def __init__(self, args):
class Config:
pass
self._config = Config()
self._config.context = 'cpu'
self._config.device_id = 0
self._config.columns_size = 2
self._config.x_length = args.x_length
self._config.mlp_model_params_path = args.mlp_model_params_path
self._config.labels_path = args.labels_path
if not os.path.isfile(self._config.mlp_model_params_path):
logger.error("Model params path {} is not found.".format(self._config.mlp_model_params_path))
sys.exit(-1)
else:
logger.info("Path of the model parameters file is {}.".format(self._config.mlp_model_params_path))
if not os.path.isfile(self._config.labels_path):
logger.error("Labels path {} is not found.".format(self._config.labels_path))
sys.exit(-1)
else:
logger.info("Path of the labels file is {}.".format(self._config.labels_path))
seed(0)
logger.info("Running in %s" % self._config.context)
self._ctx = get_extension_context(self._config.context, device_id = self._config.device_id)
nn.set_default_context(self._ctx)
nn.clear_parameters()
self._mlp = MLP(self._config)
self._labels = None
with open(self._config.labels_path) as f:
self._labels = f.readlines()
self._points_buf = pointsbuffer.PointsBuffer()
def __init__(self):
self._minx = 0.0
self._miny = 0.0
self._maxx = 255.0
self._maxy = 255.0
self._sigma = 32.0
self._factor = 1.0
self._homography_distort_range = 32.0
self._distortion_level0_repeat = 8
self._distortion_level1_repeat = 8
self._points_buf = pointsbuffer.PointsBuffer()
self._max_scale = (self._maxy - self._miny + 1.0) * 0.5 \
if (self._maxx - self._minx) > (self._maxy - self._miny) \
else (self._maxx - self._minx + 1.0) * 0.5
random.seed()
def __init__(self, args):
self._config = args
self._minx = 0
self._miny = 0
self._maxx = 255
self._maxy = 255
self._output_dir = None
self._x = 0
self._y = 0
self._points = []
self._pred_points = []
self._recognizer = None
self._window = None
self._canvas = None
self._result_area = None
self._result_txt = None
self._points_buf = pointsbuffer.PointsBuffer()
self._initWindow()
def __init__(self, args):
class Config:
pass
self._config = Config()
self._config.process = 'infer'
self._config.columns_size = 2
self._config.x_length = args.x_length
self._config.x_input_length = args.x_input_length
self._config.x_split_step = args.x_split_step
self._config.width = args.width
self._config.height = args.height
self._config.lstm_units = args.lstm_units
self._config.batch_size = 1
self._config.epochs = 0
self._config.validation_split = 0.0
self._config.learning_rate = 0.0
self._config.training_dataset_path = None
self._config.evaluation_dataset_path = None
self._net_type = args.net
logger.info("Network type is {}.".format(self._net_type))
self._mlp = None
self._lenet = None
self._lstm = None
if self._net_type == 'mlp':
self._config.model_params_path = args.mlp_model_params_path
if not os.path.isfile(self._config.model_params_path):
logger.error("Model params path {} is not found.".format(
self._config.model_params_path))
else:
logger.info("Path of the model parameters file is {}.".format(
self._config.model_params_path))
self._mlp = MLP(self._config)
self._mlp.init_for_infer()
elif self._net_type == 'lenet':
self._config.model_params_path = args.lenet_model_params_path
if not os.path.isfile(self._config.model_params_path):
logger.error("Model params path {} is not found.".format(
self._config.model_params_path))
else:
logger.info("Path of the model parameters file is {}.".format(
self._config.model_params_path))
self._lenet = LeNet(self._config)
self._lenet.init_for_infer()
elif self._net_type == 'mlp-with-lstm':
self._config.model_params_path = args.lstm_model_params_path
if not os.path.isfile(self._config.model_params_path):
logger.error("Model params path {} is not found.".format(
self._config.model_params_path))
else:
logger.info("Path of the model parameters file is {}.".format(
self._config.model_params_path))
self._lstm = LSTM(self._config)
self._lstm.init_for_infer()
self._config.model_params_path = args.mlp_model_params_path
if not os.path.isfile(self._config.model_params_path):
logger.error("Model params path {} is not found.".format(
self._config.model_params_path))
else:
logger.info("Path of the model parameters file is {}.".format(
self._config.model_params_path))
self._mlp = MLP(self._config)
self._mlp.init_for_infer()
else:
raise ValueError("Unknown network type {}".format(self._net_type))
self._labels = None
self._labels_path = args.labels_path
if not os.path.isfile(self._labels_path):
logger.error("Labels path {} is not found.".format(
self._labels_path))
else:
logger.info("Path of the labels file is {}.".format(
self._labels_path))
with open(self._labels_path) as f:
self._labels = f.readlines()
self._points_buf = pointsbuffer.PointsBuffer()
def __init__(self, args):
class Config:
pass
self._config = Config()
self._config.context = args.context
self._config.device_id = args.device_id
self._config.process = 'infer'
self._config.columns_size = 2
self._config.x_length = args.x_length
self._config.x_input_length = args.x_input_length
self._config.x_output_length = args.x_output_length
self._config.x_split_step = args.x_split_step
self._config.width = args.width
self._config.height = args.height
self._config.lstm_unit_name = args.lstm_unit_name
self._config.lstm_units = args.lstm_units
self._config.batch_size = 1
self._config.max_iter = 0
self._config.learning_rate = 0.0
self._config.weight_decay = 0.0
self._config.val_interval = 0
self._config.val_iter = 0
self._config.monitor_path = '.'
self._config.training_dataset_path = None
self._config.validation_dataset_path = None
self._config.evaluation_dataset_path = None
seed(0)
if self._config.context is None:
self._config.context = 'cpu'
logger.info("Running in %s" % self._config.context)
self._ctx = get_extension_context(self._config.context,
device_id=self._config.device_id)
nn.set_default_context(self._ctx)
self._net_type = args.net
logger.info("Network type is {}.".format(self._net_type))
self._mlp = None
self._lenet = None
self._lstm = None
nn.clear_parameters()
if self._net_type == 'mlp':
self._config.model_params_path = args.mlp_model_params_path
if not os.path.isfile(self._config.model_params_path):
logger.error("Model params path {} is not found.".format(
self._config.model_params_path))
else:
logger.info("Path of the model parameters file is {}.".format(
self._config.model_params_path))
self._mlp = MLP(self._config)
self._mlp.init_for_infer()
elif self._net_type == 'lenet':
self._config.model_params_path = args.lenet_model_params_path
if not os.path.isfile(self._config.model_params_path):
logger.error("Model params path {} is not found.".format(
self._config.model_params_path))
else:
logger.info("Path of the model parameters file is {}.".format(
self._config.model_params_path))
self._lenet = LeNet(self._config)
self._lenet.init_for_infer()
elif self._net_type == 'mlp-with-lstm':
self._config.model_params_path = args.lstm_model_params_path
if not os.path.isfile(self._config.model_params_path):
logger.error("Model params path {} is not found.".format(
self._config.model_params_path))
else:
logger.info("Path of the model parameters file is {}.".format(
self._config.model_params_path))
self._lstm = LSTM(self._config)
self._lstm.init_for_infer()
self._config.model_params_path = args.mlp_model_params_path
if not os.path.isfile(self._config.model_params_path):
logger.error("Model params path {} is not found.".format(
self._config.model_params_path))
else:
logger.info("Path of the model parameters file is {}.".format(
self._config.model_params_path))
self._mlp = MLP(self._config)
self._mlp.init_for_infer()
else:
raise ValueError("Unknown network type {}".format(self._net_type))
self._labels = None
self._labels_path = args.labels_path
if not os.path.isfile(self._labels_path):
logger.error("Labels path {} is not found.".format(
self._labels_path))
else:
logger.info("Path of the labels file is {}.".format(
self._labels_path))
with open(self._labels_path) as f:
self._labels = f.readlines()
self._points_buf = pointsbuffer.PointsBuffer()