return pred + 1
def show_result(pred, image):
"""
Show input image with a result of the test function
"""
fig = plt.figure()
title = 'predict: ' + str(pred)
fig1 = fig.add_subplot(1, 1, 1)
fig1.set_title(title)
fig1.axis("off")
plt.imshow(image)
fig.tight_layout()
plt.show()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_name = 'efficientnet-b0'
image_size = EfficientNet.get_image_size(model_name)
model = EfficientNet.from_pretrained(model_name, num_classes=15)
model = model.to(device)
model.load_state_dict(torch.load('best.pt', map_location=device))
image = Image.open('sample.png').convert(
'RGB') # directory setting. Change sample.png to path of an input image
pred = test(image)
show_result(pred, image)
def main():
"""
--------------------------------------------- MAIN --------------------------------------------------------
Instantiates the model plus loss function and defines the dataloaders for several datasets including some
data augmentation.
Defines the grid for a grid search on lambda_max_divrs and initial_centroid_value_multipliers which both
have a big influence on the sparsity (and respectively accuracy) of the resulting ternary networks.
Starts grid search.
"""
# Manual seed for reproducibility
torch.manual_seed(363636)
# Global instances
global args, use_cuda, device
# Instantiating the parser
args = parser.parse_args()
# Global CUDA flag
use_cuda = args.cuda and torch.cuda.is_available()
# Defining device and device's map locationo
device = torch.device("cuda" if use_cuda else "cpu")
print('chosen device: ', device)
# Building the model
if args.model == 'cifar_micronet':
print('Building MicroNet for CIFAR-100 with depth multiplier {} and width multiplier {} ...'.format(
args.dw_multps[0] ** args.phi, args.dw_multps[1] ** args.phi))
model = micronet(args.dw_multps[0] ** args.phi, args.dw_multps[1] ** args.phi)
elif args.model == 'imagenet_micronet':
print('Building MicroNet for ImageNet with depth multiplier {} and width multiplier {} ...'.format(
args.dw_multps[0] ** args.phi, args.dw_multps[1] ** args.phi))
model = image_micronet(args.dw_multps[0] ** args.phi, args.dw_multps[1] ** args.phi)
elif args.model == 'efficientnet-b1':
print('Building EfficientNet-B1 ...')
model = EfficientNet.efficientnet_b1()
elif args.model == 'efficientnet-b2':
print('Building EfficientNet-B2 ...')
model = EfficientNet.efficientnet_b2()
elif args.model == 'efficientnet-b3':
print('Building EfficientNet-B3 ...')
model = EfficientNet.efficientnet_b3()
elif args.model == 'efficientnet-b4':
print('Building EfficientNet-B4 ...')
model = EfficientNet.efficientnet_b4()
for name, param in model.named_parameters():
print('\n', name)
# Transfers model to device (GPU/CPU).
model.to(device)
# Defining loss function and printing CUDA information (if available)
if use_cuda:
print("PyTorch version: ")
print(torch.__version__)
print("CUDA Version: ")
print(torch.version.cuda)
print("cuDNN version is: ")
print(cudnn.version())
cudnn.benchmark = True
loss_fct = nn.CrossEntropyLoss().cuda()
else:
loss_fct = nn.CrossEntropyLoss()
# Dataloaders for CIFAR, ImageNet and MNIST
if args.dataset == 'CIFAR100':
print('Loading CIFAR-100 data ...')
normalize = transforms.Normalize(mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
std=[x / 255.0 for x in [63.0, 62.1, 66.7]])
kwargs = {'num_workers': args.workers, 'pin_memory': True} if use_cuda else {}
train_loader = torch.utils.data.DataLoader(
datasets.CIFAR100(root=args.data_path, train=True, transform=transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, 4),
transforms.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3, hue=0.075),
transforms.ToTensor(),
normalize,
Cutout(n_holes=1, length=16),
]), download=True),
batch_size=args.batch_size, shuffle=True, **kwargs)
val_loader = torch.utils.data.DataLoader(
datasets.CIFAR100(root=args.data_path, train=False, transform=transforms.Compose([
transforms.ToTensor(),
normalize,
])),
batch_size=args.val_batch_size, shuffle=False, **kwargs)
elif args.dataset == 'ImageNet':
print('Loading ImageNet data ...')
traindir = os.path.join(args.data_path, 'train')
valdir = os.path.join(args.data_path, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = datasets.ImageFolder(
traindir,
transforms.Compose([
transforms.RandomResizedCrop(args.image_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
if model.__class__.__name__ == 'EfficientNet' or 'efficientnet' in str(args.model):
image_size = EfficientNet.get_image_size(args.model)
else:
image_size = args.image_size
val_dataset = datasets.ImageFolder(
valdir,
transforms.Compose([
transforms.Resize(image_size, interpolation=PIL.Image.BICUBIC),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
normalize,
]))
val_loader = torch.utils.data.DataLoader(
val_dataset, batch_size=args.val_batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
elif args.dataset == 'MNIST':
kwargs = {'num_workers': args.workers, 'pin_memory': True} if use_cuda else {}
train_loader = torch.utils.data.DataLoader(
datasets.MNIST(args.data_path, train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.batch_size, shuffle=True, **kwargs)
val_loader = torch.utils.data.DataLoader(
datasets.MNIST(args.data_path, train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.val_batch_size, shuffle=True, **kwargs)
elif args.dataset == 'CIFAR10':
normalize = transforms.Normalize(mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
std=[x / 255.0 for x in [63.0, 62.1, 66.7]])
kwargs = {'num_workers': args.workers, 'pin_memory': True} if use_cuda else {}
train_loader = torch.utils.data.DataLoader(
datasets.CIFAR10(root=args.data_path, train=True, transform=transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, 4),
transforms.ToTensor(),
normalize,
]), download=True),
batch_size=args.batch_size, shuffle=True, **kwargs)
val_loader = torch.utils.data.DataLoader(
datasets.CIFAR10(root=args.data_path, train=False, transform=transforms.Compose([
transforms.ToTensor(),
normalize,
])),
batch_size=args.val_batch_size, shuffle=False, **kwargs)
else:
raise NotImplementedError('Undefined dataset name %s' % args.dataset)
# Gridsearch on dividers for lambda_max and initial cluster center values
for initial_c_divr in args.ini_c_divrs:
for lambda_max_divr in args.lambda_max_divrs:
print('lambda_max_divr: {}, initial_c_divr: {}'.format(lambda_max_divr, initial_c_divr))
logfile = open('./model_quantization/logfiles/logfile.txt', 'a+')
logfile.write('lambda_max_divr: {}, initial_c_divr: {}'.format(lambda_max_divr, initial_c_divr))
grid_search(train_loader, val_loader, model, loss_fct, lambda_max_divr, initial_c_divr)
def main():
# Manual seed for reproducibility
torch.manual_seed(363636)
# Global instances
global args, use_cuda, device
# Instantiating the parser
args = parser.parse_args()
# Global CUDA flag
use_cuda = args.cuda and torch.cuda.is_available()
# Defining device and device's map locationo
device = torch.device("cuda" if use_cuda else "cpu")
print('chosen device: ', device)
# Building the model
if args.model == 'cifar_micronet':
print(
'Building MicroNet for CIFAR with depth multiplier {} and width multiplier {} ...'
.format(args.dw_multps[0]**args.phi, args.dw_multps[1]**args.phi))
if args.dataset == 'CIFAR100':
num_classes = 100
elif args.dataset == 'CIFAR10':
num_classes = 10
model = micronet(args.dw_multps[0]**args.phi,
args.dw_multps[1]**args.phi, num_classes)
elif args.model == 'image_micronet':
print(
'Building MicroNet for ImageNet with depth multiplier {} and width multiplier {} ...'
.format(args.dw_multps[0]**args.phi, args.dw_multps[1]**args.phi))
model = image_micronet(args.dw_multps[0]**args.phi,
args.dw_multps[1]**args.phi)
elif args.model == 'efficientnet-b1':
print('Building EfficientNet-B1 ...')
model = EfficientNet.efficientnet_b1()
elif args.model == 'efficientnet-b2':
print('Building EfficientNet-B2 ...')
model = EfficientNet.efficientnet_b2()
elif args.model == 'efficientnet-b3':
print('Building EfficientNet-B3 ...')
model = EfficientNet.efficientnet_b3()
elif args.model == 'efficientnet-b4':
print('Building EfficientNet-B4 ...')
model = EfficientNet.efficientnet_b4()
elif args.model == 'lenet-5':
print(
'Building LeNet-5 with depth multiplier {} and width multiplier {} ...'
.format(args.dw_multps[0]**args.phi, args.dw_multps[1]**args.phi))
model = lenet5(d_multiplier=args.dw_multps[0]**args.phi,
w_multiplier=args.dw_multps[1]**args.phi)
for name, param in model.named_parameters():
print('\n', name)
# Transfers model to device (GPU/CPU).
model.to(device)
# Defining loss function and printing CUDA information (if available)
if use_cuda:
print("PyTorch version: ")
print(torch.__version__)
print("CUDA Version: ")
print(torch.version.cuda)
print("cuDNN version is: ")
print(cudnn.version())
cudnn.benchmark = True
loss_fct = nn.CrossEntropyLoss().cuda()
else:
loss_fct = nn.CrossEntropyLoss()
# Dataloaders for CIFAR, ImageNet and MNIST
if args.dataset == 'CIFAR100':
print('Loading CIFAR-100 data ...')
normalize = transforms.Normalize(
mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
std=[x / 255.0 for x in [63.0, 62.1, 66.7]])
kwargs = {
'num_workers': args.workers,
'pin_memory': True
} if use_cuda else {}
train_loader = torch.utils.data.DataLoader(datasets.CIFAR100(
root=args.data_path,
train=True,
transform=transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, 4),
transforms.ColorJitter(brightness=0.3,
contrast=0.3,
saturation=0.3,
hue=0.075),
transforms.ToTensor(),
normalize,
Cutout(n_holes=1, length=16),
]),
download=True),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
val_loader = torch.utils.data.DataLoader(
datasets.CIFAR100(root=args.data_path,
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
normalize,
])),
batch_size=args.val_batch_size,
shuffle=False,
**kwargs)
elif args.dataset == 'ImageNet':
print('Loading ImageNet data ...')
traindir = os.path.join(args.data_path, 'train')
valdir = os.path.join(args.data_path, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = datasets.ImageFolder(
traindir,
transforms.Compose([
transforms.RandomResizedCrop(args.image_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
if model.__class__.__name__ == 'EfficientNet' or 'efficientnet' in str(
args.model):
image_size = EfficientNet.get_image_size(args.model)
else:
image_size = args.image_size
val_dataset = datasets.ImageFolder(
valdir,
transforms.Compose([
transforms.Resize(image_size, interpolation=PIL.Image.BICUBIC),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
normalize,
]))
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.val_batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
elif args.dataset == 'MNIST':
kwargs = {
'num_workers': args.workers,
'pin_memory': True
} if use_cuda else {}
train_loader = torch.utils.data.DataLoader(datasets.MNIST(
args.data_path,
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
val_loader = torch.utils.data.DataLoader(
datasets.MNIST(args.data_path,
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=args.val_batch_size,
shuffle=True,
**kwargs)
elif args.dataset == 'CIFAR10':
print('Loading CIFAR-10 data ...')
normalize = transforms.Normalize(
mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
std=[x / 255.0 for x in [63.0, 62.1, 66.7]])
kwargs = {
'num_workers': args.workers,
'pin_memory': True
} if use_cuda else {}
train_loader = torch.utils.data.DataLoader(datasets.CIFAR10(
root=args.data_path,
train=True,
transform=transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, 4),
transforms.ColorJitter(brightness=0.3,
contrast=0.3,
saturation=0.3,
hue=0.075),
transforms.ToTensor(),
normalize,
Cutout(n_holes=1, length=16),
]),
download=True),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
val_loader = torch.utils.data.DataLoader(
datasets.CIFAR10(root=args.data_path,
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
normalize,
])),
batch_size=args.val_batch_size,
shuffle=False,
**kwargs)
else:
raise NotImplementedError('Undefined dataset name %s' % args.dataset)
train_w_frozen_assignment(train_loader, val_loader, model, loss_fct)