def main():
manual_seed = 1
np.random.seed(manual_seed)
torch.manual_seed(manual_seed)
torch.cuda.manual_seed_all(manual_seed)
torch.backends.cudnn.benchmark = True
create_directories_from_list(
[CONFIG_ARCH['logging']['path_to_tensorboard_logs']])
logger = get_logger(CONFIG_ARCH['logging']['path_to_log_file'])
writer = SummaryWriter(
log_dir=CONFIG_ARCH['logging']['path_to_tensorboard_logs'])
#### DataLoading
train_loader = get_loaders(1.0, CONFIG_ARCH['dataloading']['batch_size'],
CONFIG_ARCH['dataloading']['path_to_save_data'],
logger)
valid_loader = get_test_loader(
CONFIG_ARCH['dataloading']['batch_size'],
CONFIG_ARCH['dataloading']['path_to_save_data'])
#### Model
arch = args.architecture_name
model = fbnet_builder.get_model(arch, cnt_classes=10).cuda()
model = model.apply(weights_init)
model = nn.DataParallel(model, [0])
print(model)
#### Loss and Optimizer
optimizer = torch.optim.SGD(
filter(lambda p: p.requires_grad, model.parameters()),
lr=CONFIG_ARCH['optimizer']['lr'],
momentum=CONFIG_ARCH['optimizer']['momentum'],
weight_decay=CONFIG_ARCH['optimizer']['weight_decay'])
criterion = nn.CrossEntropyLoss().cuda()
#### Scheduler
if CONFIG_ARCH['train_settings']['scheduler'] == 'MultiStepLR':
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=CONFIG_ARCH['train_settings']['milestones'],
gamma=CONFIG_ARCH['train_settings']['lr_decay'])
elif CONFIG_ARCH['train_settings']['scheduler'] == 'CosineAnnealingLR':
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer,
T_max=CONFIG_ARCH['train_settings']['cnt_epochs'],
eta_min=0.001,
last_epoch=-1)
else:
logger.info(
"Please, specify scheduler in architecture_functions/config_for_arch"
)
#### Training Loop
trainer = TrainerArch(criterion, optimizer, scheduler, logger, writer)
trainer.train_loop(train_loader, valid_loader, model)
def fbneta_cifar():
model = fbnet_builder.get_model('fbnet_a', cnt_classes=10).cuda()
return model
def main():
manual_seed = 1
np.random.seed(manual_seed)
torch.manual_seed(manual_seed)
torch.cuda.manual_seed_all(manual_seed)
torch.backends.cudnn.benchmark = True
create_directories_from_list(
[CONFIG_ARCH['logging']['path_to_tensorboard_logs']])
logger = get_logger(CONFIG_ARCH['logging']['path_to_log_file'])
writer = SummaryWriter(
log_dir=CONFIG_ARCH['logging']['path_to_tensorboard_logs'])
#### DataLoading
train_loader = get_loaders(1.0, CONFIG_ARCH['dataloading']['batch_size'],
CONFIG_ARCH['dataloading']['path_to_save_data'],
logger)
valid_loader = get_test_loader(
CONFIG_ARCH['dataloading']['batch_size'],
CONFIG_ARCH['dataloading']['path_to_save_data'])
#### Model
arch = args.architecture_name
model = fbnet_builder.get_model(arch, cnt_classes=10).cuda()
#model.load_state_dict(torch.load("architecture_functions/logs/best_model.pth", map_location=torch.device('cuda')))
#checkpoint = torch.load("architecture_functions/logs/best_model.pth", map_location=torch.device('cuda'))
#state_dict = torch.load("architecture_functions/logs/best_model.pth", map_location="cuda")
state_dict = torch.load(
"/home/oza/pre-experiment/speeding/FBNet/architecture_functions/logs/best_model.pth",
map_location="cuda")
#state_dict = torch.load("/home/oza/pre-experiment/speeding/test_dist/logs/test_FBnetA0826/best.pth.tar")['state_dict']
#state_dict = torch.load("/home/oza/pre-experiment/speeding/testFB/FBNet/architecture_functions/logs/fbnet_a/best_model.pth", map_location="cuda")
#state_dict = torch.load("/home/oza/pre-experiment/speeding/testFB/distiller/examples/classifier_compression/logs/2020.08.29-035310/best.pth.tar", map_location="cuda")['state_dict']
#state_dict = torch.load("/home/oza/pre-experiment/speeding/testFB/FBNet/architecture_functions/logs/best_model.pth", map_location="cuda")
#state_dict = torch.load("/home/oza/pre-experiment/speeding/testFB/FBNet/architecture_functions/logs/fbnet_a/best_model.pth", map_location="cuda")
if "model_ema" in state_dict and state_dict["model_ema"] is not None:
state_dict = state_dict["model_ema"]
ret = {}
for name, val in state_dict.items():
if name.startswith("module."):
name = name[len("module."):]
#print(name)
ret[name] = val
# ToTensor：画像のグレースケール化（RGBの0~255を0~1の範囲に正規化）、Normalize：Z値化（RGBの平均と標準偏差を0.5で決め打ちして正規化）
#transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
CIFAR_MEAN = [0.49139968, 0.48215827, 0.44653124]
CIFAR_STD = [0.2023, 0.1994, 0.2010]
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(CIFAR_MEAN, CIFAR_STD)])
# トレーニングデータをダウンロード
trainset = torchvision.datasets.CIFAR10(root='./data',
train=True,
download=True,
transform=transform)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=4,
shuffle=True,
num_workers=2)
# テストデータをダウンロード
#testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
#testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=True, num_workers=2)
test_data = datasets.CIFAR10(root='./data',
train=False,
download=True,
transform=transform)
testloader = torch.utils.data.DataLoader(test_data,
batch_size=4,
shuffle=False,
num_workers=16)
model.load_state_dict(ret)
model.eval()
correct = 0
total = 0
topk = (1, )
'''
for data in testloader:
images, labels = data
images = images.to('cuda')
labels = labels.to('cuda')
outputs = model(images)
maxk = max(topk)
_, pred = outputs.topk(maxk, 1, True, True)
pred = pred.t()
# one-hot case
if labels.ndimension() > 1:
labels = labels.max(1)[1]
correct = pred.eq(labels.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(1.0 / 10000))
print(res)
'''
i = 0
with torch.no_grad():
for data in testloader:
images, labels = data
images = images.to('cuda')
labels = labels.to('cuda')
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
#print(total)
#print(correct)
i += 1
print(i)
print(total)
print('Accuracy of the network on the 10000 test images: %d %%' %
(100 * correct / total))
quit()
model = model.apply(weights_init)
model = nn.DataParallel(model, [0])
#### Loss and Optimizer
optimizer = torch.optim.SGD(
filter(lambda p: p.requires_grad, model.parameters()),
lr=CONFIG_ARCH['optimizer']['lr'],
momentum=CONFIG_ARCH['optimizer']['momentum'],
weight_decay=CONFIG_ARCH['optimizer']['weight_decay'])
criterion = nn.CrossEntropyLoss().cuda()
#### Scheduler
if CONFIG_ARCH['train_settings']['scheduler'] == 'MultiStepLR':
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=CONFIG_ARCH['train_settings']['milestones'],
gamma=CONFIG_ARCH['train_settings']['lr_decay'])
elif CONFIG_ARCH['train_settings']['scheduler'] == 'CosineAnnealingLR':
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer,
T_max=CONFIG_ARCH['train_settings']['cnt_epochs'],
eta_min=0.001,
last_epoch=-1)
else:
logger.info(
"Please, specify scheduler in architecture_functions/config_for_arch"
)
#### Training Loop
trainer = TrainerArch(criterion, optimizer, scheduler, logger, writer)
trainer.train_loop(train_loader, valid_loader, model)
def fbneta_cifar():
model = fbnet_builder.get_model('fbnet_a', cnt_classes=10).to(device)
return model
