args.learning_rate_min)
# for epoch in range(100):
# logging.info('Epoch: %d', epoch)
# epoch_start = time.time()
# train_acc, train_obj = train_gan(train_queue, valid_queue, gen, disc, network_params, criterion, adversarial_loss, optimizer_gen, optimizer_disc, 0, 0, 0, 0, train_arch=True)
# epoch_duration = time.time() - epoch_start
# logging.info('Epoch time: %ds', epoch_duration)
# # utils.save(disc, os.path.join(args.save, 'disc_dump.pt'))
# utils.save(gen, os.path.join(args.save, 'gen_dump.pt'))
for sp in range(len(num_to_keep)):
gen = Generator(100)
gen.cuda()
model = Resnet18()
model.cuda()
logging.info("param size gen= %fMB", utils.count_parameters_in_MB(gen))
logging.info("param size model= %fMB", utils.count_parameters_in_MB(model))
optimizer_gen = torch.optim.Adam(gen.parameters(), lr=args.lr,
betas=(args.b1, args.b2))
optimizer_model = torch.optim.SGD(model.parameters(), lr=0.1,
momentum=0.9, weight_decay=5e-4)
scheduler_model = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer_model, T_max=200)
sp = 0
disc = Network(args.init_channels + int(add_width[sp]), CIFAR_CLASSES, args.layers + int(add_layers[sp]), criterion, switches_normal=switches_normal, switches_reduce=switches_reduce, p=float(drop_rate[sp]))
disc = nn.DataParallel(disc)
disc = disc.cuda()
logging.info("param size disc= %fMB", utils.count_parameters_in_MB(disc))
network_params = []
for k, v in disc.named_parameters():
if not (k.endswith('alphas_normal') or k.endswith('alphas_reduce')):
network_params.append(v)
# optimizer_disc = torch.optim.SGD(
# network_params,
# args.learning_rate,
# momentum=args.momentum,
# weight_decay=args.weight_decay)
optimizer_disc = torch.optim.Adam(network_params, lr=args.lr,
betas=(args.b1, args.b2))
optimizer_a = torch.optim.Adam(disc.module.arch_parameters(),
lr=args.arch_learning_rate, betas=(0.5, 0.999), weight_decay=args.arch_weight_decay)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer_disc, float(args.epochs), eta_min=args.learning_rate_min)
sm_dim = -1
epochs = args.epochs
eps_no_arch = eps_no_archs[sp]
scale_factor = 0.2
# utils.load(disc, 'disc_dump.pt')
# utils.load(gen, os.path.join(args.save, 'gen_dump.pt'))
architect = Architect(gen, disc, model, network_params, criterion, adversarial_loss, CIFAR_CLASSES, args)
for epoch in range(100):
logging.info('Epoch: %d', epoch)
epoch_start = time.time()
train_acc, train_obj = train_gan(epoch, train_queue, valid_queue, gen, disc, network_params, criterion, adversarial_loss, optimizer_gen, optimizer_disc, 0, 0, 0, 0, train_arch=True)
epoch_duration = time.time() - epoch_start
logging.info('Epoch time: %ds', epoch_duration)
# for epoch in range(epochs):
for epoch in range(0):
scheduler.step()
scheduler_model.step()
lr_gen = args.lr
lr_disc = args.learning_rate
lr = scheduler.get_lr()[0]
lr_model = scheduler_model.get_lr()[0]
logging.info('Epoch: %d lr: %e lr_model: %e', epoch, lr, lr_model)
epoch_start = time.time()
# training
if epoch < eps_no_arch:
disc.module.p = float(drop_rate[sp]) * (epochs - epoch - 1) / epochs
disc.module.update_p()
train_acc, train_obj = train(train_queue, valid_queue, architect, gen, model, disc, network_params, criterion, adversarial_loss, optimizer_gen, optimizer_disc, optimizer_model, optimizer_a, lr, lr_model, lr_gen, lr_disc, train_arch=False)
else:
disc.module.p = float(drop_rate[sp]) * np.exp(-(epoch - eps_no_arch) * scale_factor)
disc.module.update_p()
train_acc, train_obj = train(train_queue, valid_queue, architect, gen, model, disc, network_params, criterion, adversarial_loss, optimizer_gen, optimizer_disc, optimizer_model, optimizer_a, lr, lr_model, lr_gen, lr_disc, train_arch=True)
logging.info('Train_acc %f', train_acc)
epoch_duration = time.time() - epoch_start
logging.info('Epoch time: %ds', epoch_duration)
# validation
if epochs - epoch < 5:
valid_acc, valid_obj = infer(valid_queue, model, criterion)
logging.info('Valid_acc %f', valid_acc)
utils.save(disc, os.path.join(args.save, 'disc.pt'))
utils.save(gen, os.path.join(args.save, 'gen.pt'))
utils.save(model, os.path.join(args.save, 'model.pt'))
print('------Dropping %d paths------' % num_to_drop[sp])
# Save switches info for s-c refinement.
if sp == len(num_to_keep) - 1:
switches_normal_2 = copy.deepcopy(switches_normal)
switches_reduce_2 = copy.deepcopy(switches_reduce)
# drop operations with low architecture weights
arch_param = disc.module.arch_parameters()
normal_prob = F.softmax(arch_param[0], dim=sm_dim).data.cpu().numpy()
for i in range(14):
idxs = []
for j in range(len(PRIMITIVES)):
if switches_normal[i][j]:
idxs.append(j)
if sp == len(num_to_keep) - 1:
# for the last stage, drop all Zero operations
drop = get_min_k_no_zero(normal_prob[i, :], idxs, num_to_drop[sp])
else:
drop = get_min_k(normal_prob[i, :], num_to_drop[sp])
for idx in drop:
switches_normal[i][idxs[idx]] = False
reduce_prob = F.softmax(arch_param[1], dim=-1).data.cpu().numpy()
for i in range(14):
idxs = []
for j in range(len(PRIMITIVES)):
if switches_reduce[i][j]:
idxs.append(j)
if sp == len(num_to_keep) - 1:
drop = get_min_k_no_zero(reduce_prob[i, :], idxs, num_to_drop[sp])
else:
drop = get_min_k(reduce_prob[i, :], num_to_drop[sp])
for idx in drop:
switches_reduce[i][idxs[idx]] = False
logging.info('switches_normal = %s', switches_normal)
logging_switches(switches_normal)
logging.info('switches_reduce = %s', switches_reduce)
logging_switches(switches_reduce)
if sp == len(num_to_keep) - 1:
arch_param = disc.module.arch_parameters()
normal_prob = F.softmax(arch_param[0], dim=sm_dim).data.cpu().numpy()
reduce_prob = F.softmax(arch_param[1], dim=sm_dim).data.cpu().numpy()
normal_final = [0 for idx in range(14)]
reduce_final = [0 for idx in range(14)]
# remove all Zero operations
for i in range(14):
if switches_normal_2[i][0] == True:
normal_prob[i][0] = 0
normal_final[i] = max(normal_prob[i])
if switches_reduce_2[i][0] == True:
reduce_prob[i][0] = 0
reduce_final[i] = max(reduce_prob[i])
# Generate Architecture, similar to DARTS
keep_normal = [0, 1]
keep_reduce = [0, 1]
n = 3
start = 2
for i in range(3):
end = start + n
tbsn = normal_final[start:end]
tbsr = reduce_final[start:end]
edge_n = sorted(range(n), key=lambda x: tbsn[x])
keep_normal.append(edge_n[-1] + start)
keep_normal.append(edge_n[-2] + start)
edge_r = sorted(range(n), key=lambda x: tbsr[x])
keep_reduce.append(edge_r[-1] + start)
keep_reduce.append(edge_r[-2] + start)
start = end
n = n + 1
# set switches according the ranking of arch parameters
for i in range(14):
if not i in keep_normal:
for j in range(len(PRIMITIVES)):
switches_normal[i][j] = False
if not i in keep_reduce:
for j in range(len(PRIMITIVES)):
switches_reduce[i][j] = False
# translate switches into genotype
genotype = parse_network(switches_normal, switches_reduce)
logging.info(genotype)
## restrict skipconnect (normal cell only)
logging.info('Restricting skipconnect...')
# generating genotypes with different numbers of skip-connect operations
for sks in range(0, 9):
max_sk = 8 - sks
num_sk = check_sk_number(switches_normal)
if not num_sk > max_sk:
continue
while num_sk > max_sk:
normal_prob = delete_min_sk_prob(switches_normal, switches_normal_2, normal_prob)
switches_normal = keep_1_on(switches_normal_2, normal_prob)
switches_normal = keep_2_branches(switches_normal, normal_prob)
num_sk = check_sk_number(switches_normal)
logging.info('Number of skip-connect: %d', max_sk)
genotype = parse_network(switches_normal, switches_reduce)
logging.info(genotype)
def main():
if not torch.cuda.is_available():
logging.info('No GPU device available')
sys.exit(1)
np.random.seed(args.seed)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled = True
torch.cuda.manual_seed(args.seed)
logging.info("args = %s", args)
logging.info("unparsed args = %s", unparsed)
num_gpus = torch.cuda.device_count()
genotype = eval("genotypes.%s" % args.arch)
print('---------Genotype---------')
logging.info(genotype)
print('--------------------------')
disc = Network(args.init_channels, CIFAR_CLASSES, args.layers,
args.auxiliary, genotype)
disc = torch.nn.DataParallel(disc)
disc = disc.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(disc))
adversarial_loss = nn.MSELoss()
adversarial_loss.cuda()
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
optimizer_disc = torch.optim.SGD(disc.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer_disc, float(args.epochs))
gen = Generator(100)
gen.cuda()
model = Resnet18()
model.cuda()
logging.info("param size gen= %fMB", utils.count_parameters_in_MB(gen))
logging.info("param size model= %fMB", utils.count_parameters_in_MB(model))
optimizer_gen = torch.optim.Adam(gen.parameters(),
lr=args.lr,
betas=(args.b1, args.b2))
optimizer_model = torch.optim.SGD(model.parameters(),
lr=0.1,
momentum=0.9,
weight_decay=5e-4)
scheduler_model = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer_model, T_max=200)
if args.cifar100:
train_transform, valid_transform = utils._data_transforms_cifar100(
args)
else:
train_transform, valid_transform = utils._data_transforms_cifar10(args)
if args.cifar100:
train_data = dset.CIFAR100(root=args.tmp_data_dir,
train=True,
download=True,
transform=train_transform)
valid_data = dset.CIFAR100(root=args.tmp_data_dir,
train=False,
download=True,
transform=valid_transform)
else:
train_data = dset.CIFAR10(root=args.tmp_data_dir,
train=True,
download=True,
transform=train_transform)
valid_data = dset.CIFAR10(root=args.tmp_data_dir,
train=False,
download=True,
transform=valid_transform)
train_queue = torch.utils.data.DataLoader(train_data,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True,
num_workers=args.workers)
valid_queue = torch.utils.data.DataLoader(valid_data,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=args.workers)
best_acc = 0.0
for epoch in range(args.epochs):
# scheduler_model.step()
# lr_gen = args.lr
# lr_disc = args.learning_rate
# lr_model = scheduler_model.get_lr()[0]
# logging.info('Epoch: %d lr_model %e', epoch, lr_model)
# disc.module.drop_path_prob = args.drop_path_prob * epoch / args.epochs
# disc.drop_path_prob = args.drop_path_prob * epoch / args.epochs
# start_time = time.time()
# train_acc, train_obj = train(train_queue, gen ,disc, model, criterion, adversarial_loss, optimizer_disc, optimizer_gen, optimizer_model)
# logging.info('Train_acc: %f', train_acc)
# valid_acc, valid_obj = infer(valid_queue, model, criterion)
# if valid_acc > best_acc:
# best_acc = valid_acc
# logging.info('Valid_acc: %f', valid_acc)
logging.info('Epoch: %d', epoch)
epoch_start = time.time()
train_acc, train_obj = train_gan(epoch,
train_queue,
valid_queue,
gen,
disc,
criterion,
adversarial_loss,
optimizer_gen,
optimizer_disc,
0,
0,
0,
0,
train_arch=True)
epoch_duration = time.time() - epoch_start
logging.info('Epoch time: %ds', epoch_duration)
end_time = time.time()
duration = end_time - start_time
print('Epoch time: %ds.' % duration)
def main_worker(gpu, ngpus_per_node, args):
if args.distributed:
if args.dist_url == "env://" and args.rank == -1:
args.rank = int(os.environ["RANK"])
if args.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
args.rank = args.rank * ngpus_per_node + gpu
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
world_size=args.world_size, rank=args.rank)
logging.info(("=> load data '{}'".format(args.dtype)))
if args.dtype == 'cifar10':
train_transform, valid_transform = utils._data_transforms_cifar10(args, cutout=True)
train_data = dset.CIFAR10(root=args.tmp_data_dir, train=True, download=True, transform=train_transform)
valid_data = dset.CIFAR10(root=args.tmp_data_dir, train=False, download=True, transform=valid_transform)
num_classes = 10
update_lrs = [150, 250, 350]
elif args.dtype == 'cifar100':
train_transform, valid_transform = utils._data_transforms_cifar100(args, cutout=True)
train_data = dset.CIFAR100(root=args.tmp_data_dir, train=True, download=True, transform=train_transform)
valid_data = dset.CIFAR100(root=args.tmp_data_dir, train=False, download=True, transform=valid_transform)
num_classes = 100
update_lrs = [40, 80, 160, 300]
else:
logging.info('no data type available')
sys.exit(1)
logging.info("update lrs: '{}'".format(update_lrs))
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size, shuffle=True, pin_memory=True, num_workers=args.workers)
valid_queue = torch.utils.data.DataLoader(
valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=args.workers)
logging.info(("=> creating model '{}'".format(args.arch)))
blocks_args, global_params = mixnet_builder.get_model_params(args.arch)
model = MixNet(input_size=32, num_classes=num_classes, blocks_args=blocks_args, global_params=global_params)
# print(model)
# exit(0)
logging.info("args = %s", args)
# logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
logging.info("param size = %fMB", model._num_params / 1e6)
# exit(0)
if args.distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model.cuda(args.gpu)
# When using a single GPU per process and per
# DistributedDataParallel, we need to divide the batch size
# ourselves based on the total number of GPUs we have
args.batch_size = int(args.batch_size / ngpus_per_node)
args.workers = int(args.workers / ngpus_per_node)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
else:
model.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
model = torch.nn.parallel.DistributedDataParallel(model)
elif args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
else:
# DataParallel will divide and allocate batch_size to all available GPUs
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
# model.make_cuda_and_parallel()
# model.avgpool = torch.nn.DataParallel(model.avgpool)
# model.classifier = torch.nn.DataParallel(model.classifier)
model = torch.nn.DataParallel(model)
model = model.cuda()
criterion = nn.CrossEntropyLoss().cuda()
if args.optim == 'adam':
optimizer = torch.optim.Adam(model.parameters(), args.lr, weight_decay=args.weight_decay)
elif args.optim == 'rmsprop':
optimizer = torch.optim.RMSprop(model.parameters(), lr=args.lr, momentum=args.momentum, eps=args.eps, weight_decay=args.weight_decay)
else:
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
cudnn.benchmark = True
# scaled_lr = args.lr * args.batch_size / 256
# optim = {
# "adam" : lambda : torch.optim.Adam(model.parameters()),
# "rmsprop" : lambda : torch.optim.RMSprop(model.parameters(), lr=scaled_lr, momentum=args.momentum, eps=args.eps, weight_decay=args.weight_decay)
# }[args.optim]()
# scheduler = get_scheduler(optim, args.scheduler, int(2.4*len(train_queue)), args.epochs * len(train_queue))
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs))
best_acc = 0.0
cur_lr = args.lr
for epoch in range(args.epochs):
scheduler.step()
logging.info('Epoch: %d lr %e', epoch, scheduler.get_lr()[0])
# cur_lr = adjust_learning_rate(optimizer, epoch, cur_lr, update_lrs)
# logging.info('Epoch: %d lr %e', epoch, cur_lr)
start_time = time.time()
train_acc, train_obj = train(train_queue, model, criterion, optimizer)
logging.info('Train_acc: %f', train_acc)
valid_acc, valid_obj = test(valid_queue, model, criterion)
if valid_acc > best_acc:
best_acc = valid_acc
logging.info('Valid_acc: %f', valid_acc)
end_time = time.time()
duration = end_time - start_time
print('Epoch time: %ds.' % duration )
utils.save(model, os.path.join(args.save, 'weights.pt'))
def main():
if not torch.cuda.is_available():
logging.info('No GPU device available')
sys.exit(1)
np.random.seed(args.seed)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled = True
torch.cuda.manual_seed(args.seed)
logging.info("args = %s", args)
# prepare dataset
if args.cifar100:
train_transform, valid_transform = utils._data_transforms_cifar100(
args)
else:
train_transform, valid_transform = utils._data_transforms_cifar10(args)
if args.cifar100:
train_data = dset.CIFAR100(root=args.tmp_data_dir,
train=True,
download=True,
transform=train_transform)
else:
train_data = dset.CIFAR10(root=args.tmp_data_dir,
train=True,
download=True,
transform=train_transform)
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True,
num_workers=args.workers)
valid_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[split:num_train]),
pin_memory=True,
num_workers=args.workers)
# build Network
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
switches = [] #switch开关，标记path中哪个op是开放状态
for i in range(14): # 一个Cell中有4个计算Node，2个输入Node，因此总的path为14
switches.append([True for j in range(len(PRIMITIVES))
]) #每个path上有len(PRIMITIVES)个op，初试状态是全部都用
switches_normal = copy.deepcopy(switches) #normal cell 中op的开关状态
switches_reduce = copy.deepcopy(switches) #reduce cell 中op的开关状态
# To be moved to args
num_to_keep = [5, 3, 1]
num_to_drop = [3, 2, 2]
if len(args.add_width) == 3: #默认参数为0
add_width = args.add_width
else:
add_width = [0, 0, 0]
if len(args.add_layers) == 3: #传进去两个args.add_layers
add_layers = args.add_layers
else:
add_layers = [0, 6, 12]
if len(args.dropout_rate) == 3: #传进去三个参数
drop_rate = args.dropout_rate
else:
drop_rate = [0.0, 0.0, 0.0]
eps_no_archs = [10, 10, 10]
for sp in range(len(num_to_keep)): #训练分为3个阶段
# args.init_channels 默认16，即网络的head输出channels为16，通过设置args.add_width可以改变这个输出channels
# args.layers 默认5，3个normal cell + 2个reduce cell，第二阶段layer为11，第三阶段为17，这通过add_layers调节
model = Network(args.init_channels + int(add_width[sp]),
CIFAR_CLASSES,
args.layers + int(add_layers[sp]),
criterion,
switches_normal=switches_normal,
switches_reduce=switches_reduce,
p=float(drop_rate[sp]))
model = nn.DataParallel(model)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
network_params = [] # 保存网络权重
for k, v in model.named_parameters():
if not (k.endswith('alphas_normal')
or k.endswith('alphas_reduce')):
network_params.append(v)
optimizer = torch.optim.SGD(
network_params, # 负责更新网络权重
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer_a = torch.optim.Adam(
model.module.arch_parameters(), # 负责更新网络架构参数
lr=args.arch_learning_rate,
betas=(0.5, 0.999),
weight_decay=args.arch_weight_decay)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs), eta_min=args.learning_rate_min)
sm_dim = -1
epochs = args.epochs
eps_no_arch = eps_no_archs[sp]
scale_factor = 0.2
for epoch in range(epochs): # 训练epoch，默认25，每个阶段训练25个epoch
scheduler.step()
lr = scheduler.get_lr()[0]
logging.info('Epoch: %d lr: %e', epoch, lr)
epoch_start = time.time()
# training
if epoch < eps_no_arch: #前eps_no_arch（10） 训练网络权重
model.module.p = float(
drop_rate[sp]) * (epochs - epoch - 1) / epochs
model.module.update_p()
train_acc, train_obj = train(train_queue,
valid_queue,
model,
network_params,
criterion,
optimizer,
optimizer_a,
lr,
train_arch=False)
else: # 训练网络架构参数
model.module.p = float(drop_rate[sp]) * np.exp(
-(epoch - eps_no_arch) * scale_factor)
model.module.update_p()
train_acc, train_obj = train(train_queue,
valid_queue,
model,
network_params,
criterion,
optimizer,
optimizer_a,
lr,
train_arch=True)
logging.info('Train_acc %f', train_acc)
epoch_duration = time.time() - epoch_start
logging.info('Epoch time: %ds', epoch_duration)
# validation
if epochs - epoch < 5: # epochs=25,即最后5个epoch，在验证集上验证下
valid_acc, valid_obj = infer(valid_queue, model, criterion)
logging.info('Valid_acc %f', valid_acc)
utils.save(model, os.path.join(args.save,
'weights.pt')) #一个阶段的训练结束后，保存下模型
# 一个阶段训练完后，丢弃一些Op
print('------Dropping %d paths------' %
num_to_drop[sp]) # num_to_drop=[3,2,2]，共有8个候选op，依次丢弃3，2，2，最后剩下一个
# Save switches info for s-c refinement.
if sp == len(num_to_keep
) - 1: # num_to_keep=[5,3,1], 只有在sp=2时，即最后一个训练阶段，才执行下面的语句
switches_normal_2 = copy.deepcopy(
switches_normal) #此时每条path中还有3个op，后面要删除掉2个，剩下最后一个
switches_reduce_2 = copy.deepcopy(switches_reduce)
# drop operations with low architecture weights
# 放弃拥有低概率的op
arch_param = model.module.arch_parameters() #获取结构参数
# 处理arch_normal
normal_prob = F.softmax(
arch_param[0],
dim=sm_dim).data.cpu().numpy() # 计算arch_normal的softmax
for i in range(14): #一个Cell中共有14条path
idxs = [] #记录每条path上选择的op的索引
for j in range(len(PRIMITIVES)): # 遍历每条path上的op
if switches_normal[i][j]: #如果为True，即选择它
idxs.append(j) #idxs中有3个元素
if sp == len(num_to_keep) - 1: # 最后一个训练阶段
# for the last stage, drop all Zero operations
# 对于最后一个训练阶段，丢弃所有 Zero 操作
drop = get_min_k_no_zero(
normal_prob[i, :], idxs,
num_to_drop[sp]) #最后一个阶段num_to_drop[2]=1
else:
drop = get_min_k(normal_prob[i, :], num_to_drop[sp])
for idx in drop:
switches_normal[i][
idxs[idx]] = False #将概率最低的k个op关闭，注意此处更新了switches_normal
# 处理arch_reduce
reduce_prob = F.softmax(arch_param[1], dim=-1).data.cpu().numpy()
for i in range(14):
idxs = []
for j in range(len(PRIMITIVES)):
if switches_reduce[i][j]:
idxs.append(j)
if sp == len(num_to_keep) - 1:
drop = get_min_k_no_zero(reduce_prob[i, :], idxs,
num_to_drop[sp])
else:
drop = get_min_k(reduce_prob[i, :], num_to_drop[sp])
for idx in drop:
switches_reduce[i][idxs[idx]] = False #注意此处更新了switches_reduce
logging.info('switches_normal = %s', switches_normal)
logging_switches(switches_normal)
logging.info('switches_reduce = %s', switches_reduce)
logging_switches(switches_reduce)
if sp == len(num_to_keep) - 1: #最后一个阶段
arch_param = model.module.arch_parameters()
normal_prob = F.softmax(arch_param[0],
dim=sm_dim).data.cpu().numpy() #计算各个op概率
reduce_prob = F.softmax(arch_param[1],
dim=sm_dim).data.cpu().numpy()
normal_final = [0 for idx in range(14)] #记录每条path上选择的op的索引
reduce_final = [0 for idx in range(14)]
# remove all Zero operations
for i in range(14):
if switches_normal_2[i][
0] == True: #如果Zero操作被选择了将其概率置为0，在最后一个阶段训练完成后，每条path还剩3个op
normal_prob[i][
0] = 0 #如果我们在第3阶段后，还有Zero operations，将其对应的概率置0
normal_final[i] = max(normal_prob[i]) #记录第i条path上选择的op的概率的最大值
if switches_reduce_2[i][0] == True:
reduce_prob[i][0] = 0
reduce_final[i] = max(reduce_prob[i])
# Generate Architecture, similar to DARTS
# 为每个计算节点选择两条输入path，由于0节点的输入是固定的，因此不用选
# 1Node候选path数3，2Node候选path数4，3Node候选path数5
keep_normal = [0, 1]
keep_reduce = [0, 1]
n = 3
start = 2
for i in range(3):
end = start + n
tbsn = normal_final[start:end]
tbsr = reduce_final[start:end]
edge_n = sorted(range(n),
key=lambda x: tbsn[x]) #从候选的path中选择两条
keep_normal.append(edge_n[-1] + start)
keep_normal.append(edge_n[-2] + start)
edge_r = sorted(range(n), key=lambda x: tbsr[x])
keep_reduce.append(edge_r[-1] + start)
keep_reduce.append(edge_r[-2] + start)
start = end
n = n + 1
# set switches according the ranking of arch parameters
# 设置switches，对于没有选择的path，将其上的op全部关掉
for i in range(14):
if not i in keep_normal:
for j in range(len(PRIMITIVES)):
switches_normal[i][j] = False
if not i in keep_reduce:
for j in range(len(PRIMITIVES)):
switches_reduce[i][j] = False
# translate switches into genotype
genotype = parse_network(switches_normal, switches_reduce)
logging.info(genotype)
## restrict skipconnect (normal cell only) 约束跳跃链接
logging.info('Restricting skipconnect...')
# generating genotypes with different numbers of skip-connect operations
# 生成不同数量skip-connect operations 的基因
for sks in range(0, 9):
max_sk = 8 - sks
num_sk = check_sk_number(switches_normal)
if not num_sk > max_sk:
continue
while num_sk > max_sk: #删除多余的skip-connection
normal_prob = delete_min_sk_prob(switches_normal,
switches_normal_2,
normal_prob)
switches_normal = keep_1_on(switches_normal_2, normal_prob)
switches_normal = keep_2_branches(switches_normal,
normal_prob)
num_sk = check_sk_number(switches_normal)
logging.info('Number of skip-connect: %d', max_sk)
genotype = parse_network(switches_normal, switches_reduce)
logging.info(genotype)
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
np.random.seed(args.seed)
if not args.is_parallel:
torch.cuda.set_device(int(args.gpu))
logging.info('gpu device = %d' % int(args.gpu))
else:
logging.info('gpu device = %s' % args.gpu)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled = True
torch.cuda.manual_seed(args.seed)
logging.info("args = %s", args)
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
if args.is_cifar100:
model = Network(args.init_channels, CIFAR100_CLASSES, args.layers,
criterion)
model1 = Network(args.init_channels, CIFAR100_CLASSES, args.layers,
criterion)
# important for initializing the two models differently.
# model1.init_weights()
else:
model = Network(args.init_channels, CIFAR_CLASSES, args.layers,
criterion)
model1 = Network(args.init_channels, CIFAR_CLASSES, args.layers,
criterion)
# model1.init_weights()
model = model.cuda()
model1 = model1.cuda()
logging.info("param size of model1 = %fMB",
utils.count_parameters_in_MB(model))
logging.info("param size of model2 = %fMB",
utils.count_parameters_in_MB(model1))
# if args.is_parallel:
# # import ipdb; ipdb.set_trace()
# gpus = [int(i) for i in args.gpu.split(',')]
# model = nn.parallel.DataParallel(
# model, device_ids=gpus, output_device=gpus[0])
# model = model.module
optimizer = torch.optim.SGD(model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer1 = torch.optim.SGD(model1.parameters(),
args.learning_rate1,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.is_cifar100:
train_transform, valid_transform = utils._data_transforms_cifar100(
args)
else:
train_transform, valid_transform = utils._data_transforms_cifar10(args)
if args.is_cifar100:
train_data = dset.CIFAR100(root=args.data,
train=True,
download=True,
transform=train_transform)
else:
train_data = dset.CIFAR10(root=args.data,
train=True,
download=True,
transform=train_transform)
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=False,
num_workers=4)
external_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=False,
num_workers=4)
valid_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[split:num_train]),
pin_memory=False,
num_workers=4)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs), eta_min=args.learning_rate_min)
scheduler1 = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer1, float(args.epochs), eta_min=args.learning_rate_min)
architect = Architect(model, model1, args)
for epoch in range(args.epochs):
lr = scheduler.get_lr()[0]
lr1 = scheduler1.get_lr()[0]
logging.info('epoch %d lr %e lr1 %e', epoch, lr, lr1)
genotype = model.genotype()
genotype1 = model1.genotype()
logging.info('genotype1 = %s', genotype)
logging.info('genotype2 = %s', genotype1)
print(F.softmax(model.alphas_normal, dim=-1))
print(F.softmax(model.alphas_reduce, dim=-1))
print(F.softmax(model1.alphas_normal, dim=-1))
print(F.softmax(model1.alphas_reduce, dim=-1))
# training
train_acc, train_obj, train_acc1, train_obj1 = train(
train_queue, valid_queue, external_queue, model, model1, architect,
criterion, optimizer, optimizer1, lr, lr1)
logging.info('train_acc %f train_acc1 %f', train_acc, train_acc1)
scheduler.step()
scheduler1.step()
# validation
valid_acc, valid_obj, valid_acc1, valid_obj1 = infer(
valid_queue, model, model1, criterion)
logging.info('valid_acc %f valid_acc1 %f', valid_acc, valid_acc1)
utils.save(model, os.path.join(args.save, 'weights.pt'))
utils.save(model1, os.path.join(args.save, 'weights1.pt'))
def __init__(self,
test_args: Namespace,
my_dataset: MyDataset,
model: nn.Module = None):
self.__device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
log_format = '%(asctime)s %(message)s'
logging.basicConfig(stream=sys.stdout,
level=logging.INFO,
format=log_format,
datefmt='%m/%d %I:%M:%S %p')
np.random.seed(test_args.seed)
torch.manual_seed(test_args.seed)
cudnn.benchmark = True
cudnn.enabled = True
logging.info(f'gpu device = {test_args.gpu}')
logging.info(f'args = {test_args}')
if model is None:
# equal to: genotype = genotypes.DARTS_v2
if not (test_args.arch or test_args.arch_path):
logging.info('need to designate arch.')
sys.exit(1)
genotype = eval(
f'genotypes.{test_args.arch}'
) if not test_args.arch_path else utils.load_genotype(
test_args.arch_path)
print('Load genotype:', genotype)
if my_dataset is MyDataset.CIFAR10:
model = NetworkCIFAR(test_args.init_ch, 10, test_args.layers,
test_args.auxiliary,
genotype).to(self.__device)
elif my_dataset is MyDataset.CIFAR100:
model = NetworkCIFAR(test_args.init_ch, 100, test_args.layers,
test_args.auxiliary,
genotype).to(self.__device)
elif my_dataset is MyDataset.ImageNet:
model = NetworkImageNet(test_args.init_ch, 1000,
test_args.layers, test_args.auxiliary,
genotype).to(self.__device)
else:
raise Exception('No match MyDataset')
utils.load(model, test_args.model_path, False)
model = model.to(self.__device)
param_size = utils.count_parameters_in_MB(model)
logging.info(f'param size = {param_size}MB')
model.drop_path_prob = test_args.drop_path_prob
self.__model = model
self.__args = test_args
self.__criterion = nn.CrossEntropyLoss().to(self.__device)
if my_dataset is MyDataset.CIFAR10:
_, test_transform = utils._data_transforms_cifar10(test_args)
test_data = dset.CIFAR10(root=test_args.data,
train=False,
download=True,
transform=test_transform)
elif my_dataset is MyDataset.CIFAR100:
_, test_transform = utils._data_transforms_cifar100(test_args)
test_data = dset.CIFAR100(root=test_args.data,
train=False,
download=True,
transform=test_transform)
elif my_dataset is MyDataset.ImageNet:
validdir = test_args.data / 'val'
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
valid_data = dset.ImageFolder(
validdir,
transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
]))
test_data = valid_data
else:
raise Exception('No match MyDataset')
self.__test_queue = torch.utils.data.DataLoader(
test_data,
batch_size=test_args.batchsz,
shuffle=False,
pin_memory=True,
num_workers=4)
def model_compress(args):
if os.path.isdir(args.save) == False:
os.makedirs(args.save)
save_dir = '{}compress-{}-{}'.format(args.save, args.note,
time.strftime("%Y%m%d-%H%M%S"))
utils.create_exp_dir(save_dir, scripts_to_save=glob.glob('*.py'))
log_format = '%(asctime)s %(message)s'
logging.basicConfig(stream=sys.stdout,
level=logging.INFO,
format=log_format,
datefmt='%m/%d %I:%M:%S %p')
fh = logging.FileHandler(os.path.join(save_dir, 'log.txt'))
fh.setFormatter(logging.Formatter(log_format))
logging.getLogger().addHandler(fh)
if args.cifar100:
CIFAR_CLASSES = 100
data_folder = 'cifar-100-python'
else:
CIFAR_CLASSES = 10
data_folder = 'cifar-10-batches-py'
if not torch.cuda.is_available():
logging.info('No GPU device available')
sys.exit(1)
np.random.seed(args.seed)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled = True
torch.cuda.manual_seed(args.seed)
logging.info("args = %s", args)
# prepare dataset
if args.cifar100:
train_transform, valid_transform = utils._data_transforms_cifar100(
args)
else:
train_transform, valid_transform = utils._data_transforms_cifar10(args)
if args.cifar100:
train_data = dset.CIFAR100(root=args.train_data_dir,
train=True,
download=True,
transform=train_transform)
else:
train_data = dset.CIFAR10(root=args.train_data_dir,
train=True,
download=True,
transform=train_transform)
num_train = len(train_data)
iter_per_one_epoch = num_train // (2 * args.batch_size)
if iter_per_one_epoch >= 100:
train_extend_rate = 1
else:
train_extend_rate = (100 // iter_per_one_epoch) + 1
iter_per_one_epoch = iter_per_one_epoch * train_extend_rate
logging.info('num original train data: %d', num_train)
logging.info('iter per one epoch: %d', iter_per_one_epoch)
indices = list(range(num_train))
random.shuffle(indices)
split = int(np.floor(args.train_portion * num_train))
train_set = torch.utils.data.Subset(train_data, indices[:split])
valid_set = torch.utils.data.Subset(train_data, indices[split:num_train])
train_set = torch.utils.data.ConcatDataset([train_set] * train_extend_rate)
# valid_set = torch.utils.data.ConcatDataset([valid_set]*train_extend_rate)
train_queue = torch.utils.data.DataLoader(
train_set,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.RandomSampler(train_set),
pin_memory=True,
num_workers=args.workers)
valid_queue = torch.utils.data.DataLoader(
valid_set,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.RandomSampler(valid_set),
pin_memory=True,
num_workers=args.workers)
# build Network
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
eps_no_arch = args.eps_no_archs
epochs = args.epochs
if args.arch in genotypes.__dict__.keys():
genotype = eval("genotypes.%s" % args.arch)
else:
genotype = eval(args.arch)
model = Network(genotype,
args.init_channels,
CIFAR_CLASSES,
args.layers,
criterion,
steps=args.inter_nodes,
multiplier=args.inter_nodes,
stem_multiplier=args.stem_multiplier,
residual_connection=args.residual_connection)
model = nn.DataParallel(model)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
network_params = []
for k, v in model.named_parameters():
if not (k.endswith('alphas_normal') or k.endswith('alphas_reduce')):
network_params.append(v)
optimizer = torch.optim.SGD(network_params,
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer_a = torch.optim.Adam(model.module.arch_parameters(),
lr=args.arch_learning_rate,
betas=(0.5, 0.999),
weight_decay=args.arch_weight_decay)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(epochs), eta_min=args.learning_rate_min)
scheduler_a = torch.optim.lr_scheduler.StepLR(optimizer_a, 30, gamma=0.2)
train_epoch_record = -1
arch_train_count = 0
prev_geno = ''
prev_rank = None
rank_geno = None
result_geno = None
arch_stable = 0
best_arch_stable = 0
for epoch in range(epochs):
lr = scheduler.get_lr()[0]
logging.info('Epoch: %d lr: %e', epoch, lr)
epoch_start = time.time()
# training
if epoch < eps_no_arch:
train_acc, train_obj = train(train_queue,
valid_queue,
model,
network_params,
criterion,
optimizer,
optimizer_a,
lr,
train_arch=False)
else:
ops, probs = compressing_parse(model)
concat = range(2, 2 + model.module._steps)
genotype = Genotype(
normal=ops[0],
normal_concat=concat,
reduce=ops[1],
reduce_concat=concat,
)
if str(prev_geno) != str(genotype):
prev_geno = genotype
logging.info(genotype)
# early stopping
stable_cond = True
rank = []
for i in range(len(probs)):
rank_tmp = ranking(probs[i])
rank.append(rank_tmp)
if prev_rank != rank:
stable_cond = False
arch_stable = 0
prev_rank = rank
rank_geno = genotype
logging.info('rank: %s', rank)
if stable_cond:
arch_stable += 1
if arch_stable > best_arch_stable:
best_arch_stable = arch_stable
result_geno = rank_geno
logging.info('arch_stable: %d', arch_stable)
logging.info('best genotype: %s', rank_geno)
if arch_stable >= args.stable_arch - 1:
logging.info('stable genotype: %s', rank_geno)
result_geno = rank_geno
break
train_acc, train_obj = train(train_queue,
valid_queue,
model,
network_params,
criterion,
optimizer,
optimizer_a,
lr,
train_arch=True)
arch_train_count += 1
scheduler_a.step()
scheduler.step()
logging.info('Train_acc %f, Objs: %e', train_acc, train_obj)
epoch_duration = time.time() - epoch_start
logging.info('Epoch time: %ds', epoch_duration)
# validation
if epoch >= eps_no_arch:
valid_acc, valid_obj = infer(valid_queue, model, criterion)
logging.info('Valid_acc %f, Objs: %e', valid_acc, valid_obj)
# # early arch training
# if train_epoch_record == -1:
# if train_acc > 70:
# arch_train_num = args.epochs - args.eps_no_archs
# eps_no_arch = 0
# train_epoch_record = epoch
# else:
# if epoch >= train_epoch_record + arch_train_num:
# break
utils.save(model, os.path.join(save_dir, 'weights.pt'))
# last geno parser
ops, probs = compressing_parse(model)
concat = range(2, 2 + model.module._steps)
genotype = Genotype(
normal=ops[0],
normal_concat=concat,
reduce=ops[1],
reduce_concat=concat,
)
logging.info('Last geno: %s', genotype)
if result_geno == None:
result_geno = genotype
return result_geno, best_arch_stable
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
np.random.seed(args.seed)
torch.cuda.set_device(args.gpu)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled = True
torch.cuda.manual_seed(args.seed)
logging.info('gpu device = %d' % args.gpu)
logging.info("args = %s", args)
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
arch1, alphas_normal1, alphas_reduce1,\
betas_normal1, betas_reduce1 = initialize_alphas()
arch2, alphas_normal2, alphas_reduce2,\
betas_normal2, betas_reduce2 = initialize_alphas()
model = Network(args.init_channels, CIFAR_CLASSES, args.layers, criterion)
model = model.cuda()
model1 = Network(args.init_channels, CIFAR_CLASSES, args.layers, criterion)
model1 = model1.cuda()
# model for pretraining.
model_pretrain = Network(args.init_channels, CIFAR_CLASSES, args.layers,
criterion)
model_pretrain = model_pretrain.cuda()
model1_pretrain = Network(args.init_channels, CIFAR_CLASSES, args.layers,
criterion)
model1_pretrain = model1_pretrain.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
model._arch_parameters = arch1
model1._arch_parameters = arch2
model.alphas_reduce = alphas_reduce1
model.alphas_normal = alphas_normal1
model1.alphas_reduce = alphas_reduce2
model1.alphas_normal = alphas_normal2
model.betas_reduce = betas_reduce1
model.betas_normal = betas_normal1
model1.betas_reduce = betas_reduce2
model1.betas_normal = betas_normal2
model_pretrain._arch_parameters = arch1
model1_pretrain._arch_parameters = arch2
model_pretrain.alphas_reduce = alphas_reduce1
model_pretrain.alphas_normal = alphas_normal1
model1_pretrain.alphas_reduce = alphas_reduce2
model1_pretrain.alphas_normal = alphas_normal2
model_pretrain.betas_reduce = betas_reduce1
model_pretrain.betas_normal = betas_normal1
model1_pretrain.betas_reduce = betas_reduce2
model1_pretrain.betas_normal = betas_normal2
optimizer = torch.optim.SGD(model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer1 = torch.optim.SGD(model1.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer_pretrain = torch.optim.SGD(model_pretrain.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer1_pretrain = torch.optim.SGD(model1_pretrain.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.set == 'cifar100':
train_transform, valid_transform = utils._data_transforms_cifar100(
args)
train_data = dset.CIFAR100(root=args.data,
train=True,
download=True,
transform=train_transform)
else:
train_transform, valid_transform = utils._data_transforms_cifar10(args)
train_data = dset.CIFAR10(root=args.data,
train=True,
download=True,
transform=train_transform)
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True,
num_workers=2)
external_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True,
num_workers=2)
valid_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[split:num_train]),
pin_memory=True,
num_workers=2)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs), eta_min=args.learning_rate_min)
scheduler1 = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer1, float(args.epochs), eta_min=args.learning_rate_min)
scheduler_pretrain = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer_pretrain,
float(args.epochs + args.pretrain_steps),
eta_min=args.learning_rate_min)
scheduler1_pretrain = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer1_pretrain,
float(args.epochs + args.pretrain_steps),
eta_min=args.learning_rate_min)
architect = Architect(model, model1, args)
for epoch in range(args.epochs + args.pretrain_steps):
lr = scheduler.get_lr()[0]
lr1 = scheduler1.get_lr()[0]
lr_pretrain = scheduler_pretrain.get_lr()[0]
lr1_pretrain = scheduler1_pretrain.get_lr()[0]
logging.info('epoch %d lr %e lr1 %e lr_pretrain %e lr1_pretrain %e',
epoch, lr, lr1, lr_pretrain, lr1_pretrain)
if epoch >= args.pretrain_steps:
genotype = model.genotype()
genotype1 = model1.genotype()
logging.info('genotype1 = %s', genotype)
logging.info('genotype2 = %s', genotype1)
print(F.softmax(model.alphas_normal, dim=-1))
print(F.softmax(model.alphas_reduce, dim=-1))
print(F.softmax(model1.alphas_normal, dim=-1))
print(F.softmax(model1.alphas_reduce, dim=-1))
# training
train_acc, train_obj, train_acc1, train_obj1 = train(
args, epoch, train_queue, valid_queue, external_queue, model,
model1, model_pretrain, model1_pretrain, architect, criterion,
optimizer, optimizer1, optimizer_pretrain, optimizer1_pretrain, lr,
lr1, lr_pretrain, lr1_pretrain)
if epoch >= args.pretrain_steps:
logging.info('train_acc %f train_acc1 %f', train_acc, train_acc1)
else:
logging.info('pretrain_acc %f pretrain_acc1 %f', train_acc,
train_acc1)
if epoch >= args.pretrain_steps:
scheduler_pretrain.step()
scheduler1_pretrain.step()
scheduler.step()
scheduler1.step()
else:
scheduler_pretrain.step()
scheduler1_pretrain.step()
# validation
if epoch >= args.pretrain_steps and (args.epochs +
args.pretrain_steps) - epoch <= 1:
valid_acc, valid_obj, valid_acc1, valid_obj1 = infer(
valid_queue, model, model1, criterion)
logging.info('valid_acc %f valid_acc1 %f', valid_acc, valid_acc1)
utils.save(model, os.path.join(args.save, 'weights.pt'))
utils.save(model1, os.path.join(args.save, 'weights1.pt'))
def main():
if not torch.cuda.is_available():
logging.info('No GPU device available')
sys.exit(1)
seed = args.seed
logging.info('Using the random seed of %d for searching...' % seed)
np.random.seed(seed)
cudnn.benchmark = True
torch.manual_seed(seed)
cudnn.enabled = True
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
logging.info("args = %s", args)
num_gpus = torch.cuda.device_count()
logging.info('Training with %d GPU(s)', num_gpus)
# build Network
# default as ResNet20 since the constrain of GPU memory when doing search process
resnet_types = {
'resnet20': 3,
'resnet32': 5,
'resnet44': 7,
'resnet56': 9,
'resnet110': 18
}
n_sizes = resnet_types[args.net_type]
logging.info('Number of attentional residual block(s): %s', n_sizes * 3)
model = att_resnet_cifar(n_size=n_sizes,
no_gpus=num_gpus,
num_classes=CIFAR_CLASSES)
if num_gpus > 1:
model = nn.DataParallel(model)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
# prepare dataset
if args.cifar100:
train_transform, valid_transform = utils._data_transforms_cifar100(
args)
else:
train_transform, valid_transform = utils._data_transforms_cifar10(args)
if args.cifar100:
train_data = dset.CIFAR100(root=args.tmp_data_dir,
train=True,
download=True,
transform=train_transform)
else:
train_data = dset.CIFAR10(root=args.tmp_data_dir,
train=True,
download=True,
transform=train_transform)
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True,
num_workers=args.workers)
valid_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[split:num_train]),
pin_memory=True,
num_workers=args.workers)
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
if num_gpus > 1:
optimizer = torch.optim.SGD(model.module.net_parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
architect = Architect_m(model, args)
else:
optimizer = torch.optim.SGD(model.net_parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
architect = Architect_s(model, args)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs), eta_min=args.learning_rate_min)
epochs = args.epochs
scale_factor = 0.19
BEST_accVal = 0.0
for epoch in range(epochs):
scheduler.step()
lr = scheduler.get_lr()[0]
logging.info('Epoch: %d lr: %e', epoch, lr)
epoch_start = time.time()
# training
if args.dropout_rate > 0.:
drop_rate = args.dropout_rate * np.exp(-epoch * scale_factor)
if num_gpus > 1:
model.module.update_p(drop_rate)
else:
model.update_p(drop_rate)
train_acc, train_obj = train(train_queue, valid_queue, model,
architect, criterion, optimizer, lr,
num_gpus)
logging.info('Train_acc %f', train_acc)
epoch_duration = time.time() - epoch_start
logging.info('Epoch time: %ds', epoch_duration)
# validation
if epochs - epoch < 10:
valid_acc, valid_obj = infer(valid_queue, model, criterion)
logging.info('Valid_acc %f', valid_acc)
if valid_acc > BEST_accVal:
BEST_accVal = valid_acc
utils.save(model, os.path.join(args.save, 'weights.pt'))
logging.info('BEST VALID ACCURACY IS: %f', BEST_accVal)
if num_gpus > 1:
genotype = model.module.genotype()
else:
genotype = model.genotype()
logging.info(
'______________________________________________\nFinal genotype = %s',
genotype)
with open('{}/result.txt'.format(args.save), 'w') as file:
file.write(str(genotype))
logging.info('____________________END_______________________')
def main():
if not torch.cuda.is_available():
logging.info('No GPU device available')
sys.exit(1)
np.random.seed(args.seed)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled = True
torch.cuda.manual_seed(args.seed)
# torch.autograd.set_detect_anomaly(True)
logging.info("args = %s", args)
# prepare dataset
if args.cifar100:
train_transform, test_transform = utils._data_transforms_cifar100(args)
else:
train_transform, test_transform = utils._data_transforms_cifar10(args)
if args.cifar100:
train_data = dset.CIFAR100(root=args.tmp_data_dir,
train=True,
download=True,
transform=train_transform)
else:
train_data = dset.CIFAR10(root=args.tmp_data_dir,
train=True,
download=True,
transform=train_transform)
test_data = dset.CIFAR10(root=args.tmp_data_dir,
train=False,
download=True,
transform=test_transform)
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True,
num_workers=args.workers)
valid_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[split:num_train]),
pin_memory=True,
num_workers=args.workers)
test_queue = torch.utils.data.DataLoader(test_data,
batch_size=args.batch_size,
pin_memory=True,
num_workers=args.workers)
# build Network
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
path_num = sum(1 for i in range(args.nodes) for n in range(2 + i))
switches = []
for i in range(path_num):
switches.append([True for j in range(len(PRIMITIVES))])
if args.drop_none:
switches[i][0] = False # switch off zero operator
if args.drop_skip:
switches[i][3] = False # switch off identity operator
switches_normal = copy.deepcopy(switches)
switches_reduce = copy.deepcopy(switches)
# To be moved to args
num_to_keep = [5, 3, 1]
num_to_drop = [2, 2, 2]
if len(args.add_width) == 3:
add_width = args.add_width
else:
add_width = [0, 0, 0]
if len(args.add_layers) == 3:
add_layers = args.add_layers
else:
add_layers = [0, 6, 12]
if len(args.dropout_rate) == 3:
drop_rate = args.dropout_rate
else:
drop_rate = [0.0, 0.0, 0.0]
eps_no_archs = [10, 10, 10]
for sp in range(len(num_to_keep)):
# if sp == len(num_to_keep)-1: # switch on zero operator in the last stage
# for i in range(path_num):
# switches_normal[i][0]=True
# for i in range(path_num):
# switches_reduce[i][0]=True
model = Network(args.init_channels + int(add_width[sp]),
CIFAR_CLASSES,
args.layers + int(add_layers[sp]),
criterion,
steps=args.nodes,
multiplier=args.nodes,
switches_normal=switches_normal,
switches_reduce=switches_reduce,
p=float(drop_rate[sp]))
model = nn.DataParallel(model)
# print(model)
# if sp==0:
# utils.save(model, os.path.join(args.save, 'cell_weights.pt')) # keep initial weights
# else:
# utils.load(model.module.cells, os.path.join(args.save, 'cell_weights.pt')) # strict=False
# print('copying weight....')
# state_dict = torch.load(os.path.join(args.save, 'cell_weights.pt'))
# for key in state_dict.keys():
# print(key)
# for key in state_dict.keys():
# if 'm_ops' in key and 'op0' not in key:
# s = re.split('op\d', key)
# copy_key = s[0]+'op0'+s[1]
# state_dict[key] = state_dict[copy_key]
# print(key)
# model.load_state_dict(state_dict)
# print('done!')
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
network_params = []
arch_params = []
for k, v in model.named_parameters():
if 'alpha' in k:
print(k)
arch_params.append(v)
else:
network_params.append(v)
# if not (k.endswith('alphas_normal_source') or k.endswith('alphas_reduce')):
# network_params.append(v)
optimizer = torch.optim.SGD(network_params,
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer_a = torch.optim.Adam(arch_params,
lr=args.arch_learning_rate,
betas=(0.5, 0.999),
weight_decay=args.arch_weight_decay)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs), eta_min=args.learning_rate_min)
sm_dim = -1
epochs = args.epochs
eps_no_arch = eps_no_archs[sp]
scale_factor = 0.2
for epoch in range(epochs): #epochs
scheduler.step()
lr = scheduler.get_lr()[0] #args.learning_rate#
logging.info('Epoch: %d lr: %e', epoch, lr)
epoch_start = time.time()
# training
if epoch < eps_no_arch:
model.module.p = float(
drop_rate[sp]) * (epochs - epoch - 1) / epochs
model.module.update_p()
train_acc, train_obj = train(train_queue,
valid_queue,
model,
network_params,
criterion,
optimizer,
optimizer_a,
lr,
train_arch=False,
train_weight=True)
elif epoch < epochs:
model.module.p = float(drop_rate[sp]) * np.exp(
-(epoch - eps_no_arch) * scale_factor)
model.module.update_p()
train_acc, train_obj = train(train_queue,
valid_queue,
model,
network_params,
criterion,
optimizer,
optimizer_a,
lr,
train_arch=True,
train_weight=True)
else: # train arch only
train_acc, train_obj = train(train_queue,
valid_queue,
model,
network_params,
criterion,
optimizer,
optimizer_a,
lr,
train_arch=True,
train_weight=False)
logging.info('Train_acc %f', train_acc)
epoch_duration = time.time() - epoch_start
logging.info('Epoch time: %ds', epoch_duration)
# validation
# if epochs - epoch < 5:
valid_acc, valid_obj = infer(valid_queue, model, criterion)
logging.info('Valid_acc %f', valid_acc)
test_acc, test_obj = infer(test_queue, model, criterion)
logging.info('Test_acc %f', test_acc)
utils.save(model, os.path.join(args.save, 'weights.pt'))
print('------Dropping %d paths------' % num_to_drop[sp])
# Save switches info for s-c refinement.
if sp == len(num_to_keep) - 1:
switches_normal_2 = copy.deepcopy(switches_normal)
switches_reduce_2 = copy.deepcopy(switches_reduce)
# drop operations with low architecture weights
arch_param = model.module.arch_parameters()
# n = 3
# start = 2
# weightsn2 = F.softmax(arch_param[2][0:2], dim=-1)
# weightsr2 = F.softmax(arch_param[3][0:2], dim=-1)
weightsn2 = F.sigmoid(arch_param[2])
weightsr2 = F.sigmoid(arch_param[3])
# for i in range(args.nodes-1):
# end = start + n
# tn2 = F.softmax(arch_param[2][start:end], dim=-1)
# tr2 = F.softmax(arch_param[3][start:end], dim=-1)
# start = end
# n += 1
# weightsn2 = torch.cat([weightsn2, tn2],dim=0)
# weightsr2 = torch.cat([weightsr2, tr2],dim=0)
weightsn2 = weightsn2.data.cpu().numpy()
weightsr2 = weightsr2.data.cpu().numpy()
normal_prob = F.softmax(arch_param[0], dim=sm_dim).data.cpu().numpy()
for i in range(path_num):
normal_prob[i] = normal_prob[i] * weightsn2[i]
idxs = []
for j in range(len(PRIMITIVES)):
if switches_normal[i][j]:
idxs.append(j)
if sp == len(num_to_keep) - 1:
# for the last stage, drop all Zero operations
drop = get_min_k_no_zero(normal_prob[i, :], idxs,
num_to_drop[sp])
else:
drop = get_min_k(normal_prob[i, :], num_to_drop[sp])
for idx in drop:
switches_normal[i][idxs[idx]] = False
reduce_prob = F.softmax(arch_param[1], dim=-1).data.cpu().numpy()
for i in range(path_num):
reduce_prob[i] = reduce_prob[i] * weightsr2[i]
idxs = []
for j in range(len(PRIMITIVES)):
if switches_reduce[i][j]:
idxs.append(j)
if sp == len(num_to_keep) - 1:
drop = get_min_k_no_zero(reduce_prob[i, :], idxs,
num_to_drop[sp])
else:
drop = get_min_k(reduce_prob[i, :], num_to_drop[sp])
for idx in drop:
switches_reduce[i][idxs[idx]] = False
logging.info('switches_normal = %s', switches_normal)
logging_switches(switches_normal)
logging.info('switches_reduce = %s', switches_reduce)
logging_switches(switches_reduce)
if sp == len(num_to_keep) - 1:
# n = 3
# start = 2
# weightsn2 = F.softmax(arch_param[2][0:2], dim=-1)
# weightsr2 = F.softmax(arch_param[3][0:2], dim=-1)
weightsn2 = F.sigmoid(arch_param[2])
weightsr2 = F.sigmoid(arch_param[3])
# for i in range(args.nodes-1):
# end = start + n
# tn2 = F.softmax(arch_param[2][start:end], dim=-1)
# tr2 = F.softmax(arch_param[3][start:end], dim=-1)
# start = end
# n += 1
# weightsn2 = torch.cat([weightsn2, tn2],dim=0)
# weightsr2 = torch.cat([weightsr2, tr2],dim=0)
weightsn2 = weightsn2.data.cpu().numpy()
weightsr2 = weightsr2.data.cpu().numpy()
arch_param = model.module.arch_parameters()
normal_prob = F.softmax(arch_param[0],
dim=sm_dim).data.cpu().numpy()
reduce_prob = F.softmax(arch_param[1],
dim=sm_dim).data.cpu().numpy()
normal_final = [0 for idx in range(path_num)]
reduce_final = [0 for idx in range(path_num)]
# remove all Zero operations
for i in range(path_num):
normal_prob[i] = normal_prob[i] * weightsn2[i]
if switches_normal_2[i][0] == True:
normal_prob[i][0] = 0
normal_final[i] = max(normal_prob[i])
reduce_prob[i] = reduce_prob[i] * weightsr2[i]
if switches_reduce_2[i][0] == True:
reduce_prob[i][0] = 0
reduce_final[i] = max(reduce_prob[i])
# Generate Architecture, similar to DARTS
keep_normal = [0, 1]
keep_reduce = [0, 1]
n = 3
start = 2
for i in range(args.nodes - 1):
end = start + n
tbsn = normal_final[start:end]
tbsr = reduce_final[start:end]
edge_n = sorted(range(n), key=lambda x: tbsn[x])
keep_normal.append(edge_n[-1] + start)
keep_normal.append(edge_n[-2] + start)
edge_r = sorted(range(n), key=lambda x: tbsr[x])
keep_reduce.append(edge_r[-1] + start)
keep_reduce.append(edge_r[-2] + start)
start = end
n = n + 1
# set switches according the ranking of arch parameters
for i in range(path_num):
if not i in keep_normal:
for j in range(len(PRIMITIVES)):
switches_normal[i][j] = False
if not i in keep_reduce:
for j in range(len(PRIMITIVES)):
switches_reduce[i][j] = False
# translate switches into genotype
genotype = parse_network(switches_normal,
switches_reduce,
steps=args.nodes)
logging.info(genotype)
## restrict skipconnect (normal cell only)
logging.info('Restricting skipconnect...')
# generating genotypes with different numbers of skip-connect operations
for sks in range(0, 9):
max_sk = 8 - sks
num_sk = check_sk_number(switches_normal)
if not num_sk > max_sk:
continue
while num_sk > max_sk:
normal_prob = delete_min_sk_prob(switches_normal,
switches_normal_2,
normal_prob)
switches_normal = keep_1_on(switches_normal_2, normal_prob)
switches_normal = keep_2_branches(switches_normal,
normal_prob)
num_sk = check_sk_number(switches_normal)
logging.info('Number of skip-connect: %d', max_sk)
genotype = parse_network(switches_normal,
switches_reduce,
steps=args.nodes)
logging.info(genotype)
def main():
if not torch.cuda.is_available():
logging.info('No GPU device available')
sys.exit(1)
np.random.seed(args.seed)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled = True
torch.cuda.manual_seed(args.seed)
logging.info("args = %s", args)
logging.info("unparsed args = %s", unparsed)
num_gpus = torch.cuda.device_count()
genotype = eval("genotypes.%s" % args.arch)
print('---------Genotype---------')
logging.info(genotype)
print('--------------------------')
model = Network(args.init_channels, CIFAR_CLASSES, args.layers,
args.auxiliary, genotype)
model = torch.nn.DataParallel(model)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
optimizer = torch.optim.SGD(model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.cifar100:
train_transform, valid_transform = utils._data_transforms_cifar100(
args)
else:
train_transform, valid_transform = utils._data_transforms_cifar10(args)
if args.cifar100:
train_data = dset.CIFAR100(root=args.tmp_data_dir,
train=True,
download=False,
transform=train_transform)
valid_data = dset.CIFAR100(root=args.tmp_data_dir,
train=False,
download=False,
transform=valid_transform)
else:
train_data = dset.CIFAR10(root=args.tmp_data_dir,
train=True,
download=False,
transform=train_transform)
valid_data = dset.CIFAR10(root=args.tmp_data_dir,
train=False,
download=False,
transform=valid_transform)
train_queue = torch.utils.data.DataLoader(train_data,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True,
num_workers=args.workers)
valid_queue = torch.utils.data.DataLoader(valid_data,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=args.workers)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs))
best_acc = 0.0
for epoch in range(args.epochs):
scheduler.step()
logging.info('Epoch: %d lr %e', epoch, scheduler.get_lr()[0])
model.module.drop_path_prob = args.drop_path_prob * epoch / args.epochs
model.drop_path_prob = args.drop_path_prob * epoch / args.epochs
start_time = time.time()
train_acc, train_obj = train(train_queue, model, criterion, optimizer)
logging.info('Train_acc: %f', train_acc)
valid_acc, valid_obj = infer(valid_queue, model, criterion)
if valid_acc > best_acc:
best_acc = valid_acc
utils.save(model.module, os.path.join(args.save, 'weights.pt'))
logging.info('Valid_acc: %f', valid_acc)
logging.info('best_acc:%f', best_acc)
end_time = time.time()
duration = end_time - start_time
print('Epoch time: %ds.' % duration)
def main():
if not torch.cuda.is_available():
logging.info('No GPU device available')
sys.exit(1)
cudnn.enabled = True
logging.info("args = %s", args)
num_gpus = torch.cuda.device_count()
genotype = eval("genotypes.%s" % args.arch)
print('---------Genotype---------')
logging.info(genotype)
print('--------------------------')
resnet_types = {
'resnet20': 3,
'resnet32': 5,
'resnet44': 7,
'resnet56': 9,
'resnet110': 18
}
n_sizes = resnet_types[args.net_type]
logging.info('Number of attentional residual block(s): %s', n_sizes * 3)
model = att_resnet_cifar(genotype,
n_size=n_sizes,
num_classes=CIFAR_CLASSES)
if num_gpus > 1:
model = torch.nn.DataParallel(model)
model = model.cuda()
try:
utils.load(model, args.model_path)
except:
model = model.module
utils.load(model, args.model_path)
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
if args.cifar100:
_, test_transform = utils._data_transforms_cifar100(args)
else:
_, test_transform = utils._data_transforms_cifar10(args)
if args.cifar100:
test_data = dset.CIFAR100(root=args.tmp_data_dir,
train=False,
download=True,
transform=test_transform)
else:
test_data = dset.CIFAR10(root=args.tmp_data_dir,
train=False,
download=True,
transform=test_transform)
test_queue = torch.utils.data.DataLoader(test_data,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=2)
if num_gpus > 1:
model.module._block.drop_path_prob = 0.
else:
model._block.drop_path_prob = 0.
test_acc, test_obj = infer(test_queue, model, criterion)
logging.info('TEST ACCURACY: --- %f% ---', test_acc)
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
np.random.seed(args.seed)
torch.cuda.set_device(args.gpu)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled = True
torch.cuda.manual_seed(args.seed)
logging.info('gpu device = %d' % args.gpu)
logging.info("args = %s", args)
genotype = eval("genotypes.%s" % args.arch)
logging.info('genotype = %s', genotype)
model = Network(args.init_channels, CIFAR_CLASSES, args.layers, args.auxiliary, genotype)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
optimizer = torch.optim.SGD(
model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay
)
# train_transform, valid_transform = utils._data_transforms_cifar10(args)
#
# train_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=train_transform)
# valid_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=valid_transform)
"""From https://github.com/chenxin061/pdarts/"""
if args.cifar100:
train_transform, valid_transform = utils._data_transforms_cifar100(args)
else:
train_transform, valid_transform = utils._data_transforms_cifar10(args)
if args.cifar100:
train_data = dset.CIFAR100(root=args.data, train=True, download=True, transform=train_transform)
valid_data = dset.CIFAR100(root=args.data, train=False, download=True, transform=valid_transform)
else:
train_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=train_transform)
valid_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=valid_transform)
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size, shuffle=True, pin_memory=True, num_workers=2)
valid_queue = torch.utils.data.DataLoader(
valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=2)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs))
for epoch in range(args.epochs):
scheduler.step()
logging.info('epoch %d lr %e', epoch, scheduler.get_lr()[0])
model.drop_path_prob = args.drop_path_prob * epoch / args.epochs
train_acc, train_obj = train(train_queue, model, criterion, optimizer)
logging.info('train_acc %f', train_acc)
valid_acc, valid_obj = infer(valid_queue, model, criterion)
logging.info('valid_acc %f', valid_acc)
utils.save(model, os.path.join(args.save, 'weights.pt'))
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
np.random.seed(args.seed)
torch.cuda.set_device(args.gpu)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled = True
torch.cuda.manual_seed(args.seed)
logging.info('gpu device = %d' % args.gpu)
logging.info("args = %s", args)
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
model = Network(args.init_channels, CIFAR_CLASSES, args.layers, criterion)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
optimizer = torch.optim.SGD(
model.parameters(), ####### 这里指定用SGD对模型的权重训练
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
# prepare dataset
if args.cifar100:
train_transform, valid_transform = utils._data_transforms_cifar100(
args)
else:
train_transform, valid_transform = utils._data_transforms_cifar10(args)
if args.cifar100:
train_data = dset.CIFAR100(root=args.data,
train=True,
download=True,
transform=train_transform)
else:
train_data = dset.CIFAR10(root=args.data,
train=True,
download=False,
transform=train_transform)
num_train = len(train_data)
indices = list(range(num_train))
split = int(
np.floor(args.train_portion *
num_train)) # 用train set 训练参数时，根据train_portion划分训练集与验证集
train_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[:split]), # 训练集的指针 from 0 到split点
pin_memory=True,
num_workers=4)
valid_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[split:num_train]), # 验证集的指针 from split点 到数据集最后点
pin_memory=True,
num_workers=4)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs), eta_min=args.learning_rate_min)
architect = Architect(model, args)
for epoch in range(args.epochs):
lr = scheduler.get_last_lr()[0]
logging.info('epoch %d lr %e', epoch, lr)
# training
train_acc, train_obj = train(train_queue, valid_queue, model,
architect, criterion, optimizer, lr)
logging.info('train_acc %f', train_acc)
genotype = model.genotype(
) # 读取当前epoch的结构参数并log保存，自己从log中读取保存的参数写入genotype中开始train_from the scratch
logging.info('genotype = %s', genotype)
print(F.softmax(model.alphas_normal, dim=-1))
print(F.softmax(model.alphas_reduce, dim=-1))
# validation
valid_acc, valid_obj = infer(valid_queue, model, criterion)
logging.info('valid_acc %f', valid_acc)
utils.save(model, os.path.join(args.save, 'weights.pt'))
scheduler.step()