similarity_focus( input, axis, indexes, name=None )
Generate a similarity focus mask with the same shape of input using the following method:
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).
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 row or j-th column will be skipped. And then the next largest number will be selected from the remaining numbers. 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.
Broadcast the 3-D similarity focus mask to the same shape of input X.
Refer to Similarity Focus Layer
* Example : Given a 4-D tensor x with the shape (BatchSize, C, A, B), where C is the number of channels and the shape of feature map is (A, B): x.shape = (2, 3, 2, 2) x.data = [[[[0.8, 0.1], [0.4, 0.5]], [[0.9, 0.7], [0.9, 0.9]], [[0.8, 0.9], [0.1, 0.2]]], [[[0.2, 0.5], [0.3, 0.4]], [[0.9, 0.7], [0.8, 0.4]], [[0.0, 0.2], [0.4, 0.7]]]] Given axis: 1 (the axis of the channel) Given indexes:  then we get a 4-D tensor out with the same shape of input x: out.shape = (2, 3, 2, 2) out.data = [[[[1.0, 0.0], [0.0, 1.0]], [[1.0, 0.0], [0.0, 1.0]], [[1.0, 0.0], [0.0, 1.0]]], [[[0.0, 1.0], [1.0, 0.0]], [[0.0, 1.0], [1.0, 0.0]], [[0.0, 1.0], [1.0, 0.0]]]]
input (Variable) – The input tensor variable(default float). It should be a 4-D tensor with shape [BatchSize, A, B, C]. Data type is float32 or float64.
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.
- A tensor variable with the same shape and same type
as the input.
- Return type
import paddle.fluid as fluid import paddle paddle.enable_static() data = fluid.data( name='data', shape=[-1, 3, 2, 2], dtype='float32') fluid.layers.similarity_focus(input=data, axis=1, indexes=)