Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into convert

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/reader/__init__.py

@@ -21,3 +21,5 @@
 #
 #     r = paddle.reader.buffered(paddle.reader.creator.text("hello.txt"))
 from decorator import *
+
+import creator

python/paddle/reader/creator.py

+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# 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.
+
+__all__ = ['np_array', 'text_file']
+
+
+def np_array(x):
+    """
+    Creates a reader that yields elements of x, if it is a
+    numpy vector. Or rows of x, if it is a numpy matrix.
+    Or any sub-hyperplane indexed by the highest dimension.
+
+    :param x: the numpy array to create reader from.
+    :returns: data reader created from x.
+    """
+
+    def reader():
+        if x.ndim < 1:
+            yield x
+
+        for e in x:
+            yield e
+
+    return reader
+
+
+def text_file(path):
+    """
+    Creates a data reader that outputs text line by line from given text file.
+    Trailing new line ('\n') of each line will be removed.
+
+    :path: path of the text file.
+    :returns: data reader of text file
+    """
+
+    def reader():
+        f = open(path, "r")
+        for l in f:
+            yield l.rstrip('\n')
+        f.close()
+
+    return reader

python/paddle/reader/tests/CMakeLists.txt

@@ -2,3 +2,8 @@ add_test(NAME reader_decorator_test
   COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/
         ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/reader/tests/decorator_test.py
     WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle)
+
+add_test(NAME reader_creator_test
+  COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/
+        ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/reader/tests/creator_test.py
+    WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle)

python/paddle/reader/tests/creator_test.py

+# Copyright PaddlePaddle contributors. All Rights Reserved
+#
+# 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.
+import unittest
+import paddle.reader.creator
+import numpy as np
+import os
+
+
+class TestNumpyArray(unittest.TestCase):
+    def test_numpy_array(self):
+        l = [[1, 2, 3], [4, 5, 6]]
+        x = np.array(l, np.int32)
+        reader = paddle.reader.creator.np_array(x)
+        for idx, e in enumerate(reader()):
+            self.assertItemsEqual(e, l[idx])
+
+
+class TestTextFile(unittest.TestCase):
+    def test_text_file(self):
+        path = os.path.join(os.path.dirname(__file__), "test_data_creator.txt")
+        reader = paddle.reader.creator.text_file(path)
+        for idx, e in enumerate(reader()):
+            self.assertEqual(e, str(idx * 2) + " " + str(idx * 2 + 1))
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/reader/tests/test_data_creator.txt

+0 1
+2 3
+4 5

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()

python/paddle/v2/optimizer.py

@@ -3,7 +3,10 @@ import paddle.trainer_config_helpers.optimizers as v1_optimizers
 import paddle.trainer_config_helpers.config_parser_utils as config_parser_utils
 import paddle.v2
 
-__all__ = ['Adam', 'Adamax']
+__all__ = [
+    'Momentum', 'Adam', 'Adamax', 'AdaGrad', 'DecayedAdaGrad', 'AdaDelta',
+    'RMSProp', 'ModelAverage', 'L2Regularization'
+]
 
 
 class Optimizer(object):
@@ -38,6 +41,14 @@ class Optimizer(object):
                                                              pass_num)
 
 
+class Momentum(Optimizer):
+    def __init__(self, momentum=None, sparse=False, **kwargs):
+        learning_method = v1_optimizers.MomentumOptimizer(
+            momentum=None, sparse=False)
+        super(Momentum, self).__init__(
+            learning_method=learning_method, **kwargs)
+
+
 class Adam(Optimizer):
     def __init__(self, beta1=0.9, beta2=0.999, epsilon=1e-8, **kwargs):
         learning_method = v1_optimizers.AdamOptimizer(
@@ -52,7 +63,45 @@ class Adamax(Optimizer):
         super(Adamax, self).__init__(learning_method=learning_method, **kwargs)
 
 
+class AdaGrad(Optimizer):
+    def __init__(self, **kwargs):
+        learning_method = v1_optimizers.AdaGradOptimizer()
+        super(AdaGrad, self).__init__(learning_method=learning_method, **kwargs)
+
+
+class DecayedAdaGrad(Optimizer):
+    def __init__(self, rho=0.95, epsilon=1e-06, **kwargs):
+        learning_method = v1_optimizers.DecayedAdaGradOptimizer(
+            rho=rho, epsilon=epsilon)
+        super(DecayedAdaGrad, self).__init__(
+            learning_method=learning_method, **kwargs)
+
+
+class AdaDelta(Optimizer):
+    def __init__(self, rho=0.95, epsilon=1e-06, **kwargs):
+        learning_method = v1_optimizers.AdaDeltaOptimizer(
+            rho=rho, epsilon=epsilon)
+        super(AdaDelta, self).__init__(
+            learning_method=learning_method, **kwargs)
+
+
+class RMSProp(Optimizer):
+    def __init__(self, rho=0.95, epsilon=1e-6, **kwargs):
+        learning_method = v1_optimizers.RMSPropOptimizer(
+            rho=rho, epsilon=epsilon)
+        super(RMSProp, self).__init__(learning_method=learning_method, **kwargs)
+
+
+ModelAverage = v1_optimizers.ModelAverage
+L2Regularization = v1_optimizers.L2Regularization
+
 if __name__ == '__main__':
     swig_api.initPaddle('--use_gpu=false')
-    opt = paddle.v2.optimizer.Adam()
-    print opt.enable_types()
+    for opt in [
+            Momentum(), Adam(), Adamax(), AdaGrad(), DecayedAdaGrad(),
+            AdaDelta(), RMSProp(), Adam(
+                model_average=ModelAverage(average_window=0.5),
+                regularization=L2Regularization(rate=0.5),
+                gradient_clipping_threshold=25)
+    ]:
+        print opt, opt.enable_types()