# meta data
data = extract_data.object_data()
# config
cfg = data.cfg
utils.saveConfig(cfg)
hoi_mapping = data.hoi_labels
# data
genTrain = DataGenerator(imagesMeta=data.trainGTMeta,
cfg=cfg,
data_type='train',
do_meta=False)
# models
Models = methods.AllModels(cfg, mode='train', do_hoi=True)
_, _, model_hoi = Models.get_models()
sys.stdout.flush()
#if False:
# train
callbacks = [callbacks.MyModelCheckpointInterval(cfg), \
callbacks.MyLearningRateScheduler(cfg), \
callbacks.MyModelCheckpointWeightsInterval(cfg),\
callbacks.SaveLog2File(cfg), \
callbacks.PrintCallBack()]
if cfg.dataset == 'TUPPMI':
model_hoi.fit_generator(generator = genTrain.begin(), \
steps_per_epoch = genTrain.nb_batches, \
# 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')
# genTest = DataGenerator(imagesMeta = data.testGTMeta, cfg=cfg, data_type='test', do_meta=True)
Models = methods.AllModels(cfg,
mode='test',
do_rpn=False,
do_det=True,
do_hoi=False)
Stages = stages.AllStages(cfg,
Models,
obj_mapping,
hoi_mapping,
mode='test')
# Val data
evalVal = det_test.saveEvalData(genVal, Stages, cfg, obj_mapping)
det_test.saveEvalResults(genVal, cfg)
# Test data
#evalTest = det_test.saveEvalData(genTest, Stages, cfg, obj_mapping)
#det_test.saveEvalResults(evalTest, genTest, cfg)
if True:
# meta data
data = extract_data.object_data()
# config
cfg = data.cfg
obj_mapping = data.class_mapping
# data
genTrain = DataGenerator(imagesMeta=data.trainGTMeta,
cfg=cfg,
data_type='train',
do_meta=False)
genVal = DataGenerator(imagesMeta=data.valGTMeta,
cfg=cfg,
data_type='val',
do_meta=False)
# models
Models = methods.AllModels(cfg,
mode='train',
do_rpn='rpn' in cfg.my_results_dir,
do_det='det' in cfg.my_results_dir,
do_hoi='hoi' in cfg.my_results_dir)
sys.stdout.flush()
#if False:
# Save stuff
Models.save_model(only_weights=True)
print('Path:', cfg.my_results_path)