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')
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.