d74b591ca9ca50189aff78f27048bb56c24d412a,python/baseline/pytorch/tagger/model.py,RNNTaggerModel,drop_inputs,#RNNTaggerModel#Any#Any#,98

Before Change

        if not self.training or v == 0:
            return field
        drop_indices = np.where((np.random.random(field.shape) < v) & (field != RNNTaggerModel.PAD))
        field[drop_indices[0], drop_indices[1]] = RNNTaggerModel.UNK
        return field

    def input_tensor(self, key, batch_dict, perm_idx):

After Change

            return x

        mask_pad = x != RNNTaggerModel.PAD
        mask_drop = x.new(x.size(0), x.size(1)).bernoulli_(v).byte()
        x.masked_fill_(mask_pad & mask_drop, RNNTaggerModel.UNK)
        return x

    def input_tensor(self, key, batch_dict, perm_idx):

Instances