add python wrap for sppLayer

doc/ui/api/trainer_config_helpers/layers.rst

@@ -46,6 +46,12 @@ conv_operator
     :members: conv_operator
     :noindex:
 
+conv_projection
+-------------
+..  automodule:: paddle.trainer_config_helpers.layers
+    :members: conv_projection
+    :noindex:
+
 conv_shift_layer
 ------------------
 ..  automodule:: paddle.trainer_config_helpers.layers
@@ -73,6 +79,12 @@ img_pool_layer
     :members: img_pool_layer
     :noindex:   
 
+spp_layer
+--------------
+..  automodule:: paddle.trainer_config_helpers.layers
+    :members: spp_layer
+    :noindex:
+
 maxout_layer
 ------------
 ..  automodule:: paddle.trainer_config_helpers.layers

paddle/gserver/layers/PoolProjection.cpp

@@ -16,7 +16,7 @@ limitations under the License. */
 
 namespace paddle {
 
-REGISTER_PROJECTION_CREATE_FUNC(pool2, &PoolProjection::create);
+REGISTER_PROJECTION_CREATE_FUNC(pool, &PoolProjection::create);
 
 PoolProjection* PoolProjection::create(const ProjectionConfig& config,
                                        ParameterPtr parameter, bool useGpu) {

paddle/gserver/layers/SpatialPyramidPoolLayer.cpp

@@ -24,7 +24,7 @@ ProjectionConfig SpatialPyramidPoolLayer::getConfig(size_t imgSizeW,
                                                     size_t pyramidLevel,
                                                     std::string& poolType) {
   ProjectionConfig config;
-  config.set_type("pool2");
+  config.set_type("pool");
   PoolConfig* conf = config.mutable_pool_conf();
   conf->set_channels(channels);
   conf->set_img_size(imgSizeW);
@@ -93,7 +93,7 @@ bool SpatialPyramidPoolLayer::init(const LayerMap& layerMap,
     startCol = endCol;
     projInput_.emplace_back(Argument());
   }
-  outputSize_ = endCol;
+  CHECK_EQ(endCol, getSize());
   return true;
 }
 
@@ -101,7 +101,7 @@ void SpatialPyramidPoolLayer::forward(PassType passType) {
   Layer::forward(passType);
 
   int batchSize = getInput(0).getBatchSize();
-  resetOutput(batchSize, outputSize_);
+  resetOutput(batchSize, getSize());
   for (size_t i = 0; i < pyramidHeight_; i++) {
     size_t startCol = projCol_[i].first;
     size_t endCol = projCol_[i].second;

paddle/gserver/layers/SpatialPyramidPoolLayer.h

@@ -27,7 +27,6 @@ protected:
   size_t imgSizeW_;
   size_t imgSizeH_;
   size_t pyramidHeight_;
-  size_t outputSize_;
   std::string poolType_;
 
   std::vector<std::unique_ptr<PoolProjection>> poolProjections_;

paddle/gserver/tests/test_LayerGrad.cpp

@@ -931,6 +931,8 @@ void testSppLayer(const string& poolType, const int pyramidHeight, bool trans,
   sppConfig->set_channels(16);
   sppConfig->set_img_size(10);
   sppConfig->set_img_size_y(20);
+  int outputSize = (std::pow(4, sppConfig->pyramid_height()) - 1) / (4 - 1);
+  config.layerConfig.set_size(outputSize * sppConfig->channels());
   testLayerGrad(config, "spp", 100, trans, useGpu);
 }
 

paddle/math/Matrix.cpp

@@ -1510,18 +1510,19 @@ void CpuMatrix::maxPoolForward(Matrix& inputMat, size_t imgSizeH,
   CHECK(inHeight * inWidth == inputMat.getWidth() / channels);
   CHECK_EQ(num, this->getHeight());
   CHECK_EQ(channels * outputH * outputW, this->getWidth());
+  size_t outStride = getStride();
 
   /* initialize the data_ */
   for (size_t i = 0; i < height_; i++) {
     for (size_t j = 0; j < width_; j++) {
-      outData[i * getStride() + j] = -(real)FLT_MAX;
+      outData[i * outStride + j] = -(real)FLT_MAX;
     }
   }
 
   /* pool max one by one */
   for (size_t n = 0; n < num; ++n) {  // frame by frame
     if (!isContiguous()) {
-      outData = data_ + n * getStride();
+      outData = data_ + n * outStride;
     }
     for (size_t c = 0; c < channels; ++c) {  // channel by channel
       for (size_t ph = 0; ph < outputH; ++ph) {
@@ -1564,10 +1565,15 @@ void CpuMatrix::maxPoolBackward(Matrix& image, size_t imgSizeH, size_t imgSizeW,
   real* inData = image.getData();
   real* otData = outV.getData();
   real* otGrad = outGrad.getData();
+
+  size_t outStride = outV.getStride();
+  real* origOutData = otData;
+  real* origOutGrad = otGrad;
+
   for (size_t n = 0; n < num; ++n) {
     if (!outV.isContiguous()) {
-      otData = outV.getData() + n * outV.getStride();
-      otGrad = outGrad.getData() + n * outGrad.getStride();
+      otData = origOutData + n * outStride;
+      otGrad = origOutGrad + n * outStride;
     }
     for (size_t c = 0; c < channels; ++c) {
       for (size_t ph = 0; ph < outputH; ++ph) {

proto/ModelConfig.proto.m4

@@ -202,11 +202,11 @@ message ProjectionConfig {
   optional ConvConfig conv_conf = 8;
   optional int32 num_filters = 9;
 
-  // For pool
-  optional PoolConfig pool_conf = 10;
-
   // For IdentityOffsetProjection
   optional uint64 offset = 11 [default = 0];
+
+  // For pool
+  optional PoolConfig pool_conf = 12;
 }
 
 message OperatorConfig {

python/paddle/trainer/config_parser.py

@@ -470,6 +470,7 @@ class Input(Cfg):
             image=None,
             block_expand=None,
             maxout=None,
+            spp=None,
             format=None,
             nnz=None,
             is_static=None,
@@ -669,7 +670,6 @@ class ConvProjection(Projection):
     def calc_parameter_dims(self, input_size, output_size):
         return None
 
-
 # Define a operator for mixed layer
 @config_class
 class Operator(Cfg):
@@ -783,6 +783,15 @@ class Pool(Cfg):
             padding_y = None):
         self.add_keys(locals())
 
+class SpatialPyramidPool(Cfg):
+    def __init__(
+            self,
+            pool_type,
+            pyramid_height,
+            channels,
+            img_width = None):
+        self.add_keys(locals())
+
 # please refer to the comments in proto/ModelConfig.proto
 @config_class
 class Norm(Cfg):
@@ -1043,6 +1052,22 @@ def parse_pool(pool, input_layer_name, pool_conf):
             2*pool_conf.padding_y - pool_conf.size_y) / \
             float(pool_conf.stride_y))) + 1
 
+def parse_spp(spp, input_layer_name, spp_conf):
+    spp_conf.pool_type = spp.pool_type
+    config_assert(spp.pool_type in ['max-projection', 'avg-projection'],
+                  "pool-type %s is not in " "['max-projection', 'avg-projection']"
+                  % spp.pool_type)
+    spp_conf.pyramid_height = spp.pyramid_height
+    spp_conf.channels = spp.channels
+
+    img_pixels = g_layer_map[input_layer_name].size / spp_conf.channels
+
+    spp_conf.img_size = default(spp.img_width, int(img_pixels ** 0.5))
+    spp_conf.img_size_y = img_pixels / spp_conf.img_size
+    config_assert(spp_conf.img_size * spp_conf.img_size_y == img_pixels,
+                  "Incorrect input image size %d for input image pixels %d"
+                  % (spp_conf.img_size, img_pixels))
+
 def parse_image(image, input_layer_name, image_conf):
     image_conf.channels = image.channels
     image_pixels = g_layer_map[input_layer_name].size / image_conf.channels
@@ -1649,6 +1674,25 @@ class PoolLayer(LayerBase):
                 name, pool_conf.output_y, pool_conf.output_x))
             self.set_layer_size((pool_conf.output_x * pool_conf.output_y) * pool_conf.channels)
 
+@config_layer('spp')
+class SpatialPyramidPoolLayer(LayerBase):
+    def __init__(
+            self,
+            name,
+            inputs,
+            device=None):
+        super(SpatialPyramidPoolLayer, self).__init__(name, 'spp', 0, inputs=inputs, device=device)
+        for input_index in xrange(len(self.inputs)):
+            input_layer = self.get_input_layer(input_index)
+            parse_spp(
+                self.inputs[input_index].spp,
+                input_layer.name,
+                self.config.inputs[input_index].spp_conf)
+            spp_conf = self.config.inputs[input_index].spp_conf
+            output_size = (pow(4, spp_conf.pyramid_height) - 1) / (4 - 1)
+            print("output size for %s is %d " % (name, output_size))
+            self.set_layer_size(output_size * spp_conf.channels)
+
 @config_layer('batch_norm')
 class BatchNormLayer(LayerBase):
     layer_type = 'batch_norm'

python/paddle/trainer_config_helpers/layers.py

@@ -55,7 +55,8 @@ __all__ = ["full_matrix_projection", "AggregateLevel", "ExpandLevel",
            'multi_binary_label_cross_entropy',
            'rank_cost', 'lambda_cost', 'huber_cost',
            'block_expand_layer',
-           'maxout_layer', 'out_prod_layer', 'print_layer'
+           'maxout_layer', 'out_prod_layer', 'print_layer', 
+           'spp_layer', 
            ]
 
 
@@ -111,6 +112,7 @@ class LayerType(object):
     LINEAR_COMBINATION_LAYER = "convex_comb"
     BLOCK_EXPAND = "blockexpand"
     MAXOUT = "maxout"
+    SPP_LAYER = "spp"
 
     PRINT_LAYER = "print"
 
@@ -868,6 +870,7 @@ def pooling_layer(input, pooling_type=None, name=None, bias_attr=None,
                        size=input.size)
 
 
+
 @wrap_bias_attr_default()
 @wrap_param_attr_default()
 @wrap_act_default(param_names=['gate_act'],
@@ -1708,6 +1711,62 @@ def img_pool_layer(input, pool_size, name=None,
                        num_filters=num_channels)
 
 
+@wrap_name_default("spp")
+@layer_support()
+def spp_layer(input, name=None, num_channels=None, pool_type=None,
+              pyramid_height=None, img_width=None, layer_attr=None):
+    pass
+    """
+    Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.
+    The details please refer to
+    `Kaiming He's paper <https://arxiv.org/abs/1406.4729>`_.
+
+    :param name: layer name.
+    :type name: basestring
+    :param input: layer's input.
+    :type input: LayerOutput
+    :param num_channels: number of input channel.
+    :type num_channels: int
+    :param pool_type: Pooling type. MaxPooling or AveragePooling. Default is MaxPooling.
+    :type scale: BasePoolingType
+    :param pyramid_height: pyramid height.
+    :type pyramid_height: int
+    :param img_width: the width of input feature map. If it is None, the input feature
+                      map should be square.
+    :type img_width: int|None
+    :param layer_attr: Extra Layer Attribute.
+    :type layer_attr: ExtraLayerAttribute
+    :return: LayerOutput object.
+    :rtype: LayerOutput
+    """
+    if num_channels is None:
+        assert input.num_filters is not None
+        num_channels = input.num_filters
+
+    if pool_type is None:
+        pool_type = MaxPooling()
+    elif isinstance(pool_type, AvgPooling):
+        pool_type.name = 'avg'
+
+    type_name = pool_type.name
+    if (isinstance(pool_type, AvgPooling) or isinstance(pool_type, MaxPooling)):
+        type_name += '-projection'
+
+    Layer(
+        name=name,
+        type=LayerType.SPP_LAYER,
+        inputs=Input(input.name,
+                     spp=SpatialPyramidPool(pool_type=type_name,
+                                            channels=num_channels,
+                                            pyramid_height=pyramid_height,
+                                            img_width=img_width)
+        ),
+        **ExtraLayerAttribute.to_kwargs(layer_attr)
+    )
+    return LayerOutput(name, LayerType.SPP_LAYER, parents=[input], 
+                       num_filters=num_channels)
+
+
 def __img_norm_layer__(name, input, size, norm_type, scale, power,
                        num_channels, blocked, layer_attr):
     if num_channels is None:

python/paddle/trainer_config_helpers/tests/configs/check.md5

@@ -20,3 +20,4 @@ fded24727338fb8ce44d9951ed8aea08  test_rnn_group.protostr
 67d6fde3afb54f389d0ce4ff14726fe1  test_sequence_pooling.protostr
 f586a548ef4350ba1ed47a81859a64cb  unused_layers.protostr
 f937a5a6e7e8864b4d8cf56b0f7c7f44  util_layers.protostr
+60c9a71e19bd4b2a1253712799d0ae70  test_spp_layer.protostr

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

@@ -9,7 +9,7 @@ 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 shared_fc shared_lstm test_cost_layers_with_weight
-test_maxout test_bi_grumemory)
+test_maxout test_bi_grumemory test_spp_layer)
 
 
 for conf in ${configs[*]}

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

+from paddle.trainer_config_helpers import *
+
+settings(
+	  batch_size=100,
+    learning_rate=1e-5
+)
+
+data = data_layer(name='data', size=3200)
+
+
+spp = spp_layer(input=data,
+                pyramid_height=2,
+                num_channels=16,
+                pool_type=MaxPooling(),
+                img_width=10)
+
+outputs(spp)