Add the AMR graph constrction and RGCN in example #578
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cminusQAQ
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graph4nlp/pytorch/data/data.py
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| @@ -195,6 +218,18 @@ def node_features(self) -> NodeFeatView: | |||
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| return self.nodes[:].features | |||
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| @property | |||
| def ntypes(self) -> List[str]: | |||
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If the graph is homogeneous, it should be an exception.
graph4nlp/pytorch/data/data.py
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| @@ -157,6 +168,12 @@ def add_nodes(self, node_num: int): | |||
| node_num > 0 | |||
| ), "The number of nodes to be added should be greater than 0. (Got {})".format(node_num) | |||
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| if not self.is_hetero: | |||
| assert ntypes is None, "The graph is homogeneous, ntypes should be None." | |||
graph4nlp/pytorch/data/data.py
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| @@ -326,6 +361,18 @@ def edges(self) -> EdgeView: | |||
| """ | |||
| return EdgeView(self) | |||
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| @property | |||
| def etypes(self) -> List[Tuple[str, str, str]]: | |||
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When it is homogeneous, self._etypes is not assigned.
graph4nlp/pytorch/data/data.py
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| @@ -641,6 +713,41 @@ def edge_attributes(self) -> List[Dict[str, torch.Tensor]]: | |||
| return self._edge_attributes | |||
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| # Conversion utility functions | |||
| def make_data_dict(self) -> Dict[Tuple[str, str, str], Tuple[torch.Tensor, torch.Tensor]]: | |||
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I think this could be a utility function.
graph4nlp/pytorch/data/dataset.py
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| edge_token = graph.edge_attributes[edge_idx]["token"] | ||
| s.add(edge_token) | ||
| except Exception as e: | ||
| pass |
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please handle the catched exception
graph4nlp/pytorch/data/dataset.py
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| @@ -705,6 +709,41 @@ def _process(self): | |||
| self.test = self.build_topology(self.test) | |||
| if "val" in self.__dict__: | |||
| self.val = self.build_topology(self.val) | |||
| # build_edge_vocab and save | |||
| if self.init_edge_vocab: | |||
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It should be a new function similar to "build_vocab()". E.g., build_edge_vocab()
| @@ -311,6 +311,7 @@ def __init__( | |||
| for_inference=False, | |||
| reused_vocab_model=None, | |||
| nlp_processor_args=None, | |||
| init_edge_vocab=False, | |||
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init_edge_vocab is not clear. It covers 1.build edge vocab, 2. an indicator to use heterogeneous graph.
TODO: discuss
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- build_edge_vocab: bool
- is_hetero: bool
| ) | ||
| data_item_collect.append(data_item) | ||
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| data_item_collect = self.dataset.build_topology(data_items=data_item_collect) | ||
| for i in range(len(data_item_collect)): | ||
| data_item_collect[i] = self.dataset._vectorize_one_dataitem( | ||
| data_item_collect[i], self.vocab_model, use_ie=use_ie | ||
| data_item_collect[i], self.vocab_model, use_ie=use_ie, edge_vocab=self.edge_vocab |
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But the api in dataset._vectorize_one_dataitem doesn't have the parameter: "edge_vocab", please check it.
_vectorize_one_dataitem
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- move the edge vocab to the library code
- unify edge_vocab with vocab_model
| @@ -140,6 +140,9 @@ def __init__( | |||
| "w2v_bert", | |||
| "w2v_bert_bilstm", | |||
| "w2v_bert_bigru", | |||
| "w2v_amr", | |||
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Why amr shoud be a general embedding strategy?
I don't think it should be regarded as a new general embedding strategy.
| @@ -211,6 +222,17 @@ def __init__( | |||
| else: | |||
| rnn_input_size = word_emb_size | |||
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| if "pos" in word_emb_type: | |||
| self.word_emb_layers["pos"] = WordEmbedding(37, 50) | |||
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please don't use magic numbers.
cminusQAQ
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