Add EditDistance op for GE.
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@ -190,6 +190,7 @@ constexpr const char kNameSquareSumAll[] = "SquareSumAll";
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constexpr const char kNameAscendQuant[] = "Quant";
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constexpr const char kNameAscendDequant[] = "Dequant";
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constexpr const char kNameReverseSequence[] = "ReverseSequence";
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constexpr const char kNameEditDistance[] = "EditDistance";
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constexpr const char kNameCase[] = "Case";
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class OpAdapterMap {
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@ -87,4 +87,12 @@ ATTR_MAP(ReverseSequence) = {{"seq_dim", ATTR_DESC(seq_dim, AnyTraits<int>())},
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{"batch_dim", ATTR_DESC(batch_dim, AnyTraits<int>())}};
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OUTPUT_MAP(ReverseSequence) = {{0, OUTPUT_DESC(y)}};
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REG_ADPT_DESC(ReverseSequence, kNameReverseSequence, ADPT_DESC(ReverseSequence))
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// EditDistance
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INPUT_MAP(EditDistance) = {{1, INPUT_DESC(hypothesis_indices)}, {2, INPUT_DESC(hypothesis_values)},
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{3, INPUT_DESC(hypothesis_shape)}, {4, INPUT_DESC(truth_indices)},
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{5, INPUT_DESC(truth_values)}, {6, INPUT_DESC(truth_shape)}};
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ATTR_MAP(EditDistance) = {{"normalize", ATTR_DESC(normalize, AnyTraits<bool>())}};
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OUTPUT_MAP(EditDistance) = {{0, OUTPUT_DESC(output)}};
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REG_ADPT_DESC(EditDistance, kNameEditDistance, ADPT_DESC(EditDistance))
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} // namespace mindspore::transform
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@ -54,5 +54,8 @@ DECLARE_OP_ADAPTER(Data)
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DECLARE_OP_ADAPTER(ReverseSequence)
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DECLARE_OP_USE_OUTPUT(ReverseSequence)
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DECLARE_OP_ADAPTER(EditDistance)
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DECLARE_OP_USE_OUTPUT(EditDistance)
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} // namespace mindspore::transform
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#endif // MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_OP_DECLARE_ARRAY_OPS_DECLARE_H_
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@ -23,7 +23,6 @@ from .acos_grad import _acos_grad_tbe
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from .acosh import _acosh_tbe
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from .acosh_grad import _acosh_grad_tbe
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from .adam_apply_one_with_decay import _adam_apply_one_with_decay_tbe
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from .add import _add_tbe
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from .apply_centered_rms_prop import _apply_centered_rms_prop_tbe
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from .add_n import _add_n_tbe
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from .accumulate_n_v2 import _accumulate_n_v2_tbe
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@ -1,37 +0,0 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Add op"""
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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add_op_info = TBERegOp("Add") \
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.fusion_type("ELEMWISE") \
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.async_flag(False) \
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.binfile_name("add.so") \
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.compute_cost(10) \
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.kernel_name("add") \
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.partial_flag(True) \
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.op_pattern("dynamicFormat") \
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.input(0, "x1", False, "required", "all") \
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.input(1, "x2", False, "required", "all") \
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.output(0, "y", False, "required", "all") \
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.dtype_format(DataType.None_None, DataType.None_None, DataType.None_None) \
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.get_op_info()
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@op_info_register(add_op_info)
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def _add_tbe():
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"""Add TBE register"""
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return
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@ -29,7 +29,7 @@ from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Unpack,
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ScatterUpdate, ScalarToArray, ScalarToTensor, ScatterNd, ScatterNdUpdate, Select,
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Shape, Size, Slice, Split, TransShape, ParallelConcat, Padding,
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ScatterNdAdd, ScatterNdSub, ScatterNonAliasingAdd, ReverseV2, Rint,
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Squeeze, StridedSlice, Tile, TensorScatterUpdate,
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Squeeze, StridedSlice, Tile, TensorScatterUpdate, EditDistance,
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Transpose, TruncatedNormal, TupleToArray, UnsortedSegmentMin, UnsortedSegmentProd,
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UnsortedSegmentSum, SpaceToDepth, DepthToSpace, SpaceToBatch, BatchToSpace,
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SpaceToBatchND, BatchToSpaceND, BroadcastTo, InplaceUpdate, ReverseSequence, EmbeddingLookup,
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@ -92,6 +92,7 @@ from .sparse_ops import SparseToDense
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__all__ = [
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'ReverseSequence',
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'EditDistance',
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'CropAndResize',
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'TensorAdd',
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'Argmax',
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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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@ -684,6 +684,18 @@ class ParallelConcatNet(nn.Cell):
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return self.parallel_concat((x1, x2))
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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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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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test_case_math_ops = [
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('BitwiseAnd', {
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'block': P.BitwiseAnd(),
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@ -1978,6 +1990,15 @@ test_case_array_ops = [
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'desc_inputs': [Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]).astype(np.float32)),
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Tensor(np.array([1, 2, 3]).astype(np.int32))],
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'desc_bprop': [[3, 3]]}),
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('EditDistance', {
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'block': EditDistance(Tensor(np.array([1, 1, 2]).astype(np.int64)),
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Tensor(np.array([2, 2, 2]).astype(np.int64))),
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'desc_inputs': [Tensor(np.array([[0, 0, 0], [1, 0, 1], [1, 1, 1]]).astype(np.int64)),
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Tensor(np.array([1, 2, 3]).astype(np.float32)),
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Tensor(np.array([[0, 1, 0], [0, 0, 1], [1, 1, 0], [1, 0, 1]]).astype(np.int64)),
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Tensor(np.array([1, 3, 2, 1]).astype(np.float32))],
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'skip': ['backward'],
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}),
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('LinSpace', {
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'block': inner.LinSpace(),
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'desc_inputs': [Tensor([5, 5.5], mstype.float32),
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