How to use _get_params method in Airtest

Best Python code snippet using Airtest

projection_layers.py

Source:projection_layers.py

...21class ProjectionLayer(base_layers.BaseLayer):22  """Base class for encoders."""23  def __init__(self, model_config, mode):24    """Create projection."""25    def _get_params(varname, default_value=None):26      value = model_config[varname] if varname in model_config else default_value27      default = "" if varname in model_config else " (default)"28      logging.info("%s = %s%s", varname, value, default)29      setattr(self, varname, value)30    self.mode = mode31    _get_params("feature_size")32    _get_params("max_seq_len", 0)33    _get_params("add_eos_tag", False)34    _get_params("add_bos_tag", False)35    _get_params("hashtype", "murmur")36    _get_params("split_on_space", True)37    _get_params("token_separators", "")38    _get_params("vocabulary", "")39    _get_params("quantize")40    _get_params("word_novelty_bits", 0)41    _get_params("doc_size_levels", 0)42    self.distortion_probability = 0.043    if mode == base_layers.TRAIN:44      _get_params("distortion_probability", 0.0)45    parameters = base_layers.Parameters(mode, self.quantize)46    super(ProjectionLayer, self).__init__(parameters=parameters)47  def call(self, inputs):48    projection, _, seq_length = ssp.sequence_string_projection(49        input=inputs,50        feature_size=self.feature_size,51        max_splits=self.max_seq_len - 1,52        hashtype=self.hashtype,53        distortion_probability=self.distortion_probability,54        split_on_space=self.split_on_space,55        token_separators=self.token_separators,56        word_novelty_bits=self.word_novelty_bits,57        doc_size_levels=self.doc_size_levels,58        add_eos_tag=self.add_eos_tag,59        add_bos_tag=self.add_bos_tag,60        vocabulary=self.vocabulary)61    modes = [base_layers.PREDICT, base_layers.TFLITE]62    if self.mode not in modes and self.max_seq_len > 0:63      short_by = self.max_seq_len - tf.shape(projection)[1]64      projection = tf.pad(projection, [[0, 0], [0, short_by], [0, 0]])65      batch_size = self.get_batch_dimension(inputs)66      projection = tf.reshape(projection,67                              [batch_size, self.max_seq_len, self.feature_size])68    if self.mode in modes:69      projection = self.qrange_tanh(projection)70    return projection, seq_length71class ProjectionLayerPreSegmented(base_layers.BaseLayer):72  """Base class for encoders."""73  def __init__(self, model_config, mode):74    """Create projection."""75    def _get_params(varname, default_value=None):76      value = model_config[varname] if varname in model_config else default_value77      default = "" if varname in model_config else " (default)"78      logging.info("%s = %s%s", varname, value, default)79      setattr(self, varname, value)80    self.mode = mode81    _get_params("feature_size")82    _get_params("add_eos_tag", False)83    _get_params("add_bos_tag", False)84    _get_params("vocabulary", "")85    _get_params("quantize")86    self.distortion_probability = 0.087    if mode == base_layers.TRAIN:88      _get_params("distortion_probability", 0.0)89    parameters = base_layers.Parameters(mode, self.quantize)90    super(ProjectionLayerPreSegmented, self).__init__(parameters=parameters)91  def call(self, inputs, sequence_length):92    projection = sspv2.sequence_string_projection_v2(93        input=inputs,94        sequence_length=sequence_length,95        feature_size=self.feature_size,96        distortion_probability=self.distortion_probability,97        add_eos_tag=self.add_eos_tag,98        add_bos_tag=self.add_bos_tag,99        vocabulary=self.vocabulary)100    modes = [base_layers.PREDICT, base_layers.TFLITE]101    if self.mode in modes:102      projection = self.qrange_tanh(projection)...

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