def __init__(self):
self.segmentations = Queue(1)
self.features = Queue(1)
rospy.Subscriber(SEGMENTATION_TOPIC, SegmentedImage,
self._segmentation_callback)
rospy.Subscriber(FEATURES_TOPIC, VGG16Features,
self._features_callback)
self.object_found = rospy.Publisher(OBJECT_TOPIC,
PointStamped,
queue_size=1)
self.category = rospy.Publisher(CATEGORY_TOPIC, String, queue_size=1)
self.status = rospy.Publisher(STATUS_TOPIC,
String,
queue_size=1,
latch=True)
self.eval_config = self._load_config(EVAL_CONF_PATH)
self.guesser_config = self._load_config(GUESS_CONF_PATH)
self.qgen_config = self._load_config(QGEN_CONF_PATH)
self.tokenizer = GWTokenizer(TOKENS_PATH)
self.tf_config = tf.ConfigProto(log_device_placement=False)
self.tf_config.gpu_options.allow_growth = True
self.image_dim = ros_utils.get_image_dim()
if 'image' in config['model']:
logger.info('Loading images..')
image_builder = get_img_builder(config['model']['image'], args.img_dir)
use_resnet = image_builder.is_raw_image()
assert False, "Guesser + Image is not yet available"
# Load data
logger.info('Loading data..')
trainset = Dataset(args.data_dir, "train", image_builder, crop_builder)
validset = Dataset(args.data_dir, "valid", image_builder, crop_builder)
testset = Dataset(args.data_dir, "test", image_builder, crop_builder)
# Load dictionary
logger.info('Loading dictionary..')
tokenizer = GWTokenizer(os.path.join(args.data_dir, args.dict_file))
# Build Network
logger.info('Building network..')
network = GuesserNetwork(config['model'], num_words=tokenizer.no_words)
# Build Optimizer
logger.info('Building optimizer..')
optimizer, outputs = create_optimizer(network, config["optimizer"])
###############################
# START TRAINING
#############################
# Load config
batch_size = config['model']['batch_size']
logger.info('Loading images..')
image_builder = get_img_builder(config['model']['image'], args.img_dir)
use_resnet = image_builder.is_raw_image()
# Load data
logger.info('Loading data..')
trainset = Dataset(args.data_dir, "train", image_builder, crop_builder,
rcnn, args.no_games_to_load)
validset = Dataset(args.data_dir, "valid", image_builder, crop_builder,
rcnn, args.no_games_to_load)
testset = Dataset(args.data_dir, "test", image_builder, crop_builder, rcnn,
args.no_games_to_load)
# Load dictionary
logger.info('Loading dictionary..')
tokenizer = GWTokenizer(args.dict_file)
# Build Network
logger.info('Building network..')
network, batchifier_cstor = create_qgen(config["model"],
num_words=tokenizer.no_words)
# Build Optimizer
logger.info('Building optimizer..')
optimizer, outputs = create_optimizer(
network, config["optimizer"]) # output:[loss, accuracy]
###############################
# START TRAINING
#############################
# Load image
logger.info('Loading images..')
use_resnet = False
if 'image' in config['model']:
logger.info('Loading images..')
image_builder = get_img_builder(config['model']['image'], args.img_dir)
# Load data
logger.info('Loading data..')
trainset = Dataset(args.data_dir, "train", "guesswhat", image_builder, crop_builder)
validset = Dataset(args.data_dir, "valid", "guesswhat", image_builder, crop_builder)
testset = Dataset(args.data_dir, "test", "guesswhat", image_builder, crop_builder)
# Load dictionary
logger.info('Loading dictionary..')
tokenizer = GWTokenizer(args.dict_file)
# Build Network
logger.info('Building network..')
if args.rl_model:
network = QGenNetworkLSTM(config["model"], num_words=tokenizer.no_words, policy_gradient=True)
qgen_var = [v for v in tf.global_variables() if "qgen" in v.name] # and 'rl_baseline' not in v.name
else:
network = QGenNetworkLSTM(config["model"], num_words=tokenizer.no_words, policy_gradient=False)
qgen_var = [v for v in tf.global_variables() if "qgen" in v.name and 'rl_baseline' not in v.name]
saver = tf.train.Saver(var_list=qgen_var)
# Build Optimizer
logger.info('Building optimizer..')
# optimizer, outputs = create_optimizer(network, config)
outputs = [network.decoder_state]
