class TestClassifier:
def __init__(self):
self.clf = Classifier()
self.d = Dataset('full_pascal_trainval')
def test_load_svm(self):
self.clf.name = 'csc'
self.clf.suffix = 'default'
self.clf.cls = 'dog'
self.clf.train_dataset = self.d
self.clf.load_svm()
class TestClassifier:
def __init__(self):
self.clf = Classifier()
self.d = Dataset('full_pascal_trainval')
def test_load_svm(self):
self.clf.name = 'csc'
self.clf.suffix = 'default'
self.clf.cls = 'dog'
self.clf.train_dataset = self.d
self.clf.load_svm()
def __init__(self, cls, train_d, gist_table=None, val_d=None):
"""
Load all gist features right away
"""
self.train_d = train_d
self.val_d = val_d
Classifier.__init__(self)
self.tt.tic()
if gist_table == None:
print("Started loading GIST")
self.gist_table = np.load(config.get_gist_dict_filename(train_d))
print("Time spent loading gist: %.3f"%self.tt.qtoc())
else:
self.gist_table = gist_table
self.cls = cls
self.svm = self.load_svm()
def __init__(self):
self.clf = Classifier()
self.d = Dataset('full_pascal_trainval')
def __init__(self):
self.clf = Classifier()
self.d = Dataset('full_pascal_trainval')
from synthetic.classifier import Classifier
if __name__=='__main__':
train_set = 'full_pascal_train'
train_dataset = Dataset(train_set)
images = train_dataset.images
classes = config.pascal_classes
suffix = 'default'
filename = config.get_ext_dets_filename(train_dataset, 'csc_'+suffix)
csc_train = np.load(filename)
csc_train = csc_train[()]
csc_train = csc_train.subset(['score', 'cls_ind', 'img_ind'])
score = csc_train.subset(['score']).arr
classif = Classifier()
csc_train.arr = classif.normalize_dpm_scores(csc_train.arr)
numpos = train_dataset.get_ground_truth().shape[0]
threshs = np.arange(0,1.01,0.05)
result_filename = config.res_dir + 'thresh_classify.txt'
result_file = open(result_filename, 'a')
threshs = np.array([0.15])
for thrindex in range(comm_rank, threshs.shape[0], comm_size):
for cls in range(len(classes)):
from synthetic.classifier import Classifier
if __name__ == '__main__':
train_set = 'full_pascal_train'
train_dataset = Dataset(train_set)
images = train_dataset.images
classes = config.pascal_classes
suffix = 'default'
filename = config.get_ext_dets_filename(train_dataset, 'csc_' + suffix)
csc_train = np.load(filename)
csc_train = csc_train[()]
csc_train = csc_train.subset(['score', 'cls_ind', 'img_ind'])
score = csc_train.subset(['score']).arr
classif = Classifier()
csc_train.arr = classif.normalize_dpm_scores(csc_train.arr)
numpos = train_dataset.get_ground_truth().shape[0]
threshs = np.arange(0, 1.01, 0.05)
result_filename = config.res_dir + 'thresh_classify.txt'
result_file = open(result_filename, 'a')
threshs = np.array([0.15])
for thrindex in range(comm_rank, threshs.shape[0], comm_size):
for cls in range(len(classes)):
tp = 0.
