Merge remote-tracking branch 'baidu/develop' into feature/sppnet

paddle/math/Matrix.cpp

@@ -187,6 +187,15 @@ MatrixPtr Matrix::subMatrix(size_t startRow, size_t endRow, size_t startCol,
                         trans_, useGpu_);
 }
 
+void Matrix::setDiag(real value) {
+  CHECK(data_ != NULL);
+  CHECK_EQ(height_, width_);
+
+  zeroMem();
+  BaseMatrix diag(height_, 1, stride_ + 1, data_, false, useGpu_);
+  diag.assign(value);
+}
+
 GpuMatrix::GpuMatrix(size_t height, size_t width, bool trans)
     : Matrix(std::make_shared<GpuMemoryHandle>(height * width * sizeof(real)),
              height, width, trans, true) {}
@@ -202,6 +211,7 @@ void GpuMatrix::resetOne() {
   CHECK(data_ != NULL);
   one();
 }
+
 void GpuMatrix::resize(size_t newHeight, size_t newWidth) {
   size_t newSize = newHeight * newWidth;
   if (NULL == memoryHandle_.get() ||

paddle/math/Matrix.h

@@ -195,6 +195,8 @@ public:
 
   virtual void resetOne() { LOG(FATAL) << "Not implemented"; }
 
+  void setDiag(real value);
+
   virtual void copyFrom(const Matrix& src) { LOG(FATAL) << "Not implemented"; }
 
   virtual void trimFrom(const CpuSparseMatrix& src) {
@@ -330,6 +332,7 @@ public:
 
   virtual MatrixPtr getInverse() {
     LOG(FATAL) << "Not implemented";
+    return nullptr;
   }
 
   /**
@@ -1016,6 +1019,7 @@ public:
 
   void zeroMem();
   void resetOne();
+  void setDiag(real value);
 
   void resize(size_t newHeight, size_t newWidth);
   void resize(size_t newHeight, size_t newWidth,
@@ -1280,6 +1284,8 @@ public:
 
   void zeroMem();
   void resetOne();
+  void setDiag(real value);
+
   void resize(size_t newHeight, size_t newWidth);
   void resize(size_t newHeight, size_t newWidth,
               size_t newNnz, /* used to allocate space */

paddle/math/tests/test_matrixCompare.cpp

@@ -647,20 +647,23 @@ void testMatrixInverse(int height) {
   MatrixPtr cpuI = std::make_shared<CpuMatrix>(height, height);
   MatrixPtr gpuI = std::make_shared<GpuMatrix>(height, height);
 
+  /* Make matrix well conditioned: cpu * cpuT + Identity */
   cpu->randomizeUniform();
+  MatrixPtr cpuT = cpu->getTranspose();
+  MatrixPtr outputCheck = std::make_shared<CpuMatrix>(height, height);
+  outputCheck->mul(cpu, cpuT);
+  cpu->setDiag(1.0);
+  cpu->add(*outputCheck);
+
   gpu->copyFrom(*cpu);
   cpu->inverse(cpuI, false);
   gpu->inverse(gpuI, false);
 
-  MatrixPtr outputCheck = std::make_shared<CpuMatrix>(height, height);
   outputCheck->copyFrom(*gpuI);
   MatrixCheckErr(*cpuI, *outputCheck);
 
   outputCheck->mul(cpu, cpuI);
-  cpu->zeroMem();
-  for (int i = 0; i < height; i++) {
-    cpu->getRowBuf(i)[i] = 1.0;
-  }
+  cpu->setDiag(1.0);
   MatrixCheckErr(*cpu, *outputCheck);
 }
 

python/paddle/trainer_config_helpers/layers.py

@@ -592,7 +592,7 @@ class MixedLayerType(LayerOutput):
     def __exit__(self, *args, **kwargs):
         del args, kwargs  # unused parameter to suppress warning
         assert len(self.inputs) != 0
-        MixedLayer(
+        ml = MixedLayer(
             name=self.name,
             size=self.size,
             active_type=self.activation.name,
@@ -600,6 +600,9 @@ class MixedLayerType(LayerOutput):
             inputs=self.inputs,
             **ExtraLayerAttribute.to_kwargs(self.layer_attr)
         )
+        # update the size which might be computed inside MixedLayer
+        # according to the operator's output size
+        self.size = ml.config.size
 
 
 @wrap_name_default("mixed")
@@ -2104,7 +2107,7 @@ def concat_layer(input, act=None, name=None, layer_attr=None, bias_attr=None):
 
     if layer_type == LayerType.CONCAT_LAYER:
         assert not bias_attr
-    
+
     Layer(
         name=name, type=layer_type,
         inputs=[x.name for x in input] if is_concat_layer else input,
@@ -2682,7 +2685,7 @@ def out_prod_layer(input1, input2, name=None, layer_attr=None):
     assert isinstance(input1, LayerOutput)
     assert isinstance(input2, LayerOutput)
     Layer(name=name,
-          type="out_prod",
+          type=LayerType.OUT_PROD_LAYER,
           inputs=[input1.name, input2.name],
           **ExtraLayerAttribute.to_kwargs(layer_attr))
     return LayerOutput(name=name,
@@ -2849,7 +2852,7 @@ def beam_search(step, input, bos_id, eos_id, beam_size,
 
 def __cost_input__(input, label, weight=None):
     """
-    inputs and parents for cost layers. 
+    inputs and parents for cost layers.
     """
     ipts = [Input(input.name), Input(label.name)]
     parents = [input, label]
@@ -2858,7 +2861,7 @@ def __cost_input__(input, label, weight=None):
         ipts.append(Input(weight.name))
         parents.append(weight)
     return ipts, parents
-    
+
 
 @wrap_name_default()
 @layer_support()
@@ -2943,7 +2946,7 @@ def classification_cost(input, label, weight=None, name=None,
 
 
 def conv_operator(img, filter, filter_size, num_filters,
-                  num_channel=None, stride=1, padding=0,
+                  num_channels=None, stride=1, padding=0,
                   filter_size_y=None, stride_y=None, padding_y=None):
     """
     Different from img_conv_layer, conv_op is an Operator, which can be used
@@ -2973,8 +2976,8 @@ def conv_operator(img, filter, filter_size, num_filters,
     :type filter_size_y: int
     :param num_filters: channel of output data.
     :type num_filters: int
-    :param num_channel: channel of input data.
-    :type num_channel: int
+    :param num_channels: channel of input data.
+    :type num_channels: int
     :param stride: The x dimension of the stride.
     :type stride: int
     :param stride_y: The y dimension of the stride.
@@ -2993,19 +2996,19 @@ def conv_operator(img, filter, filter_size, num_filters,
     if padding_y is None:
         padding_y = padding
 
-    if num_channel is None:
-        num_channel = img.num_filters
+    if num_channels is None:
+        num_channels = img.num_filters
 
     assert isinstance(filter, LayerOutput)
     if filter.size is not None:
-        filter.size = filter_size * filter_size_y * num_filters * num_channel
+        filter.size = filter_size * filter_size_y * num_filters * num_channels
 
     op = ConvOperator(input_layer_names=[img.name, filter.name],
                       num_filters=num_filters,
                       conv_conf=Conv(filter_size=filter_size,
                                      padding=padding,
                                      stride=stride,
-                                     channels=num_channel,
+                                     channels=num_channels,
                                      filter_size_y=filter_size_y,
                                      padding_y=padding_y,
                                      stride_y=stride_y,
@@ -3045,8 +3048,8 @@ def conv_projection(input, filter_size, num_filters,
     :type filter_size_y: int
     :param num_filters: channel of output data.
     :type num_filters: int
-    :param num_channel: channel of input data.
-    :type num_channel: int
+    :param num_channels: channel of input data.
+    :type num_channels: int
     :param stride: The x dimension of the stride.
     :type stride: int
     :param stride_y: The y dimension of the stride.
@@ -3537,15 +3540,15 @@ def maxout_layer(input,
       - Input: output of a conv layer.
       - Output: feature map size same as input. Channel is (input channel) / groups.
 
-    So groups should be larger than 1, and the num of channels should be able 
+    So groups should be larger than 1, and the num of channels should be able
     to devided by groups.
 
-    Please refer to Paper: 
+    Please refer to Paper:
       - Maxout Networks: http://www.jmlr.org/proceedings/papers/v28/goodfellow13.pdf
       - Multi-digit Number Recognition from Street View \
         Imagery using Deep Convolutional Neural Networks: \
         https://arxiv.org/pdf/1312.6082v4.pdf
-    
+
     The simple usage is:
 
     .. code-block:: python
@@ -3790,9 +3793,9 @@ def nce_layer(input, label, num_classes, weight=None,
     :param weight: weight layer, can be None(default)
     :type weight: LayerOutput
     :param num_classes: number of classes.
-    :type num_classes: int 
+    :type num_classes: int
     :param num_neg_samples: number of negative samples. Default is 10.
-    :type num_neg_samples: int 
+    :type num_neg_samples: int
     :param neg_distribution: The distribution for generating the random negative labels.
                              A uniform distribution will be used if not provided.
                              If not None, its length must be equal to num_classes.
@@ -3813,7 +3816,7 @@ def nce_layer(input, label, num_classes, weight=None,
         assert isinstance(neg_distribution, collections.Sequence)
         assert len(neg_distribution) == num_classes
         assert sum(neg_distribution) == 1
-    
+
     ipts_for_layer = []
     parents = []
     for each_input in input:

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

@@ -35,7 +35,7 @@ flt = data_layer(name='filter', size=3*3*1*64)
 
 with mixed_layer() as m7:
     m7 += conv_operator(img=img, filter=flt, num_filters=64,
-                        num_channel=1, filter_size=3)
+                        num_channels=1, filter_size=3)
 
 end = mixed_layer(input=[full_matrix_projection(input=m5),
                          trans_full_matrix_projection(input=m6),

python/paddle/trainer_config_helpers/tests/layers_test_config.py

@@ -29,9 +29,11 @@ z1 = mixed_layer(act=LinearActivation(),
                                       filter=y1,
                                       filter_size=1,
                                       num_filters=5,
-                                      num_channel=5,
+                                      num_channels=5,
                                       stride=1)])
 
+assert z1.size > 0
+
 y2 = fc_layer(input=y, size=15)
 
 cos1 = cos_sim(a=x1, b=y1)