Merge branch 'develop' into layer

demo/image_classification/prediction.py

@@ -126,7 +126,7 @@ class ImageClassifier():
         # For oversampling, average predictions across crops.
         # If not, the shape of output[name]: (1, class_number),
         # the mean is also applicable.
-        return output[output_layer].mean(0)
+        return output[output_layer]['value'].mean(0)
 
     def predict(self, image=None, output_layer=None):
         assert isinstance(image, basestring)

demo/model_zoo/resnet/classify.py

@@ -156,7 +156,7 @@ class ImageClassifier():
             # For oversampling, average predictions across crops.
             # If not, the shape of output[name]: (1, class_number),
             # the mean is also applicable.
-            res[name] = output[name].mean(0)
+            res[name] = output[name]['value'].mean(0)
 
         return res
 

paddle/api/Arguments.cpp

@@ -38,6 +38,13 @@ Arguments* Arguments::createByPaddleArgumentVector(void* ptr) {
   return args;
 }
 
+Arguments* Arguments::createByPaddleArgument(const void* ptr) {
+  auto p = (paddle::Argument*)(ptr);
+  auto args = new Arguments();
+  args->m->outputs.push_back(*p);
+  return args;
+}
+
 Matrix* Arguments::getSlotValue(size_t idx) const throw(RangeError) {
   auto& a = m->getArg(idx);
   return Matrix::createByPaddleMatrixPtr(&a.value);

paddle/api/GradientMachine.cpp

@@ -144,12 +144,12 @@ Parameter* GradientMachine::getParameter(size_t i) throw(RangeError) {
 
 void GradientMachine::randParameters() { m->machine->randParameters(); }
 
-Matrix* GradientMachine::getLayerOutput(const std::string& layerName) const
+Arguments* GradientMachine::getLayerOutput(const std::string& layerName) const
     throw(UnsupportError) {
-  auto nn = std::dynamic_pointer_cast<paddle::NeuralNetwork>(m->machine);
+  auto nn = m->machine;
   if (nn) {
-    auto mat = nn->getLayerOutput(layerName);
-    return Matrix::createByPaddleMatrixPtr(&mat);
+    auto arg = nn->getLayerOutput(layerName);
+    return Arguments::createByPaddleArgument(&arg);
   } else {
     throw UnsupportError();
   }

paddle/api/PaddleAPI.h

@@ -454,6 +454,7 @@ public:
 
 private:
   static Arguments* createByPaddleArgumentVector(void* ptr);
+  static Arguments* createByPaddleArgument(const void* ptr);
   void* getInternalArgumentsPtr() const;
 
 private:
@@ -769,7 +770,7 @@ public:
 
   void randParameters();
 
-  Matrix* getLayerOutput(const std::string& layerName) const
+  Arguments* getLayerOutput(const std::string& layerName) const
       throw(UnsupportError);
 
   /**
@@ -956,7 +957,7 @@ public:
 
   Arguments* getForwardOutput();
 
-  Matrix* getLayerOutput(const std::string& layerName);
+  Arguments* getLayerOutput(const std::string& layerName) const;
 };
 
 /// the N-Best results generated from one input sequence.

paddle/api/Trainer.cpp

@@ -131,12 +131,11 @@ void Trainer::testOneDataBatch(size_t batchSize, const Arguments& args) {
 void TrainerPrivate::finishTestPeriod() { tester_->finishTestPeriod(); }
 void Trainer::finishTestPeriod() { m->finishTestPeriod(); }
 
-Matrix* Trainer::getLayerOutput(const std::string& layerName) {
-  auto nn = std::dynamic_pointer_cast<paddle::NeuralNetwork>(
-      this->m->getGradientMachine());
+Arguments* Trainer::getLayerOutput(const std::string& layerName) const {
+  auto nn = this->m->getGradientMachine();
   CHECK(nn) << "trainerInternal_.getGradientMachine() is not NeuralNetwork";
-  auto m = nn->getLayerOutput(layerName);
-  return Matrix::createByPaddleMatrixPtr(&m);
+  auto arg = nn->getLayerOutput(layerName);
+  return Arguments::createByPaddleArgument(&arg);
 }
 
 void Trainer::forwardOneBatch(size_t batchSize) {

paddle/gserver/gradientmachines/GradientMachine.h

@@ -134,6 +134,10 @@ public:
     backward(callback);
   }
 
+  virtual Argument getLayerOutput(const std::string& layerName) {
+    return *((Argument*)nullptr);
+  }
+
   // see comment in Layer.h for the function with the same name
   virtual void resetState() {}
 

paddle/gserver/gradientmachines/MultiGradientMachine.cpp

@@ -282,6 +282,18 @@ void MultiGradientMachine::forwardBackward(const std::vector<Argument>& inArgs,
   backwardImp(callback);
 }
 
+Argument MultiGradientMachine::getLayerOutput(const std::string& layerName) {
+  std::vector<Argument> args;
+  args.reserve(threads_.size());
+
+  for (auto& thread : threads_) {
+    args.push_back(thread->getGradientMachine()->getLayerOutput(layerName));
+  }
+  outLayerArgs_.concat(args, false /* use_gpu */, outArgStream_, passType_);
+
+  return outLayerArgs_;
+}
+
 void MultiGradientMachine::backwardImp(const UpdateCallback& callback) {
   for (size_t i = 0; i < parameters_.size(); i++) {
     if (!parameters_[i]->useGpu() || parameters_[i]->isStatic()) continue;

paddle/gserver/gradientmachines/MultiGradientMachine.h

@@ -189,6 +189,8 @@ public:
                        PassType passType,
                        const UpdateCallback& callback);
 
+  virtual Argument getLayerOutput(const std::string& layerName);
+
   virtual void onPassEnd();
 
   virtual void finish();
@@ -314,6 +316,8 @@ protected:
   std::vector<Argument> outArgs_;
   hl_stream_t outArgStream_;
 
+  Argument outLayerArgs_;
+
   /// ParameterType which needs to be merged from each GPU
   std::vector<ParameterType> mergeTypes_;
   int numDevices_;         /* number of gpu devices */

paddle/gserver/gradientmachines/NeuralNetwork.cpp

@@ -293,11 +293,10 @@ void NeuralNetwork::backward(const UpdateCallback& callback) {
   }
 }
 
-MatrixPtr NeuralNetwork::getLayerOutput(const std::string& layerName) {
-  auto it = layerMap_.find(layerName);
-  CHECK(it != layerMap_.end()) << "Cannot find layer: " << layerName;
-  return it->second->getOutputValue();
+Argument NeuralNetwork::getLayerOutput(const std::string& layerName) {
+  return getLayer(layerName)->getOutput();
 }
+
 void NeuralNetwork::onPassEnd() {
   for (auto& layer : layers_) {
     layer->onPassEnd();

paddle/gserver/gradientmachines/NeuralNetwork.h

@@ -87,7 +87,8 @@ public:
 
   virtual void backward(const UpdateCallback& callback = nullptr);
 
-  MatrixPtr getLayerOutput(const std::string& layerName);
+  virtual Argument getLayerOutput(const std::string& layerName);
+
   const LayerPtr& getLayer(const std::string& layerName) const {
     auto it = layerMap_.find(layerName);
     CHECK(it != layerMap_.end()) << "Unknown layer " << layerName;

paddle/gserver/layers/CosSimLayer.cpp

@@ -42,7 +42,7 @@ void CosSimLayer::forward(PassType passType) {
   /* malloc memory for the output_ if necessary */
   int batchSize = getInputValue(0)->getHeight();
   int size = getSize();
-  CHECK_EQ(forward_.size(), 1) << "Only one forward function needed";
+  CHECK_EQ(forward_.size(), 1UL) << "Only one forward function needed";
 
   {
     REGISTER_TIMER_INFO("CosFwResetTimer", getName().c_str());
@@ -68,7 +68,7 @@ void CosSimLayer::forward(PassType passType) {
 void CosSimLayer::backward(const UpdateCallback& callback) {
   /* activation */ {
     REGISTER_TIMER_INFO("CosBpAtvTimer", getName().c_str());
-    CHECK_EQ(backward_.size(), 1) << "Only one backward function needed";
+    CHECK_EQ(backward_.size(), 1UL) << "Only one backward function needed";
 
     const auto outG = this->getOutputGrad();
     const auto outV = this->getOutputValue();

paddle/gserver/layers/CosSimVecMatLayer.cpp

@@ -112,7 +112,7 @@ bool CosSimVecMatLayer::init(const LayerMap& layerMap,
 
 void CosSimVecMatLayer::forward(PassType passType) {
   Layer::forward(passType);
-  CHECK_EQ(forward_.size(), 1) << "Only one forward function needed";
+  CHECK_EQ(forward_.size(), 1UL) << "Only one forward function needed";
 
   MatrixPtr inV0 = getInputValue(0);
   MatrixPtr inV1 = getInputValue(1);
@@ -145,7 +145,7 @@ void CosSimVecMatLayer::forward(PassType passType) {
 }
 
 void CosSimVecMatLayer::backward(const UpdateCallback& callback) {
-  CHECK_EQ(backward_.size(), 1) << "Only one forward function needed";
+  CHECK_EQ(backward_.size(), 1UL) << "Only one forward function needed";
 
   MatrixPtr inV0 = getInputValue(0);
   MatrixPtr inV1 = getInputValue(1);

paddle/math/tests/test_RowBuffer.cpp

@@ -17,10 +17,10 @@ limitations under the License. */
 
 TEST(RowBuffer, testAutoGrow) {
   paddle::RowBuffer buf(128);
-  ASSERT_EQ(128, buf.getWidth());
+  ASSERT_EQ(128UL, buf.getWidth());
   ASSERT_TRUE(buf.isAutoGrowth());
   buf.resize(2);
-  ASSERT_EQ(2, buf.getRowCount());
+  ASSERT_EQ(2UL, buf.getRowCount());
   for (size_t i = 0; i < buf.getWidth() * 2; ++i) {
     buf.data()[i] = i;
   }
@@ -35,7 +35,7 @@ TEST(RowBuffer, testAutoGrow) {
     data[i] = i;
   }
 
-  ASSERT_EQ(3, buf.getRowCount());
+  ASSERT_EQ(3UL, buf.getRowCount());
   for (size_t i = 0; i < buf.getRowCount() - 1; ++i) {
     for (size_t j = 0; j < buf.getWidth(); ++j) {
       ASSERT_NEAR(i * buf.getWidth() + j, buf.get(i)[j], 1e-5);
@@ -51,7 +51,7 @@ TEST(RowBuffer, testWithMemBuf) {
       std::make_shared<paddle::CpuMemoryHandle>(128 * 2 * sizeof(real));
   paddle::RowBuffer buf(mem, 128);
   ASSERT_TRUE(!buf.isAutoGrowth());
-  ASSERT_EQ(2, buf.getRowCount());
+  ASSERT_EQ(2UL, buf.getRowCount());
   for (size_t i = 0; i < buf.getWidth() * 2; ++i) {
     buf.data()[i] = i;
   }

paddle/py_paddle/util.py

@@ -208,7 +208,7 @@ def __monkeypatch_gradient_machine__():
 
         output = dict()
         for name in layerNames:
-            output[name] = __matrix_to_numpy__(self.getLayerOutput(name))
+            output[name] = __arguments_to_numpy__(0, self.getLayerOutput(name))
         return output
 
     swig_paddle.GradientMachine.getLayerOutputs = getLayerOutputs

python/paddle/v2/dataset/config.py

+import os
+
+__all__ = ['DATA_HOME']
+
+DATA_HOME = os.path.expanduser('~/.cache/paddle_data_set')
+
+if not os.path.exists(DATA_HOME):
+    os.makedirs(DATA_HOME)

python/paddle/v2/dataset/mnist.py

+import sklearn.datasets.mldata
+import sklearn.model_selection
+import numpy
+from config import DATA_HOME
+
+__all__ = ['train_creator', 'test_creator']
+
+
+def __mnist_reader_creator__(data, target):
+    def reader():
+        n_samples = data.shape[0]
+        for i in xrange(n_samples):
+            yield (data[i] / 255.0).astype(numpy.float32), int(target[i])
+
+    return reader
+
+
+TEST_SIZE = 10000
+
+data = sklearn.datasets.mldata.fetch_mldata(
+    "MNIST original", data_home=DATA_HOME)
+X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
+    data.data, data.target, test_size=TEST_SIZE, random_state=0)
+
+
+def train_creator():
+    return __mnist_reader_creator__(X_train, y_train)
+
+
+def test_creator():
+    return __mnist_reader_creator__(X_test, y_test)
+
+
+def unittest():
+    assert len(list(test_creator()())) == TEST_SIZE
+
+
+if __name__ == '__main__':
+    unittest()