Merge pull request #6100 from guoshengCS/enhance-include-pool

Enhance AvgPooling to support both include_mode and exclude_mode

paddle/cuda/include/hl_cnn.h

@@ -116,6 +116,7 @@ extern void hl_maxpool_backward(const int frameCnt,
  * @param[in]   paddingW    padding width.
  * @param[out]  tgtData     output data.
  * @param[in]   tgtStride   stride between output data samples.
+ * @param[in]   excludeMode whether to consider paddings for size.
  *
  */
 extern void hl_avgpool_forward(const int frameCnt,
@@ -132,7 +133,8 @@ extern void hl_avgpool_forward(const int frameCnt,
                                const int paddingH,
                                const int paddingW,
                                real* tgtData,
-                               const int tgtStride);
+                               const int tgtStride,
+                               bool excludeMode);
 
 /**
  * @brief   Maximum pool backward.
@@ -154,6 +156,7 @@ extern void hl_avgpool_forward(const int frameCnt,
  * @param[in]   scaleB      scale.
  * @param[out]  backGrad    output grad.
  * @param[in]   outStride   stride between output data samples.
+ * @param[in]   excludeMode whether to consider paddings for size.
  *
  */
 extern void hl_avgpool_backward(const int frameCnt,
@@ -172,7 +175,8 @@ extern void hl_avgpool_backward(const int frameCnt,
                                 real scaleA,
                                 real scaleB,
                                 real* backGrad,
-                                const int outStride);
+                                const int outStride,
+                                bool excludeMode);
 
 extern void hl_maxpool3D_forward(const int frameCnt,
                                  const real* inputData,

paddle/cuda/include/stub/hl_cnn_stub.h

@@ -68,7 +68,8 @@ inline void hl_avgpool_forward(const int frameCnt,
                                const int paddingH,
                                const int paddingW,
                                real* tgtData,
-                               const int tgtStride) {}
+                               const int tgtStride,
+                               const bool excludeMode) {}
 
 inline void hl_avgpool_backward(const int frameCnt,
                                 const real* outGrad,
@@ -86,7 +87,8 @@ inline void hl_avgpool_backward(const int frameCnt,
                                 real scaleA,
                                 real scaleB,
                                 real* backGrad,
-                                const int outStride) {}
+                                const int outStride,
+                                const bool excludeMode) {}
 
 inline void hl_maxpool3D_forward(const int frameCnt,
                                  const real* inputData,

paddle/cuda/src/hl_cuda_cnn.cu

@@ -210,7 +210,8 @@ __global__ void KeAvgPoolForward(const int nthreads,
                                  const int padH,
                                  const int padW,
                                  real* tgtData,
-                                 const int tgtStride) {
+                                 const int tgtStride,
+                                 const bool excludeMode) {
   int index = blockIdx.x * blockDim.x + threadIdx.x;
   if (index < nthreads) {
     int pw = index % pooledW;
@@ -224,7 +225,8 @@ __global__ void KeAvgPoolForward(const int nthreads,
     int wend = min(wstart + sizeX, width);
     hstart = max(hstart, 0);
     wstart = max(wstart, 0);
-    int pool_size = (hend - hstart) * (wend - wstart);
+    int poolSize =
+        excludeMode ? (hend - hstart) * (wend - wstart) : sizeY * sizeX;
 
     real aveval = 0;
     inputData += (frameNum * channels + c) * height * width;
@@ -235,7 +237,7 @@ __global__ void KeAvgPoolForward(const int nthreads,
     }
     int tgtIndex =
         index % (pooledW * pooledH * channels) + frameNum * tgtStride;
-    tgtData[tgtIndex] = aveval / pool_size;
+    tgtData[tgtIndex] = aveval / poolSize;
   }
 }
 
@@ -253,7 +255,8 @@ void hl_avgpool_forward(const int frameCnt,
                         const int paddingH,
                         const int paddingW,
                         real* tgtData,
-                        const int tgtStride) {
+                        const int tgtStride,
+                        const bool excludeMode) {
   int num_kernels = pooledH * pooledW * channels * frameCnt;
   int blocks = (num_kernels + 1024 - 1) / 1024;
   KeAvgPoolForward<<<blocks, 1024, 0, STREAM_DEFAULT>>>(num_kernels,
@@ -270,7 +273,8 @@ void hl_avgpool_forward(const int frameCnt,
                                                         paddingH,
                                                         paddingW,
                                                         tgtData,
-                                                        tgtStride);
+                                                        tgtStride,
+                                                        excludeMode);
   CHECK_SYNC("hl_avgpool_forward failed");
 }
 
@@ -290,7 +294,8 @@ __global__ void KeAvgPoolBackward(const int nthreads,
                                   real scaleA,
                                   real scaleB,
                                   real* tgtGrad,
-                                  const int outStride) {
+                                  const int outStride,
+                                  const bool excludeMode) {
   int index = blockIdx.x * blockDim.x + threadIdx.x;
   if (index < nthreads) {
     int offsetW = index % width + padW;
@@ -314,8 +319,9 @@ __global__ void KeAvgPoolBackward(const int nthreads,
         int wstart = pw * strideW - padW;
         int wend = min(wstart + sizeX, width);
         wstart = max(wstart, 0);
-        int poolsize = (hend - hstart) * (wend - wstart);
-        gradient += outGrad[ph * pooledW + pw] / poolsize;
+        int poolSize =
+            excludeMode ? (hend - hstart) * (wend - wstart) : sizeY * sizeX;
+        gradient += outGrad[ph * pooledW + pw] / poolSize;
       }
     }
     tgtGrad[index] = scaleB * tgtGrad[index] + scaleA * gradient;
@@ -338,7 +344,8 @@ void hl_avgpool_backward(const int frameCnt,
                          real scaleA,
                          real scaleB,
                          real* backGrad,
-                         const int outStride) {
+                         const int outStride,
+                         const bool excludeMode) {
   int num_kernels = height * width * channels * frameCnt;
   int blocks = (num_kernels + 1024 - 1) / 1024;
 
@@ -358,7 +365,8 @@ void hl_avgpool_backward(const int frameCnt,
                                                          scaleA,
                                                          scaleB,
                                                          backGrad,
-                                                         outStride);
+                                                         outStride,
+                                                         excludeMode);
   CHECK_SYNC("hl_avgpool_backward failed");
 }
 

paddle/gserver/layers/PoolLayer.cpp

@@ -45,6 +45,8 @@ bool PoolLayer::init(const LayerMap& layerMap,
   strideY_ = conf.has_stride_y() ? conf.stride_y() : conf.stride();
   confPaddingY_ = conf.has_padding_y() ? conf.padding_y() : conf.padding();
   outputY_ = conf.has_output_y() ? conf.output_y() : conf.output_x();
+
+  excludeMode_ = conf.has_exclude_mode() ? conf.exclude_mode() : true;
   return true;
 }
 

paddle/gserver/layers/PoolLayer.h

@@ -38,6 +38,8 @@ protected:
 
   std::string poolType_;
 
+  bool excludeMode_;
+
 public:
   explicit PoolLayer(const LayerConfig& config) : Layer(config) {}
 

paddle/gserver/layers/PoolProjection.cpp

@@ -36,6 +36,8 @@ PoolProjection::PoolProjection(const ProjectionConfig& config,
   strideY_ = conf.has_stride_y() ? conf.stride_y() : conf.stride();
   confPaddingY_ = conf.has_padding_y() ? conf.padding_y() : conf.padding();
   outputY_ = conf.has_output_y() ? conf.output_y() : conf.output_x();
+
+  excludeMode_ = conf.has_exclude_mode() ? conf.exclude_mode() : true;
 }
 
 size_t PoolProjection::getSize() {
@@ -141,7 +143,8 @@ void AvgPoolProjection::forward() {
                        outputY_,
                        outputX_,
                        confPaddingY_,
-                       confPadding_);
+                       confPadding_,
+                       excludeMode_);
 }
 
 void AvgPoolProjection::backward(const UpdateCallback& callback) {
@@ -166,6 +169,7 @@ void AvgPoolProjection::backward(const UpdateCallback& callback) {
                              1,
                              1,
                              confPaddingY_,
-                             confPadding_);
+                             confPadding_,
+                             excludeMode_);
 }
 }  // namespace paddle

paddle/gserver/layers/PoolProjection.h

@@ -28,6 +28,7 @@ protected:
   int confPaddingY_, confPadding_;
   size_t channels_;
   std::string poolType_;
+  bool excludeMode_;
 
 public:
   PoolProjection(const ProjectionConfig& config,

paddle/gserver/tests/test_LayerGrad.cpp

@@ -1211,7 +1211,10 @@ void setPoolConfig(TestConfig* config,
   pool->set_output_y(oh);
 }
 
-void testPoolLayer(const string& poolType, bool trans, bool useGpu) {
+void testPoolLayer(const string& poolType,
+                   bool trans,
+                   bool useGpu,
+                   bool excludeMode = true) {
   TestConfig config;
   config.inputDefs.push_back({INPUT_DATA, "layer_0", 3136, 0});
   LayerInputConfig* input = config.layerConfig.add_inputs();
@@ -1219,6 +1222,7 @@ void testPoolLayer(const string& poolType, bool trans, bool useGpu) {
 
   pool->set_img_size(14);
   pool->set_img_size_y(14);
+  pool->set_exclude_mode(excludeMode);
   setPoolConfig(&config, pool, poolType);
   config.layerConfig.set_size(pool->output_x() * pool->output_y() *
                               pool->channels());
@@ -1250,16 +1254,26 @@ void testPoolLayer2(const string& poolType, bool trans, bool useGpu) {
 
 TEST(Layer, PoolLayer) {
   testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ false);
+  testPoolLayer("avg-projection",
+                /* trans= */ false,
+                /* useGpu= */ false,
+                /* excludeMode= */ false);
   testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ false);
   testPoolLayer("max-pool-with-mask", /* trans= */ false, /* useGpu= */ false);
 
 #ifdef PADDLE_WITH_CUDA
   testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ true);
+  testPoolLayer("avg-projection",
+                /* trans= */ false,
+                /* useGpu= */ true,
+                /* excludeMode= */ false);
   testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ true);
   testPoolLayer("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true);
   testPoolLayer("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true);
   testPoolLayer2("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true);
   testPoolLayer2("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true);
+  testPoolLayer2(
+      "cudnn-avg-incl-pad-pool", /* trans= */ false, /* useGpu= */ true);
   testPoolLayer("max-pool-with-mask", /* trans= */ false, /* useGpu= */ true);
 #endif
 }

paddle/math/Matrix.cpp

@@ -1130,7 +1130,8 @@ void GpuMatrix::avgPoolForward(Matrix& inputMat,
                                size_t outputH,
                                size_t outputW,
                                size_t paddingH,
-                               size_t paddingW) {
+                               size_t paddingW,
+                               bool excludeMode) {
   CHECK(inputMat.useGpu_ == true) << "Matrix type are not equal";
 
   real* inputData = inputMat.getData();
@@ -1153,7 +1154,8 @@ void GpuMatrix::avgPoolForward(Matrix& inputMat,
                      paddingH,
                      paddingW,
                      data_,
-                     getStride());
+                     getStride(),
+                     excludeMode);
 }
 
 void GpuMatrix::avgPoolBackward(Matrix& outGrad,
@@ -1168,7 +1170,8 @@ void GpuMatrix::avgPoolBackward(Matrix& outGrad,
                                 real scaleTargets,
                                 real scaleOutput,
                                 size_t paddingH,
-                                size_t paddingW) {
+                                size_t paddingW,
+                                bool excludeMode) {
   CHECK(outGrad.useGpu_ == true) << "Matrix type are not equal";
 
   real* outDiff = outGrad.getData();
@@ -1194,7 +1197,8 @@ void GpuMatrix::avgPoolBackward(Matrix& outGrad,
                       scaleTargets,
                       scaleOutput,
                       data_,
-                      outGrad.getStride());
+                      outGrad.getStride(),
+                      excludeMode);
 }
 
 void GpuMatrix::maxPool3DForward(Matrix& inputMat,
@@ -2136,7 +2140,8 @@ void CpuMatrix::avgPoolForward(Matrix& input,
                                size_t outputH,
                                size_t outputW,
                                size_t paddingH,
-                               size_t paddingW) {
+                               size_t paddingW,
+                               bool excludeMode) {
   // The main loop
   size_t num = input.getHeight();
   size_t inLength = imgSizeH * imgSizeW;
@@ -2165,7 +2170,8 @@ void CpuMatrix::avgPoolForward(Matrix& input,
               tgtData[ph * outputW + pw] += inData[h * imgSizeW + w];
             }
           }
-          int poolSize = (hend - hstart) * (wend - wstart);
+          int poolSize =
+              excludeMode ? (hend - hstart) * (wend - wstart) : sizeY * sizeX;
           CHECK(poolSize);
           tgtData[ph * outputW + pw] /= poolSize;
         }
@@ -2189,7 +2195,8 @@ void CpuMatrix::avgPoolBackward(Matrix& input,
                                 real scaleTargets,
                                 real scaleOutput,
                                 size_t paddingH,
-                                size_t paddingW) {
+                                size_t paddingW,
+                                bool excludeMode) {
   size_t num = input.getHeight();
   size_t channels = input.getWidth() / outputH / outputW;
   size_t inLength = imgSizeH * imgSizeW;
@@ -2211,7 +2218,8 @@ void CpuMatrix::avgPoolBackward(Matrix& input,
           int wstart = pw * strideW - paddingW;
           int wend = std::min(wstart + sizeX, imgSizeW);
           wstart = std::max(wstart, 0);
-          int poolSize = (hend - hstart) * (wend - wstart);
+          int poolSize =
+              excludeMode ? (hend - hstart) * (wend - wstart) : sizeY * sizeX;
           CHECK(poolSize);
 
           for (int h = hstart; h < hend; ++h) {

paddle/math/Matrix.h

@@ -911,7 +911,8 @@ public:
                               size_t outputH,
                               size_t outputW,
                               size_t paddingH,
-                              size_t paddingW) {
+                              size_t paddingW,
+                              bool excludeMode = true) {
     LOG(FATAL) << "Not implemeted";
   }
 
@@ -927,9 +928,11 @@ public:
                                real scaleTargets,
                                real scaleOutput,
                                size_t paddingH,
-                               size_t paddingW) {
+                               size_t paddingW,
+                               bool excludeMode = true) {
     LOG(FATAL) << "Not implemeted";
   }
+
   /**
    * Pooling 3D forward operation, pick out the largest element
    * in the sizeX of value
@@ -1458,7 +1461,8 @@ public:
                       size_t outputH,
                       size_t outputW,
                       size_t paddingH,
-                      size_t paddingW);
+                      size_t paddingW,
+                      bool excludeMode = true);
 
   void avgPoolBackward(Matrix& input,
                        size_t imgSizeH,
@@ -1472,7 +1476,8 @@ public:
                        real scaleTargets,
                        real scaleOutput,
                        size_t paddingH,
-                       size_t paddingW);
+                       size_t paddingW,
+                       bool excludeMode = true);
 
   void maxPool3DForward(Matrix& inputMat,
                         Matrix& maxPoolIdx,
@@ -1730,7 +1735,8 @@ public:
                       size_t outputH,
                       size_t outputW,
                       size_t paddingH,
-                      size_t paddingW);
+                      size_t paddingW,
+                      bool excludeMode = true);
 
   void avgPoolBackward(Matrix& input,
                        size_t imgSizeH,
@@ -1744,7 +1750,8 @@ public:
                        real scaleTargets,
                        real scaleOutput,
                        size_t paddingH,
-                       size_t paddingW);
+                       size_t paddingW,
+                       bool excludeMode = true);
 
   void maxPool3DForward(Matrix& inputMat,
                         Matrix& maxPoolIdx,

proto/ModelConfig.proto

@@ -139,6 +139,8 @@ message PoolConfig {
   optional uint32 output_z = 16 [ default = 1 ];
   optional uint32 img_size_z = 17 [ default = 1 ];
   optional uint32 padding_z = 18 [ default = 1 ];
+
+  optional bool exclude_mode = 19;
 }
 
 message SppConfig {

python/paddle/trainer/config_parser.py

@@ -1233,7 +1233,7 @@ def parse_bilinear(bilinear, input_layer_name, bilinear_conf):
     bilinear_conf.out_size_y = bilinear.out_size_y
 
 
-def parse_pool(pool, input_layer_name, pool_conf, ceil_mode):
+def parse_pool(pool, input_layer_name, pool_conf, ceil_mode, exclude_mode):
     pool_conf.pool_type = pool.pool_type
     config_assert(pool.pool_type in [
         'max-projection', 'avg-projection', 'max-pool-with-mask', 'cudnn-max-pool', 'cudnn-avg-pool'
@@ -1262,6 +1262,8 @@ def parse_pool(pool, input_layer_name, pool_conf, ceil_mode):
     pool_conf.output_y = cnn_output_size(pool_conf.img_size_y, pool_conf.size_y,
                                          pool_conf.padding_y,
                                          pool_conf.stride_y, not ceil_mode)
+    if exclude_mode != None:
+        pool_conf.exclude_mode = exclude_mode
 
 
 def parse_pool3d(pool, input_layer_name, pool_conf, ceil_mode):
@@ -2303,7 +2305,8 @@ class NormLayer(LayerBase):
 class PoolLayer(LayerBase):
     layer_type = 'pool'
 
-    def __init__(self, name, inputs, ceil_mode=True, **xargs):
+    def __init__(self, name, inputs, ceil_mode=True, exclude_mode=None,
+                 **xargs):
         use_mkldnn = int(g_command_config_args.get("use_mkldnn", 0))
         if self.layer_type == "mkldnn_pool":
             config_assert(use_mkldnn, "mkldnn_pool only support MKLDNN")
@@ -2314,7 +2317,7 @@ class PoolLayer(LayerBase):
             input_layer = self.get_input_layer(input_index)
             pool_conf = self.config.inputs[input_index].pool_conf
             parse_pool(self.inputs[input_index].pool, input_layer.name,
-                       pool_conf, ceil_mode)
+                       pool_conf, ceil_mode, exclude_mode)
             self.set_cnn_layer(name, pool_conf.output_y, pool_conf.output_x,
                                pool_conf.channels)
 

python/paddle/trainer_config_helpers/layers.py

@@ -21,7 +21,7 @@ from .activations import LinearActivation, SigmoidActivation, TanhActivation, \
     ReluActivation, IdentityActivation, SoftmaxActivation, BaseActivation
 from .evaluators import *
 from .poolings import MaxPooling, AvgPooling, MaxWithMaskPooling, BasePoolingType, \
-    CudnnAvgPooling, CudnnMaxPooling
+    CudnnAvgPooling, CudnnAvgInclPadPooling, CudnnMaxPooling
 from .attrs import *
 from .default_decorators import *
 
@@ -2709,7 +2709,8 @@ def img_pool_layer(input,
                    pool_size_y=None,
                    stride_y=None,
                    padding_y=None,
-                   ceil_mode=True):
+                   ceil_mode=True,
+                   exclude_mode=None):
     """
     Image pooling Layer.
 
@@ -2773,10 +2774,15 @@ def img_pool_layer(input,
     :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
                        details.
     :type layer_attr: ExtraLayerAttribute
-    :param ceil_mode: Wether to use the ceil function to calculate output height and width.
+    :param ceil_mode: Whether to use the ceil function to calculate output height and width.
                       True is the default. If it is set to False, the floor function will
                       be used.
     :type ceil_mode: bool
+    :param exclude_mode: Whether to exclude the padding cells when calculating, but only 
+                         work when pool_type is AvgPooling. If None, also exclude the padding 
+                         cells. If use cudnn, use CudnnAvgPooling or CudnnAvgInclPadPooling 
+                         as pool_type to identify the mode.
+    :type exclude_mode: bool
     :return: LayerOutput object.
     :rtype: LayerOutput
     """
@@ -2790,7 +2796,7 @@ def img_pool_layer(input,
         pool_type.name = 'avg'
 
     assert type(pool_type) in [AvgPooling, MaxPooling, MaxWithMaskPooling, CudnnAvgPooling,
-                               CudnnMaxPooling], \
+                               CudnnMaxPooling, CudnnAvgInclPadPooling], \
         "only (Cudnn)AvgPooling, (Cudnn)MaxPooling, MaxWithMaskPooling are supported"
 
     type_name = pool_type.name + '-projection' \
@@ -2819,6 +2825,7 @@ def img_pool_layer(input,
                     padding_y=padding_y))
         ],
         ceil_mode=ceil_mode,
+        exclude_mode=exclude_mode,
         **ExtraLayerAttribute.to_kwargs(layer_attr))
     return LayerOutput(
         name,

python/paddle/trainer_config_helpers/poolings.py

@@ -16,7 +16,8 @@
 
 __all__ = [
     "BasePoolingType", "MaxPooling", "AvgPooling", "MaxWithMaskPooling",
-    "CudnnMaxPooling", "CudnnAvgPooling", "SumPooling", "SquareRootNPooling"
+    "CudnnMaxPooling", "CudnnAvgPooling", "CudnnAvgInclPadPooling",
+    "SumPooling", "SquareRootNPooling"
 ]
 
 
@@ -88,6 +89,16 @@ class CudnnAvgPooling(BasePoolingType):
         BasePoolingType.__init__(self, "cudnn-avg-pool")
 
 
+class CudnnAvgInclPadPooling(BasePoolingType):
+    """
+    Cudnn average pooling only support GPU. Return the average value in the
+    pooling window taking into account the padding cells.
+    """
+
+    def __init__(self):
+        BasePoolingType.__init__(self, "cudnn-avg-incl-pad-pool")
+
+
 class AvgPooling(BasePoolingType):
     """
     Average pooling.