def __init__()

in ocr/utils/encoder_decoder.py [0:0]

• 40 lines of code
• 2 McCabe index (conditional complexity)

    def __init__(self, attention_cell='multi_head', num_layers=2,
                 units=128, hidden_size=2048, max_length=50,
                 num_heads=4, scaled=True, dropout=0.0,
                 use_residual=True, output_attention=False,
                 weight_initializer=None, bias_initializer='zeros',
                 prefix=None, params=None):
        super(TransformerDecoder, self).__init__(prefix=prefix, params=params)
        assert units % num_heads == 0, 'In TransformerDecoder, the units should be divided ' \
                                       'exactly by the number of heads. Received units={}, ' \
                                       'num_heads={}'.format(units, num_heads)
        self._num_layers = num_layers
        self._units = units
        self._hidden_size = hidden_size
        self._num_states = num_heads
        self._max_length = max_length
        self._dropout = dropout
        self._use_residual = use_residual
        self._output_attention = output_attention
        self._scaled = scaled
        with self.name_scope():
            self.dropout_layer = nn.Dropout(dropout)
            self.layer_norm = nn.LayerNorm()
            self.position_weight = self.params.get_constant('const',
                                                            _position_encoding_init(max_length,
                                                                                    units))
            self.transformer_cells = nn.HybridSequential()
            for i in range(num_layers):
                self.transformer_cells.add(
                    TransformerDecoderCell(
                        units=units,
                        hidden_size=hidden_size,
                        num_heads=num_heads,
                        attention_cell=attention_cell,
                        weight_initializer=weight_initializer,
                        bias_initializer=bias_initializer,
                        dropout=dropout,
                        scaled=scaled,
                        use_residual=use_residual,
                        output_attention=output_attention,
                        prefix='transformer%d_' % i))