Add bilinear interpolation layer

doc/ui/api/trainer_config_helpers/layers.rst

@@ -263,6 +263,12 @@ interpolation_layer
     :members: interpolation_layer
     :noindex:
 
+bilinear_interp_layer
+-------------------
+..  automodule:: paddle.trainer_config_helpers.layers
+    :members: bilinear_interp_layer
+    :noindex:
+
 power_layer
 -----------
 ..  automodule:: paddle.trainer_config_helpers.layers

paddle/cuda/include/hl_cnn.h

@@ -240,4 +240,60 @@ extern void hl_CMRNorm_backward(
     size_t channels, size_t height, size_t width, size_t sizeX,
     real alpha, real beta);
 
+/**
+ * @brief   Bilinear interpolation forward.
+ *
+ * @param[in]   inData      input value.
+ * @param[in]   inImgH      input image height.
+ * @param[in]   inImgW      input image width.
+ * @param[in]   inputH      input batchSize.
+ * @param[in]   inputW      input image data dim.
+ * @param[out]  outData     output value.
+ * @param[in]   outImgH     output image height.
+ * @param[in]   outImgW     output image width.
+ * @param[in]   outputH     output batchSize.
+ * @param[in]   outputW     output image data dim.
+ * @param[in]   numChannels number of channels.
+ *
+ */
+extern void hl_bilinear_forward(const real* inData,
+                                const size_t inImgH,
+                                const size_t inImgW,
+                                const size_t inputH,
+                                const size_t inputW,
+                                real* outData,
+                                const size_t outImgH,
+                                const size_t outImgW,
+                                const size_t outputH,
+                                const size_t outputW,
+                                const size_t numChannels);
+
+ /**
+ * @brief   Bilinear interpolation backward.
+ *
+ * @param[out]  inGrad      input gradient.
+ * @param[in]   inImgH      input image height.
+ * @param[in]   inImgW      input image width.
+ * @param[in]   inputH      input batchSize.
+ * @param[in]   inputW      input image data dim.
+ * @param[in]   outGrad     output gradient.
+ * @param[in]   outImgH     output image height.
+ * @param[in]   outImgW     output image width.
+ * @param[in]   outputH     output batchSize.
+ * @param[in]   outputW     output image data dim.
+ * @param[in]   numChannels number of channels.
+ *
+ */                               
+extern void hl_bilinear_backward(real* inGrad,
+                                 const size_t inImgH,
+                                 const size_t inImgW,
+                                 const size_t inputH,
+                                 const size_t inputW,
+                                 const real* outGrad,
+                                 const size_t outImgH,
+                                 const size_t outImgW,
+                                 const size_t outputH,
+                                 const size_t outputW,
+                                 const size_t numChannels);
+
 #endif /* HL_CNN_H_ */

paddle/cuda/include/stub/hl_cnn_stub.h

@@ -89,4 +89,28 @@ inline void hl_CMRNorm_backward(
     size_t channels, size_t height, size_t width, size_t sizeX,
     real alpha, real beta) {}
 
+inline void hl_bilinear_forward(const real* inData,
+                                const size_t inImgH,
+                                const size_t inImgW,
+                                const size_t inputH,
+                                const size_t inputW,
+                                real* outData,
+                                const size_t outImgH,
+                                const size_t outImgW,
+                                const size_t outputH,
+                                const size_t outputW,
+                                const size_t numChannels) {}
+
+inline void hl_bilinear_backward(real* inGrad,
+                                const size_t inImgH,
+                                const size_t inImgW,
+                                const size_t inputH,
+                                const size_t inputW,
+                                const real* outGrad,
+                                const size_t outImgH,
+                                const size_t outImgW,
+                                const size_t outputH,
+                                const size_t outputW,
+                                const size_t numChannels) {}
+
 #endif  // HL_CNN_STUB_H_

paddle/cuda/src/hl_cuda_cnn.cu

@@ -522,7 +522,7 @@ void hl_CMRNorm_backward(size_t frameCnt, const real* inV,
                          size_t height, size_t width, size_t sizeX,
                          real alpha, real beta) {
   size_t threadsNum = frameCnt * height * width;
-  size_t blocksX = (threadsNum + 1024 -1) / 1024;
+  size_t blocksX = (threadsNum + 1024 - 1) / 1024;
   size_t blocksY = 1;
   dim3 threads(1024, 1);
   dim3 grid(blocksX, blocksY);
@@ -531,3 +531,135 @@ void hl_CMRNorm_backward(size_t frameCnt, const real* inV,
            height, width, sizeX, alpha, beta, inDiff);
   CHECK_SYNC("hl_CMRNorm_backward");
 }
+
+__global__ void KeBilinearInterpFw(const size_t nthreads,
+                                   const real* in,
+                                   const size_t inImgH,
+                                   const size_t inImgW,
+                                   const size_t inputH,
+                                   const size_t inputW,
+                                   real* out,
+                                   const size_t outImgH,
+                                   const size_t outImgW,
+                                   const size_t outputH,
+                                   const size_t outputW,
+                                   const size_t numChannels,
+                                   const real ratioH,
+                                   const real ratioW) {
+  int tid = blockIdx.x * blockDim.x + threadIdx.x;
+  if(tid < nthreads) {
+    int outIdH = tid / (outputW / numChannels);
+    int outIdW = tid % (outputW / numChannels);
+
+    int inIdH = ratioH * (outIdW / outImgW);
+    int hId = (inIdH < inImgH - 1) ? 1 : 0;
+    real hlambda = ratioH * (outIdW / outImgW) - inIdH;
+
+    int inIdW = ratioW * (tid % outImgW);
+    int wId = (inIdW < inImgW - 1) ? 1 : 0;
+    real wlambda = ratioW * (tid % outImgW) - inIdW;
+
+    const real* inPos = &in[outIdH * inputW + inIdH * inImgW + inIdW];
+    real* outPos = &out[outIdH * outputW + outIdW];
+    for (int c = 0; c < numChannels; ++c) {
+      // bilinear interpolation
+      outPos[0] = (1.f - hlambda) *
+        ((1.f - wlambda) * inPos[0] + wlambda * inPos[wId]) + 
+        hlambda * ((1.f - wlambda) * inPos[hId * inImgW] +
+        wlambda * inPos[hId * inImgW + wId]);
+      inPos += inImgH * inImgW;
+      outPos += outImgH * outImgW;
+    }
+  }
+}
+
+void hl_bilinear_forward(const real* inData,
+                         const size_t inImgH,
+                         const size_t inImgW,
+                         const size_t inputH,
+                         const size_t inputW,
+                         real* outData,
+                         const size_t outImgH,
+                         const size_t outImgW,
+                         const size_t outputH,
+                         const size_t outputW,
+                         const size_t numChannels) {
+  int threadNum = outputH * outImgH * outImgW;
+  int blocks = (threadNum + 1024 - 1) / 1024;
+
+  real ratioH = (outImgH > 1) ?
+      static_cast<float>(inImgH - 1) / (outImgH - 1) : 0.f;
+  real ratioW = (outImgW > 1) ?
+      static_cast<float>(inImgW - 1) / (outImgW - 1) : 0.f;
+
+  KeBilinearInterpFw<<< blocks, 1024, 0, STREAM_DEFAULT>>>(
+    threadNum, inData, inImgH, inImgW, inputH, inputW, outData,
+    outImgH, outImgW, outputH, outputW, numChannels, ratioH, ratioW);
+  CHECK_SYNC("hl_bilinear_forward failed");
+}
+
+__global__ void KeBilinearInterpBw(const size_t nthreads,
+                                   real* in,
+                                   const size_t inImgH,
+                                   const size_t inImgW,
+                                   const size_t inputH,
+                                   const size_t inputW,
+                                   const real* out,
+                                   const size_t outImgH,
+                                   const size_t outImgW,
+                                   const size_t outputH,
+                                   const size_t outputW,
+                                   const size_t numChannels,
+                                   const real ratioH,
+                                   const real ratioW) {
+  int tid = blockIdx.x * blockDim.x + threadIdx.x;
+
+  if(tid < nthreads) {
+    int outIdH = tid / (outputW / numChannels);
+    int outIdW = tid % (outputW / numChannels);
+
+    int inIdH = ratioH * (outIdW / outImgW);
+    int hId = (inIdH < inImgH - 1) ? 1 : 0;
+    real hlambda = ratioH * (outIdW / outImgW) - inIdH;
+
+    int inIdW = ratioW * (tid % outImgW);
+    int wId = (inIdW < inImgW - 1) ? 1 : 0;
+    real wlambda = ratioW * (tid % outImgW) - inIdW;
+
+    const real* outPos = &out[outIdH * outputW + outIdW];
+    real* inPos = &in[outIdH * inputW + inIdH * inImgW + inIdW];
+    for (int c = 0; c < numChannels; ++c) {
+      atomicAdd(&inPos[0], (1.f - hlambda) * (1.f - wlambda) * outPos[0]);
+      atomicAdd(&inPos[wId], (1.f - hlambda) * wlambda * outPos[0]);
+      atomicAdd(&inPos[hId * inImgW], hlambda * (1.f - wlambda) * outPos[0]);
+      atomicAdd(&inPos[hId * inImgW + wId], hlambda * wlambda * outPos[0]);
+      inPos += inImgH * inImgW;
+      outPos += outImgH * outImgW;
+    }
+  }
+}
+
+void hl_bilinear_backward(real* inGrad,
+                          const size_t inImgH,
+                          const size_t inImgW,
+                          const size_t inputH,
+                          const size_t inputW,
+                          const real* outGrad,
+                          const size_t outImgH,
+                          const size_t outImgW,
+                          const size_t outputH,
+                          const size_t outputW,
+                          const size_t numChannels) {
+  int threadNum = outputH * outImgH * outImgW;
+  int blocks = (threadNum + 1024 - 1) / 1024;
+ 
+  real ratioH = (outImgH > 1) ?
+      static_cast<float>(inImgH - 1) / (outImgH - 1) : 0.f;
+  real ratioW = (outImgW > 1) ?
+      static_cast<float>(inImgW - 1) / (outImgW - 1) : 0.f;
+
+  KeBilinearInterpBw<<< blocks, 1024, 0, STREAM_DEFAULT>>>(
+    threadNum, inGrad, inImgH, inImgW, inputH, inputW, outGrad,
+    outImgH, outImgW, outputH, outputW, numChannels, ratioH, ratioW);
+  CHECK_SYNC("hl_bilinear_backward failed");
+}
\ No newline at end of file

paddle/gserver/layers/BilinearInterpLayer.cpp

+/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
+
+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 "BilinearInterpLayer.h"
+#include "paddle/utils/Logging.h"
+#include "paddle/utils/Stat.h"
+
+namespace paddle {
+
+REGISTER_LAYER(bilinear_interp, BilinearInterpLayer);
+
+size_t BilinearInterpLayer::getDataDimSize() {
+  getOutput().setFrameHeight(outImgH_);
+  getOutput().setFrameWidth(outImgW_);
+  return outImgH_ * outImgW_ * numChannels_;
+}
+
+bool BilinearInterpLayer::init(const LayerMap& layerMap,
+                               const ParameterMap& parameterMap) {
+  /* Initialize the basic parent class */
+  Layer::init(layerMap, parameterMap);
+
+  CHECK_EQ(1, config_.inputs_size());
+
+  const BilinearInterpConfig& conf = config_.inputs(0).bilinear_interp_conf();
+  inImgH_ = inputLayers_[0]->getOutput().getFrameHeight();
+  inImgW_ = inputLayers_[0]->getOutput().getFrameWidth();
+  if (inImgH_ == 0) {
+    inImgH_ = conf.img_size_y();
+  }
+  if (inImgW_ == 0) {
+    inImgW_ = conf.img_size_x();
+  }
+  outImgH_ = conf.out_size_y();
+  outImgW_ = conf.out_size_x();
+  numChannels_ = conf.num_channels();
+
+  CHECK(outImgH_ > 0 && outImgW_ > 0);
+  CHECK(inImgH_ > 0 && inImgW_ > 0);
+  CHECK(numChannels_);
+
+  return true;
+}
+
+void BilinearInterpLayer::forward(PassType passType) {
+  Layer::forward(passType);
+  size_t batchSize = getInput(0).getBatchSize();
+  size_t size = getDataDimSize();
+  {
+    REGISTER_TIMER_INFO("FwResetTimer", getName().c_str());
+    resetOutput(batchSize, size);
+  }
+
+  MatrixPtr inV = getInputValue(0);
+  MatrixPtr outV = getOutputValue();
+  {
+    REGISTER_TIMER_INFO("FwBilinearInterpTimer", getName().c_str());
+    outV->bilinearForward(*inV, inImgH_, inImgW_, outImgH_, outImgW_,
+      numChannels_);
+  }
+}
+
+void BilinearInterpLayer::backward(const UpdateCallback& callback) {
+  (void) callback;
+
+  MatrixPtr inputG = getInputGrad(0);
+  MatrixPtr outG = getOutputGrad();
+  {
+    REGISTER_TIMER_INFO("BwBilinearInterpTimer", getName().c_str());
+    if (inputG) {
+      inputG->bilinearBackward(*outG, outImgH_, outImgW_, inImgH_, inImgW_,
+        numChannels_);
+    }
+  }
+}
+}  // namespace paddle

paddle/gserver/layers/BilinearInterpLayer.h

+/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
+
+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 "Layer.h"
+#include "paddle/math/Matrix.h"
+
+namespace paddle {
+
+/**
+ * @brief A layer for bilinear interpolation which is
+ *        used on conv layer output.
+ *
+ * @note  The config file api is bilinear_interp_layer.
+ */
+class BilinearInterpLayer : public Layer {
+protected:
+  size_t outImgH_, outImgW_;
+  size_t inImgH_, inImgW_;
+  size_t numChannels_;
+
+public:
+  explicit BilinearInterpLayer(const LayerConfig& config) : Layer(config) {}
+
+  virtual ~BilinearInterpLayer() {}
+
+  size_t getDataDimSize();
+  bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
+  void forward(PassType passType);
+  void backward(const UpdateCallback& callback = nullptr);
+};
+
+}  // namespace paddle

paddle/gserver/tests/test_LayerGrad.cpp

@@ -31,6 +31,26 @@ P_DECLARE_double(checkgrad_eps);
 P_DECLARE_bool(thread_local_rand_use_global_seed);
 P_DECLARE_bool(prev_batch_state);
 
+TEST(Layer, BilinearInterpLayer) {
+  TestConfig config;
+  config.layerConfig.set_type("bilinear_interp");
+  config.biasSize = 0;
+
+  config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0});
+  LayerInputConfig* input = config.layerConfig.add_inputs();
+  BilinearInterpConfig* bilinear = input->mutable_bilinear_interp_conf();
+
+  bilinear->set_img_size_x(32);
+  bilinear->set_img_size_y(32);
+  bilinear->set_out_size_x(64);
+  bilinear->set_out_size_y(64);
+  bilinear->set_num_channels(4);
+
+  for (auto useGpu : {false, true}) {
+    testLayerGrad(config, "bilinear_interp", 10, false, useGpu);
+  }
+}
+
 TEST(Operator, dot_mul) {
   TestConfig config;
   config.layerConfig.set_size(10);

paddle/math/Matrix.cpp

@@ -23,6 +23,7 @@ limitations under the License. */
 
 #include "paddle/utils/Logging.h"
 #include <string.h>
+#include "hl_cnn.h"
 #include "hl_gpu.h"
 #include "hl_table_apply.h"
 #include "hl_top_k.h"
@@ -1144,6 +1145,56 @@ void GpuMatrix::addColumnVector(const Matrix& b) {
   BaseMatrix::addColVector(const_cast<Matrix&>(b));
 }
 
+void GpuMatrix::bilinearForward(const Matrix& in,
+                                const size_t inImgH,
+                                const size_t inImgW,
+                                const size_t outImgH,
+                                const size_t outImgW,
+                                const size_t numChannels) {
+  CHECK(dynamic_cast<const GpuMatrix*>(&in));
+
+  const size_t outputW = getWidth();
+  const size_t outputH = getHeight();
+  const size_t inputW = in.getWidth();
+  const size_t inputH = in.getHeight();
+
+  real* outData = getData();
+  const real* inData  = in.getData();
+
+  if (inImgH == outImgW && inImgW == outImgW) {
+    this->copyFrom(in);
+  } else {
+    hl_bilinear_forward(inData, inImgH, inImgW,
+      inputH, inputW, outData, outImgH, outImgW,
+      outputH, outputW, numChannels);
+  }
+}
+
+void GpuMatrix::bilinearBackward(const Matrix& out,
+                                 const size_t outImgH,
+                                 const size_t outImgW,
+                                 const size_t inImgH,
+                                 const size_t inImgW,
+                                 const size_t numChannels) {
+  CHECK(dynamic_cast<const GpuMatrix*>(&out));
+
+  const size_t inputW = getWidth();
+  const size_t inputH = getHeight();
+  const size_t outputW = out.getWidth();
+  const size_t outputH = out.getHeight();
+
+  real* inGrad = getData();
+  const real* outGrad = out.getData();
+
+  if (outImgH == inImgH && outImgW == inImgW) {
+    this->copyFrom(out);
+  } else {
+    hl_bilinear_backward(inGrad, inImgH, inImgW,
+      inputH, inputW, outGrad, outImgH, outImgW,
+      outputH, outputW, numChannels);
+  }
+}
+
 /**
  * CpuMatrix
  */
@@ -3598,6 +3649,109 @@ void CpuMatrix::classificationErrorMulti(Matrix& output, Matrix& label,
   }
 }
 
+void CpuMatrix::bilinearForward(const Matrix& in,
+                                const size_t inImgH,
+                                const size_t inImgW,
+                                const size_t outImgH,
+                                const size_t outImgW,
+                                const size_t numChannels) {
+  CHECK(dynamic_cast<const CpuMatrix*>(&in));
+
+  size_t outputW = getWidth();
+  size_t outputH = getHeight();
+  size_t inputW = in.getWidth();
+  size_t inputH = in.getHeight();
+
+  real* outData = getData();
+  const real* inData  = in.getData();
+
+  const real ratioH = (outImgH > 1) ?
+    static_cast<real>(inImgH - 1) / (outImgH - 1) : 0.f;
+  const real ratioW = (outImgW > 1) ?
+    static_cast<real>(inImgW - 1) / (outImgW - 1) : 0.f;
+
+  if (inImgH == outImgH && inImgW == outImgW) {
+    this->copyFrom(in);
+  } else {
+    for (int k = 0; k < outputH; ++k) {   // loop for batches
+      for (int i = 0; i < outImgH; ++i) {  // loop for images
+        int h = ratioH * i;
+        int hid = (h < inImgH - 1) ? 1 : 0;
+        real hlambda = ratioH * i - h;
+
+        for (int j = 0; j < outImgW; ++j) {
+          int w = ratioW * j;
+          int wid = (w < inImgW - 1) ? 1 : 0;
+          real wlambda = ratioW * j - w;
+          // calculate four position for bilinear interpolation
+          const real* inPos = &inData[k * inputW + h * inImgW + w];
+          real* outPos = &outData[k * outputW + i * outImgW + j];
+          for (int c = 0; c < numChannels; ++c) {  // loop for channels
+            // bilinear interpolation
+            outPos[0] = (1.f - hlambda) *
+              ((1.f - wlambda) * inPos[0] + wlambda * inPos[wid]) +
+              hlambda * ((1.f - wlambda) * inPos[hid * inImgW] +
+              wlambda * inPos[hid * inImgW + wid]);
+            inPos += inImgH * inImgW;
+            outPos += outImgH * outImgW;
+          }
+        }
+      }
+    }
+  }
+}
+
+void CpuMatrix::bilinearBackward(const Matrix& out,
+                                 const size_t outImgH,
+                                 const size_t outImgW,
+                                 const size_t inImgH,
+                                 const size_t inImgW,
+                                 const size_t numChannels) {
+  CHECK(dynamic_cast<const CpuMatrix*>(&out));
+
+  size_t inputW = getWidth();
+  size_t inputH = getHeight();
+  size_t outputW = out.getWidth();
+  size_t outputH = out.getHeight();
+
+  real* inGrad = getData();
+  const real* outGrad = out.getData();
+
+  const real ratioH = (outImgH > 1) ?
+    static_cast<real>(inImgH - 1) / (outImgH - 1) : 0.f;
+  const real ratioW = (outImgW > 1) ?
+    static_cast<real>(inImgW - 1) / (outImgW - 1) : 0.f;
+
+  if (inImgH == outImgH && inImgW == outImgW) {
+    this->copyFrom(out);
+  } else {
+    for (int k = 0; k < outputH; ++k) {   // loop for batches
+      for (int i = 0; i < outImgH; ++i) {  // loop for images
+        int h = ratioH * i;
+        int hid = (h < inImgH - 1) ? 1 : 0;
+        real hlambda = ratioH * i - h;
+
+        for (int j = 0; j < outImgW; ++j) {
+          int w = ratioW * j;
+          int wid = (w < inImgW - 1) ? 1 : 0;
+          real wlambda = ratioW * j - w;
+
+          real* inPos = &inGrad[k * inputW + h * inImgW + w];
+          const real* outPos = &outGrad[k * outputW + i * outImgW + j];
+          for (int c = 0; c < numChannels; ++c) {  // loop for channels
+            inPos[0] += (1.f - hlambda) * (1.f - wlambda) * outPos[0];
+            inPos[wid] += (1.f - hlambda) * wlambda * outPos[0];
+            inPos[hid * inImgW] += hlambda * (1.f - wlambda) * outPos[0];
+            inPos[hid * inImgW + wid] += hlambda * wlambda * outPos[0];
+            inPos += inImgH * inImgW;
+            outPos += outImgH * outImgW;
+          }
+        }
+      }
+    }
+  }
+}
+
 ////////////////////////////////////////////////////////////////
 //               functions executed via cpu                   //
 ////////////////////////////////////////////////////////////////

paddle/math/Matrix.h

@@ -930,6 +930,22 @@ public:
   virtual void paramReluBackwardDiff(Matrix& oGrad, Matrix& data, Matrix& W) {
     LOG(FATAL) << "Not implemented";
   }
+  virtual void bilinearForward(const Matrix& in,
+                               const size_t inImgH,
+                               const size_t inImgW,
+                               const size_t outImgH,
+                               const size_t outImgW,
+                               const size_t numChannels) {
+    LOG(FATAL) << "Not implemented";
+  }
+  virtual void bilinearBackward(const Matrix& out,
+                                const size_t outImgH,
+                                const size_t outImgW,
+                                const size_t inImgH,
+                                const size_t inImgW,
+                                const size_t numChannels) {
+    LOG(FATAL) << "Not implemented";
+  }
 };
 
 inline std::ostream& operator<<(std::ostream& os, const Matrix& mat) {
@@ -1191,6 +1207,20 @@ public:
                                        int contextLength,
                                        int contextStart, int totalPad,
                                        size_t beginPad);
+
+  void bilinearForward(const Matrix& in,
+                       const size_t inImgH,
+                       const size_t inImgW,
+                       const size_t outImgH,
+                       const size_t outImgW,
+                       const size_t numChannels);
+
+  void bilinearBackward(const Matrix& out,
+                        const size_t outImgH,
+                        const size_t outImgW,
+                        const size_t inImgH,
+                        const size_t inImgW,
+                        const size_t numChannels);
 };
 
 class CpuMatrix : public Matrix {
@@ -1469,6 +1499,20 @@ public:
   void multiBinaryLabelCrossEntropy(Matrix& output, Matrix& label);
   void multiBinaryLabelCrossEntropyBp(Matrix& output, Matrix& label);
   void classificationErrorMulti(Matrix& output, Matrix& label, real threshold);
+
+  void bilinearForward(const Matrix& in,
+                       const size_t inImgH,
+                       const size_t inImgW,
+                       const size_t outImgH,
+                       const size_t outImgW,
+                       const size_t numChannels);
+
+  void bilinearBackward(const Matrix& out,
+                        const size_t outImgH,
+                        const size_t outImgW,
+                        const size_t inImgH,
+                        const size_t inImgW,
+                        const size_t numChannels);
 };
 
 class SharedCpuMatrix : public CpuMatrix {

paddle/math/tests/test_matrixCompare.cpp

@@ -88,6 +88,72 @@ void MatrixCheckErr(const Matrix& matrix1, const Matrix& matrix2) {
   EXPECT_EQ(count, 0) << "There are " << count << " different element.";
 }
 
+void testBilinearFwdBwd(int numSamples, int imgSizeH, int imgSizeW,
+                        int channels) {
+  int inWidth = imgSizeH * imgSizeW * channels;
+  int outWidth = 2 * imgSizeH * 2 * imgSizeW * channels;
+
+  // forward
+  MatrixPtr input = CpuMatrix::create(numSamples, inWidth, false, false);
+  MatrixPtr inputGpu = GpuMatrix::create(numSamples, inWidth, false, true);
+
+  MatrixPtr target = CpuMatrix::create(numSamples, outWidth, false, false);
+  MatrixPtr targetGpu = GpuMatrix::create(numSamples, outWidth, false, true);
+  MatrixPtr targetCheck = CpuMatrix::create(numSamples, outWidth, false, false);
+
+  input->randomizeUniform();
+  inputGpu->copyFrom(*input);
+
+  target->bilinearForward(*input, imgSizeH, imgSizeW,
+      2 * imgSizeH, 2 * imgSizeW, channels);
+  targetGpu->bilinearForward(*inputGpu, imgSizeH, imgSizeW,
+      2 * imgSizeH, 2 * imgSizeW, channels);
+
+  // check
+  targetCheck->copyFrom(*targetGpu);
+  MatrixCheckErr(*target, *targetCheck);
+
+  // backward
+  MatrixPtr inputGrad = CpuMatrix::create(numSamples, inWidth, false, false);
+  MatrixPtr inputGpuGrad = GpuMatrix::create(numSamples, inWidth, false, true);
+
+  MatrixPtr targetGrad = CpuMatrix::create(numSamples, outWidth, false, false);
+  MatrixPtr targetGpuGrad = GpuMatrix::create(numSamples, outWidth, false,
+                                              true);
+  MatrixPtr targetCheckGrad =
+      CpuMatrix::create(numSamples, inWidth, false, false);
+
+  inputGrad->randomizeUniform();
+  targetGrad->randomizeUniform();
+  inputGpuGrad->copyFrom(*inputGrad);
+  targetGpuGrad->copyFrom(*targetGrad);
+
+  inputGrad->bilinearBackward(*targetGrad, 2 * imgSizeH, 2 * imgSizeW,
+      imgSizeH, imgSizeW, channels);
+  inputGpuGrad->bilinearBackward(*targetGpuGrad, 2 * imgSizeH, 2 * imgSizeW,
+      imgSizeH, imgSizeW, channels);
+
+  // check
+  targetCheckGrad->copyFrom(*inputGpuGrad);
+  MatrixCheckErr(*inputGrad, *targetCheckGrad);
+}
+
+TEST(Matrix, BilinearFwdBwd) {
+  for (auto numSamples : {5, 10}) {
+    for (auto channels : {8, 16}) {
+      for (auto imgSizeH : {14, 28}) {
+        for (auto imgSizeW : {16, 30}) {
+          VLOG(3) << " numSamples=" << numSamples
+                  << " channels=" << channels
+                  << " imgSizeH=" << imgSizeH
+                  << " imgSizeW=" << imgSizeW;
+          testBilinearFwdBwd(numSamples, imgSizeH, imgSizeW, channels);
+        }
+      }
+    }
+  }
+}
+
 void testMatrixProjectionForward(int contextStart, int contextLength,
                                  bool padding, int batchSize, int inputDim) {
   MatrixPtr cpuInput = std::make_shared<CpuMatrix>(batchSize, inputDim);

proto/ModelConfig.proto.m4

@@ -203,6 +203,15 @@ message OperatorConfig {
   optional int32 num_filters = 7;
 }
 
+message BilinearInterpConfig {
+  // The size if input feature map.
+  required uint32 img_size_x = 1;
+  required uint32 img_size_y = 2;
+  // The size if output feature map.
+  required uint32 out_size_x = 3;
+  required uint32 out_size_y = 4;
+  required uint32 num_channels = 5;
+}
 
 message ImageConfig {
   // The image data dimensionality.
@@ -225,6 +234,7 @@ message LayerInputConfig {
   // If the input layer has multi-output.
   // Set the argument name.
   optional string input_layer_argument = 9;
+  optional BilinearInterpConfig bilinear_interp_conf = 10;
 }
 
 message LayerConfig {

python/paddle/trainer/config_parser.py

@@ -461,6 +461,7 @@ class Input(Cfg):
             sparse_update=None,
             gradient_clipping_threshold=None,
             conv=None,
+            bilinear_interp=None,
             norm=None,
             pool=None,
             image=None,
@@ -723,6 +724,18 @@ class Conv(Cfg):
         if output_x is not None:
           config_assert(output_x <= 0)
 
+# please refer to the comments in proto/ModelConfig.proto
+@config_class
+class BilinearInterp(Cfg):
+    def __init__(
+            self,
+            img_size_x = None,
+            img_size_y=None,
+            out_size_x = None,
+            out_size_y = None,
+            num_channels = None):
+        self.add_keys(locals())
+
 # please refer to the comments in proto/ModelConfig.proto
 @config_class
 class Pool(Cfg):
@@ -953,6 +966,13 @@ def TestData(data_config, async_load_data=None):
                        " Data definition")
         g_config.test_data_config.async_load_data = async_load_data
 
+def parse_bilinear(bilinear, input_layer_name, bilinear_conf):
+    bilinear_conf.img_size_x = bilinear.img_size_x;
+    bilinear_conf.img_size_y = bilinear.img_size_y;
+    bilinear_conf.out_size_x = bilinear.out_size_x;
+    bilinear_conf.out_size_y = bilinear.out_size_y;
+    bilinear_conf.num_channels = bilinear.num_channels;
+
 def parse_pool(pool, input_layer_name, pool_conf):
     pool_conf.pool_type = pool.pool_type
     config_assert(pool.pool_type in ['max-projection', 'avg-projection',
@@ -2306,6 +2326,21 @@ class InterpolationLayer(LayerBase):
         config_assert(input_layer1.size == input_layer2.size,
                       'the two vector inputs should be of the same size')
 
+@config_layer('bilinear_interp')
+class BilinearInterpLayer(LayerBase):
+    def __init__(
+            self,
+            name,
+            inputs,
+            device=None):
+        super(BilinearInterpLayer, self).__init__(
+            name, 'bilinear_interp', 0, inputs=inputs, device=device)
+        input_layer = self.get_input_layer(0)
+        self.set_layer_size(input_layer.size)
+        parse_bilinear(self.inputs[0].bilinear_interp,
+                       input_layer.name,
+                       self.config.inputs[0].bilinear_interp_conf);
+  
 @config_layer('sum_to_one_norm')
 class SumToOneNormLayer(LayerBase):
     def __init__(

python/paddle/trainer_config_helpers/layers.py

@@ -40,8 +40,8 @@ __all__ = ["full_matrix_projection", "AggregateLevel", "ExpandLevel",
            'img_cmrnorm_layer', 'addto_layer',
            'concat_layer', 'lstm_step_layer', 'recurrent_group',
            'memory', 'StaticInput', 'expand_layer', 'scaling_layer',
-           'power_layer', 'interpolation_layer', 'trans_layer',
-           'sum_to_one_norm_layer',
+           'power_layer', 'interpolation_layer', 'bilinear_interp_layer',
+           'trans_layer', 'sum_to_one_norm_layer',
            'get_output_layer', 'LayerType', 'context_projection',
            'beam_search', 'maxid_layer', 'GeneratedInput', 'SubsequenceInput',
            'gru_step_layer', 'recurrent_layer',
@@ -92,6 +92,7 @@ class LayerType(object):
 
     EXPAND_LAYER = 'expand'
     INTERPOLATION_LAYER = 'interpolation'
+    BILINEAR_INTERP_LAYER = 'bilinear_interp'
     POWER_LAYER = 'power'
     SCALING_LAYER = 'scaling'
     TRANS_LAYER = 'trans'
@@ -1252,6 +1253,70 @@ def interpolation_layer(input, weight, name=None, layer_attr=None):
                        size=input[0].size)
 
 
+@wrap_name_default()
+@layer_support()
+def bilinear_interp_layer(input,
+                          img_size_x=None,   
+                          img_size_y=None,
+                          out_size_x=None,
+                          out_size_y=None,
+                          num_channels=None,
+                          name=None,
+                          layer_attr=None):
+    """
+    This layer is to implement bilinear interpolation on conv layer output.
+
+    Please refer to Wikipedia: https://en.wikipedia.org/wiki/Bilinear_interpolation
+
+    The simple usage is:
+
+    .. code-block:: python
+
+       bilinear = bilinear_interp_layer(input,
+                                        img_size_x,
+                                        img_size_y,
+                                        out_size_x,
+                                        out_size_y,
+                                        num_channels)
+    
+    :para    input:        A input layer.
+    :type    input:        LayerOutput.
+    :para    img_size_x:   previous layer output width.
+    :type    img_size_x:   int|None 
+    :para    img_size_y:   previous layer output height.
+    :type    img_size_y:   int|None
+    :para    out_size_x:   bilinear interpolation output width.
+    :type    out_size_x:   int|None 
+    :para    out_size_y:   bilinear interpolation output height.
+    :type    out_size_y:   int|None
+    :para    num_channels: number of channels of input layer. If None,
+                           it will be set automatically from previous output.
+    :type    num_channels: int|None
+    :para    name:         The layer's name, which cna not be specified.
+    :type    name:         None|basestring
+    :para    layer_attr:   Extra Layer attribute.
+    :type    layer_attr:   ExtraLayerAttribute
+    :return: LayerOutput object.
+    :rtype:  LayerOutput
+    """
+    assert input.layer_type == LayerType.CONV_LAYER
+    assert isinstance(input.activation, LinearActivation)
+    assert img_size_x > 0 and img_size_y > 0
+    assert out_size_x > 0 and out_size_y > 0
+    if num_channels is None:
+        assert input.numfilters is not None
+        num_channels = input.num_filters
+    Layer(name=name,
+          inputs=Input(input.name,
+                       bilinear_interp=BilinearInterp(img_size_x=img_size_x,
+                                                      img_size_y=img_size_y,
+                                                      out_size_x=out_size_x,
+                                                      out_size_y=out_size_y,
+                                                      num_channels=num_channels)),
+          type=LayerType.BILINEAR_INTERP_LAYER,
+          **ExtraLayerAttribute.to_kwargs(layer_attr))
+    return LayerOutput(name, LayerType.BILINEAR_INTERP_LAYER, parents=[input])
+
 @wrap_name_default()
 @layer_support()
 def power_layer(input, weight, name=None, layer_attr=None):

python/paddle/trainer_config_helpers/tests/configs/generate_protostr.sh

@@ -8,7 +8,7 @@ configs=(test_fc layer_activations projections test_print_layer
 test_sequence_pooling test_lstmemory_layer test_grumemory_layer
 last_first_seq test_expand_layer test_ntm_layers test_hsigmoid
 img_layers util_layers simple_rnn_layers unused_layers test_cost_layers
-test_rnn_group)
+test_rnn_group test_bilinear_interp)
 
 
 for conf in ${configs[*]}

python/paddle/trainer_config_helpers/tests/configs/test_bilinear_interp.py

+from paddle.trainer_config_helpers import *
+
+settings(
+    batch_size=1000,
+    learning_rate=1e-5
+)
+
+data = data_layer(name='data', size=2304)
+
+conv = img_conv_layer(input=data,
+                      filter_size = 3,
+                      num_channels=1,
+                      num_filters=16,
+                      padding=1,
+                      act=LinearActivation(),
+                      bias_attr=True)
+
+bilinear = bilinear_interp_layer(input=conv,
+                                 img_size_x=32,
+                                 img_size_y=32,
+                                 out_size_x=64,
+                                 out_size_y=64,
+                                 num_channels=16)
+
+pool = img_pool_layer(input=bilinear,
+                      num_channels=4,
+                      pool_size=2,
+                      stride=2,
+                      pool_type=MaxPooling())
+
+fc = fc_layer(input=pool, size=384, bias_attr=False)
+
+outputs(fc)
\ No newline at end of file