提交 5e7bb6a9 编写于 作者: B barrierye

update docs test=develop

上级 316e020a
......@@ -35,14 +35,17 @@ class SimilarityFocusOpMaker : public framework::OpProtoAndCheckerMaker {
SimilarityFocus Operator.
Generate a similarity focus mask with the same shape of input using the following method:
1. Extract the 4-D matrix(here the first dimension is BatchSize) corresponding
to the axis according to the indexes. For example, if axis=1 and indexes=[a],
it will get the matrix T=X[:, a, :, :]. In this case, if the shape of input X
is (BatchSize, A, B, C), the shape of matrix T is (BatchSize, B, C).
2. For each index, find the largest numbers in the matrix T, so that the same
row and same column has at most one number(obviously there will be min(B, C)
numbers), and mark the corresponding position of the 3-D similarity focus mask
as 1, otherwise as 0. Do elementwise-or for each index.
1. Extract the 3-D tensor(here the first dimension is BatchSize) corresponding
to the axis according to the indexes. For example, if axis=1 and indexes=[a],
it will get the matrix T=X[:, a, :, :]. In this case, if the shape of input X
is (BatchSize, A, B, C), the shape of tensor T is (BatchSize, B, C).
2. For each index, find the largest numbers in the tensor T, so that the same
row and same column has at most one number(what it means is that if the
largest number has been found in the i-th row and the j-th column, then
the numbers in the i-th or j-th column will be skipped. Obviously there
will be min(B, C) numbers), and mark the corresponding position of the
3-D similarity focus mask as 1, otherwise as 0. Do elementwise-or for
each index.
3. Broadcast the 3-D similarity focus mask to the same shape of input X.
Refer to `Similarity Focus Layer <http://www.aclweb.org/anthology/N16-1108>`_
......
......@@ -7560,14 +7560,17 @@ def similarity_focus(input, axis, indexes, name=None):
SimilarityFocus Operator
Generate a similarity focus mask with the same shape of input using the following method:
1. Extract the 4-D matrix(here the first dimension is BatchSize) corresponding
1. Extract the 3-D tensor(here the first dimension is BatchSize) corresponding
to the axis according to the indexes. For example, if axis=1 and indexes=[a],
it will get the matrix T=X[:, a, :, :]. In this case, if the shape of input X
is (BatchSize, A, B, C), the shape of matrix T is (BatchSize, B, C).
2. For each index, find the largest numbers in the matrix T, so that the same
row and same column has at most one number(obviously there will be min(B, C)
numbers), and mark the corresponding position of the 3-D similarity focus mask
as 1, otherwise as 0. Do elementwise-or for each index.
is (BatchSize, A, B, C), the shape of tensor T is (BatchSize, B, C).
2. For each index, find the largest numbers in the tensor T, so that the same
row and same column has at most one number(what it means is that if the
largest number has been found in the i-th row and the j-th column, then
the numbers in the i-th or j-th column will be skipped. Obviously there
will be min(B, C) numbers), and mark the corresponding position of the
3-D similarity focus mask as 1, otherwise as 0. Do elementwise-or for
each index.
3. Broadcast the 3-D similarity focus mask to the same shape of input X.
Refer to `Similarity Focus Layer <http://www.aclweb.org/anthology/N16-1108>`_
......@@ -7624,9 +7627,9 @@ def similarity_focus(input, axis, indexes, name=None):
Args:
input(Variable): The input tensor variable(default float). It should
be a 4-D tensor with shape [BatchSize, A, B, C].
axis(int): Indicating the dimension to be select. It can only be
axis(int): Indicating the dimension to be selected. It can only be
1, 2 or 3.
indexes(list): indicating the indexes of the selected dimension.
indexes(list): Indicating the indexes of the selected dimension.
Returns:
Variable: A tensor variable with the same shape and same type
......@@ -7649,7 +7652,11 @@ def similarity_focus(input, axis, indexes, name=None):
if len(indexes) == 0:
raise ValueError("indexes can not be empty.")
out = helper.create_tmp_variable(dtype=helper.input_dtype())
if name is None:
out = helper.create_variable_for_type_inference(dtype=input.dtype)
else:
out = helper.create_variable(
name=name, dtype=input.dtype, persistable=False)
helper.append_op(
type='similarity_focus',
inputs={'X': input},
......
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