Merge remote-tracking branch 'upstream/develop' into windows/build

paddle/fluid/API.spec

@@ -179,6 +179,7 @@ paddle.fluid.layers.space_to_depth ArgSpec(args=['x', 'blocksize', 'name'], vara
 paddle.fluid.layers.affine_grid ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.sequence_reverse ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.affine_channel ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None))
+paddle.fluid.layers.similarity_focus ArgSpec(args=['input', 'axis', 'indexes', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.hash ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None))
 paddle.fluid.layers.grid_sampler ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.log_loss ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None))
@@ -201,6 +202,7 @@ paddle.fluid.layers.create_tensor ArgSpec(args=['dtype', 'name', 'persistable'],
 paddle.fluid.layers.create_parameter ArgSpec(args=['shape', 'dtype', 'name', 'attr', 'is_bias', 'default_initializer'], varargs=None, keywords=None, defaults=(None, None, False, None))
 paddle.fluid.layers.create_global_var ArgSpec(args=['shape', 'value', 'dtype', 'persistable', 'force_cpu', 'name'], varargs=None, keywords=None, defaults=(False, False, None))
 paddle.fluid.layers.cast ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.layers.tensor_array_to_tensor ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None))
 paddle.fluid.layers.concat ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(0, None))
 paddle.fluid.layers.sums ArgSpec(args=['input', 'out'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.assign ArgSpec(args=['input', 'output'], varargs=None, keywords=None, defaults=(None,))

paddle/fluid/operators/CMakeLists.txt

@@ -320,6 +320,7 @@ op_library(save_op DEPS lod_tensor)
 op_library(load_op DEPS lod_tensor)
 op_library(save_combine_op DEPS lod_tensor)
 op_library(load_combine_op DEPS lod_tensor)
+op_library(tensor_array_to_tensor_op DEPS concat_op)
 op_library(concat_op DEPS concat_and_split)
 
 list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})

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 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 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.
+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

paddle/fluid/operators/tensor_array_to_tensor_op.cc

+/* 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. */
+
+#include <string>
+#include <vector>
+
+#include "paddle/fluid/framework/lod_tensor_array.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/variable.h"
+
+namespace paddle {
+namespace operators {
+using framework::Tensor;
+
+void LodTensorArray2LodTensorVector(const framework::Scope &scope,
+                                    const std::string &base_name,
+                                    const std::string &lod_tensor_array_name,
+                                    std::vector<std::string> *res_names) {
+  auto &inx =
+      scope.FindVar(lod_tensor_array_name)->Get<framework::LoDTensorArray>();
+  for (size_t i = 0; i < inx.size(); i++) {
+    std::string var_name = base_name + std::to_string(i);
+    framework::Variable *g_feed_value =
+        const_cast<framework::Scope &>(scope).Var(var_name);
+    auto &feed_input =
+        *(g_feed_value->GetMutable<paddle::framework::LoDTensor>());
+    feed_input.ShareDataWith(inx[i]);
+    res_names->push_back(var_name);
+  }
+}
+
+void LodTensorVectorResizeFromLodTensorArray(
+    const framework::Scope &scope, const std::string &base_name,
+    const std::string &lod_tensor_array_name,
+    std::vector<std::string> *res_names) {
+  auto &inx =
+      scope.FindVar(lod_tensor_array_name)->Get<framework::LoDTensorArray>();
+  for (size_t i = 0; i < inx.size(); i++) {
+    std::string var_name = base_name + std::to_string(i);
+    framework::Variable *g_feed_value =
+        const_cast<framework::Scope &>(scope).Var(var_name);
+    auto &feed_input =
+        *(g_feed_value->GetMutable<paddle::framework::LoDTensor>());
+    auto dims = inx[i].dims();
+    feed_input.Resize(dims);
+    res_names->push_back(var_name);
+  }
+}
+
+void LodTensorArrayCreateFromLodTensorArray(
+    const framework::Scope &scope,
+    const std::string &input_lod_tensor_array_name,
+    const std::string &output_lod_tensor_array_name) {
+  auto &inx = scope.FindVar(input_lod_tensor_array_name)
+                  ->Get<framework::LoDTensorArray>();
+  auto &grad_inx = *scope.FindVar(output_lod_tensor_array_name)
+                        ->GetMutable<framework::LoDTensorArray>();
+
+  for (size_t i = 0; i < inx.size(); i++) {
+    std::string var_name = output_lod_tensor_array_name + std::to_string(i);
+    framework::Variable *g_feed_value =
+        const_cast<framework::Scope &>(scope).Var(var_name);
+    auto &feed_input =
+        *(g_feed_value->GetMutable<paddle::framework::LoDTensor>());
+    grad_inx.push_back(feed_input);
+  }
+}
+
+class LoDTensorArray2TensorOp : public framework::OperatorBase {
+ public:
+  using OperatorBase::OperatorBase;
+
+ private:
+  void RunImpl(const framework::Scope &scope,
+               const platform::Place &place) const override {
+    auto axis = Attr<int>("axis");
+
+    framework::AttributeMap attrs;
+    attrs["axis"] = axis;
+
+    auto &inx = scope.FindVar(Input("X"))->Get<framework::LoDTensorArray>();
+    auto &out =
+        *scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
+    auto &out_inx =
+        *scope.FindVar(Output("OutIndex"))->GetMutable<framework::LoDTensor>();
+
+    const size_t n = inx.size();
+    PADDLE_ENFORCE_GT(n, 0, "Input tensorarray size should > 0.");
+
+    std::string base_name = Inputs("X")[0];
+    std::vector<std::string> names;
+
+    // get the input tensorarray items' dim in out_inx
+    auto out_inx_dim = out_inx.dims();
+    out_inx_dim[0] = inx.size();
+    out_inx.Resize(out_inx_dim);
+
+    std::string var_name = "out_index";
+    framework::Variable *tmp_index_var =
+        const_cast<framework::Scope &>(scope).Var(var_name);
+    auto &tmp_index_tensor =
+        *(tmp_index_var->GetMutable<paddle::framework::LoDTensor>());
+    tmp_index_tensor.Resize(out_inx_dim);
+    int *tmp_index_data =
+        tmp_index_tensor.mutable_data<int>(platform::CPUPlace());
+
+    auto out_dims = inx[0].dims();
+    size_t out_dim_sum = 0;
+    for (size_t index = 0; index < inx.size(); index++) {
+      auto inx_dims = inx[index].dims();
+      out_dim_sum += inx_dims[axis];
+      tmp_index_data[index] = inx_dims[axis];
+    }
+    out_inx.ShareDataWith(tmp_index_tensor);
+
+    // get input array items' dims
+    out_dims[axis] = out_dim_sum;
+    out.Resize(out_dims);
+
+    LodTensorArray2LodTensorVector(scope, base_name, Input("X"), &names);
+    // Invoke Reshape Op
+    auto concat_op = framework::OpRegistry::CreateOp(
+        "concat", {{"X", names}}, {{"Out", {Output("Out")}}}, attrs);
+
+    concat_op->Run(scope, place);
+  }
+};
+
+class LoDTensorArray2TensorOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "Input LoDTensorArray of tensor_array_to_tensor operator.");
+    AddOutput("Out", "Output tensor of tensor_array_to_tensor operator.");
+    AddOutput("OutIndex",
+              "Output input LoDTensorArray items' dims of "
+              "tensor_array_to_tensor operator.");
+    AddAttr<int>("axis",
+                 "The axis along which the input tensors will be concatenated.")
+        .SetDefault(0);
+    AddComment(R"DOC(
+tensor_array_to_tensor Operator.
+
+Concatenate the input LoDTensorArray along dimension axis to the output Tensor.
+Examples:
+  Input = {[1,2], [3,4], [5,6]}
+  axis = 0
+  Output = [[1,2],
+            [3,4],
+            [5,6]]
+  OutputIndex = [1,1,1]
+
+)DOC");
+  }
+};
+
+class LoDTensorArray2TensorOpInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *ctx) const override {}
+};
+
+class LoDTensorArray2TensorGradInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *context) const override {}
+};
+
+class LoDTensorArray2TensorGradInferVarType
+    : public framework::VarTypeInference {
+ public:
+  void operator()(const framework::OpDesc &op_desc,
+                  framework::BlockDesc *block) const override {
+    for (auto &out_var : op_desc.Output(framework::GradVarName("X"))) {
+      block->Var(out_var)->SetType(framework::proto::VarType::LOD_TENSOR_ARRAY);
+    }
+  }
+};
+
+class LoDTensorArray2TensorGradOp : public framework::OperatorBase {
+ public:
+  using OperatorBase::OperatorBase;
+
+ private:
+  void RunImpl(const framework::Scope &scope,
+               const platform::Place &place) const override {
+    auto axis = Attr<int>("axis");
+    framework::AttributeMap attrs;
+    attrs["axis"] = axis;
+
+    auto &inx = scope.FindVar(Input("X"))->Get<framework::LoDTensorArray>();
+    const size_t n = inx.size();
+    PADDLE_ENFORCE_GT(n, 0, "Input tensorarray size should > 0.");
+
+    std::string base_name = Inputs("X")[0];
+    std::vector<std::string> names;
+
+    LodTensorArray2LodTensorVector(scope, base_name, Input("X"), &names);
+
+    // grad
+    auto dx_name = Output(framework::GradVarName("X"));
+    auto dout_name = Input(framework::GradVarName("Out"));
+
+    std::vector<std::string> grad_names;
+
+    LodTensorVectorResizeFromLodTensorArray(scope, "grad_name", Input("X"),
+                                            &grad_names);
+
+    auto concat_grad_op = framework::OpRegistry::CreateOp(
+        "concat_grad", {{"X", names}, {"Out@GRAD", {dout_name}}},
+        {{"X@GRAD", grad_names}}, attrs);
+
+    concat_grad_op->Run(scope, place);
+
+    LodTensorArrayCreateFromLodTensorArray(scope, Input("X"), dx_name);
+    auto &grad_inx =
+        *scope.FindVar(dx_name)->GetMutable<framework::LoDTensorArray>();
+
+    for (size_t i = 0; i < grad_names.size(); i++) {
+      std::string var_name = grad_names[i];
+      auto &feed_input = scope.FindVar(var_name)->Get<framework::LoDTensor>();
+      grad_inx[i].ShareDataWith(feed_input);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+USE_OP(concat);
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(tensor_array_to_tensor, ops::LoDTensorArray2TensorOp,
+                  ops::LoDTensorArray2TensorOpMaker,
+                  ops::LoDTensorArray2TensorOpInferShape,
+                  paddle::framework::DefaultGradOpDescMaker<true>);
+REGISTER_OPERATOR(tensor_array_to_tensor_grad, ops::LoDTensorArray2TensorGradOp,
+                  ops::LoDTensorArray2TensorGradInferShape,
+                  ops::LoDTensorArray2TensorGradInferVarType);

python/paddle/fluid/layers/nn.py

@@ -161,6 +161,7 @@ __all__ = [
     'affine_grid',
     'sequence_reverse',
     'affine_channel',
+    'similarity_focus',
     'hash',
     'grid_sampler',
     'log_loss',
@@ -7937,6 +7938,118 @@ def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
     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 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 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.
+    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>`_
+
+    .. code-block:: text
+
+        * 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: [0]
+
+            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]]]]
+
+    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 selected. 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=[2, 3, 2, 2], dtype='float32')
+            x = fluid.layers.layer_norm(input=data, axis=1, indexes=[0])
+    """
+    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.")
+
+    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},
+        outputs={'Out': out},
+        attrs={"axis": axis,
+               "indexes": indexes})
+    return out
+
+
 def hash(input, hash_size, num_hash=1, name=None):
     """
     Hash the input to an integer whose value is less than the given hash size.

python/paddle/fluid/layers/tensor.py

@@ -24,10 +24,10 @@ from .layer_function_generator import templatedoc
 import numpy
 
 __all__ = [
-    'create_tensor', 'create_parameter', 'create_global_var', 'cast', 'concat',
-    'sums', 'assign', 'fill_constant_batch_size_like', 'fill_constant',
-    'argmin', 'argmax', 'argsort', 'ones', 'zeros', 'reverse', 'has_inf',
-    'has_nan', 'isfinite'
+    'create_tensor', 'create_parameter', 'create_global_var', 'cast',
+    'tensor_array_to_tensor', 'concat', 'sums', 'assign',
+    'fill_constant_batch_size_like', 'fill_constant', 'argmin', 'argmax',
+    'argsort', 'ones', 'zeros', 'reverse', 'has_inf', 'has_nan', 'isfinite'
 ]
 
 
@@ -193,6 +193,60 @@ def concat(input, axis=0, name=None):
     return out
 
 
+def tensor_array_to_tensor(input, axis=1, name=None):
+    """
+    This function concatenates the input LodTensorArray along the axis mentioned
+    and returns that as the output.
+
+    A simple example as below:
+    
+    .. code-block:: text
+    
+        Given:
+
+        input.data = {[[0.6, 0.1, 0.3],
+                       [0.5, 0.3, 0.2]],
+                      [[1.3],
+                       [1.8]],
+                      [[2.3, 2.1],
+                       [2.5, 2.4]]}
+        
+        axis = 1
+    
+        Then:
+
+        output.data = [[0.6, 0.1, 0.3, 1.3, 2.3, 2.1],
+                       [0.5, 0.3, 0.2, 1.8, 2.5, 2.4]]
+
+        output_index.data = [3, 1, 2]
+
+    Args:
+        input(list): Input LodTensorArray
+        axis(int): Integer axis along which the tensors will be concatenated
+        name(str|None): A name for this layer(optional). If set None, the layer
+                       will be named automatically.
+
+    Returns:
+        Variable: Output variable of the concatenation
+        Variable: The input LodTensorArray items' dims along the axis
+
+    Examples:
+        .. code-block:: python
+
+           output, output_index = fluid.layers.tensor_array_to_tensor(input=tensor_array)
+    """
+    helper = LayerHelper('tensor_array_concat', **locals())
+    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
+    out_index = helper.create_variable_for_type_inference(dtype="int32")
+    helper.append_op(
+        type='tensor_array_concat',
+        inputs={'X': input},
+        outputs={'Out': [out],
+                 'OutIndex': [out_index]},
+        attrs={'axis': axis})
+    return out, out_index
+
+
 def sums(input, out=None):
     """
     This function performs the sum operation on the input and returns the

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(OpTest):
+    def setUp(self):
+        self.op_type = "similarity_focus"
+        batch_size = 2
+        x_dim, y_dim, z_dim = 3, 2, 2
+        self.inputs = {
+            'X': np.array([[[[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]]]]),
+        }
+        self.attrs = {
+            'axis': 1,
+            'indexes': [0],
+        }
+
+        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).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_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()

python/paddle/fluid/tests/unittests/test_tensor_array_to_tensor.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
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid.op import Operator
+from paddle.fluid.executor import Executor
+
+
+class TestLoDTensorArrayConcat(unittest.TestCase):
+    def setUp(self):
+        self.op_type = "tensor_array_to_tensor"
+        self.attrs = {"axis": 0}
+        self.outputs = ["Out"]
+
+    def test_get_set(self):
+        scope = core.Scope()
+        program = fluid.Program()
+        block = program.global_block()
+
+        input_arr = block.create_var(
+            name="tmp_lod_tensor_array",
+            type=core.VarDesc.VarType.LOD_TENSOR_ARRAY)
+        input_arr.persistable = True
+        input_arr_var = scope.var('tmp_lod_tensor_array')
+        input_tensor_array = input_arr_var.get_lod_tensor_array()
+        self.assertEqual(0, len(input_tensor_array))
+
+        cpu = core.CPUPlace()
+        for i in range(10):
+            t = core.LoDTensor()
+            if i == 0:
+                t.set(numpy.array([[i], [i]], dtype='float32'), cpu)
+            else:
+                t.set(numpy.array([[i]], dtype='float32'), cpu)
+            input_tensor_array.append(t)
+
+        self.assertEqual(10, len(input_tensor_array))
+
+        random_grad = numpy.random.random_sample([11]).astype(numpy.float32)
+
+        y_out = block.create_var(name="Out")
+        y_out.persistable = True
+        y_out_index = block.create_var(name="OutIndex")
+        y_out_index.persistable = True
+
+        y_grad_arr = block.create_var(
+            name='Out@GRAD', dtype='float32', shape=[11])
+        y_grad_arr.persistable = True
+        y_grad = scope.var('Out@GRAD')
+        y_grad_tensor = y_grad.get_tensor()
+        y_grad_tensor.set(random_grad, cpu)
+
+        op = block.append_op(
+            type=self.op_type,
+            inputs={"X": input_arr},
+            outputs={"Out": y_out,
+                     "OutIndex": y_out_index},
+            attrs=self.attrs)
+
+        out_grad = block.create_var(
+            name="tmp_lod_tensor_array@GRAD",
+            type=core.VarDesc.VarType.LOD_TENSOR_ARRAY)
+        out_grad.persistable = True
+
+        grad_op_desc_list, op_grad_to_var = core.get_grad_op_desc(op.desc,
+                                                                  set(), [])
+        grad_op_desc = grad_op_desc_list[0]
+        new_op_desc = block.desc.append_op()
+        new_op_desc.copy_from(grad_op_desc)
+        for var_name in grad_op_desc.output_arg_names():
+            block.desc.var(var_name.encode("ascii"))
+
+        grad_op_desc.infer_var_type(block.desc)
+        grad_op_desc.infer_shape(block.desc)
+        for arg in grad_op_desc.output_arg_names():
+            grad_var = block.desc.find_var(arg.encode("ascii"))
+            grad_var.set_dtype(core.VarDesc.VarType.FP32)
+
+        fetch_list = []
+        fetch_list.append(block.var('Out'))
+        fetch_list.append(block.var('OutIndex'))
+
+        exe = fluid.Executor(fluid.CPUPlace())
+        out = exe.run(program, fetch_list=fetch_list, scope=scope)
+        #print ("index: ", numpy.array(out[1]))  
+
+        # test forward
+        tensor_res = numpy.array(out[0])
+        tensor_res_out_idx = numpy.array(out[1])
+        tensor_gt = numpy.array(
+            [0] + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='float32')
+
+        self.assertEqual(len(tensor_res), len(tensor_gt))
+        self.assertEqual(len(tensor_res_out_idx), 10)
+
+        for i in range(len(tensor_res)):
+            self.assertEqual(tensor_res[i], tensor_gt[i])
+
+        for i in range(len(tensor_res_out_idx)):
+            if i == 0:
+                self.assertEqual(tensor_res_out_idx[i], 2)
+            else:
+                self.assertEqual(tensor_res_out_idx[i], 1)
+
+        # test backward
+        grad_tensor = scope.var('tmp_lod_tensor_array@GRAD')
+        grad_tensor_array = grad_tensor.get_lod_tensor_array()
+
+        self.assertEqual(10, len(grad_tensor_array))
+
+        for i in range(len(grad_tensor_array)):
+            if i == 0:
+                self.assertEqual(
+                    numpy.array(grad_tensor_array[i])[0],
+                    numpy.array(random_grad[i]))
+                self.assertEqual(
+                    numpy.array(grad_tensor_array[i])[1],
+                    numpy.array(random_grad[i + 1]))
+            if i == 1:
+                self.assertEqual(
+                    numpy.array(grad_tensor_array[i]),
+                    numpy.array(random_grad[i + 1]))
+
+
+if __name__ == '__main__':
+    unittest.main()