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@ -3470,6 +3470,93 @@ class ReverseSequence(PrimitiveWithInfer):
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return x
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class EditDistance(PrimitiveWithInfer):
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"""
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Computes the Levebshtein Edit Distance. It is used to measure the similarity of two sequences.
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Args:
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normalize (bool): If True, edit distances are normalized by length of truth. Default: True.
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Inputs:
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- **hypothesis_indices** (Tensor) - The indices of the hypothesis list SparseTensor. With int64 data type.
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The shape of tensor is :math:`(N, R)`.
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- **hypothesis_values** (Tensor) - The values of the hypothesis list SparseTensor.
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Must be 1-D vector with length of N.
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- **hypothesis_shape** (Tensor) - The values of the hypothesis list SparseTensor.
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Must be R-length vector with int64 data type. Only constant value is allowed.
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- **truth_indices** (Tensor) - The indices of the truth list SparseTensor. With int64 data type.
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The shape of tensor is :math:`(M, R)`.
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- **truth_values** (Tensor) - The values of the truth list SparseTensor. Must be 1-D vector with length of M.
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- **truth_shape** (Tensor) - The values of the truth list SparseTensor.
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Must be R-length vector with int64 data type. Only constant value is allowed.
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Outputs:
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Tensor, a dense tensor with rank `R-1` and float32 data type.
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Examples:
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>>> class EditDistance(nn.Cell):
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>>> def __init__(self, hypothesis_shape, truth_shape, normalize=True):
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>>> super(EditDistance, self).__init__()
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>>> self.edit_distance = P.EditDistance(normalize)
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>>> self.hypothesis_shape = hypothesis_shape
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>>> self.truth_shape = truth_shape
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>>>
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>>> def construct(self, hypothesis_indices, hypothesis_values, truth_indices, truth_values):
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>>> return self.edit_distance(hypothesis_indices, hypothesis_values, self.hypothesis_shape,
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>>> truth_indices, truth_values, self.truth_shape)
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>>>
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>>> hypothesis_indices = Tensor(np.array([[0, 0, 0], [1, 0, 1], [1, 1, 1]]).astype(np.int64))
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>>> hypothesis_values = Tensor(np.array([1, 2, 3]).astype(np.float32))
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>>> hypothesis_shape = Tensor(np.array([1, 1, 2]).astype(np.int64))
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>>> truth_indices = Tensor(np.array([[0, 1, 0], [0, 0, 1], [1, 1, 0], [1, 0, 1]]).astype(np.int64))
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>>> truth_values = Tensor(np.array([1, 3, 2, 1]).astype(np.float32))
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>>> truth_shape = Tensor(np.array([2, 2, 2]).astype(np.int64))
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>>> edit_distance = EditDistance(hypothesis_shape, truth_shape)
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>>> out = edit_distance(hypothesis_indices, hypothesis_values, truth_indices, truth_values)
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>>> [[1.0, 1.0], [1.0, 1.0]]
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"""
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@prim_attr_register
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def __init__(self, normalize=True):
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"""init EditDistance"""
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self.normalize = validator.check_value_type("normalize", normalize, [bool], self.name)
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def __infer__(self, h_indices, h_values, h_shape, truth_indices, truth_values, truth_shape):
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validator.check_const_input('hypothesis_shape', h_shape['value'], self.name)
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validator.check_const_input('truth_shape', truth_shape['value'], self.name)
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args_int = {"hypothesis_indices": h_indices['dtype'], "hypothesis_shape": h_shape['dtype'],
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"truth_indices": truth_indices['dtype'], "truth_shape": truth_shape['dtype']}
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validator.check_tensor_type_same(args_int, [mstype.int64], self.name)
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args = {"hypothesis_values": h_values['dtype'], "truth_values": truth_values['dtype']}
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validator.check_tensor_type_same(args, mstype.number_type, self.name)
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hypothesis_indices_shp, truth_indices_shp = h_indices['shape'], truth_indices['shape']
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validator.check("hypothesis_indices rank", len(hypothesis_indices_shp), "expected", 2, Rel.EQ, self.name)
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validator.check("truth_indices rank", len(truth_indices_shp), "expected", 2, Rel.EQ, self.name)
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validator.check("hypothesis_values rank", len(h_values['shape']), "expected", 1, Rel.EQ, self.name)
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validator.check("hypothesis_shape rank", len(h_shape['shape']), "expected", 1, Rel.EQ, self.name)
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validator.check("truth_values rank", len(truth_values['shape']), "expected", 1, Rel.EQ, self.name)
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validator.check("truth_shape rank", len(truth_shape['shape']), "expected", 1, Rel.EQ, self.name)
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validator.check("hypothesis_values shape", h_values['shape'][0],
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"hypothesis_indices shape[0]", hypothesis_indices_shp[0], Rel.EQ, self.name)
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validator.check("hypothesis_shape", h_shape['shape'][0],
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"hypothesis_indices shape[1]", hypothesis_indices_shp[1], Rel.EQ, self.name)
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validator.check("truth_values shape", truth_values['shape'][0],
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"truth_indices shape[0]", truth_indices_shp[0], Rel.EQ, self.name)
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validator.check("hypothesis_shape", h_shape['shape'][0],
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"truth_shape", truth_shape['shape'][0], Rel.EQ, self.name)
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hypothesis_shape_v = h_shape['value'].asnumpy()
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truth_shape_v = truth_shape['value'].asnumpy()
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out_shape_rank = len(hypothesis_shape_v) - 1
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out_shape = []
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for i in range(out_shape_rank):
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out_shape.append(max(hypothesis_shape_v[i], truth_shape_v[i]))
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return {'shape': tuple(out_shape),
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'dtype': mstype.tensor_type(mstype.float32),
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'value': None}
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class TransShape(PrimitiveWithInfer):
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"""
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Transform the shape of input tensor to target shape.
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mindspore-ci-bot
5 years ago
committed by
Gitee