a7b0ffb1a047f855762d4e76acd7b8bffc7202f0,projects/personality_captions/transresnet/modules.py,TransresnetModel,_build_text_encoder,#TransresnetModel#Any#,149

Before Change

                self.dictionary, self.opt["embedding_size"], self.opt["datapath"]
            )

        self.text_encoder = TransformerEncoder(
            n_heads=self.opt["n_heads"],
            n_layers=self.opt["n_layers"],
            embedding_size=self.opt["embedding_size"],
            ffn_size=self.opt["ffn_size"],
            vocabulary_size=len(self.dictionary),
            embedding=self.embeddings,
            dropout=self.opt["dropout"],
            attention_dropout=self.opt["attention_dropout"],
            relu_dropout=self.opt["relu_dropout"],
            padding_idx=self.dictionary.tok2ind[self.dictionary.null_token],
            learn_positional_embeddings=self.opt["learn_positional_embeddings"],
            embeddings_scale=False,
            n_positions=self.opt["n_positions"],
            activation=self.opt["activation"],
            variant=self.opt["variant"],
            n_segments=self.opt["n_segments"],
        )
        if self.opt.get("load_encoder_from") is not None:
            self._load_text_encoder_state()

        self.additional_layer = LinearWrapper(

After Change

                self.dictionary, self.opt["embedding_size"], self.opt["datapath"]
            )

        self.text_encoder = TransformerEncoder(
            opt=self.opt,
            embedding=self.embeddings,
            vocabulary_size=len(self.dictionary),
            padding_idx=self.dictionary.tok2ind[self.dictionary.null_token],
            embeddings_scale=False,
        )
        if self.opt.get("load_encoder_from") is not None:
            self._load_text_encoder_state()

        self.additional_layer = LinearWrapper(

Instances