sys.path.append('../../') sys.path.append('../../shared/') sys.path.append('../models/') sys.path.append('../filters/') sys.path.append('../data/') import extract_data from det_generators import DataGenerator import methods,\ stages import det_test if True: # Load data data = extract_data.object_data() cfg = data.cfg obj_mapping = data.class_mapping hoi_mapping = data.hoi_labels # Create batch generators genTrain = DataGenerator(imagesMeta=data.trainGTMeta, cfg=cfg, data_type='train', do_meta=True, mode='test') genVal = DataGenerator(imagesMeta=data.valGTMeta, cfg=cfg, data_type='val', do_meta=True, mode='test')
sys.path.append('../filters/') sys.path.append('../data/') import numpy as np import utils,\ extract_data,\ methods,\ losses,\ callbacks,\ filters_helper as helper from det_generators import DataGenerator # meta data data = extract_data.object_data(False) # config cfg = data.cfg obj_mapping = data.class_mapping # data genTrain = DataGenerator(imagesMeta = data.trainGTMeta, cfg=cfg, data_type='train', do_meta=True) #genVal = DataGenerator(imagesMeta = data.valGTMeta, cfg=cfg, data_type='val', do_meta=True) genItr = genTrain.begin() for batchidx in range(genTrain.nb_batches): [X,rois], y, imageMeta, imageDims, _ = next(genItr) # if batchidx+1 % 100 == 0: utils.update_progress_new(batchidx+1, genTrain.nb_batches, '')