diff --git a/demo/image_classification/prediction.py b/demo/image_classification/prediction.py index 9a86aafcb2fa4d4354d1dd9443c1b73ddcda980b..49c0ff600c40e0222093ff0a8a2f7e8e38ccba29 100755 --- a/demo/image_classification/prediction.py +++ b/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) diff --git a/demo/mnist/api_train_v2.py b/demo/mnist/api_train_v2.py index 6fc01ce58be57c77144c6558d039430b22d3a746..767dffdad82f59d73b1505586260a61f5008f1f8 100644 --- a/demo/mnist/api_train_v2.py +++ b/demo/mnist/api_train_v2.py @@ -27,19 +27,14 @@ def main(): cost = paddle.layer.classification_cost(input=inference, label=label) parameters = paddle.parameters.create(cost) - for param_name in parameters.keys(): - array = parameters.get(param_name) - array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape) - parameters.set(parameter_name=param_name, value=array) adam_optimizer = paddle.optimizer.Adam(learning_rate=0.01) def event_handler(event): if isinstance(event, paddle.event.EndIteration): - para = parameters.get('___fc_2__.w0') - print "Pass %d, Batch %d, Cost %f, Weight Mean Of Fc 2 is %f" % ( - event.pass_id, event.batch_id, event.cost, para.mean()) - + if event.batch_id % 100 == 0: + print "Pass %d, Batch %d, Cost %f, %s" % ( + event.pass_id, event.batch_id, event.cost, event.metrics) else: pass @@ -50,11 +45,12 @@ def main(): parameters=parameters, event_handler=event_handler, batch_size=32, # batch size should be refactor in Data reader - data_types={ # data_types will be removed, It should be in + data_types=[ # data_types will be removed, It should be in # network topology - 'pixel': images.type, - 'label': label.type - }) + ('pixel', images.type), + ('label', label.type)], + reader_dict={'pixel':0, 'label':1} + ) if __name__ == '__main__': diff --git a/demo/model_zoo/resnet/classify.py b/demo/model_zoo/resnet/classify.py index 4631816c43ef48839df1863a0a86c3ab00924d3f..6074cc1d3a85e13e3e8d336d81e22104f9d8e7cf 100755 --- a/demo/model_zoo/resnet/classify.py +++ b/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 diff --git a/paddle/api/Arguments.cpp b/paddle/api/Arguments.cpp index 41beed38a87601cb57072c8966cd0fd2ea156524..a3f4bfffc9f074900ebcc52876c04bbfc0e570b2 100644 --- a/paddle/api/Arguments.cpp +++ b/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); diff --git a/paddle/api/Evaluator.cpp b/paddle/api/Evaluator.cpp index c30e09876397e37ef9ed4ec3200d1aa372ceb609..681e3a380912339c531c16c88f43255c2f34c32f 100644 --- a/paddle/api/Evaluator.cpp +++ b/paddle/api/Evaluator.cpp @@ -27,3 +27,18 @@ std::string Evaluator::toString() { m->rawPtr->printStats(sout); return sout.str(); } + +std::vector Evaluator::getNames() const { + std::vector retv; + m->rawPtr->getNames(&retv); + return retv; +} + +double Evaluator::getValue(const std::string name) const { + paddle::Error err; + double v = m->rawPtr->getValue(name, &err); + if (err) { + throw std::runtime_error(err.msg()); + } + return v; +} diff --git a/paddle/api/GradientMachine.cpp b/paddle/api/GradientMachine.cpp index 66115f8293b905809639afff779abfdb2bb3a54e..538ca2999f8f05afc45ac2d2f526133c8024f066 100644 --- a/paddle/api/GradientMachine.cpp +++ b/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(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(); } diff --git a/paddle/api/PaddleAPI.h b/paddle/api/PaddleAPI.h index f5af8b0035b44d97832dd90ca2eeba079503715c..1831b8e170087c909f77948f2d9077c946c72507 100644 --- a/paddle/api/PaddleAPI.h +++ b/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); /** @@ -900,6 +901,10 @@ public: */ std::string toString(); + std::vector getNames() const; + + double getValue(const std::string name) const; + private: EvaluatorPrivate* m; @@ -952,7 +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. diff --git a/paddle/api/Trainer.cpp b/paddle/api/Trainer.cpp index d83dc380beeec3747451a483f4811eb833e8c226..84e4ca054abb0100a02c8a40e31c49c17684ef40 100644 --- a/paddle/api/Trainer.cpp +++ b/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( - 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) { diff --git a/paddle/api/test/testTrain.py b/paddle/api/test/testTrain.py index a90d15c272a3a2b56e35c979e053deb2b54eebc1..7061a4c43bf01158b5f084d0c310dedd81773a04 100644 --- a/paddle/api/test/testTrain.py +++ b/paddle/api/test/testTrain.py @@ -89,9 +89,14 @@ def main(): except Exception as e: print e + ev = m.makeEvaluator() + ev.start() m.forwardBackward(inArgs, outArgs, swig_paddle.PASS_TRAIN, update_callback) - + m.eval(ev) + ev.finish() + for name in ev.getNames(): + print name, ev.getValue(name) for optimizer in optimizers: optimizer.finishBatch() diff --git a/paddle/gserver/gradientmachines/GradientMachine.h b/paddle/gserver/gradientmachines/GradientMachine.h index 0829968d87c5dc7eeb2d1b70c758ff305d89496f..bc2f2f8563526aa045ea89f15152ee2d639b5774 100644 --- a/paddle/gserver/gradientmachines/GradientMachine.h +++ b/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() {} diff --git a/paddle/gserver/gradientmachines/MultiGradientMachine.cpp b/paddle/gserver/gradientmachines/MultiGradientMachine.cpp index 80f223824d8dccfb0e9386f4c076b28f9332a958..123273f916f5d33e2543d9f5f28573c3b5761e28 100644 --- a/paddle/gserver/gradientmachines/MultiGradientMachine.cpp +++ b/paddle/gserver/gradientmachines/MultiGradientMachine.cpp @@ -282,6 +282,18 @@ void MultiGradientMachine::forwardBackward(const std::vector& inArgs, backwardImp(callback); } +Argument MultiGradientMachine::getLayerOutput(const std::string& layerName) { + std::vector 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; diff --git a/paddle/gserver/gradientmachines/MultiGradientMachine.h b/paddle/gserver/gradientmachines/MultiGradientMachine.h index 9be15ef4bcf34f26b7eceb9047252e537f20a4a8..838a52b5153af63adbce5788824b9f541f22517c 100644 --- a/paddle/gserver/gradientmachines/MultiGradientMachine.h +++ b/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 outArgs_; hl_stream_t outArgStream_; + Argument outLayerArgs_; + /// ParameterType which needs to be merged from each GPU std::vector mergeTypes_; int numDevices_; /* number of gpu devices */ diff --git a/paddle/gserver/gradientmachines/NeuralNetwork.cpp b/paddle/gserver/gradientmachines/NeuralNetwork.cpp index 273a9111c35a21c01f0cd8d283ecc6eaa4ef0c61..4512aacc81f86bf87fc9ea30adcf081327663f16 100644 --- a/paddle/gserver/gradientmachines/NeuralNetwork.cpp +++ b/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(); diff --git a/paddle/gserver/gradientmachines/NeuralNetwork.h b/paddle/gserver/gradientmachines/NeuralNetwork.h index 25af4abcf81700e200feea806fa3daed19df1275..e7b6c438407e7eab6eab1f6ed496f35caa9f2177 100644 --- a/paddle/gserver/gradientmachines/NeuralNetwork.h +++ b/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; diff --git a/paddle/gserver/layers/CosSimLayer.cpp b/paddle/gserver/layers/CosSimLayer.cpp index a6c0300acf6752a3536e7939577b561fd97d1eb8..57ba124e40cbd098fa8b0012ff31d6935b16862a 100644 --- a/paddle/gserver/layers/CosSimLayer.cpp +++ b/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(); diff --git a/paddle/gserver/layers/CosSimVecMatLayer.cpp b/paddle/gserver/layers/CosSimVecMatLayer.cpp index aabafd473aa1e06a767d48d4c49b7b8662e992e7..0f887d8adfa053e8fe88ac4fa4e2a9ba08ac07b5 100644 --- a/paddle/gserver/layers/CosSimVecMatLayer.cpp +++ b/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); diff --git a/paddle/math/tests/test_RowBuffer.cpp b/paddle/math/tests/test_RowBuffer.cpp index 5f66f22ef73dcff1868c1a3e03139a680b1ce2b5..8cc4c69a1a4d8afec08bf7fb13408e135a06c09c 100644 --- a/paddle/math/tests/test_RowBuffer.cpp +++ b/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(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; } diff --git a/paddle/py_paddle/dataprovider_converter.py b/paddle/py_paddle/dataprovider_converter.py index 21d1cb75f4d40e6ed011b33c6366c9d31c0fcc7c..2690cafe1d8d32bf52cd9e5fa4dc69fbacb2d66c 100644 --- a/paddle/py_paddle/dataprovider_converter.py +++ b/paddle/py_paddle/dataprovider_converter.py @@ -23,7 +23,8 @@ __all__ = ['DataProviderConverter'] class IScanner(object): def __init__(self, input_type, pos): self.input_type = input_type - assert isinstance(self.input_type, dp2.InputType) + if not isinstance(self.input_type, dp2.InputType): + raise ValueError("input type should be dataprovider2.InputType") self.pos = pos def scan(self, dat): @@ -50,7 +51,6 @@ class DenseScanner(IScanner): def finish_scan(self, argument): assert isinstance(argument, swig_paddle.Arguments) - assert isinstance(self.input_type, dp2.InputType) if self.__mat__.dtype != numpy.float32: self.__mat__ = self.__mat__.astype(numpy.float32) m = swig_paddle.Matrix.createDenseFromNumpy(self.__mat__, True, False) @@ -63,7 +63,6 @@ class SparseBinaryScanner(IScanner): self.__rows__ = [0] self.__cols__ = [] self.__height__ = 0 - self.__nnz__ = 0 self.__value__ = [] def scan(self, dat): @@ -76,7 +75,6 @@ class SparseBinaryScanner(IScanner): def finish_scan(self, argument): assert isinstance(argument, swig_paddle.Arguments) - assert isinstance(self.input_type, dp2.InputType) m = swig_paddle.Matrix.createSparse(self.__height__, self.input_type.dim, len(self.__cols__), diff --git a/paddle/py_paddle/util.py b/paddle/py_paddle/util.py index ce105d249aaf3e838443d3e0cf5996fe8c783a22..a708def1d2d7f6da2998a5905f9473accc6db969 100644 --- a/paddle/py_paddle/util.py +++ b/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 diff --git a/paddle/scripts/docker/Dockerfile b/paddle/scripts/docker/Dockerfile index d4845a72b61833b356779d44de5163f161e0cd4d..27d19dc15c8bd64832243d75bf22f0e8adeecbd1 100644 --- a/paddle/scripts/docker/Dockerfile +++ b/paddle/scripts/docker/Dockerfile @@ -10,28 +10,30 @@ RUN apt-get update && \ apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \ apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \ apt-get install -y python-numpy python-matplotlib gcc g++ gfortran && \ - apt-get install -y automake clang-3.8 llvm-3.8 libclang-3.8-dev && \ + apt-get install -y automake && \ apt-get clean -y RUN pip install --upgrade pip && \ - pip install -U protobuf && \ + pip install -U "protobuf==3.1.0" && \ pip install -U wheel pillow BeautifulSoup && \ pip install -U docopt PyYAML sphinx && \ pip install -U sphinx_rtd_theme recommonmark jupyter RUN curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz | tar -xz && \ - cd cmake-3.4.1 && ./bootstrap && make -j4 && make install && \ + cd cmake-3.4.1 && ./bootstrap && make -j `nproc` && make install && \ cd .. && rm -rf cmake-3.4.1 +ARG BUILD_WOBOQ ARG BUILD_AND_INSTALL ARG WITH_AVX ARG WITH_DOC ARG WITH_STYLE_CHECK +ENV BUILD_WOBOQ=${BUILD_WOBOQ:-OFF} ENV BUILD_AND_INSTALL=${BUILD_AND_INSTALL:-OFF} ENV WITH_GPU=OFF ENV WITH_AVX=${WITH_AVX:-ON} -ENV WITH_DOC=${WITH_DOC:-ON} +ENV WITH_DOC=${WITH_DOC:-OFF} ENV WITH_STYLE_CHECK=${WITH_STYLE_CHECK:-OFF} RUN mkdir /paddle diff --git a/paddle/scripts/docker/Dockerfile.gpu b/paddle/scripts/docker/Dockerfile.gpu index da20b2635e10e702d7c121a4f95e6a52a68487b0..538233dd0c7bb5088ea9473eb4d1a1ab62e98cb5 100644 --- a/paddle/scripts/docker/Dockerfile.gpu +++ b/paddle/scripts/docker/Dockerfile.gpu @@ -10,28 +10,30 @@ RUN apt-get update && \ apt-get install -y wget unzip tar xz-utils bzip2 gzip coreutils && \ apt-get install -y curl sed grep graphviz libjpeg-dev zlib1g-dev && \ apt-get install -y python-numpy python-matplotlib gcc g++ gfortran && \ - apt-get install -y automake clang-3.8 llvm-3.8 libclang-3.8-dev && \ + apt-get install -y automake && \ apt-get clean -y RUN pip install --upgrade pip && \ - pip install -U protobuf && \ + pip install -U "protobuf==3.1.0" && \ pip install -U wheel pillow BeautifulSoup && \ pip install -U docopt PyYAML sphinx && \ pip install -U sphinx_rtd_theme recommonmark jupyter RUN curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz | tar -xz && \ - cd cmake-3.4.1 && ./bootstrap && make -j4 && make install && \ + cd cmake-3.4.1 && ./bootstrap && make -j `nproc` && make install && \ cd .. && rm -rf cmake-3.4.1 +ARG BUILD_WOBOQ ARG BUILD_AND_INSTALL ARG WITH_AVX ARG WITH_DOC ARG WITH_STYLE_CHECK +ENV BUILD_WOBOQ=${BUILD_WOBOQ:-OFF} ENV BUILD_AND_INSTALL=${BUILD_AND_INSTALL:-OFF} ENV WITH_GPU=ON ENV WITH_AVX=${WITH_AVX:-ON} -ENV WITH_DOC=${WITH_DOC:-ON} +ENV WITH_DOC=${WITH_DOC:-OFF} ENV WITH_STYLE_CHECK=${WITH_STYLE_CHECK:-OFF} RUN mkdir /paddle diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh index 6197b41d6b5f191a452cbf32b47e0ff490b61046..d9c44f42340323afb4930dab35114f2adc5fbb3a 100755 --- a/paddle/scripts/docker/build.sh +++ b/paddle/scripts/docker/build.sh @@ -11,7 +11,7 @@ set -e # If Dockerfile.* sets BUILD_AND_INSTALL to 'ON', it would have copied # source tree to /paddle, and this scripts should build it into # /paddle/build. -if [[ ${BUILD_AND_INSTALL:-ON} == 'ON' ]]; then +if [[ ${BUILD_AND_INSTALL:-OFF} == 'ON' ]]; then if [[ ${WITH_GPU:-OFF} == 'ON' ]]; then ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so /usr/lib/libcudnn.so fi @@ -19,7 +19,7 @@ if [[ ${BUILD_AND_INSTALL:-ON} == 'ON' ]]; then mkdir -p /paddle/build # -p means no error if exists cd /paddle/build cmake .. \ - -DWITH_DOC=ON \ + -DWITH_DOC=${WITH_DOC:-OFF} \ -DWITH_GPU=${WITH_GPU:-OFF} \ -DWITH_AVX=${WITH_AVX:-OFF} \ -DWITH_SWIG_PY=ON \ @@ -29,28 +29,32 @@ if [[ ${BUILD_AND_INSTALL:-ON} == 'ON' ]]; then make -j `nproc` make install - # Install woboq_codebrowser. - git clone https://github.com/woboq/woboq_codebrowser /woboq - cd /woboq - cmake -DLLVM_CONFIG_EXECUTABLE=/usr/bin/llvm-config-3.8 \ - -DCMAKE_BUILD_TYPE=Release \ - . - make - - export WOBOQ_OUT=/usr/share/nginx/html/paddle - export BUILD_DIR=/paddle/build - mkdir -p $WOBOQ_OUT - cp -rv /woboq/data $WOBOQ_OUT/../data - /woboq/generator/codebrowser_generator \ - -b /paddle/build \ - -a \ - -o $WOBOQ_OUT \ - -p paddle:/paddle - /woboq/indexgenerator/codebrowser_indexgenerator $WOBOQ_OUT - cd /woboq - make clean - - pip install /usr/local/opt/paddle/share/wheels/*.whl + if [[ ${BUILD_WOBOQ:-OFF} == 'ON' ]]; then + apt-get install -y clang-3.8 llvm-3.8 libclang-3.8-dev + # Install woboq_codebrowser. + git clone https://github.com/woboq/woboq_codebrowser /woboq + cd /woboq + cmake -DLLVM_CONFIG_EXECUTABLE=/usr/bin/llvm-config-3.8 \ + -DCMAKE_BUILD_TYPE=Release \ + . + make + + export WOBOQ_OUT=/usr/share/nginx/html/paddle + export BUILD_DIR=/paddle/build + mkdir -p $WOBOQ_OUT + cp -rv /woboq/data $WOBOQ_OUT/../data + /woboq/generator/codebrowser_generator \ + -b /paddle/build \ + -a \ + -o $WOBOQ_OUT \ + -p paddle:/paddle + /woboq/indexgenerator/codebrowser_indexgenerator $WOBOQ_OUT + cd /woboq + make clean + fi + + pip install /usr/local/opt/paddle/share/wheels/py_paddle*linux*.whl + pip install /usr/local/opt/paddle/share/wheels/paddle*.whl paddle version fi diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt index 357637e20346f8e1179d3a28ff580722cdfcccff..71af50a9a4ed835635362c24d6c1ae5e92de050b 100644 --- a/python/CMakeLists.txt +++ b/python/CMakeLists.txt @@ -25,6 +25,7 @@ add_custom_target(paddle_python ALL DEPENDS add_subdirectory(paddle/trainer_config_helpers/tests) add_subdirectory(paddle/reader/tests) +add_subdirectory(paddle/v2/tests) install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/dist/ DESTINATION opt/paddle/share/wheels diff --git a/python/paddle/reader/__init__.py b/python/paddle/reader/__init__.py index 493b410e8299ebe167be43ead1401a6ab245a631..7373dc461b1d3115c03b37c5102a469a52aa7441 100644 --- a/python/paddle/reader/__init__.py +++ b/python/paddle/reader/__init__.py @@ -21,3 +21,5 @@ # # r = paddle.reader.buffered(paddle.reader.creator.text("hello.txt")) from decorator import * + +import creator diff --git a/python/paddle/reader/creator.py b/python/paddle/reader/creator.py new file mode 100644 index 0000000000000000000000000000000000000000..5a91bb0b8ef6d1874737386897f6c555eaec18d4 --- /dev/null +++ b/python/paddle/reader/creator.py @@ -0,0 +1,53 @@ +# 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 diff --git a/python/paddle/reader/tests/CMakeLists.txt b/python/paddle/reader/tests/CMakeLists.txt index 502c897d8946a838847c1c23b1236358c58c088e..da072fb3dbeed5bee15fa1f64372ad8dec497070 100644 --- a/python/paddle/reader/tests/CMakeLists.txt +++ b/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) diff --git a/python/paddle/reader/tests/creator_test.py b/python/paddle/reader/tests/creator_test.py new file mode 100644 index 0000000000000000000000000000000000000000..eda8ab6715b2be0c9cb6163adf60d8fbdf2d7e8c --- /dev/null +++ b/python/paddle/reader/tests/creator_test.py @@ -0,0 +1,38 @@ +# 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() diff --git a/python/paddle/reader/tests/test_data_creator.txt b/python/paddle/reader/tests/test_data_creator.txt new file mode 100644 index 0000000000000000000000000000000000000000..a2a8d47d43868d369083808497697da79e620e31 --- /dev/null +++ b/python/paddle/reader/tests/test_data_creator.txt @@ -0,0 +1,3 @@ +0 1 +2 3 +4 5 diff --git a/python/paddle/v2/__init__.py b/python/paddle/v2/__init__.py index 0cf7b8e9039f7393ae9fcf73faeeeb8fbf11df31..49d1983a2a422b7e105c66dd92419426f0853212 100644 --- a/python/paddle/v2/__init__.py +++ b/python/paddle/v2/__init__.py @@ -18,12 +18,13 @@ import parameters import trainer import event import data_type +import data_feeder import attr import py_paddle.swig_paddle as api __all__ = [ 'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer', - 'event', 'data_type', 'attr' + 'event', 'data_type', 'attr', 'data_feeder' ] diff --git a/python/paddle/v2/data_feeder.py b/python/paddle/v2/data_feeder.py new file mode 100644 index 0000000000000000000000000000000000000000..2a16d46dda47f822dd2d6c168528dd6cec53ab4e --- /dev/null +++ b/python/paddle/v2/data_feeder.py @@ -0,0 +1,100 @@ +# 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. + +from py_paddle import swig_paddle +from py_paddle import DataProviderConverter +import data_type + +__all__ = ['DataFeeder'] + + +class DataFeeder(DataProviderConverter): + """ + DataFeeder converts the data returned by paddle.reader into a data structure + of Arguments which is defined in the API. The paddle.reader usually returns + a list of mini-batch data entries. Each data entry in the list is one sampe. + Each sample is a list or a tuple with one feature or multiple features. + DataFeeder converts this mini-batch data entries into Arguments in order + to feed it to C++ interface. + + The example usage: + + data_types = [('image', paddle.data_type.dense_vector(784)), + ('label', paddle.data_type.integer_value(10))] + reader_dict = {'image':0, 'label':1} + feeder = DataFeeder(data_types=data_types, reader_dict=reader_dict) + minibatch_data = [ + ( [1.0,2.0,3.0,4.0], 5, [6,7,8] ), # first sample + ( [1.0,2.0,3.0,4.0], 5, [6,7,8] ) # second sample + ] + # or minibatch_data = [ + # [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ], # first sample + # [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ] # second sample + # ] + arg = feeder(minibatch_data) + """ + + def __init__(self, data_types, reader_dict): + """ + :param data_types: A list to specify data name and type. Each item is + a tuple of (data_name, data_type). For example: + [('image', paddle.data_type.dense_vector(784)), + ('label', paddle.data_type.integer_value(10))] + + :type data_types: A list of tuple + :param reader_dict: A dictionary to specify the position of each data + in the input data. + :type reader_dict: dict() + """ + self.input_names = [] + input_types = [] + self.reader_dict = reader_dict + for each in data_types: + self.input_names.append(each[0]) + assert isinstance(each[1], data_type.InputType) + input_types.append(each[1]) + DataProviderConverter.__init__(self, input_types) + + def convert(self, dat, argument=None): + """ + :param dat: A list of mini-batch data. Each sample is a list or tuple + one feature or multiple features. + for example: + [ + ([0.2, 0.2], ), # first sample + ([0.8, 0.3], ), # second sample + ] + or, + [ + [[0.2, 0.2], ], # first sample + [[0.8, 0.3], ], # second sample + ] + + :type dat: List + :param argument: An Arguments object contains this mini-batch data with + one or multiple features. The Arguments definition is + in the API. + :type argument: swig_paddle.Arguments + """ + + def reorder_data(data): + retv = [] + for each in data: + reorder = [] + for name in self.input_names: + reorder.append(each[self.reader_dict[name]]) + retv.append(reorder) + return retv + + return DataProviderConverter.convert(self, reorder_data(dat), argument) diff --git a/python/paddle/v2/data_set/mnist.py b/python/paddle/v2/data_set/mnist.py deleted file mode 100644 index 6f35acf683686e3030e4c89784486ea0a3c0317f..0000000000000000000000000000000000000000 --- a/python/paddle/v2/data_set/mnist.py +++ /dev/null @@ -1,54 +0,0 @@ -import sklearn.datasets.mldata -import sklearn.model_selection -import numpy -from config import DATA_HOME - -__all__ = ['MNIST', '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 - - -class MNIST(object): - """ - mnist dataset reader. The `train_reader` and `test_reader` method returns - a iterator of each sample. Each sample is combined by 784-dim float and a - one-dim label - """ - - def __init__(self, random_state=0, test_size=10000, **options): - data = sklearn.datasets.mldata.fetch_mldata( - "MNIST original", data_home=DATA_HOME) - self.X_train, self.X_test, self.y_train, self.y_test = sklearn.model_selection.train_test_split( - data.data, - data.target, - test_size=test_size, - random_state=random_state, - **options) - - def train_creator(self): - return __mnist_reader_creator__(self.X_train, self.y_train) - - def test_creator(self): - return __mnist_reader_creator__(self.X_test, self.y_test) - - -__default_instance__ = MNIST() -train_creator = __default_instance__.train_creator -test_creator = __default_instance__.test_creator - - -def unittest(): - size = 12045 - mnist = MNIST(test_size=size) - assert len(list(mnist.test_creator()())) == size - - -if __name__ == '__main__': - unittest() diff --git a/python/paddle/v2/data_type.py b/python/paddle/v2/data_type.py index 5b01ba4cd4866cf7b355fc0a6a667409cf9c4419..dd3ebfcb4267e1bb59011c81cb5a2716b8e45a6d 100644 --- a/python/paddle/v2/data_type.py +++ b/python/paddle/v2/data_type.py @@ -14,9 +14,9 @@ from paddle.trainer.PyDataProvider2 import \ InputType, dense_vector, sparse_binary_vector,\ - sparse_vector, integer_value + sparse_vector, integer_value, integer_value_sequence __all__ = [ 'InputType', 'dense_vector', 'sparse_binary_vector', 'sparse_vector', - 'integer_value' + 'integer_value', 'integer_value_sequence' ] diff --git a/python/paddle/v2/data_set/__init__.py b/python/paddle/v2/dataset/__init__.py similarity index 100% rename from python/paddle/v2/data_set/__init__.py rename to python/paddle/v2/dataset/__init__.py diff --git a/python/paddle/v2/data_set/config.py b/python/paddle/v2/dataset/config.py similarity index 100% rename from python/paddle/v2/data_set/config.py rename to python/paddle/v2/dataset/config.py diff --git a/python/paddle/v2/dataset/mnist.py b/python/paddle/v2/dataset/mnist.py new file mode 100644 index 0000000000000000000000000000000000000000..db84f37aa4fc3477b17599a48a4de9b45cfb6c1f --- /dev/null +++ b/python/paddle/v2/dataset/mnist.py @@ -0,0 +1,39 @@ +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() diff --git a/python/paddle/v2/event.py b/python/paddle/v2/event.py index a16cfa91f062a60a141ea8fa962b3ecf6f5f0a22..835e28e6218df22e1cad7f7bb31c3c9941657252 100644 --- a/python/paddle/v2/event.py +++ b/python/paddle/v2/event.py @@ -3,8 +3,6 @@ All training events. There are: -* BeginTraining -* EndTraining * BeginIteration * EndIteration * BeginPass @@ -12,15 +10,62 @@ There are: TODO(yuyang18): Complete it! """ -__all__ = ['EndIteration'] +import py_paddle.swig_paddle as api +__all__ = ['EndIteration', 'BeginIteration', 'BeginPass', 'EndPass'] -class EndIteration(object): +class WithMetric(object): + def __init__(self, evaluator): + if not isinstance(evaluator, api.Evaluator): + raise TypeError("Evaluator should be api.Evaluator type") + self.__evaluator__ = evaluator + + @property + def metrics(self): + names = self.__evaluator__.getNames() + retv = dict() + for each_name in names: + val = self.__evaluator__.getValue(each_name) + retv[each_name] = val + return retv + + +class BeginPass(object): + """ + Event On One Pass Training Start. + """ + + def __init__(self, pass_id): + self.pass_id = pass_id + + +class EndPass(WithMetric): + """ + Event On One Pass Training Complete. + """ + + def __init__(self, pass_id, evaluator): + self.pass_id = pass_id + WithMetric.__init__(self, evaluator) + + +class BeginIteration(object): + """ + Event On One Batch Training Start. + """ + + def __init__(self, pass_id, batch_id): + self.pass_id = pass_id + self.batch_id = batch_id + + +class EndIteration(WithMetric): """ Event On One Batch Training Complete. """ - def __init__(self, pass_id, batch_id, cost): + def __init__(self, pass_id, batch_id, cost, evaluator): self.pass_id = pass_id self.batch_id = batch_id self.cost = cost + WithMetric.__init__(self, evaluator) diff --git a/python/paddle/v2/optimizer.py b/python/paddle/v2/optimizer.py index aa2942bc9faeb2a353459cd619886f56ea32f450..10e255dc945efb8b20f09dc1806d2ba7ef856c55 100644 --- a/python/paddle/v2/optimizer.py +++ b/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() diff --git a/python/paddle/v2/tests/CMakeLists.txt b/python/paddle/v2/tests/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..2f08ceed534c58c3353be7861f45d024b7c60328 --- /dev/null +++ b/python/paddle/v2/tests/CMakeLists.txt @@ -0,0 +1,6 @@ +add_test(NAME test_v2_layer + COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/ + ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/v2/tests/test_layer.py + WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle) +add_test(NAME test_v2_api + COMMAND bash ${PROJ_ROOT}/python/paddle/v2/tests/run_tests.sh ${PYTHON_EXECUTABLE}) diff --git a/python/paddle/v2/tests/run_tests.sh b/python/paddle/v2/tests/run_tests.sh new file mode 100755 index 0000000000000000000000000000000000000000..b96f54fe9cc78a436bc67e6c542b6e842aba997b --- /dev/null +++ b/python/paddle/v2/tests/run_tests.sh @@ -0,0 +1,36 @@ +#!/bin/bash +# 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. + + +pushd `dirname $0` > /dev/null +SCRIPTPATH=$PWD +popd > /dev/null + +cd $SCRIPTPATH + +$1 -m pip install ../../../../paddle/dist/*.whl + +test_list="test_data_feeder.py" + +export PYTHONPATH=$PWD/../../../../python/ + +for fn in $test_list +do + echo "test $fn" + $1 $fn + if [ $? -ne 0 ]; then + exit 1 + fi +done diff --git a/python/paddle/v2/tests/test_data_feeder.py b/python/paddle/v2/tests/test_data_feeder.py new file mode 100644 index 0000000000000000000000000000000000000000..5f67da6a5b32d74228d727d94ec79b9f7a06dab7 --- /dev/null +++ b/python/paddle/v2/tests/test_data_feeder.py @@ -0,0 +1,238 @@ +# 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. + +import unittest + +import py_paddle.swig_paddle as api +import numpy as np + +from paddle.v2 import data_type +from paddle.v2.data_feeder import DataFeeder + + +class DataFeederTest(unittest.TestCase): + def dense_reader(self, size): + data = np.random.random(size) + return data + + def sparse_binary_reader(self, high, size_limit, non_empty=False): + num = np.random.randint(size_limit) # num could be 0 + while non_empty and num == 0: + num = np.random.randint(size_limit) + return np.random.randint(high, size=num).tolist() + + def test_dense(self): + def compare(input): + feeder = DataFeeder([('image', data_type.dense_vector(784))], + {'image': 0}) + arg = feeder(input) + output = arg.getSlotValue(0).copyToNumpyMat() + input = np.array(input, dtype='float32') + self.assertAlmostEqual(input.all(), output.all()) + + # test numpy array + batch_size = 32 + dim = 784 + data = [] + for i in xrange(batch_size): + each_sample = [] + each_sample.append(self.dense_reader(dim)) + data.append(each_sample) + compare(data) + + # each feature is a list + data = [] + for i in xrange(batch_size): + each_sample = [] + each_sample.append(self.dense_reader(dim).tolist()) + data.append(each_sample) + compare(data) + + # test tuple + data = [] + for i in xrange(batch_size): + each_sample = (self.dense_reader(dim).tolist(), ) + data.append(each_sample) + compare(data) + + def test_sparse_binary(self): + dim = 10000 + batch_size = 32 + data = [] + for i in xrange(batch_size): + each_sample = [] + each_sample.append(self.sparse_binary_reader(dim, 50)) + data.append(each_sample) + feeder = DataFeeder([('input', data_type.sparse_binary_vector(dim))], + {'input': 0}) + arg = feeder(data) + output = arg.getSlotValue(0) + assert isinstance(output, api.Matrix) + for i in xrange(batch_size): + self.assertEqual(output.getSparseRowCols(i), data[i][0]) + + def test_sparse(self): + dim = 10000 + batch_size = 32 + v = [] + w = [] + data = [] + for dat in xrange(batch_size): + each_sample = [] + a = self.sparse_binary_reader(dim, 40, non_empty=True) + b = self.dense_reader(len(a)).tolist() + v.append(a) + w.append(np.array(b, dtype="float32")) + each_sample.append(zip(a, b)) + data.append(each_sample) + + feeder = DataFeeder([('input', data_type.sparse_vector(dim))], + {'input': 0}) + arg = feeder(data) + output = arg.getSlotValue(0) + assert isinstance(output, api.Matrix) + for i in xrange(batch_size): + self.assertEqual(output.getSparseRowCols(i), v[i]) + cols_value = output.getSparseRowColsVal(i) + value = [val[1] for val in cols_value] + value = np.array(value, dtype="float32") + self.assertAlmostEqual(value.all(), w[i].all()) + + def test_integer(self): + dim = 100 + batch_size = 32 + index = [] + for i in xrange(batch_size): + each_sample = [] + each_sample.append(np.random.randint(dim)) + index.append(each_sample) + feeder = DataFeeder([('input', data_type.integer_value(dim))], + {'input': 0}) + arg = feeder(index) + output = arg.getSlotIds(0).copyToNumpyArray() + index = np.array(index, dtype='int') + self.assertEqual(output.all(), index.flatten().all()) + + def test_integer_sequence(self): + dim = 10000 + batch_size = 32 + start = [0] + data = [] + for i in xrange(batch_size): + each_sample = [] + each_sample.append( + self.sparse_binary_reader( + dim, 30, non_empty=True)) + data.append(each_sample) + start.append(len(each_sample[0]) + start[-1]) + feeder = DataFeeder([('input', data_type.integer_value_sequence(dim))], + {'input': 0}) + arg = feeder(data) + output_data = arg.getSlotIds(0).copyToNumpyArray() + output_start = arg.getSlotSequenceStartPositions(0).copyToNumpyArray() + + index = [] + for dat in data: + index.extend(x for x in dat[0]) # only one feature, so dat[0] + index = np.array(index, dtype='int') + start = np.array(start, dtype='int') + self.assertEqual(output_data.all(), index.all()) + self.assertEqual(output_start.all(), start.all()) + + def test_multiple_features(self): + batch_size = 2 + data = [] + for i in xrange(batch_size): + each_sample = [] + each_sample.append(np.random.randint(10)) + each_sample.append( + self.sparse_binary_reader( + 20000, 40, non_empty=True)) + each_sample.append(self.dense_reader(100)) + data.append(each_sample) + + # test multiple features + data_types = [('fea0', data_type.dense_vector(100)), + ('fea1', data_type.sparse_binary_vector(20000)), + ('fea2', data_type.integer_value(10))] + feeder = DataFeeder(data_types, {'fea0': 2, 'fea1': 1, 'fea2': 0}) + arg = feeder(data) + output_dense = arg.getSlotValue(0).copyToNumpyMat() + output_sparse = arg.getSlotValue(1) + output_index = arg.getSlotIds(2).copyToNumpyArray() + for i in xrange(batch_size): + self.assertEqual(output_dense[i].all(), data[i][2].all()) + self.assertEqual(output_sparse.getSparseRowCols(i), data[i][1]) + self.assertEqual(output_index[i], data[i][0]) + + # reader returns 3 features, but only use 2 features + data_types = [('fea0', data_type.dense_vector(100)), + ('fea2', data_type.integer_value(10))] + feeder = DataFeeder(data_types, {'fea0': 2, 'fea2': 0}) + arg = feeder(data) + output_dense = arg.getSlotValue(0).copyToNumpyMat() + output_index = arg.getSlotIds(1).copyToNumpyArray() + for i in xrange(batch_size): + self.assertEqual(output_dense[i].all(), data[i][2].all()) + self.assertEqual(output_index[i], data[i][0]) + + # reader returns 3 featreus, one is duplicate data + data_types = [('fea0', data_type.dense_vector(100)), + ('fea1', data_type.sparse_binary_vector(20000)), + ('fea2', data_type.integer_value(10)), + ('fea3', data_type.dense_vector(100))] + feeder = DataFeeder(data_types, + {'fea0': 2, + 'fea1': 1, + 'fea2': 0, + 'fea3': 2}) + arg = feeder(data) + fea0 = arg.getSlotValue(0).copyToNumpyMat() + fea1 = arg.getSlotValue(1) + fea2 = arg.getSlotIds(2).copyToNumpyArray() + fea3 = arg.getSlotValue(3).copyToNumpyMat() + for i in xrange(batch_size): + self.assertEqual(fea0[i].all(), data[i][2].all()) + self.assertEqual(fea1.getSparseRowCols(i), data[i][1]) + self.assertEqual(fea2[i], data[i][0]) + self.assertEqual(fea3[i].all(), data[i][2].all()) + + def test_multiple_features_tuple(self): + batch_size = 2 + data = [] + for i in xrange(batch_size): + a = np.random.randint(10) + b = self.sparse_binary_reader(20000, 40, non_empty=True) + c = self.dense_reader(100) + each_sample = (a, b, c) + data.append(each_sample) + + # test multiple features + data_types = [('fea0', data_type.dense_vector(100)), + ('fea1', data_type.sparse_binary_vector(20000)), + ('fea2', data_type.integer_value(10))] + feeder = DataFeeder(data_types, {'fea0': 2, 'fea1': 1, 'fea2': 0}) + arg = feeder(data) + out_dense = arg.getSlotValue(0).copyToNumpyMat() + out_sparse = arg.getSlotValue(1) + out_index = arg.getSlotIds(2).copyToNumpyArray() + for i in xrange(batch_size): + self.assertEqual(out_dense[i].all(), data[i][2].all()) + self.assertEqual(out_sparse.getSparseRowCols(i), data[i][1]) + self.assertEqual(out_index[i], data[i][0]) + + +if __name__ == '__main__': + api.initPaddle("--use_gpu=0") + unittest.main() diff --git a/python/paddle/v2/tests/test_layer.py b/python/paddle/v2/tests/test_layer.py new file mode 100644 index 0000000000000000000000000000000000000000..b600e8cf765122ab6cfe8530465391c92be0590f --- /dev/null +++ b/python/paddle/v2/tests/test_layer.py @@ -0,0 +1,63 @@ +# 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 difflib +import unittest + +import paddle.trainer_config_helpers as conf_helps +import paddle.v2.activation as activation +import paddle.v2.attr as attr +import paddle.v2.data_type as data_type +import paddle.v2.layer as layer +from paddle.trainer_config_helpers.config_parser_utils import \ + parse_network_config as parse_network + +pixel = layer.data(name='pixel', type=data_type.dense_vector(784)) +label = layer.data(name='label', type=data_type.integer_value(10)) +weight = layer.data(name='weight', type=data_type.dense_vector(10)) +score = layer.data(name='score', type=data_type.dense_vector(1)) +hidden = layer.fc(input=pixel, + size=100, + act=activation.Sigmoid(), + param_attr=attr.Param(name='hidden')) +inference = layer.fc(input=hidden, size=10, act=activation.Softmax()) + + +class CostLayerTest(unittest.TestCase): + def test_cost_layer(self): + cost1 = layer.classification_cost(input=inference, label=label) + cost2 = layer.classification_cost( + input=inference, label=label, weight=weight) + cost3 = layer.cross_entropy_cost(input=inference, label=label) + cost4 = layer.cross_entropy_with_selfnorm_cost( + input=inference, label=label) + cost5 = layer.regression_cost(input=inference, label=label) + cost6 = layer.regression_cost( + input=inference, label=label, weight=weight) + cost7 = layer.multi_binary_label_cross_entropy_cost( + input=inference, label=label) + cost8 = layer.rank_cost(left=score, right=score, label=score) + cost9 = layer.lambda_cost(input=inference, score=score) + cost10 = layer.sum_cost(input=inference) + cost11 = layer.huber_cost(input=score, label=label) + + print dir(layer) + layer.parse_network(cost1, cost2) + print dir(layer) + #print layer.parse_network(cost3, cost4) + #print layer.parse_network(cost5, cost6) + #print layer.parse_network(cost7, cost8, cost9, cost10, cost11) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 4365bd41e7073bce4112e5813dbf1517856c06f5..097814d2f4619797470668cbd0ea95f112a1fde6 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -2,7 +2,7 @@ import collections import py_paddle.swig_paddle as api from paddle.proto.ModelConfig_pb2 import ModelConfig -from py_paddle import DataProviderConverter +from data_feeder import DataFeeder from . import event as v2_event from . import layer as v2_layer @@ -69,7 +69,8 @@ class SGD(ITrainer): test_data_reader=None, event_handler=None, batch_size=32, - data_types=None): + data_types=None, + reader_dict=None): """ Training method. Will train num_passes of input data. @@ -96,28 +97,34 @@ class SGD(ITrainer): topology, api.CREATE_MODE_NORMAL, self.__optimizer__.enable_types()) assert isinstance(gm, api.GradientMachine) parameters.append_gradient_machine(gm) - + gm.randParameters() updater = self.__optimizer__.create_local_updater() updater.init(gm) gm.start() + batch_evaluator = gm.makeEvaluator() + assert isinstance(batch_evaluator, api.Evaluator) + pass_evaluator = gm.makeEvaluator() + assert isinstance(pass_evaluator, api.Evaluator) out_args = api.Arguments.createArguments(0) - data_types_lists = [] - for each in topology.input_layer_names: - if each not in data_types: - raise ValueError() - data_types_lists.append(data_types[each]) - - converter = DataProviderConverter(input_types=data_types_lists) + feeder = DataFeeder(data_types, reader_dict) for pass_id in xrange(num_passes): + event_handler(v2_event.BeginPass(pass_id)) + pass_evaluator.start() updater.startPass() for batch_id, data_batch in enumerate( __data_reader_to_batch__(train_data_reader, batch_size, topology)): + batch_evaluator.start() + event_handler( + v2_event.BeginIteration( + pass_id=pass_id, batch_id=batch_id)) pass_type = updater.startBatch(len(data_batch)) - gm.forwardBackward(converter(data_batch), out_args, pass_type) + gm.forwardBackward(feeder(data_batch), out_args, pass_type) + gm.eval(pass_evaluator) + gm.eval(batch_evaluator) for each_param in gm.getParameters(): updater.update(each_param) # Get cost. We use numpy to calculate total cost for this batch. @@ -125,11 +132,17 @@ class SGD(ITrainer): cost_vec = cost_vec.copyToNumpyMat() cost = cost_vec.sum() / len(data_batch) updater.finishBatch(cost) + batch_evaluator.finish() event_handler( v2_event.EndIteration( - pass_id=pass_id, batch_id=batch_id, cost=cost)) + pass_id=pass_id, + batch_id=batch_id, + cost=cost, + evaluator=batch_evaluator)) updater.finishPass() + pass_evaluator.finish() + event_handler(v2_event.EndPass(pass_id, evaluator=pass_evaluator)) gm.finish()