db7b74579181f9cbae3583f447d83148714a1c3d,stanza/models/classifiers/cnn_classifier.py,CNNClassifier,forward,#CNNClassifier#Any#Any#,83

Before Change

            max_phrase_len = self.max_window

        if max_phrase_len > min(len(x) for x in inputs):
            idx = torch.tensor(self.vocab_map[self.pad], requires_grad=False, device=device)
            pad_vector = self.embedding(idx)

        input_tensor = []
        for phrase in inputs:
            // build a list of the vectors we want for this sentence / phrase

After Change

            indices = torch.tensor(indices, requires_grad=False, device=device)
            input_vectors = self.embedding(indices)
            for unknown in unknowns:
                input_vectors[unknown, :] = self.unk

            // we will now have an N x emb_size tensor
            // this is the input to the CNN
            // there are two ways in which this padding is suboptimal
            // the first is that for short sentences, smaller windows will
            //   be padded to the point that some windows are entirely pad
            // the second is that a sentence S will have more or less padding
            //   depending on what other sentences are in its batch
            // we assume these effects are pretty minimal

            // reshape x to 1xNxE
            x = input_vectors.unsqueeze(0)
            input_tensor.append(x)
        x = torch.stack(input_tensor)

Instances