import tensorflow as tf
import os
tf.app.flags.DEFINE_string("configfile", "config/config_mpii.cfg",
"config file name")
tf.app.flags.DEFINE_string("loadmodel", None,
"model name used to continue training")
FLAGS = tf.app.flags.FLAGS
if __name__ == '__main__':
print('--Parsing Config File')
params = process_config(FLAGS.configfile)
os.system('mkdir -p {}'.format(params['saver_directory']))
os.system('cp {0} {1}'.format(FLAGS.configfile, params['saver_directory']))
print('--Creating Dataset')
dataset = DataGenerator(params['joint_list'], params['img_directory'],
params['training_txt_file'], params['img_size'])
dataset._create_train_table()
dataset._randomize()
dataset._create_sets()
model = HourglassModel(params=params, dataset=dataset, training=True)
model.create_model()
model.do_train(nEpochs=params['nepochs'],
epochSize=params['epoch_size'],
saveStep=params['saver_step'],
dataset=None,
load=FLAGS.loadmodel)
params = process_config('config.cfg')
print('--Creating Dataset')
dataset = DataGenerator(params['img_directory'],
params['training_txt_file'],
params['num_joints'],
params['val_directory'],
params['val_txt_file'],
resolutions=params['resolutions'],
headed=params['headed'],
head_train=params['head_train'],
head_test=params['head_test'],
head_val=params['head_val'])
dataset._create_train_table(
) #creates the lists with dicts of the coord. of boxes, joints and the corresp. weights
dataset._randomize() # shuffles the previous lists
dataset._create_sets() # validation and training lists
model = HourglassModel(nFeat=params['nfeats'],
nStack=params['nstacks'],
nModules=params['nmodules'],
nLow=params['nlow'],
outputDim=params['num_joints'],
batch_size=params['batch_size'],
attention=params['mcam'],
training=True,
drop_rate=params['dropout_rate'],
lear_rate=params['learning_rate'],
decay=params['learning_rate_decay'],
decay_step=params['decay_step'],
dataset=dataset,
img_dir_test=params['img_directory_test2'],
test_data_file=params['test_txt_file2'])
dataset3 = DataGenerator(params['joint_list'],
params['img_directory3'],
params['training_txt_file3'],
remove_joints=params['remove_joints'],
img_dir_test=params['img_directory_test3'],
test_data_file=params['test_txt_file3'])
dataset4 = DataGenerator(params['joint_list'],
params['img_directory4'],
params['training_txt_file4'],
remove_joints=params['remove_joints'],
img_dir_test=params['img_directory_test4'],
test_data_file=params['test_txt_file4'])
dataset1._create_train_table()
dataset1._randomize()
dataset1._create_sets()
dataset2._create_train_table()
dataset2._randomize()
dataset2._create_sets()
dataset3._create_train_table()
dataset3._randomize()
dataset3._create_sets()
dataset4._create_train_table()
dataset4._randomize()
dataset4._create_sets()
dataset = [dataset1, dataset2, dataset3, dataset4]
camera1 = scipy.io.loadmat(params['camera1'])
camera2 = scipy.io.loadmat(params['camera2'])
camera3 = scipy.io.loadmat(params['camera3'])
params[option] = eval(config.get(section, option))
if section == 'Network':
for option in config.options(section):
params[option] = eval(config.get(section, option))
if section == 'Train':
for option in config.options(section):
params[option] = eval(config.get(section, option))
if section == 'Validation':
for option in config.options(section):
params[option] = eval(config.get(section, option))
if section == 'Saver':
for option in config.options(section):
params[option] = eval(config.get(section, option))
return params
if __name__ == '__main__':
print('--Parsing Config File')
params = process_config('config.cfg')
print('--Creating Dataset')
dataset = DataGenerator(params['joint_list'], params['img_directory'], params['training_txt_file'], remove_joints=params['remove_joints'])
dataset._create_train_table()
dataset._randomize()
dataset._create_sets()
model = HourglassModel(nFeat=params['nfeats'], nStack=params['nstacks'], nModules=params['nmodules'], nLow=params['nlow'], outputDim=params['num_joints'], batch_size=params['batch_size'], attention = params['mcam'],training=True, drop_rate= params['dropout_rate'], lear_rate=params['learning_rate'], decay=params['learning_rate_decay'], decay_step=params['decay_step'], dataset=dataset, name=params['name'], logdir_train=params['log_dir_train'], logdir_test=params['log_dir_test'], tiny= params['tiny'], w_loss=params['weighted_loss'] , joints= params['joint_list'],modif=False)
model.generate_model()
model.training_init(nEpochs=params['nepochs'], epochSize=params['epoch_size'], saveStep=params['saver_step'], dataset = None)
