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, '')
