module)
if X2 is None:
return -1, -1
neg_samples = np.where(Y1[0, :, -1] == 1)
pos_samples = np.where(Y1[0, :, -1] == 0)
if len(pos_samples) > 0:
pos_samples = pos_samples[0]
else:
pos_samples = []
return len(pos_samples), pos_samples
all_imgs, classes_count, class_mapping = get_data('')
if 'bg' not in classes_count:
classes_count['bg'] = 0
class_mapping['bg'] = len(class_mapping)
C.class_mapping = class_mapping
inv_map = {v: k for k, v in class_mapping.items()}
print('Training images per class:')
pprint.pprint(classes_count)
print('Num classes (including bg) = {}'.format(len(classes_count)))
config_output_filename = 'config_options'
C = config.Config()
C.use_horizontal_flips = horizontal_flips
C.use_vertical_flips = vertical_flips
C.rot_90 = rot_90
C.network = 'vgg'
#C.record_path = record_path
C.model_path = output_weight_path
C.num_rois = num_rois
C.base_net_weights = base_weight_path
st = time.time()
all_imgs, classes_count, class_mapping = simple_parser.get_data(train_path)
if 'bg' not in classes_count:
classes_count['bg'] = 0
class_mapping['bg'] = len(class_mapping)
C.class_mapping = class_mapping
# inv_map = {v: k for k, v in class_mapping.items()}
print('Training images per class:')
pprint.pprint(classes_count)
print('Num classes (including bg) = {}'.format(len(classes_count)))
print(class_mapping)
# config_output_filename = options.config_filename