def main():
"""Main pipleine for image similarity using deep ranking."""
# Instantiate the parser
parser = argparse.ArgumentParser()
parser.add_argument(
'--dataroot',
type=str,
default="/home/colin/Github/Computer-Vision/data/tiny-imagenet-200",
help='train/val data root')
parser.add_argument('--batch_size_train',
type=int,
default=30,
help='training set input batch size')
parser.add_argument('--batch_size_test',
type=int,
default=1,
help='test set input batch size')
parser.add_argument('--is_gpu',
type=bool,
default=True,
help='whether training using GPU')
# parse the arguments
args = parser.parse_args()
# load triplet dataset
trainloader, testloader = TinyImageNetLoader(args.dataroot,
args.batch_size_train,
args.batch_size_test)
# calculate test accuracy
calculate_accuracy(trainloader, testloader, args.is_gpu)
def main():
"""Main pipleine for image similarity using deep ranking."""
# Instantiate the parser
parser = argparse.ArgumentParser()
parser.add_argument('--dataroot',
type=str,
default="",
help='train/val data root')
parser.add_argument('--batch_size_train',
type=int,
default=25,
help='training set input batch size')
parser.add_argument('--batch_size_test',
type=int,
default=1,
help='test set input batch size')
parser.add_argument('--is_gpu',
type=bool,
default=True,
help='whether training using GPU')
parser.add_argument('--predict_similar_images',
type=str,
default="",
help='path to query image')
parser.add_argument('--predict_top_N',
type=int,
default=5,
help='Search top N images')
# parse the arguments
args = parser.parse_args()
# load triplet dataset
trainloader, testloader = TinyImageNetLoader(args.dataroot,
args.batch_size_train,
args.batch_size_test)
training_images = load_train_images()
embedding_space = load_train_embedding()
if args.predict_similar_images != "":
predict_unseen(args.predict_similar_images, args.predict_top_N,
training_images, embedding_space, args.is_gpu)
else:
# calculate test accuracy
calculate_accuracy(training_images, embedding_space, testloader,
args.is_gpu)
def main():
"""Main pipeline of Image Similarity using Deep Ranking."""
net = TripletNet(resnet101())
# For training on GPU, we need to transfer net and data onto the GPU
# http://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#training-on-gpu
if args.is_gpu:
print("==> Initialize CUDA support for TripletNet model ...")
net = torch.nn.DataParallel(net).cuda()
cudnn.benchmark = True
# resume training from the last time
if args.resume:
# Load checkpoint
print('==> Resuming training from checkpoint ...')
checkpoint = torch.load(args.ckptroot)
args.start_epoch = checkpoint['epoch']
net.load_state_dict(checkpoint['state_dict'])
print("==> Loaded checkpoint '{}' (epoch {})".format(
args.ckptroot, checkpoint['epoch']))
else:
# start over
print('==> Building new TripletNet model ...')
net = TripletNet(resnet101())
# Loss function, optimizer and scheduler
criterion = nn.TripletMarginLoss(margin=args.g, p=args.p)
optimizer = torch.optim.SGD(net.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=args.nesterov)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode='min',
factor=0.1,
patience=10,
verbose=True)
# load triplet dataset
trainloader, testloader = TinyImageNetLoader(args.dataroot,
args.batch_size_train,
args.batch_size_test)
# train model
train(net, criterion, optimizer, scheduler, trainloader, testloader,
args.start_epoch, args.epochs, args.is_gpu)
def main():
"""Main pipleine for image similarity using deep ranking."""
# Instantiate the parser
parser = argparse.ArgumentParser()
parser.add_argument('--dataroot',
type=str,
default="",
help='train/val data root')
parser.add_argument('--3',
type=int,
default=25,
help='training set input batch size')
parser.add_argument('--3',
type=int,
default=3,
help='test set input batch size')
parser.add_argument('--is_gpu',
type=bool,
default=True,
help='whether training using GPU')
parser.add_argument(
'D:/academic/homework&assignment/senior design/data/edgedetection/data2sampler22/l1/r.jpg',
type=str,
default="",
help='path to query image')
parser.add_argument('2', type=int, default=5, help='Search top N images')
# parse the arguments
args = parser.parse_args()
# load triplet dataset
trainloader, testloader = TinyImageNetLoader(args.dataroot,
args.batch_size_train,
args.batch_size_test)
training_images = load_train_images()
embedding_space = load_train_embedding()
predict_unseen(args.predict_similar_images, args.predict_top_N,
training_images, embedding_space, args.is_gpu)