def add_args()

in pytorch_translate/hybrid_transformer_rnn.py [0:0]

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

    def add_args(parser):
        """Add model-specific arguments to the parser."""
        parser.add_argument(
            "--dropout", type=float, metavar="D", help="dropout probability"
        )
        parser.add_argument(
            "--attention-dropout",
            type=float,
            metavar="D",
            help="dropout probability for attention weights",
        )
        parser.add_argument(
            "--relu-dropout",
            type=float,
            metavar="D",
            help="dropout probability after ReLU in FFN",
        )
        parser.add_argument(
            "--encoder-pretrained-embed",
            type=str,
            metavar="STR",
            help="path to pre-trained encoder embedding",
        )
        parser.add_argument(
            "--encoder-embed-dim",
            type=int,
            metavar="N",
            help="encoder embedding dimension",
        )
        parser.add_argument(
            "--encoder-ffn-embed-dim",
            type=int,
            metavar="N",
            help="encoder embedding dimension for FFN",
        )
        parser.add_argument(
            "--encoder-freeze-embed",
            default=False,
            action="store_true",
            help=(
                "whether to freeze the encoder embedding or allow it to be "
                "updated during training"
            ),
        )
        parser.add_argument(
            "--encoder-layers", type=int, metavar="N", help="num encoder layers"
        )
        parser.add_argument(
            "--encoder-attention-heads",
            type=int,
            metavar="N",
            help="num encoder attention heads",
        )
        parser.add_argument(
            "--encoder-normalize-before",
            default=False,
            action="store_true",
            help="apply layernorm before each encoder block",
        )
        parser.add_argument(
            "--encoder-learned-pos",
            default=False,
            action="store_true",
            help="use learned positional embeddings in the encoder",
        )
        parser.add_argument(
            "--decoder-pretrained-embed",
            type=str,
            metavar="STR",
            help="path to pre-trained decoder embedding",
        )
        parser.add_argument(
            "--decoder-embed-dim",
            type=int,
            metavar="N",
            help="decoder embedding dimension",
        )
        parser.add_argument(
            "--decoder-freeze-embed",
            default=False,
            action="store_true",
            help=(
                "whether to freeze the encoder embedding or allow it to be "
                "updated during training"
            ),
        )
        parser.add_argument(
            "--decoder-layers", type=int, metavar="N", help="num decoder layers"
        )
        parser.add_argument(
            "--decoder-attention-heads",
            type=int,
            metavar="N",
            help="num decoder attention heads",
        )
        parser.add_argument(
            "--decoder-reduced-attention-dim",
            type=int,
            default=None,
            metavar="N",
            help="if specified, computes attention with this dimensionality "
            "(instead of using encoder output dims)",
        )
        parser.add_argument(
            "--decoder-lstm-units",
            type=int,
            metavar="N",
            help="num LSTM units for each decoder layer",
        )
        parser.add_argument(
            "--decoder-out-embed-dim",
            default=None,
            type=int,
            metavar="N",
            help="decoder output embedding dimension",
        )

        # Args for vocab reduction
        vocab_reduction.add_args(parser)