add similarity_focus op

paddle/fluid/operators/similarity_focus_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/operators/similarity_focus_op.h"
+
+namespace paddle {
+namespace operators {
+class SimilarityFocusOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X",
+             "(Tensor, default Tensor<float>), a 4-D tensor with shape,"
+             " [BatchSize, X, Y, Z]");
+    AddOutput("Out",
+              "(Tensor, default Tensor<float>), the similarity focus mask"
+              " with the same shape of input X.");
+    AddAttr<int>("axis",
+                 "(int32), indicating the dimension to be select. It can"
+                 " only be 1, 2, or 3.");
+    AddAttr<std::vector<int>>("indexes",
+                              "(std::vector<int32>), indicating the indexes"
+                              " of the selected dimension.");
+    AddComment(R"DOC(
+SimilarityFocus Operator.
+
+Generate a similarity focus mask with the same shape of input using the following method:
+1. Extract the 3-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 casr, 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.
+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>`_
+)DOC");
+  }
+};
+
+class SimilarityFocusOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should be not null.");
+    auto x_dims = ctx->GetInputDim("X");
+    PADDLE_ENFORCE_EQ(x_dims.size(), 4, "Input(X)'s rank should be 4.");
+    ctx->SetOutputDim("Out", x_dims);
+    ctx->ShareLoD("X", /*->*/ "Out");
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()),
+        platform::CPUPlace());
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(similarity_focus, ops::SimilarityFocusOp,
+                  ops::SimilarityFocusOpMaker,
+                  paddle::framework::EmptyGradOpMaker);
+REGISTER_OP_CPU_KERNEL(similarity_focus, ops::SimilarityFocusKernel<float>,
+                       ops::SimilarityFocusKernel<double>);

paddle/fluid/operators/similarity_focus_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include <algorithm>
+#include <cstring>
+#include <utility>
+#include <vector>
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+using Tensor = framework::Tensor;
+
+template <typename T>
+class SimilarityFocusKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    Tensor* out = context.Output<Tensor>("Out");
+    const Tensor* x = context.Input<Tensor>("X");
+    T* out_data = out->mutable_data<T>(context.GetPlace());
+    const T* x_data = x->data<T>();
+
+    int axis = context.Attr<int>("axis");
+    std::vector<int> indexes = context.Attr<std::vector<int>>("indexes");
+
+    int64_t batch_size = x->dims()[0];
+    int64_t dim[4];
+    for (int i = 1; i <= 3; ++i) {
+      dim[i] = x->dims()[i];
+    }
+
+    if (indexes.size() < 1) {
+      PADDLE_THROW("Indexes' size can not be 0.");
+    }
+    for (auto index : indexes) {
+      if (dim[axis] < index) {
+        PADDLE_THROW("Index exceeds tensor shape limit.");
+      }
+    }
+
+    int64_t array_size = 1;
+    for (int i = 1; i <= 3; ++i) {
+      if (i != axis) {
+        array_size *= dim[i];
+      }
+    }
+
+    std::vector<std::pair<T, int64_t>> array(array_size);
+
+    bool (*cmp)(std::pair<T, int64_t>, std::pair<T, int64_t>) = [](
+        std::pair<T, int64_t> x, std::pair<T, int64_t> y) {
+      return x.first > y.first;
+    };
+
+    int64_t (*compute_index)(int64_t*, int, int, int, int) = [](
+        int64_t* dim, int d1, int d2, int d3, int d4) {
+      return d1 * dim[1] * dim[2] * dim[3] + d2 * dim[2] * dim[3] +
+             d3 * dim[3] + d4;
+    };
+
+    memset(out_data, 0, sizeof(T) * batch_size * dim[1] * dim[2] * dim[3]);
+    for (int i = 0; i < batch_size; ++i) {
+      for (auto index : indexes) {
+        if (axis == 1) {
+          for (int j = 0; j < dim[2]; ++j) {
+            for (int k = 0; k < dim[3]; ++k) {
+              array[j * dim[3] + k] = std::make_pair(
+                  x_data[compute_index(dim, i, index, j, k)], j * dim[3] + k);
+            }
+          }
+
+          std::sort(array.begin(), array.end(), cmp);
+          int tag_num = 0;
+          std::vector<bool> tag2(dim[2]), tag3(dim[3]);
+          for (auto x : array) {
+            int idx2 = x.second / dim[3];
+            int idx3 = x.second % dim[3];
+            if (tag2[idx2] || tag3[idx3]) {
+              continue;
+            }
+            tag_num++;
+            tag2[idx2] = true;
+            tag3[idx3] = true;
+            for (int j = 0; j < dim[1]; ++j) {
+              out_data[compute_index(dim, i, j, idx2, idx3)] = 1;
+            }
+            if (tag_num == std::min(dim[2], dim[3])) {
+              break;
+            }
+          }
+        } else if (axis == 2) {
+          for (int j = 0; j < dim[1]; ++j) {
+            for (int k = 0; k < dim[3]; ++k) {
+              array[j * dim[3] + k] = std::make_pair(
+                  x_data[compute_index(dim, i, j, index, k)], j * dim[3] + k);
+            }
+          }
+
+          std::sort(array.begin(), array.end(), cmp);
+          int tag_num = 0;
+          std::vector<bool> tag1(dim[1]), tag3(dim[3]);
+          for (auto x : array) {
+            int idx1 = x.second / dim[3];
+            int idx3 = x.second % dim[3];
+            if (tag1[idx1] || tag3[idx3]) {
+              continue;
+            }
+            tag_num++;
+            tag1[idx1] = true;
+            tag3[idx3] = true;
+            for (int j = 0; j < dim[2]; ++j) {
+              out_data[compute_index(dim, i, idx1, j, idx3)] = 1;
+            }
+            if (tag_num == std::min(dim[1], dim[3])) {
+              break;
+            }
+          }
+        } else if (axis == 3) {
+          for (int j = 0; j < dim[1]; ++j) {
+            for (int k = 0; k < dim[2]; ++k) {
+              array[j * dim[2] + k] = std::make_pair(
+                  x_data[compute_index(dim, i, j, k, index)], j * dim[2] + k);
+            }
+          }
+
+          std::sort(array.begin(), array.end(), cmp);
+          int tag_num = 0;
+          std::vector<bool> tag1(dim[1]), tag2(dim[2]);
+          for (auto x : array) {
+            int idx1 = x.second / dim[2];
+            int idx2 = x.second % dim[2];
+            if (tag1[idx1] || tag2[idx2]) {
+              continue;
+            }
+            tag_num++;
+            tag1[idx1] = true;
+            tag2[idx2] = true;
+            for (int j = 0; j < dim[3]; ++j) {
+              out_data[compute_index(dim, i, idx1, idx2, j)] = 1;
+            }
+            if (tag_num == std::min(dim[1], dim[2])) {
+              break;
+            }
+          }
+        } else {
+          PADDLE_THROW("Axis must be 1 or 2 or 3");
+        }
+      }
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/fluid/layers/nn.py

@@ -155,6 +155,7 @@ __all__ = [
     'sigmoid_cross_entropy_with_logits',
     'maxout',
     'affine_channel',
+    'similarity_focus',
 ]
 
 
@@ -7494,3 +7495,58 @@ def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
         attrs={"data_layout": data_layout},
         outputs={"Out": out})
     return out
+
+
+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 3-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 casr, 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.
+    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>`_
+
+    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
+            1, 2, or 3.
+        indexes(list): indicating the indexes of the selected dimension.
+
+    Returns:
+        Variable: A tensor variable with the same shape and same type 
+            as the input.
+        
+    Examples:
+        .. code-block:: python
+            data = fluid.layers.data(
+              name='data', shape=[128, 13, 48, 48], dtype='float32')
+            x = fluid.layers.layer_norm(input=data, axis=1, indexes=[9, 10])
+    """
+    helper = LayerHelper('similarity_focus', **locals())
+    # check attrs
+    if isinstance(axis, int) is False:
+        raise TypeError("axis must be int type.")
+    if isinstance(indexes, list) is False:
+        raise TypeError("indexes must be list type.")
+    if axis != 1 and axis != 2 and axis != 3:
+        raise ValueError("axis must be 1, 2 or 3.")
+    if len(indexes) == 0:
+        raise ValueError("indexes can not be empty.")
+
+    out = helper.create_tmp_variable(dtype=helper.input_dtype())
+    helper.append_op(
+        type='similarity_focus',
+        inputs={'X': input},
+        outputs={'Out': out},
+        attrs={"axis": axis,
+               "indexes": indexes})
+    return out

python/paddle/fluid/tests/unittests/test_similarity_focus_op.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import paddle.fluid.core as core
+from op_test import OpTest
+
+
+class TestSimilarityFocusOp_axis1(OpTest):
+    def setUp(self):
+        self.op_type = "similarity_focus"
+        batch_size = 3
+        x_dim, y_dim, z_dim = 4, 5, 6
+        self.inputs = {
+            'X': np.random.random(
+                (batch_size, x_dim, y_dim, z_dim)).astype("float32"),
+        }
+        self.attrs = {
+            'axis': 1,
+            'indexes': [0, 3],
+        }
+
+        output = None
+        for batch in range(batch_size):
+            res = np.zeros((1, y_dim, z_dim)).astype("float32").reshape(-1)
+            for index in self.attrs['indexes']:
+                channel = self.inputs['X'][batch, index, :, :].reshape(-1).copy(
+                )
+                tag1 = [0 for i in range(y_dim)]
+                tag2 = [0 for i in range(z_dim)]
+                cnt = 0
+                for i in range(channel.size):
+                    index = channel.argmax()
+                    idx1 = index / z_dim
+                    idx2 = index % z_dim
+                    if tag1[idx1] + tag2[idx2] == 0:
+                        tag1[idx1] = 1
+                        tag2[idx2] = 1
+                        res[index] = 1
+                        cnt += 1
+                        if cnt == min(y_dim, z_dim):
+                            break
+                    channel[index] = -1
+            res = res.reshape(1, y_dim, z_dim)
+            res = res.repeat([x_dim], axis=0)
+            res = res.reshape(1, x_dim, y_dim, z_dim)
+            if output is not None:
+                output = np.concatenate((output, res), axis=0)
+            else:
+                output = res
+        self.outputs = {'Out': output}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestSimilarityFocusOp_axis2(OpTest):
+    def setUp(self):
+        self.op_type = "similarity_focus"
+        batch_size = 6
+        x_dim, y_dim, z_dim = 7, 8, 9
+        self.inputs = {
+            'X': np.random.random(
+                (batch_size, x_dim, y_dim, z_dim)).astype("float32"),
+        }
+        self.attrs = {
+            'axis': 2,
+            'indexes': [0, 3, 5],
+        }
+
+        output = None
+        for batch in range(batch_size):
+            res = np.zeros((x_dim, 1, z_dim)).astype("float32").reshape(-1)
+            for index in self.attrs['indexes']:
+                channel = self.inputs['X'][batch, :, index, :].reshape(-1).copy(
+                )
+                tag1 = [0 for i in range(x_dim)]
+                tag2 = [0 for i in range(z_dim)]
+                cnt = 0
+                for i in range(channel.size):
+                    index = channel.argmax()
+                    idx1 = index / z_dim
+                    idx2 = index % z_dim
+                    if tag1[idx1] + tag2[idx2] == 0:
+                        tag1[idx1] = 1
+                        tag2[idx2] = 1
+                        res[index] = 1
+                        cnt += 1
+                        if cnt == min(x_dim, z_dim):
+                            break
+                    channel[index] = -1
+            res = res.reshape(x_dim, 1, z_dim)
+            res = res.repeat([y_dim], axis=1)
+            res = res.reshape(1, x_dim, y_dim, z_dim)
+            if output is not None:
+                output = np.concatenate((output, res), axis=0)
+            else:
+                output = res
+        self.outputs = {'Out': output}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestSimilarityFocusOp_axis3(OpTest):
+    def setUp(self):
+        self.op_type = "similarity_focus"
+        batch_size = 64
+        x_dim, y_dim, z_dim = 48, 48, 13
+        self.inputs = {
+            'X': np.random.random(
+                (batch_size, x_dim, y_dim, z_dim)).astype("float32"),
+        }
+        self.attrs = {
+            'axis': 3,
+            'indexes': [0, 2, 7, 9],
+        }
+
+        output = None
+        for batch in range(batch_size):
+            res = np.zeros((x_dim, y_dim, 1)).astype("float32").reshape(-1)
+            for index in self.attrs['indexes']:
+                channel = self.inputs['X'][batch, :, :, index].reshape(-1).copy(
+                )
+                tag1 = [0 for i in range(x_dim)]
+                tag2 = [0 for i in range(y_dim)]
+                cnt = 0
+                for i in range(channel.size):
+                    index = channel.argmax()
+                    idx1 = index / y_dim
+                    idx2 = index % y_dim
+                    if tag1[idx1] + tag2[idx2] == 0:
+                        tag1[idx1] = 1
+                        tag2[idx2] = 1
+                        res[index] = 1
+                        cnt += 1
+                        if cnt == min(x_dim, y_dim):
+                            break
+                    channel[index] = -1
+            res = res.reshape(x_dim, y_dim, 1)
+            res = res.repeat([z_dim], axis=2)
+            res = res.reshape(1, x_dim, y_dim, z_dim)
+            if output is not None:
+                output = np.concatenate((output, res), axis=0)
+            else:
+                output = res
+        self.outputs = {'Out': output}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+if __name__ == "__main__":
+    unittest.main()