boxp = 0.2
f_loss = False
minL = False
print('boxp= {}\n pre_model= {}\n config= {}\n focal_loss={}\n minL={}\n '.
format(boxp, model_n, conf, f_loss, minL))
params = process_config(conf)
print('--Creating Dataset')
dataset = DataGenerator(minL,
boxp,
params['joint_list'],
params['img_directory'],
params['training_txt_file'],
remove_joints=params['remove_joints'])
dataset._create_train_table()
dataset._create_val_table()
dataset._randomize()
dataset._create_sets()
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
modif = True
model = HourglassModel(f_loss=f_loss,
nFeat=params['nfeats'],
nStack=params['nstacks'],
nModules=params['nmodules'],
nLow=params['nlow'],
outputDim=params['num_joints'],
batch_size=params['batch_size'],
attention=params['mcam'],
