def build_weibull(mean, distance, tail):
weibull_model = {}
for i in range(len(mean)):
weibull_model[label[i]] = {}
weibull = weibull_tailfitting(mean[i], distance[i], tailsize=tail)
weibull_model[label[i]] = weibull
return weibull_model
def main(train_args):
# Setting network architecture.
if (conv_name == 'fcnwideresnet50'):
net = FCNWideResNet50(4, num_classes=args['num_classes'], pretrained=False, skip=True, hidden_classes=hidden).cuda(args['device'])
elif (conv_name == 'fcndensenet121'):
net = FCNDenseNet121(4, num_classes=args['num_classes'], pretrained=False, skip=True, hidden_classes=hidden).cuda(args['device'])
print('Loading pretrained weights from file "' + pretrained_path + '"')
net.load_state_dict(torch.load(pretrained_path))
print(net)
train_args['best_record'] = {'epoch': 0, 'lr': 1e-4, 'val_loss': 1e10, 'acc': 0, 'acc_cls': 0, 'iou': 0}
# Setting datasets.
val_set = list_dataset.ListDataset(dataset_name, 'Validate', (train_args['h_size'], train_args['w_size']), 'statistical', hidden, overlap=False, use_dsm=True)
val_loader = DataLoader(val_set, batch_size=1, num_workers=train_args['num_workers'], shuffle=False)
test_set = list_dataset.ListDataset(dataset_name, 'Test', (train_args['h_size'], train_args['w_size']), 'statistical', hidden, overlap=True, use_dsm=True)
test_loader = DataLoader(test_set, batch_size=1, num_workers=train_args['num_workers'], shuffle=False)
# Setting criterion.
criterion = CrossEntropyLoss2d(size_average=False, ignore_index=5).cuda(args['device'])
# Making sure checkpoint and output directories are created.
check_mkdir(ckpt_path)
check_mkdir(os.path.join(ckpt_path, exp_name))
check_mkdir(outp_path)
check_mkdir(os.path.join(outp_path, exp_name))
# Validation function.
mean_list, dist_list = validate(val_loader, net, criterion, epoch, num_known_classes, num_unknown_classes, hidden, train_args)
print('Fitting model...')
tic = time.time()
weibull_model = weibull_tailfitting(mean_list, dist_list, num_known_classes, tailsize=1000)
toc = time.time()
print(' Model Fitted - Elapsed Time %.2f' % (toc - tic))
mean_list.clear()
dist_list.clear()
del mean_list
del dist_list
gc.collect()
# Computing test.
test(test_loader, net, criterion, epoch, num_known_classes, num_unknown_classes, hidden, train_args, True, epoch % args['save_freq'] == 0, weibull_model)
print('Exiting...')
def openmax(test_features, test_softmax, mean, distances, num_classes,
distance_type, weibull_tailsize, alpha_rank):
weibull_model = weibull_tailfitting(mean,
distances,
num_classes,
tailsize=weibull_tailsize)
openmax = recalibrate_scores(weibull_model,
test_features,
test_softmax,
num_classes,
alpharank=alpha_rank,
distance_type=distance_type)
return openmax
def main():
parser = argparse.ArgumentParser()
# Optional arguments.
parser.add_argument("--weibull_tailsize",
type=int,
default=WEIBULL_TAIL_SIZE,
help="Tail size used for weibull fitting")
parser.add_argument(
"--alpha_rank",
type=int,
default=ALPHA_RANK,
help="Alpha rank to be used as a weight multiplier for top K scores")
parser.add_argument(
"--distance",
default='eucos',
help="Type of distance to be used for calculating distance \
between mean vector and query image \
(eucos, cosine, euclidean)")
parser.add_argument(
"--mean_files_path",
default='data/mean_files/',
help="Path to directory where mean activation vector (MAV) is saved.")
parser.add_argument("--synsetfname",
default='synset_words_caffe_ILSVRC12.txt',
help="Path to Synset filename from caffe website")
parser.add_argument(
"--image_arrname",
default='data/train_features/n01440764/n01440764_14280.JPEG.mat',
help="Image Array name for which openmax scores are to be computed")
parser.add_argument(
"--distance_path",
default='data/mean_distance_files/',
help="Path to directory where distances of training data \
from Mean Activation Vector is saved")
args = parser.parse_args()
distance_path = args.distance_path
mean_path = args.mean_files_path
alpha_rank = args.alpha_rank
weibull_tailsize = args.weibull_tailsize
synsetfname = args.synsetfname
image_arrname = args.image_arrname
labellist = getlabellist(synsetfname)
weibull_model = weibull_tailfitting(mean_path,
distance_path,
labellist,
tailsize=WEIBULL_TAIL_SIZE)
print "Completed Weibull fitting on %s models" % len(weibull_model.keys())
imgarr = loadmat(image_arrname)
openmax, softmax = recalibrate_scores(weibull_model, labellist, imgarr)
print "Image ArrName: %s" % image_arrname
print "Softmax Scores ", softmax
print "Openmax Scores ", openmax
print openmax.shape, softmax.shape
def main():
parser = argparse.ArgumentParser()
# Optional arguments.
parser.add_argument(
"--weibull_tailsize",
type=int,
default=WEIBULL_TAIL_SIZE,
help="Tail size used for weibull fitting"
)
parser.add_argument(
"--alpha_rank",
type=int,
default=ALPHA_RANK,
help="Alpha rank to be used as a weight multiplier for top K scores"
)
parser.add_argument(
"--distance",
default='eucos',
help="Type of distance to be used for calculating distance \
between mean vector and query image \
(eucos, cosine, euclidean)"
)
parser.add_argument(
"--mean_files_path",
default='data/mean_files/',
help="Path to directory where mean activation vector (MAV) is saved."
)
parser.add_argument(
"--synsetfname",
default='synset_words_caffe_ILSVRC12.txt',
help="Path to Synset filename from caffe website"
)
parser.add_argument(
"--image_arrname",
default='data/train_features/n01440764/n01440764_14280.JPEG.mat',
help="Image Array name for which openmax scores are to be computed"
)
parser.add_argument(
"--distance_path",
default='data/mean_distance_files/',
help="Path to directory where distances of training data \
from Mean Activation Vector is saved"
)
args = parser.parse_args()
distance_path = args.distance_path
mean_path = args.mean_files_path
alpha_rank = args.alpha_rank
weibull_tailsize = args.weibull_tailsize
synsetfname = args.synsetfname
image_arrname = args.image_arrname
labellist = getlabellist(synsetfname)
weibull_model = weibull_tailfitting(mean_path, distance_path, labellist,
tailsize = WEIBULL_TAIL_SIZE)
print "Completed Weibull fitting on %s models" %len(weibull_model.keys())
imgarr = loadmat(image_arrname)
openmax, softmax = recalibrate_scores(weibull_model, labellist, imgarr)
print "Image ArrName: %s" %image_arrname
print "Softmax Scores ", softmax
print "Openmax Scores ", openmax
print openmax.shape, softmax.shape
def main():
parser = argparse.ArgumentParser()
# Optional arguments.
parser.add_argument("--weibull_tailsize",
type=int,
default=WEIBULL_TAIL_SIZE,
help="Tail size used for weibull fitting")
parser.add_argument(
"--alpha_rank",
type=int,
default=ALPHA_RANK,
help="Alpha rank to be used as a weight multiplier for top K scores")
parser.add_argument(
"--distance",
default='eucos',
help="Type of distance to be used for calculating distance \
between mean vector and query image \
(eucos, cosine, euclidean)")
parser.add_argument("--synsetfname",
default='synset_words_caffe_ILSVRC12.txt',
help="Path to Synset filename from caffe website")
parser.add_argument(
"--image_folder",
default='data/',
help=
"Image folder directory for which openmax scores are to be computed")
parser.add_argument('-cls',
'--classes',
type=int,
nargs='+',
default=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9))
args = parser.parse_args()
alpha_rank = args.alpha_rank
weibull_tailsize = args.weibull_tailsize
synsetfname = args.synsetfname
labellist = getlabellist(synsetfname)
image_folder = args.image_folder
mean_path = os.path.join(image_folder, 'train', 'means')
distance_path = os.path.join(image_folder, 'train', 'distances')
weibull_model = weibull_tailfitting(mean_path,
distance_path,
labellist,
tailsize=weibull_tailsize)
print("Completed Weibull fitting on %s models" % len(weibull_model.keys()))
# per class
categories = list(
filter(lambda x: os.path.isdir(os.path.join(image_folder, 'test', x)),
os.listdir(os.path.join(image_folder, 'test'))))
categories.sort()
kkc = list(
filter(lambda x: os.path.isdir(os.path.join(image_folder, 'test', x)),
os.listdir(os.path.join(image_folder, 'train'))))
kkc.sort()
uuc = list(set(categories) - set(kkc))
kkc_scores = []
uuc_scores = []
for category in categories:
# per image
if category in kkc:
scores = kkc_scores
else:
scores = uuc_scores
for image_arrname in glob.glob(
os.path.join(image_folder, 'test', category, '*.mat')):
imgarr = loadmat(image_arrname)
openmax, softmax = recalibrate_scores(weibull_model,
labellist,
imgarr,
alpharank=alpha_rank)
scores.append(np.max(openmax))
kkc_scores = np.array(kkc_scores)
uuc_scores = np.array(uuc_scores)
print(kkc_scores.mean(), uuc_scores.mean())
y_true = np.concatenate(
(np.ones_like(kkc_scores), np.zeros_like(uuc_scores)))
y_score = np.concatenate((kkc_scores, uuc_scores))
fpr, tpr, thresholds = roc_curve(y_true, y_score)
auc_score = auc(fpr, tpr)
plt.plot(fpr, tpr)
print(auc_score)
plt.title('Openmax_ROC_Curve of {}, AUC: {:.4f}'.format(
image_folder.split('/')[-1], auc_score))
plt.xlabel('fpr')
plt.ylabel('tpr')
plt.savefig(os.path.join(image_folder, 'openmax_roc.png'))
plt.show()