diff options
| author | Jeff Donahue <jeff.donahue@gmail.com> | 2015-01-07 20:41:09 -0800 |
|---|---|---|
| committer | Jeff Donahue <jeff.donahue@gmail.com> | 2015-02-05 14:49:21 -0800 |
| commit | 3b13846486875be2911716bcd1947dca71a97817 (patch) | |
| tree | 082b89cb75f1369635ca1365e41182a7f8730b40 | |
| parent | 648aed72acf1c506009ddb33d8cace40b75e176e (diff) | |
| download | caffeonacl-3b13846486875be2911716bcd1947dca71a97817.tar.gz caffeonacl-3b13846486875be2911716bcd1947dca71a97817.tar.bz2 caffeonacl-3b13846486875be2911716bcd1947dca71a97817.zip | |
Layer type is a string
56 files changed, 487 insertions, 592 deletions
diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp index 9718b825..3a5ccd21 100644 --- a/include/caffe/common_layers.hpp +++ b/include/caffe/common_layers.hpp @@ -43,9 +43,7 @@ class ArgMaxLayer : public Layer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_ARGMAX; - } + virtual inline const char* type() const { return "ArgMax"; } virtual inline int ExactNumBottomBlobs() const { return 1; } virtual inline int ExactNumTopBlobs() const { return 1; } @@ -86,9 +84,7 @@ class ConcatLayer : public Layer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_CONCAT; - } + virtual inline const char* type() const { return "Concat"; } virtual inline int MinBottomBlobs() const { return 2; } virtual inline int ExactNumTopBlobs() const { return 1; } @@ -166,9 +162,7 @@ class EltwiseLayer : public Layer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_ELTWISE; - } + virtual inline const char* type() const { return "Eltwise"; } virtual inline int MinBottomBlobs() const { return 2; } virtual inline int ExactNumTopBlobs() const { return 1; } @@ -207,9 +201,7 @@ class FlattenLayer : public Layer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_FLATTEN; - } + virtual inline const char* type() const { return "Flatten"; } virtual inline int ExactNumBottomBlobs() const { return 1; } virtual inline int ExactNumTopBlobs() const { return 1; } @@ -260,9 +252,7 @@ class InnerProductLayer : public Layer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_INNER_PRODUCT; - } + virtual inline const char* type() const { return "InnerProduct"; } virtual inline int ExactNumBottomBlobs() const { return 1; } virtual inline int ExactNumTopBlobs() const { return 1; } @@ -296,9 +286,7 @@ class MVNLayer : public Layer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_MVN; - } + virtual inline const char* type() const { return "MVN"; } virtual inline int ExactNumBottomBlobs() const { return 1; } virtual inline int ExactNumTopBlobs() const { return 1; } @@ -330,9 +318,7 @@ class SilenceLayer : public Layer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {} - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_SILENCE; - } + virtual inline const char* type() const { return "Silence"; } virtual inline int MinBottomBlobs() const { return 1; } virtual inline int ExactNumTopBlobs() const { return 0; } @@ -362,9 +348,7 @@ class SoftmaxLayer : public Layer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_SOFTMAX; - } + virtual inline const char* type() const { return "Softmax"; } virtual inline int ExactNumBottomBlobs() const { return 1; } virtual inline int ExactNumTopBlobs() const { return 1; } @@ -426,9 +410,7 @@ class SplitLayer : public Layer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_SPLIT; - } + virtual inline const char* type() const { return "Split"; } virtual inline int ExactNumBottomBlobs() const { return 1; } virtual inline int MinTopBlobs() const { return 1; } @@ -461,9 +443,7 @@ class SliceLayer : public Layer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_SLICE; - } + virtual inline const char* type() const { return "Slice"; } virtual inline int ExactNumBottomBlobs() const { return 1; } virtual inline int MinTopBlobs() const { return 2; } diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp index e9c83856..e55a0bbb 100644 --- a/include/caffe/data_layers.hpp +++ b/include/caffe/data_layers.hpp @@ -90,9 +90,7 @@ class DataLayer : public BasePrefetchingDataLayer<Dtype> { virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_DATA; - } + virtual inline const char* type() const { return "Data"; } virtual inline int ExactNumBottomBlobs() const { return 0; } virtual inline int MinTopBlobs() const { return 1; } virtual inline int MaxTopBlobs() const { return 2; } @@ -120,9 +118,7 @@ class DummyDataLayer : public Layer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {} - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_DUMMY_DATA; - } + virtual inline const char* type() const { return "DummyData"; } virtual inline int ExactNumBottomBlobs() const { return 0; } virtual inline int MinTopBlobs() const { return 1; } @@ -155,9 +151,7 @@ class HDF5DataLayer : public Layer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {} - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_HDF5_DATA; - } + virtual inline const char* type() const { return "HDF5Data"; } virtual inline int ExactNumBottomBlobs() const { return 0; } virtual inline int MinTopBlobs() const { return 1; } @@ -195,9 +189,7 @@ class HDF5OutputLayer : public Layer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {} - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_HDF5_OUTPUT; - } + virtual inline const char* type() const { return "HDF5Output"; } // TODO: no limit on the number of blobs virtual inline int ExactNumBottomBlobs() const { return 2; } virtual inline int ExactNumTopBlobs() const { return 0; } @@ -235,9 +227,7 @@ class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> { virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_IMAGE_DATA; - } + virtual inline const char* type() const { return "ImageData"; } virtual inline int ExactNumBottomBlobs() const { return 0; } virtual inline int ExactNumTopBlobs() const { return 2; } @@ -263,9 +253,7 @@ class MemoryDataLayer : public BaseDataLayer<Dtype> { virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_MEMORY_DATA; - } + virtual inline const char* type() const { return "MemoryData"; } virtual inline int ExactNumBottomBlobs() const { return 0; } virtual inline int ExactNumTopBlobs() const { return 2; } @@ -309,9 +297,7 @@ class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> { virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_WINDOW_DATA; - } + virtual inline const char* type() const { return "WindowData"; } virtual inline int ExactNumBottomBlobs() const { return 0; } virtual inline int ExactNumTopBlobs() const { return 2; } diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp index 8a8330bc..c6461c19 100644 --- a/include/caffe/layer.hpp +++ b/include/caffe/layer.hpp @@ -179,18 +179,9 @@ class Layer { } /** - * @brief Returns the layer type as an enum value. + * @brief Returns the layer type. */ - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_NONE; - } - - /** - * @brief Returns the layer type name. - */ - virtual inline const string& type_name() const { - return LayerParameter_LayerType_Name(type()); - } + virtual inline const char* type() const { return ""; } /** * @brief Returns the exact number of bottom blobs required by the layer, @@ -347,37 +338,37 @@ class Layer { const vector<Blob<Dtype>*>& top) { if (ExactNumBottomBlobs() >= 0) { CHECK_EQ(ExactNumBottomBlobs(), bottom.size()) - << type_name() << " Layer takes " << ExactNumBottomBlobs() + << type() << " Layer takes " << ExactNumBottomBlobs() << " bottom blob(s) as input."; } if (MinBottomBlobs() >= 0) { CHECK_LE(MinBottomBlobs(), bottom.size()) - << type_name() << " Layer takes at least " << MinBottomBlobs() + << type() << " Layer takes at least " << MinBottomBlobs() << " bottom blob(s) as input."; } if (MaxBottomBlobs() >= 0) { CHECK_GE(MaxBottomBlobs(), bottom.size()) - << type_name() << " Layer takes at most " << MaxBottomBlobs() + << type() << " Layer takes at most " << MaxBottomBlobs() << " bottom blob(s) as input."; } if (ExactNumTopBlobs() >= 0) { CHECK_EQ(ExactNumTopBlobs(), top.size()) - << type_name() << " Layer produces " << ExactNumTopBlobs() + << type() << " Layer produces " << ExactNumTopBlobs() << " top blob(s) as output."; } if (MinTopBlobs() >= 0) { CHECK_LE(MinTopBlobs(), top.size()) - << type_name() << " Layer produces at least " << MinTopBlobs() + << type() << " Layer produces at least " << MinTopBlobs() << " top blob(s) as output."; } if (MaxTopBlobs() >= 0) { CHECK_GE(MaxTopBlobs(), top.size()) - << type_name() << " Layer produces at most " << MaxTopBlobs() + << type() << " Layer produces at most " << MaxTopBlobs() << " top blob(s) as output."; } if (EqualNumBottomTopBlobs()) { CHECK_EQ(bottom.size(), top.size()) - << type_name() << " Layer produces one top blob as output for each " + << type() << " Layer produces one top blob as output for each " << "bottom blob input."; } } diff --git a/include/caffe/layer_factory.hpp b/include/caffe/layer_factory.hpp index c1fd6aa0..ede5d1fa 100644 --- a/include/caffe/layer_factory.hpp +++ b/include/caffe/layer_factory.hpp @@ -12,17 +12,13 @@ * // your implementations * }; * - * and its type is defined in the protobuffer as - * - * enum LayerType { - * // other definitions - * AWESOME = 46, - * } + * and its type is its C++ class name, but without the "Layer" at the end + * ("MyAwesomeLayer" -> "MyAwesome"). * * If the layer is going to be created simply by its constructor, in your c++ * file, add the following line: * - * REGISTER_LAYER_CLASS(AWESOME, MyAwesomeLayer); + * REGISTER_LAYER_CLASS(MyAwesome); * * Or, if the layer is going to be created by another creator function, in the * format of: @@ -35,7 +31,7 @@ * (for example, when your layer has multiple backends, see GetConvolutionLayer * for a use case), then you can register the creator function instead, like * - * REGISTER_LAYER_CREATOR(AWESOME, GetMyAwesomeLayer) + * REGISTER_LAYER_CREATOR(MyAwesome, GetMyAwesomeLayer) * * Note that each layer type should only be registered once. */ @@ -44,6 +40,7 @@ #define CAFFE_LAYER_FACTORY_H_ #include <map> +#include <string> #include "caffe/common.hpp" #include "caffe/proto/caffe.pb.h" @@ -57,7 +54,7 @@ template <typename Dtype> class LayerRegistry { public: typedef Layer<Dtype>* (*Creator)(const LayerParameter&); - typedef std::map<LayerParameter_LayerType, Creator> CreatorRegistry; + typedef std::map<string, Creator> CreatorRegistry; static CreatorRegistry& Registry() { static CreatorRegistry* g_registry_ = new CreatorRegistry(); @@ -65,8 +62,7 @@ class LayerRegistry { } // Adds a creator. - static void AddCreator(const LayerParameter_LayerType& type, - Creator creator) { + static void AddCreator(const string& type, Creator creator) { CreatorRegistry& registry = Registry(); CHECK_EQ(registry.count(type), 0) << "Layer type " << type << " already registered."; @@ -76,9 +72,10 @@ class LayerRegistry { // Get a layer using a LayerParameter. static Layer<Dtype>* CreateLayer(const LayerParameter& param) { LOG(INFO) << "Creating layer " << param.name(); - const LayerParameter_LayerType& type = param.type(); + const string& type = param.type(); CreatorRegistry& registry = Registry(); - CHECK_EQ(registry.count(type), 1); + CHECK_EQ(registry.count(type), 1) << "Unknown layer type: " << type + << " (known types: " << LayerTypeList() << ")"; return registry[type](param); } @@ -86,13 +83,26 @@ class LayerRegistry { // Layer registry should never be instantiated - everything is done with its // static variables. LayerRegistry() {} + + static string LayerTypeList() { + CreatorRegistry& registry = Registry(); + string layer_types; + for (typename CreatorRegistry::iterator iter = registry.begin(); + iter != registry.end(); ++iter) { + if (iter != registry.begin()) { + layer_types += ", "; + } + layer_types += iter->first; + } + return layer_types; + } }; template <typename Dtype> class LayerRegisterer { public: - LayerRegisterer(const LayerParameter_LayerType& type, + LayerRegisterer(const string& type, Layer<Dtype>* (*creator)(const LayerParameter&)) { // LOG(INFO) << "Registering layer type: " << type; LayerRegistry<Dtype>::AddCreator(type, creator); @@ -101,17 +111,15 @@ class LayerRegisterer { #define REGISTER_LAYER_CREATOR(type, creator) \ - static LayerRegisterer<float> g_creator_f_##type( \ - LayerParameter_LayerType_##type, creator<float>); \ - static LayerRegisterer<double> g_creator_d_##type( \ - LayerParameter_LayerType_##type, creator<double>) + static LayerRegisterer<float> g_creator_f_##type(#type, creator<float>); \ + static LayerRegisterer<double> g_creator_d_##type(#type, creator<double>) \ -#define REGISTER_LAYER_CLASS(type, clsname) \ +#define REGISTER_LAYER_CLASS(type) \ template <typename Dtype> \ - Layer<Dtype>* Creator_##clsname(const LayerParameter& param) { \ - return new clsname<Dtype>(param); \ + Layer<Dtype>* Creator_##type##Layer(const LayerParameter& param) { \ + return new type##Layer<Dtype>(param); \ } \ - REGISTER_LAYER_CREATOR(type, Creator_##clsname) + REGISTER_LAYER_CREATOR(type, Creator_##type##Layer) } // namespace caffe diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp index 600a13b8..f14b9716 100644 --- a/include/caffe/loss_layers.hpp +++ b/include/caffe/loss_layers.hpp @@ -37,10 +37,7 @@ class AccuracyLayer : public Layer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_ACCURACY; - } - + virtual inline const char* type() const { return "Accuracy"; } virtual inline int ExactNumBottomBlobs() const { return 2; } virtual inline int ExactNumTopBlobs() const { return 1; } @@ -154,9 +151,7 @@ class ContrastiveLossLayer : public LossLayer<Dtype> { const vector<Blob<Dtype>*>& top); virtual inline int ExactNumBottomBlobs() const { return 3; } - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_CONTRASTIVE_LOSS; - } + virtual inline const char* type() const { return "ContrastiveLoss"; } /** * Unlike most loss layers, in the ContrastiveLossLayer we can backpropagate * to the first two inputs. @@ -242,10 +237,7 @@ class EuclideanLossLayer : public LossLayer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_EUCLIDEAN_LOSS; - } - + virtual inline const char* type() const { return "EuclideanLoss"; } /** * Unlike most loss layers, in the EuclideanLossLayer we can backpropagate * to both inputs -- override to return true and always allow force_backward. @@ -351,9 +343,7 @@ class HingeLossLayer : public LossLayer<Dtype> { explicit HingeLossLayer(const LayerParameter& param) : LossLayer<Dtype>(param) {} - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_HINGE_LOSS; - } + virtual inline const char* type() const { return "HingeLoss"; } protected: /// @copydoc HingeLossLayer @@ -440,9 +430,7 @@ class InfogainLossLayer : public LossLayer<Dtype> { virtual inline int MinBottomBlobs() const { return 2; } virtual inline int MaxBottomBlobs() const { return 3; } - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_INFOGAIN_LOSS; - } + virtual inline const char* type() const { return "InfogainLoss"; } protected: /// @copydoc InfogainLossLayer @@ -524,9 +512,7 @@ class MultinomialLogisticLossLayer : public LossLayer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS; - } + virtual inline const char* type() const { return "MultinomialLogisticLoss"; } protected: /// @copydoc MultinomialLogisticLossLayer @@ -606,9 +592,7 @@ class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS; - } + virtual inline const char* type() const { return "SigmoidCrossEntropyLoss"; } protected: /// @copydoc SigmoidCrossEntropyLossLayer @@ -711,9 +695,7 @@ class SoftmaxWithLossLayer : public LossLayer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_SOFTMAX_LOSS; - } + virtual inline const char* type() const { return "SoftmaxWithLoss"; } virtual inline int ExactNumBottomBlobs() const { return -1; } virtual inline int MinBottomBlobs() const { return 2; } virtual inline int MaxBottomBlobs() const { return 3; } diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp index 5daeeefe..b65d9843 100644 --- a/include/caffe/neuron_layers.hpp +++ b/include/caffe/neuron_layers.hpp @@ -29,9 +29,6 @@ class NeuronLayer : public Layer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_NONE; - } virtual inline int ExactNumBottomBlobs() const { return 1; } virtual inline int ExactNumTopBlobs() const { return 1; } }; @@ -54,9 +51,7 @@ class AbsValLayer : public NeuronLayer<Dtype> { virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_ABSVAL; - } + virtual inline const char* type() const { return "AbsVal"; } virtual inline int ExactNumBottomBlobs() const { return 1; } virtual inline int ExactNumTopBlobs() const { return 1; } @@ -113,9 +108,7 @@ class BNLLLayer : public NeuronLayer<Dtype> { explicit BNLLLayer(const LayerParameter& param) : NeuronLayer<Dtype>(param) {} - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_BNLL; - } + virtual inline const char* type() const { return "BNLL"; } protected: /// @copydoc BNLLLayer @@ -173,9 +166,7 @@ class DropoutLayer : public NeuronLayer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_DROPOUT; - } + virtual inline const char* type() const { return "Dropout"; } protected: /** @@ -233,9 +224,7 @@ class ExpLayer : public NeuronLayer<Dtype> { virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_EXP; - } + virtual inline const char* type() const { return "Exp"; } protected: /** @@ -298,9 +287,7 @@ class PowerLayer : public NeuronLayer<Dtype> { virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_POWER; - } + virtual inline const char* type() const { return "Power"; } protected: /** @@ -369,9 +356,7 @@ class ReLULayer : public NeuronLayer<Dtype> { explicit ReLULayer(const LayerParameter& param) : NeuronLayer<Dtype>(param) {} - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_RELU; - } + virtual inline const char* type() const { return "ReLU"; } protected: /** @@ -465,9 +450,7 @@ class SigmoidLayer : public NeuronLayer<Dtype> { explicit SigmoidLayer(const LayerParameter& param) : NeuronLayer<Dtype>(param) {} - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_SIGMOID; - } + virtual inline const char* type() const { return "Sigmoid"; } protected: /** @@ -549,9 +532,7 @@ class TanHLayer : public NeuronLayer<Dtype> { explicit TanHLayer(const LayerParameter& param) : NeuronLayer<Dtype>(param) {} - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_TANH; - } + virtual inline const char* type() const { return "TanH"; } protected: /** @@ -639,9 +620,7 @@ class ThresholdLayer : public NeuronLayer<Dtype> { virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_THRESHOLD; - } + virtual inline const char* type() const { return "Threshold"; } protected: /** diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp index 45483685..0627afe6 100644 --- a/include/caffe/util/upgrade_proto.hpp +++ b/include/caffe/util/upgrade_proto.hpp @@ -27,7 +27,7 @@ void UpgradeV0PaddingLayers(const NetParameter& param, bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection, LayerParameter* layer_param); -LayerParameter_LayerType UpgradeV0LayerType(const string& type); +const char* UpgradeV0LayerType(const string& type); // Return true iff any layer contains deprecated data transformation parameters. bool NetNeedsDataUpgrade(const NetParameter& net_param); diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp index fc178e4c..38e369c4 100644 --- a/include/caffe/vision_layers.hpp +++ b/include/caffe/vision_layers.hpp @@ -160,9 +160,8 @@ class ConvolutionLayer : public BaseConvolutionLayer<Dtype> { */ explicit ConvolutionLayer(const LayerParameter& param) : BaseConvolutionLayer<Dtype>(param) {} - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_CONVOLUTION; - } + + virtual inline const char* type() const { return "Convolution"; } protected: virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom, @@ -196,9 +195,8 @@ class DeconvolutionLayer : public BaseConvolutionLayer<Dtype> { public: explicit DeconvolutionLayer(const LayerParameter& param) : BaseConvolutionLayer<Dtype>(param) {} - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_DECONVOLUTION; - } + + virtual inline const char* type() const { return "Deconvolution"; } protected: virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom, @@ -272,9 +270,7 @@ class Im2colLayer : public Layer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_IM2COL; - } + virtual inline const char* type() const { return "Im2col"; } virtual inline int ExactNumBottomBlobs() const { return 1; } virtual inline int ExactNumTopBlobs() const { return 1; } @@ -314,9 +310,7 @@ class LRNLayer : public Layer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_LRN; - } + virtual inline const char* type() const { return "LRN"; } virtual inline int ExactNumBottomBlobs() const { return 1; } virtual inline int ExactNumTopBlobs() const { return 1; } @@ -392,9 +386,7 @@ class PoolingLayer : public Layer<Dtype> { virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); - virtual inline LayerParameter_LayerType type() const { - return LayerParameter_LayerType_POOLING; - } + virtual inline const char* type() const { return "Pooling"; } virtual inline int ExactNumBottomBlobs() const { return 1; } virtual inline int MinTopBlobs() const { return 1; } // MAX POOL layers can output an extra top blob for the mask; diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp index 80694834..c3fd1f30 100644 --- a/src/caffe/layer_factory.cpp +++ b/src/caffe/layer_factory.cpp @@ -29,7 +29,7 @@ Layer<Dtype>* GetConvolutionLayer( } } -REGISTER_LAYER_CREATOR(CONVOLUTION, GetConvolutionLayer); +REGISTER_LAYER_CREATOR(Convolution, GetConvolutionLayer); // Get pooling layer according to engine. template <typename Dtype> @@ -59,7 +59,7 @@ Layer<Dtype>* GetPoolingLayer(const LayerParameter& param) { } } -REGISTER_LAYER_CREATOR(POOLING, GetPoolingLayer); +REGISTER_LAYER_CREATOR(Pooling, GetPoolingLayer); // Get relu layer according to engine. template <typename Dtype> @@ -82,7 +82,7 @@ Layer<Dtype>* GetReLULayer(const LayerParameter& param) { } } -REGISTER_LAYER_CREATOR(RELU, GetReLULayer); +REGISTER_LAYER_CREATOR(ReLU, GetReLULayer); // Get sigmoid layer according to engine. template <typename Dtype> @@ -105,7 +105,7 @@ Layer<Dtype>* GetSigmoidLayer(const LayerParameter& param) { } } -REGISTER_LAYER_CREATOR(SIGMOID, GetSigmoidLayer); +REGISTER_LAYER_CREATOR(Sigmoid, GetSigmoidLayer); // Get softmax layer according to engine. template <typename Dtype> @@ -128,7 +128,7 @@ Layer<Dtype>* GetSoftmaxLayer(const LayerParameter& param) { } } -REGISTER_LAYER_CREATOR(SOFTMAX, GetSoftmaxLayer); +REGISTER_LAYER_CREATOR(Softmax, GetSoftmaxLayer); // Get tanh layer according to engine. template <typename Dtype> @@ -151,7 +151,7 @@ Layer<Dtype>* GetTanHLayer(const LayerParameter& param) { } } -REGISTER_LAYER_CREATOR(TANH, GetTanHLayer); +REGISTER_LAYER_CREATOR(TanH, GetTanHLayer); // Layers that use their constructor as their default creator should be // registered in their corresponding cpp files. Do not register them here. diff --git a/src/caffe/layers/absval_layer.cpp b/src/caffe/layers/absval_layer.cpp index 0d054ee5..5ce28c9e 100644 --- a/src/caffe/layers/absval_layer.cpp +++ b/src/caffe/layers/absval_layer.cpp @@ -10,7 +10,7 @@ template <typename Dtype> void AbsValLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) { NeuronLayer<Dtype>::LayerSetUp(bottom, top); - CHECK_NE(top[0], bottom[0]) << this->type_name() << " Layer does not " + CHECK_NE(top[0], bottom[0]) << this->type() << " Layer does not " "allow in-place computation."; } @@ -40,5 +40,6 @@ STUB_GPU(AbsValLayer); #endif INSTANTIATE_CLASS(AbsValLayer); -REGISTER_LAYER_CLASS(ABSVAL, AbsValLayer); +REGISTER_LAYER_CLASS(AbsVal); + } // namespace caffe diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp index 800b848f..3e8df34c 100644 --- a/src/caffe/layers/accuracy_layer.cpp +++ b/src/caffe/layers/accuracy_layer.cpp @@ -64,5 +64,6 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom, } INSTANTIATE_CLASS(AccuracyLayer); -REGISTER_LAYER_CLASS(ACCURACY, AccuracyLayer); +REGISTER_LAYER_CLASS(Accuracy); + } // namespace caffe diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp index 15e199eb..c4040cdc 100644 --- a/src/caffe/layers/argmax_layer.cpp +++ b/src/caffe/layers/argmax_layer.cpp @@ -58,6 +58,6 @@ void ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom, } INSTANTIATE_CLASS(ArgMaxLayer); -REGISTER_LAYER_CLASS(ARGMAX, ArgMaxLayer); +REGISTER_LAYER_CLASS(ArgMax); } // namespace caffe diff --git a/src/caffe/layers/bnll_layer.cpp b/src/caffe/layers/bnll_layer.cpp index cb3583ae..9ba0ea9a 100644 --- a/src/caffe/layers/bnll_layer.cpp +++ b/src/caffe/layers/bnll_layer.cpp @@ -43,5 +43,6 @@ STUB_GPU(BNLLLayer); #endif INSTANTIATE_CLASS(BNLLLayer); -REGISTER_LAYER_CLASS(BNLL, BNLLLayer); +REGISTER_LAYER_CLASS(BNLL); + } // namespace caffe diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp index 42082195..fc88433c 100644 --- a/src/caffe/layers/concat_layer.cpp +++ b/src/caffe/layers/concat_layer.cpp @@ -105,5 +105,6 @@ STUB_GPU(ConcatLayer); #endif INSTANTIATE_CLASS(ConcatLayer); -REGISTER_LAYER_CLASS(CONCAT, ConcatLayer); +REGISTER_LAYER_CLASS(Concat); + } // namespace caffe diff --git a/src/caffe/layers/contrastive_loss_layer.cpp b/src/caffe/layers/contrastive_loss_layer.cpp index 0d0b443b..0692c11c 100644 --- a/src/caffe/layers/contrastive_loss_layer.cpp +++ b/src/caffe/layers/contrastive_loss_layer.cpp @@ -97,5 +97,6 @@ STUB_GPU(ContrastiveLossLayer); #endif INSTANTIATE_CLASS(ContrastiveLossLayer); -REGISTER_LAYER_CLASS(CONTRASTIVE_LOSS, ContrastiveLossLayer); +REGISTER_LAYER_CLASS(ContrastiveLoss); + } // namespace caffe diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp index 9fd2fc6a..c0c9f6f3 100644 --- a/src/caffe/layers/conv_layer.cpp +++ b/src/caffe/layers/conv_layer.cpp @@ -79,4 +79,5 @@ STUB_GPU(ConvolutionLayer); #endif INSTANTIATE_CLASS(ConvolutionLayer); + } // namespace caffe diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp index 96964566..227db201 100644 --- a/src/caffe/layers/data_layer.cpp +++ b/src/caffe/layers/data_layer.cpp @@ -129,6 +129,6 @@ void DataLayer<Dtype>::InternalThreadEntry() { } INSTANTIATE_CLASS(DataLayer); -REGISTER_LAYER_CLASS(DATA, DataLayer); +REGISTER_LAYER_CLASS(Data); } // namespace caffe diff --git a/src/caffe/layers/deconv_layer.cpp b/src/caffe/layers/deconv_layer.cpp index 59114f01..e6d65ab5 100644 --- a/src/caffe/layers/deconv_layer.cpp +++ b/src/caffe/layers/deconv_layer.cpp @@ -81,5 +81,6 @@ STUB_GPU(DeconvolutionLayer); #endif INSTANTIATE_CLASS(DeconvolutionLayer); -REGISTER_LAYER_CLASS(DECONVOLUTION, DeconvolutionLayer); +REGISTER_LAYER_CLASS(Deconvolution); + } // namespace caffe diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp index 8c8936a7..5f81cc1c 100644 --- a/src/caffe/layers/dropout_layer.cpp +++ b/src/caffe/layers/dropout_layer.cpp @@ -73,5 +73,6 @@ STUB_GPU(DropoutLayer); #endif INSTANTIATE_CLASS(DropoutLayer); -REGISTER_LAYER_CLASS(DROPOUT, DropoutLayer); +REGISTER_LAYER_CLASS(Dropout); + } // namespace caffe diff --git a/src/caffe/layers/dummy_data_layer.cpp b/src/caffe/layers/dummy_data_layer.cpp index 15cf5a58..d254eb1f 100644 --- a/src/caffe/layers/dummy_data_layer.cpp +++ b/src/caffe/layers/dummy_data_layer.cpp @@ -93,5 +93,6 @@ void DummyDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom, } INSTANTIATE_CLASS(DummyDataLayer); -REGISTER_LAYER_CLASS(DUMMY_DATA, DummyDataLayer); +REGISTER_LAYER_CLASS(DummyData); + } // namespace caffe diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp index 0c239f4a..bbc34449 100644 --- a/src/caffe/layers/eltwise_layer.cpp +++ b/src/caffe/layers/eltwise_layer.cpp @@ -163,5 +163,6 @@ STUB_GPU(EltwiseLayer); #endif INSTANTIATE_CLASS(EltwiseLayer); -REGISTER_LAYER_CLASS(ELTWISE, EltwiseLayer); +REGISTER_LAYER_CLASS(Eltwise); + } // namespace caffe diff --git a/src/caffe/layers/euclidean_loss_layer.cpp b/src/caffe/layers/euclidean_loss_layer.cpp index d965027f..b539d348 100644 --- a/src/caffe/layers/euclidean_loss_layer.cpp +++ b/src/caffe/layers/euclidean_loss_layer.cpp @@ -54,5 +54,6 @@ STUB_GPU(EuclideanLossLayer); #endif INSTANTIATE_CLASS(EuclideanLossLayer); -REGISTER_LAYER_CLASS(EUCLIDEAN_LOSS, EuclideanLossLayer); +REGISTER_LAYER_CLASS(EuclideanLoss); + } // namespace caffe diff --git a/src/caffe/layers/exp_layer.cpp b/src/caffe/layers/exp_layer.cpp index 92cb5deb..c7e7c60c 100644 --- a/src/caffe/layers/exp_layer.cpp +++ b/src/caffe/layers/exp_layer.cpp @@ -64,5 +64,6 @@ STUB_GPU(ExpLayer); #endif INSTANTIATE_CLASS(ExpLayer); -REGISTER_LAYER_CLASS(EXP, ExpLayer); +REGISTER_LAYER_CLASS(Exp); + } // namespace caffe diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp index ec43caba..eb7b42bc 100644 --- a/src/caffe/layers/flatten_layer.cpp +++ b/src/caffe/layers/flatten_layer.cpp @@ -34,5 +34,6 @@ STUB_GPU(FlattenLayer); #endif INSTANTIATE_CLASS(FlattenLayer); -REGISTER_LAYER_CLASS(FLATTEN, FlattenLayer); +REGISTER_LAYER_CLASS(Flatten); + } // namespace caffe diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp index 706c4a18..77555211 100644 --- a/src/caffe/layers/hdf5_data_layer.cpp +++ b/src/caffe/layers/hdf5_data_layer.cpp @@ -121,5 +121,6 @@ STUB_GPU_FORWARD(HDF5DataLayer, Forward); #endif INSTANTIATE_CLASS(HDF5DataLayer); -REGISTER_LAYER_CLASS(HDF5_DATA, HDF5DataLayer); +REGISTER_LAYER_CLASS(HDF5Data); + } // namespace caffe diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp index 4a72a18a..d2fdeffc 100644 --- a/src/caffe/layers/hdf5_output_layer.cpp +++ b/src/caffe/layers/hdf5_output_layer.cpp @@ -70,5 +70,6 @@ STUB_GPU(HDF5OutputLayer); #endif INSTANTIATE_CLASS(HDF5OutputLayer); -REGISTER_LAYER_CLASS(HDF5_OUTPUT, HDF5OutputLayer); +REGISTER_LAYER_CLASS(HDF5Output); + } // namespace caffe diff --git a/src/caffe/layers/hinge_loss_layer.cpp b/src/caffe/layers/hinge_loss_layer.cpp index 4dfafcc8..a2fb2a18 100644 --- a/src/caffe/layers/hinge_loss_layer.cpp +++ b/src/caffe/layers/hinge_loss_layer.cpp @@ -47,7 +47,7 @@ template <typename Dtype> void HingeLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) { if (propagate_down[1]) { - LOG(FATAL) << this->type_name() + LOG(FATAL) << this->type() << " Layer cannot backpropagate to label inputs."; } if (propagate_down[0]) { @@ -77,5 +77,6 @@ void HingeLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top, } INSTANTIATE_CLASS(HingeLossLayer); -REGISTER_LAYER_CLASS(HINGE_LOSS, HingeLossLayer); +REGISTER_LAYER_CLASS(HingeLoss); + } // namespace caffe diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp index 2c4bb902..11222611 100644 --- a/src/caffe/layers/im2col_layer.cpp +++ b/src/caffe/layers/im2col_layer.cpp @@ -88,5 +88,6 @@ STUB_GPU(Im2colLayer); #endif INSTANTIATE_CLASS(Im2colLayer); -REGISTER_LAYER_CLASS(IM2COL, Im2colLayer); +REGISTER_LAYER_CLASS(Im2col); + } // namespace caffe diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp index ef6a342c..b96be6ad 100644 --- a/src/caffe/layers/image_data_layer.cpp +++ b/src/caffe/layers/image_data_layer.cpp @@ -149,5 +149,6 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() { } INSTANTIATE_CLASS(ImageDataLayer); -REGISTER_LAYER_CLASS(IMAGE_DATA, ImageDataLayer); +REGISTER_LAYER_CLASS(ImageData); + } // namespace caffe diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp index 8910431d..a1e0b40d 100644 --- a/src/caffe/layers/infogain_loss_layer.cpp +++ b/src/caffe/layers/infogain_loss_layer.cpp @@ -75,11 +75,11 @@ void InfogainLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) { if (propagate_down[1]) { - LOG(FATAL) << this->type_name() + LOG(FATAL) << this->type() << " Layer cannot backpropagate to label inputs."; } if (propagate_down.size() > 2 && propagate_down[2]) { - LOG(FATAL) << this->type_name() + LOG(FATAL) << this->type() << " Layer cannot backpropagate to infogain inputs."; } if (propagate_down[0]) { @@ -106,5 +106,5 @@ void InfogainLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top, } INSTANTIATE_CLASS(InfogainLossLayer); -REGISTER_LAYER_CLASS(INFOGAIN_LOSS, InfogainLossLayer); +REGISTER_LAYER_CLASS(InfogainLoss); } // namespace caffe diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp index ecb849ba..b1ec6cb2 100644 --- a/src/caffe/layers/inner_product_layer.cpp +++ b/src/caffe/layers/inner_product_layer.cpp @@ -104,5 +104,6 @@ STUB_GPU(InnerProductLayer); #endif INSTANTIATE_CLASS(InnerProductLayer); -REGISTER_LAYER_CLASS(INNER_PRODUCT, InnerProductLayer); +REGISTER_LAYER_CLASS(InnerProduct); + } // namespace caffe diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp index a878cf84..5e3e7c42 100644 --- a/src/caffe/layers/lrn_layer.cpp +++ b/src/caffe/layers/lrn_layer.cpp @@ -252,5 +252,6 @@ STUB_GPU_BACKWARD(LRNLayer, CrossChannelBackward); #endif INSTANTIATE_CLASS(LRNLayer); -REGISTER_LAYER_CLASS(LRN, LRNLayer); +REGISTER_LAYER_CLASS(LRN); + } // namespace caffe diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp index 613ca2d4..3272b66a 100644 --- a/src/caffe/layers/memory_data_layer.cpp +++ b/src/caffe/layers/memory_data_layer.cpp @@ -72,5 +72,6 @@ void MemoryDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom, } INSTANTIATE_CLASS(MemoryDataLayer); -REGISTER_LAYER_CLASS(MEMORY_DATA, MemoryDataLayer); +REGISTER_LAYER_CLASS(MemoryData); + } // namespace caffe diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cpp b/src/caffe/layers/multinomial_logistic_loss_layer.cpp index 78a1f60f..4267a594 100644 --- a/src/caffe/layers/multinomial_logistic_loss_layer.cpp +++ b/src/caffe/layers/multinomial_logistic_loss_layer.cpp @@ -41,7 +41,7 @@ void MultinomialLogisticLossLayer<Dtype>::Backward_cpu( const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) { if (propagate_down[1]) { - LOG(FATAL) << this->type_name() + LOG(FATAL) << this->type() << " Layer cannot backpropagate to label inputs."; } if (propagate_down[0]) { @@ -62,5 +62,6 @@ void MultinomialLogisticLossLayer<Dtype>::Backward_cpu( } INSTANTIATE_CLASS(MultinomialLogisticLossLayer); -REGISTER_LAYER_CLASS(MULTINOMIAL_LOGISTIC_LOSS, MultinomialLogisticLossLayer); +REGISTER_LAYER_CLASS(MultinomialLogisticLoss); + } // namespace caffe diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp index 104ad95c..b74d7b4f 100644 --- a/src/caffe/layers/mvn_layer.cpp +++ b/src/caffe/layers/mvn_layer.cpp @@ -159,5 +159,6 @@ STUB_GPU(MVNLayer); #endif INSTANTIATE_CLASS(MVNLayer); -REGISTER_LAYER_CLASS(MVN, MVNLayer); +REGISTER_LAYER_CLASS(MVN); + } // namespace caffe diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp index 2bfbb01f..6f4c69c8 100644 --- a/src/caffe/layers/pooling_layer.cpp +++ b/src/caffe/layers/pooling_layer.cpp @@ -314,5 +314,4 @@ STUB_GPU(PoolingLayer); INSTANTIATE_CLASS(PoolingLayer); - } // namespace caffe diff --git a/src/caffe/layers/power_layer.cpp b/src/caffe/layers/power_layer.cpp index 69bd120e..4fe34c49 100644 --- a/src/caffe/layers/power_layer.cpp +++ b/src/caffe/layers/power_layer.cpp @@ -99,5 +99,6 @@ STUB_GPU(PowerLayer); #endif INSTANTIATE_CLASS(PowerLayer); -REGISTER_LAYER_CLASS(POWER, PowerLayer); +REGISTER_LAYER_CLASS(Power); + } // namespace caffe diff --git a/src/caffe/layers/relu_layer.cpp b/src/caffe/layers/relu_layer.cpp index 7d5e6034..cc00319a 100644 --- a/src/caffe/layers/relu_layer.cpp +++ b/src/caffe/layers/relu_layer.cpp @@ -43,5 +43,4 @@ STUB_GPU(ReLULayer); INSTANTIATE_CLASS(ReLULayer); - } // namespace caffe diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp index d1e327a5..077d9499 100644 --- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp +++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp @@ -53,7 +53,7 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_cpu( const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) { if (propagate_down[1]) { - LOG(FATAL) << this->type_name() + LOG(FATAL) << this->type() << " Layer cannot backpropagate to label inputs."; } if (propagate_down[0]) { @@ -75,5 +75,6 @@ STUB_GPU(SigmoidCrossEntropyLossLayer); #endif INSTANTIATE_CLASS(SigmoidCrossEntropyLossLayer); -REGISTER_LAYER_CLASS(SIGMOID_CROSS_ENTROPY_LOSS, SigmoidCrossEntropyLossLayer); +REGISTER_LAYER_CLASS(SigmoidCrossEntropyLoss); + } // namespace caffe diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu index d9db4af6..08f7f492 100644 --- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu +++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu @@ -33,7 +33,7 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu( const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) { if (propagate_down[1]) { - LOG(FATAL) << this->type_name() + LOG(FATAL) << this->type() << " Layer cannot backpropagate to label inputs."; } if (propagate_down[0]) { diff --git a/src/caffe/layers/silence_layer.cpp b/src/caffe/layers/silence_layer.cpp index 9bd20574..4abf9eff 100644 --- a/src/caffe/layers/silence_layer.cpp +++ b/src/caffe/layers/silence_layer.cpp @@ -22,5 +22,6 @@ STUB_GPU(SilenceLayer); #endif INSTANTIATE_CLASS(SilenceLayer); -REGISTER_LAYER_CLASS(SILENCE, SilenceLayer); +REGISTER_LAYER_CLASS(Silence); + } // namespace caffe diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp index 60a5ecfa..46c3acd6 100644 --- a/src/caffe/layers/slice_layer.cpp +++ b/src/caffe/layers/slice_layer.cpp @@ -137,5 +137,6 @@ STUB_GPU(SliceLayer); #endif INSTANTIATE_CLASS(SliceLayer); -REGISTER_LAYER_CLASS(SLICE, SliceLayer); +REGISTER_LAYER_CLASS(Slice); + } // namespace caffe diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp index c7b09fff..25142fde 100644 --- a/src/caffe/layers/softmax_layer.cpp +++ b/src/caffe/layers/softmax_layer.cpp @@ -92,4 +92,5 @@ STUB_GPU(SoftmaxLayer); #endif INSTANTIATE_CLASS(SoftmaxLayer); + } // namespace caffe diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp index 14bd3837..bf20b605 100644 --- a/src/caffe/layers/softmax_loss_layer.cpp +++ b/src/caffe/layers/softmax_loss_layer.cpp @@ -14,7 +14,7 @@ void SoftmaxWithLossLayer<Dtype>::LayerSetUp( const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) { LossLayer<Dtype>::LayerSetUp(bottom, top); LayerParameter softmax_param(this->layer_param_); - softmax_param.set_type(LayerParameter_LayerType_SOFTMAX); + softmax_param.set_type("Softmax"); softmax_layer_.reset(LayerRegistry<Dtype>::CreateLayer(softmax_param)); softmax_bottom_vec_.clear(); softmax_bottom_vec_.push_back(bottom[0]); @@ -80,7 +80,7 @@ template <typename Dtype> void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) { if (propagate_down[1]) { - LOG(FATAL) << this->type_name() + LOG(FATAL) << this->type() << " Layer cannot backpropagate to label inputs."; } if (propagate_down[0]) { @@ -116,6 +116,6 @@ void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top, } INSTANTIATE_CLASS(SoftmaxWithLossLayer); -REGISTER_LAYER_CLASS(SOFTMAX_LOSS, SoftmaxWithLossLayer); +REGISTER_LAYER_CLASS(SoftmaxWithLoss); } // namespace caffe diff --git a/src/caffe/layers/split_layer.cpp b/src/caffe/layers/split_layer.cpp index 51ac61f4..d6929b99 100644 --- a/src/caffe/layers/split_layer.cpp +++ b/src/caffe/layers/split_layer.cpp @@ -16,7 +16,7 @@ void SplitLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom, // the backward pass. (Technically, it should be possible to share the diff // blob of the first split output with the input, but this seems to cause // some strange effects in practice...) - CHECK_NE(top[i], bottom[0]) << this->type_name() << " Layer does not " + CHECK_NE(top[i], bottom[0]) << this->type() << " Layer does not " "allow in-place computation."; top[i]->Reshape(bottom[0]->num(), bottom[0]->channels(), bottom[0]->height(), bottom[0]->width()); @@ -56,5 +56,6 @@ STUB_GPU(SplitLayer); #endif INSTANTIATE_CLASS(SplitLayer); -REGISTER_LAYER_CLASS(SPLIT, SplitLayer); +REGISTER_LAYER_CLASS(Split); + } // namespace caffe diff --git a/src/caffe/layers/threshold_layer.cpp b/src/caffe/layers/threshold_layer.cpp index 9e68c32d..2365e7b9 100644 --- a/src/caffe/layers/threshold_layer.cpp +++ b/src/caffe/layers/threshold_layer.cpp @@ -29,5 +29,6 @@ STUB_GPU_FORWARD(ThresholdLayer, Forward); #endif INSTANTIATE_CLASS(ThresholdLayer); -REGISTER_LAYER_CLASS(THRESHOLD, ThresholdLayer); +REGISTER_LAYER_CLASS(Threshold); + } // namespace caffe diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp index 83372e07..23ec83d1 100644 --- a/src/caffe/layers/window_data_layer.cpp +++ b/src/caffe/layers/window_data_layer.cpp @@ -463,5 +463,6 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() { } INSTANTIATE_CLASS(WindowDataLayer); -REGISTER_LAYER_CLASS(WINDOW_DATA, WindowDataLayer); +REGISTER_LAYER_CLASS(WindowData); + } // namespace caffe diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index dd2c1471..1fea3445 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -225,59 +225,7 @@ message LayerParameter { repeated NetStateRule include = 32; repeated NetStateRule exclude = 33; - // NOTE - // Add new LayerTypes to the enum below in lexicographical order (other than - // starting with NONE), starting with the next available ID in the comment - // line above the enum. Update the next available ID when you add a new - // LayerType. - // - // LayerType next available ID: 40 (last added: DECONVOLUTION) - enum LayerType { - // "NONE" layer type is 0th enum element so that we don't cause confusion - // by defaulting to an existent LayerType (instead, should usually error if - // the type is unspecified). - NONE = 0; - ABSVAL = 35; - ACCURACY = 1; - ARGMAX = 30; - BNLL = 2; - CONCAT = 3; - CONTRASTIVE_LOSS = 37; - CONVOLUTION = 4; - DATA = 5; - DECONVOLUTION = 39; - DROPOUT = 6; - DUMMY_DATA = 32; - EUCLIDEAN_LOSS = 7; - ELTWISE = 25; - EXP = 38; - FLATTEN = 8; - HDF5_DATA = 9; - HDF5_OUTPUT = 10; - HINGE_LOSS = 28; - IM2COL = 11; - IMAGE_DATA = 12; - INFOGAIN_LOSS = 13; - INNER_PRODUCT = 14; - LRN = 15; - MEMORY_DATA = 29; - MULTINOMIAL_LOGISTIC_LOSS = 16; - MVN = 34; - POOLING = 17; - POWER = 26; - RELU = 18; - SIGMOID = 19; - SIGMOID_CROSS_ENTROPY_LOSS = 27; - SILENCE = 36; - SOFTMAX = 20; - SOFTMAX_LOSS = 21; - SPLIT = 22; - SLICE = 33; - TANH = 23; - WINDOW_DATA = 24; - THRESHOLD = 31; - } - optional LayerType type = 5; // the layer type from the enum above + optional string type = 5; // the layer type from the enum above // The blobs containing the numeric parameters of the layer repeated BlobProto blobs = 6; diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index 65de52aa..1d8192ae 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -66,7 +66,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> { " name: 'TestNetwork' " " layers: { " " name: 'data' " - " type: DUMMY_DATA " + " type: 'DummyData' " " dummy_data_param { " " num: " << num_ << " " " channels: " << channels_ << " " @@ -85,7 +85,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> { " } " " layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 1 " " weight_filler { " @@ -102,7 +102,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> { " } " " layers: { " " name: 'loss' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'innerprod' " " bottom: 'targets' " " } " diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp index 319958fe..a4e14f91 100644 --- a/src/caffe/test/test_net.cpp +++ b/src/caffe/test/test_net.cpp @@ -61,7 +61,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "name: 'TinyTestNetwork' " "layers: { " " name: 'data' " - " type: DUMMY_DATA " + " type: 'DummyData' " " dummy_data_param { " " num: 5 " " channels: 2 " @@ -85,7 +85,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'innerproduct' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 1000 " " weight_filler { " @@ -106,7 +106,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerproduct' " " bottom: 'label' " " top: 'top_loss' " @@ -115,7 +115,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { proto += "layers: { " " name: 'loss' " - " type: ACCURACY " + " type: 'Accuracy' " " bottom: 'innerproduct' " " bottom: 'label' " " top: 'accuracy' " @@ -132,7 +132,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "name: 'TinyTestEuclidLossNetwork' " "layers: { " " name: 'data' " - " type: DUMMY_DATA " + " type: 'DummyData' " " dummy_data_param { " " num: 5 " " channels: 2 " @@ -152,7 +152,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'innerproduct' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 1 " " weight_filler { " @@ -173,7 +173,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'loss' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'innerproduct' " " bottom: 'label' " "} "; @@ -192,7 +192,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "name: 'TrickyTestNetwork' " "layers: { " " name: 'data' " - " type: DUMMY_DATA " + " type: 'DummyData' " " dummy_data_param { " " num: 5 " " channels: 2 " @@ -212,7 +212,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'innerproduct' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 1000 " " weight_filler { " @@ -233,7 +233,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'innerproduct' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 1 " " weight_filler { " @@ -254,7 +254,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + + " type: 'SoftmaxWithLoss' " + loss_weight_stream.str() + " bottom: 'transformed_data' " " bottom: 'transformed_label' " @@ -262,8 +262,8 @@ class NetTest : public MultiDeviceTest<TypeParam> { InitNetFromProtoString(proto); } - // loss_weight is the loss weight for the EUCLIDEAN_LOSS layer output. - // midnet_loss_weight is the loss weight for the first INNER_PRODUCT layer + // loss_weight is the loss weight for the 'EuclideanLoss' layer output. + // midnet_loss_weight is the loss weight for the first 'InnerProduct' layer // output. Should both default to 0.0 if unspecified (i.e., if NULL is // passed to this function). virtual void InitUnsharedWeightsNet(const Dtype* loss_weight = NULL, @@ -279,7 +279,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { proto << "layers: { " " name: 'data' " - " type: DUMMY_DATA " + " type: 'DummyData' " " dummy_data_param { " " num: 5 " " channels: 2 " @@ -294,7 +294,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'innerproduct1' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 10 " " bias_term: " << bias_term << @@ -322,7 +322,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'innerproduct2' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 10 " " bias_term: " << bias_term << @@ -346,7 +346,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'loss' " - " type: EUCLIDEAN_LOSS "; + " type: 'EuclideanLoss' "; if (loss_weight) { proto << " loss_weight: " << *loss_weight << " "; } @@ -362,7 +362,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "name: 'SharedWeightsNetwork' " "layers: { " " name: 'data' " - " type: DUMMY_DATA " + " type: 'DummyData' " " dummy_data_param { " " num: 5 " " channels: 2 " @@ -377,7 +377,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'innerproduct1' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 10 " " bias_term: false " @@ -392,7 +392,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'innerproduct2' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 10 " " bias_term: false " @@ -407,7 +407,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'loss' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'innerproduct1' " " bottom: 'innerproduct2' " "} "; @@ -419,7 +419,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "name: 'DiffDataUnsharedWeightsNetwork' " "layers: { " " name: 'data' " - " type: DUMMY_DATA " + " type: 'DummyData' " " dummy_data_param { " " num: 10 " " channels: 10 " @@ -439,7 +439,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'innerproduct1' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 10 " " bias_term: false " @@ -454,7 +454,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'innerproduct2' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 10 " " bias_term: false " @@ -469,7 +469,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'loss' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'data2' " " bottom: 'innerproduct2' " "} "; @@ -481,7 +481,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "name: 'DiffDataSharedWeightsNetwork' " "layers: { " " name: 'data' " - " type: DUMMY_DATA " + " type: 'DummyData' " " dummy_data_param { " " num: 10 " " channels: 10 " @@ -501,7 +501,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'innerproduct1' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 10 " " bias_term: false " @@ -516,7 +516,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'innerproduct2' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 10 " " bias_term: false " @@ -531,7 +531,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'loss' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'data2' " " bottom: 'innerproduct2' " "} "; @@ -548,7 +548,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "input_dim: 100 " "layers: { " " name: 'conv1' " - " type: CONVOLUTION " + " type: 'Convolution' " " bottom: 'data' " " top: 'conv1' " " convolution_param { " @@ -567,13 +567,13 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'relu1' " - " type: RELU " + " type: 'ReLU' " " bottom: 'conv1' " " top: 'conv1' " "} " "layers: { " " name: 'pool1' " - " type: POOLING " + " type: 'Pooling' " " bottom: 'conv1' " " top: 'pool1' " " pooling_param { " @@ -584,7 +584,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'norm1' " - " type: LRN " + " type: 'LRN' " " bottom: 'pool1' " " top: 'norm1' " " lrn_param { " @@ -593,7 +593,7 @@ class NetTest : public MultiDeviceTest<TypeParam> { "} " "layers: { " " name: 'softmax' " - " type: SOFTMAX " + " type: 'Softmax' " " bottom: 'norm1' " " top: 'softmax' " "} "; @@ -702,7 +702,7 @@ TYPED_TEST(NetTest, TestBottomNeedBackwardTricky) { TYPED_TEST(NetTest, TestLossWeight) { typedef typename TypeParam::Dtype Dtype; // First, compute the loss and gradients with no loss_weight specified. - // In this case, the loss weight for the EUCLIDEAN_LOSS layer should default + // In this case, the loss weight for the 'EuclideanLoss' layer should default // to 1. vector<Blob<Dtype>*> bottom; Caffe::set_random_seed(this->seed_); @@ -796,8 +796,8 @@ TYPED_TEST(NetTest, TestComboLossWeight) { const bool kForceBackward = true; const Dtype kErrorMargin = 1e-4; - // Get the loss and gradients with EUCLIDEAN_LOSS weight 1, - // INNER_PRODUCT weight 1. + // Get the loss and gradients with 'EuclideanLoss' weight 1, + // 'InnerProduct' weight 1. loss_weight = 1; midnet_loss_weight = 1; Caffe::set_random_seed(this->seed_); @@ -925,7 +925,7 @@ TYPED_TEST(NetTest, TestBackwardWithAccuracyLayer) { this->InitTinyNet(kForceBackward, kAccuracyLayer); EXPECT_TRUE(this->net_->has_blob("accuracy")); vector<Blob<Dtype>*> bottom; - // Test that we can do Backward even though we have an ACCURACY layer. + // Test that we can do Backward even though we have an 'Accuracy' layer. this->net_->ForwardBackward(bottom); } @@ -1263,19 +1263,19 @@ TEST_F(FilterNetTest, TestNoFilter) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " "} "; @@ -1287,7 +1287,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) { "name: 'LeNet' " "layers { " " name: 'mnist' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " " data_param { " @@ -1301,7 +1301,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) { "} " "layers { " " name: 'mnist' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " " data_param { " @@ -1315,7 +1315,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) { "} " "layers { " " name: 'conv1' " - " type: CONVOLUTION " + " type: 'Convolution' " " bottom: 'data' " " top: 'conv1' " " blobs_lr: 1 " @@ -1334,7 +1334,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) { "} " "layers { " " name: 'ip1' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'conv1' " " top: 'ip1' " " blobs_lr: 1 " @@ -1351,7 +1351,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) { "} " "layers { " " name: 'accuracy' " - " type: ACCURACY " + " type: 'Accuracy' " " bottom: 'ip1' " " bottom: 'label' " " top: 'accuracy' " @@ -1359,7 +1359,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) { "} " "layers { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'ip2' " " bottom: 'label' " " top: 'loss' " @@ -1370,7 +1370,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) { "name: 'LeNet' " "layers { " " name: 'mnist' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " " data_param { " @@ -1384,7 +1384,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) { "} " "layers { " " name: 'conv1' " - " type: CONVOLUTION " + " type: 'Convolution' " " bottom: 'data' " " top: 'conv1' " " blobs_lr: 1 " @@ -1403,7 +1403,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) { "} " "layers { " " name: 'ip1' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'conv1' " " top: 'ip1' " " blobs_lr: 1 " @@ -1420,7 +1420,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) { "} " "layers { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'ip2' " " bottom: 'label' " " top: 'loss' " @@ -1429,7 +1429,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) { "name: 'LeNet' " "layers { " " name: 'mnist' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " " data_param { " @@ -1443,7 +1443,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) { "} " "layers { " " name: 'conv1' " - " type: CONVOLUTION " + " type: 'Convolution' " " bottom: 'data' " " top: 'conv1' " " blobs_lr: 1 " @@ -1462,7 +1462,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) { "} " "layers { " " name: 'ip1' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'conv1' " " top: 'ip1' " " blobs_lr: 1 " @@ -1479,7 +1479,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) { "} " "layers { " " name: 'accuracy' " - " type: ACCURACY " + " type: 'Accuracy' " " bottom: 'ip1' " " bottom: 'label' " " top: 'accuracy' " @@ -1487,7 +1487,7 @@ TEST_F(FilterNetTest, TestFilterLeNetTrainTest) { "} " "layers { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'ip2' " " bottom: 'label' " " top: 'loss' " @@ -1519,20 +1519,20 @@ TEST_F(FilterNetTest, TestFilterOutByStage) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " " include: { stage: 'mystage' } " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " "} "; @@ -1540,13 +1540,13 @@ TEST_F(FilterNetTest, TestFilterOutByStage) { "name: 'TestNetwork' " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " "} "; @@ -1558,20 +1558,20 @@ TEST_F(FilterNetTest, TestFilterOutByStage2) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " " include: { stage: 'mystage' } " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " "} "; @@ -1579,13 +1579,13 @@ TEST_F(FilterNetTest, TestFilterOutByStage2) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " "} "; @@ -1598,20 +1598,20 @@ TEST_F(FilterNetTest, TestFilterInByStage) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " " include: { stage: 'mystage' } " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " "} "; @@ -1623,20 +1623,20 @@ TEST_F(FilterNetTest, TestFilterInByStage2) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " " exclude: { stage: 'mystage' } " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " "} "; @@ -1649,20 +1649,20 @@ TEST_F(FilterNetTest, TestFilterOutByMultipleStage) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " " include: { stage: 'mystage' stage: 'myotherstage' } " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " " include: { stage: 'mystage' } " @@ -1672,13 +1672,13 @@ TEST_F(FilterNetTest, TestFilterOutByMultipleStage) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " " include: { stage: 'mystage' } " @@ -1692,13 +1692,13 @@ TEST_F(FilterNetTest, TestFilterInByMultipleStage) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " " include: { stage: 'myotherstage' } " @@ -1706,7 +1706,7 @@ TEST_F(FilterNetTest, TestFilterInByMultipleStage) { "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " " include: { stage: 'mystage' } " @@ -1720,20 +1720,20 @@ TEST_F(FilterNetTest, TestFilterInByMultipleStage2) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " " include: { stage: 'mystage' stage: 'myotherstage' } " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " " include: { stage: 'mystage' } " @@ -1747,20 +1747,20 @@ TEST_F(FilterNetTest, TestFilterInByNotStage) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " " include: { not_stage: 'myotherstage' } " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " " include: { not_stage: 'myotherstage' } " @@ -1774,20 +1774,20 @@ TEST_F(FilterNetTest, TestFilterOutByNotStage) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " " include: { not_stage: 'mystage' } " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " " include: { not_stage: 'mystage' } " @@ -1797,7 +1797,7 @@ TEST_F(FilterNetTest, TestFilterOutByNotStage) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} "; @@ -1809,20 +1809,20 @@ TEST_F(FilterNetTest, TestFilterOutByMinLevel) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " " include: { min_level: 3 } " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " "} "; @@ -1830,13 +1830,13 @@ TEST_F(FilterNetTest, TestFilterOutByMinLevel) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " "} "; @@ -1848,20 +1848,20 @@ TEST_F(FilterNetTest, TestFilterOutByMaxLevel) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " " include: { max_level: -3 } " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " "} "; @@ -1869,13 +1869,13 @@ TEST_F(FilterNetTest, TestFilterOutByMaxLevel) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " "} "; @@ -1887,20 +1887,20 @@ TEST_F(FilterNetTest, TestFilterInByMinLevel) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " " include: { min_level: 0 } " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " "} "; @@ -1913,20 +1913,20 @@ TEST_F(FilterNetTest, TestFilterInByMinLevel2) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " " include: { min_level: 3 } " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " "} "; @@ -1938,20 +1938,20 @@ TEST_F(FilterNetTest, TestFilterInByMaxLevel) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " " include: { max_level: 0 } " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " "} "; @@ -1964,20 +1964,20 @@ TEST_F(FilterNetTest, TestFilterInByMaxLevel2) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " " include: { max_level: -3 } " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " "} "; @@ -1989,20 +1989,20 @@ TEST_F(FilterNetTest, TestFilterInOutByIncludeMultiRule) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " " include: { min_level: 2 phase: TRAIN } " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " " include: { min_level: 2 phase: TEST } " @@ -2016,13 +2016,13 @@ TEST_F(FilterNetTest, TestFilterInOutByIncludeMultiRule) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " " include: { min_level: 2 phase: TRAIN } " @@ -2032,13 +2032,13 @@ TEST_F(FilterNetTest, TestFilterInOutByIncludeMultiRule) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " " include: { min_level: 2 phase: TEST } " @@ -2052,13 +2052,13 @@ TEST_F(FilterNetTest, TestFilterInByIncludeMultiRule) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " " include: { min_level: 2 phase: TRAIN } " @@ -2066,7 +2066,7 @@ TEST_F(FilterNetTest, TestFilterInByIncludeMultiRule) { "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " " include: { min_level: 2 phase: TEST } " @@ -2085,20 +2085,20 @@ TEST_F(FilterNetTest, TestFilterInOutByExcludeMultiRule) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " " exclude: { min_level: 2 phase: TRAIN } " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " " exclude: { min_level: 2 phase: TEST } " @@ -2112,13 +2112,13 @@ TEST_F(FilterNetTest, TestFilterInOutByExcludeMultiRule) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " " exclude: { min_level: 2 phase: TEST } " @@ -2128,13 +2128,13 @@ TEST_F(FilterNetTest, TestFilterInOutByExcludeMultiRule) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " " exclude: { min_level: 2 phase: TRAIN } " diff --git a/src/caffe/test/test_protobuf.cpp b/src/caffe/test/test_protobuf.cpp index 0c502d6d..01de461a 100644 --- a/src/caffe/test/test_protobuf.cpp +++ b/src/caffe/test/test_protobuf.cpp @@ -16,7 +16,7 @@ class ProtoTest : public ::testing::Test {}; TEST_F(ProtoTest, TestSerialization) { LayerParameter param; param.set_name("test"); - param.set_type(LayerParameter_LayerType_NONE); + param.set_type("Test"); std::cout << "Printing in binary format." << std::endl; std::cout << param.SerializeAsString() << std::endl; std::cout << "Printing in text format." << std::endl; diff --git a/src/caffe/test/test_solver.cpp b/src/caffe/test/test_solver.cpp index a7dbf77f..d3f646c5 100644 --- a/src/caffe/test/test_solver.cpp +++ b/src/caffe/test/test_solver.cpp @@ -53,7 +53,7 @@ TYPED_TEST(SolverTest, TestInitTrainTestNets) { " name: 'TestNetwork' " " layers: { " " name: 'data' " - " type: DUMMY_DATA " + " type: 'DummyData' " " dummy_data_param { " " num: 5 " " channels: 3 " @@ -69,7 +69,7 @@ TYPED_TEST(SolverTest, TestInitTrainTestNets) { " } " " layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 10 " " } " @@ -78,7 +78,7 @@ TYPED_TEST(SolverTest, TestInitTrainTestNets) { " } " " layers: { " " name: 'accuracy' " - " type: ACCURACY " + " type: 'Accuracy' " " bottom: 'innerprod' " " bottom: 'label' " " top: 'accuracy' " @@ -86,7 +86,7 @@ TYPED_TEST(SolverTest, TestInitTrainTestNets) { " } " " layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " " include: { phase: TRAIN } " diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp index 38e76219..584d0a4a 100644 --- a/src/caffe/test/test_split_layer.cpp +++ b/src/caffe/test/test_split_layer.cpp @@ -116,19 +116,19 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertion1) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " "} "; @@ -140,32 +140,32 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertion2) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'data_split' " - " type: SPLIT " + " type: 'Split' " " bottom: 'data' " " top: 'data_split_0' " " top: 'data_split_1' " "} " "layers: { " " name: 'innerprod1' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data_split_0' " " top: 'innerprod1' " "} " "layers: { " " name: 'innerprod2' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data_split_1' " " top: 'innerprod2' " "} " "layers: { " " name: 'loss' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'innerprod1' " " bottom: 'innerprod2' " "} "; @@ -177,7 +177,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) { "name: 'CaffeNet' " "layers { " " name: 'data' " - " type: DATA " + " type: 'Data' " " data_param { " " source: '/home/jiayq/Data/ILSVRC12/train-leveldb' " " batch_size: 256 " @@ -192,7 +192,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) { "} " "layers { " " name: 'conv1' " - " type: CONVOLUTION " + " type: 'Convolution' " " convolution_param { " " num_output: 96 " " kernel_size: 11 " @@ -215,13 +215,13 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) { "} " "layers { " " name: 'relu1' " - " type: RELU " + " type: 'ReLU' " " bottom: 'conv1' " " top: 'conv1' " "} " "layers { " " name: 'pool1' " - " type: POOLING " + " type: 'Pooling' " " pooling_param { " " pool: MAX " " kernel_size: 3 " @@ -232,7 +232,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) { "} " "layers { " " name: 'norm1' " - " type: LRN " + " type: 'LRN' " " lrn_param { " " local_size: 5 " " alpha: 0.0001 " @@ -243,7 +243,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) { "} " "layers { " " name: 'conv2' " - " type: CONVOLUTION " + " type: 'Convolution' " " convolution_param { " " num_output: 256 " " group: 2 " @@ -267,13 +267,13 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) { "} " "layers { " " name: 'relu2' " - " type: RELU " + " type: 'ReLU' " " bottom: 'conv2' " " top: 'conv2' " "} " "layers { " " name: 'pool2' " - " type: POOLING " + " type: 'Pooling' " " pooling_param { " " pool: MAX " " kernel_size: 3 " @@ -284,7 +284,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) { "} " "layers { " " name: 'norm2' " - " type: LRN " + " type: 'LRN' " " lrn_param { " " local_size: 5 " " alpha: 0.0001 " @@ -295,7 +295,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) { "} " "layers { " " name: 'conv3' " - " type: CONVOLUTION " + " type: 'Convolution' " " convolution_param { " " num_output: 384 " " kernel_size: 3 " @@ -318,13 +318,13 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) { "} " "layers { " " name: 'relu3' " - " type: RELU " + " type: 'ReLU' " " bottom: 'conv3' " " top: 'conv3' " "} " "layers { " " name: 'conv4' " - " type: CONVOLUTION " + " type: 'Convolution' " " convolution_param { " " num_output: 384 " " group: 2 " @@ -348,13 +348,13 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) { "} " "layers { " " name: 'relu4' " - " type: RELU " + " type: 'ReLU' " " bottom: 'conv4' " " top: 'conv4' " "} " "layers { " " name: 'conv5' " - " type: CONVOLUTION " + " type: 'Convolution' " " convolution_param { " " num_output: 256 " " group: 2 " @@ -378,13 +378,13 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) { "} " "layers { " " name: 'relu5' " - " type: RELU " + " type: 'ReLU' " " bottom: 'conv5' " " top: 'conv5' " "} " "layers { " " name: 'pool5' " - " type: POOLING " + " type: 'Pooling' " " pooling_param { " " kernel_size: 3 " " pool: MAX " @@ -395,7 +395,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) { "} " "layers { " " name: 'fc6' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 4096 " " weight_filler { " @@ -416,13 +416,13 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) { "} " "layers { " " name: 'relu6' " - " type: RELU " + " type: 'ReLU' " " bottom: 'fc6' " " top: 'fc6' " "} " "layers { " " name: 'drop6' " - " type: DROPOUT " + " type: 'Dropout' " " dropout_param { " " dropout_ratio: 0.5 " " } " @@ -431,7 +431,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) { "} " "layers { " " name: 'fc7' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 4096 " " weight_filler { " @@ -452,13 +452,13 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) { "} " "layers { " " name: 'relu7' " - " type: RELU " + " type: 'ReLU' " " bottom: 'fc7' " " top: 'fc7' " "} " "layers { " " name: 'drop7' " - " type: DROPOUT " + " type: 'Dropout' " " dropout_param { " " dropout_ratio: 0.5 " " } " @@ -467,7 +467,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) { "} " "layers { " " name: 'fc8' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 1000 " " weight_filler { " @@ -488,7 +488,7 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionImageNet) { "} " "layers { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'fc8' " " bottom: 'label' " "} "; @@ -500,25 +500,25 @@ TEST_F(SplitLayerInsertionTest, TestNoInsertionWithInPlace) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod' " "} " "layers: { " " name: 'relu' " - " type: RELU " + " type: 'ReLU' " " bottom: 'innerprod' " " top: 'innerprod' " "} " "layers: { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'innerprod' " " bottom: 'label' " "} "; @@ -531,7 +531,7 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) { "force_backward: true " "layers: { " " name: 'data' " - " type: DUMMY_DATA " + " type: 'DummyData' " " dummy_data_param { " " num: 5 " " channels: 2 " @@ -546,7 +546,7 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) { "} " "layers: { " " name: 'innerproduct1' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 10 " " bias_term: false " @@ -562,7 +562,7 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) { "} " "layers: { " " name: 'innerproduct2' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 10 " " bias_term: false " @@ -577,7 +577,7 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) { "} " "layers: { " " name: 'loss' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'innerproduct1' " " bottom: 'innerproduct2' " "} "; @@ -586,7 +586,7 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) { "force_backward: true " "layers: { " " name: 'data' " - " type: DUMMY_DATA " + " type: 'DummyData' " " dummy_data_param { " " num: 5 " " channels: 2 " @@ -601,14 +601,14 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) { "} " "layers: { " " name: 'data_data_0_split' " - " type: SPLIT " + " type: 'Split' " " bottom: 'data' " " top: 'data_data_0_split_0' " " top: 'data_data_0_split_1' " "} " "layers: { " " name: 'innerproduct1' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 10 " " bias_term: false " @@ -623,7 +623,7 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) { "} " "layers: { " " name: 'innerproduct1_innerproduct1_0_split' " - " type: SPLIT " + " type: 'Split' " " bottom: 'innerproduct1' " " top: 'innerproduct1_innerproduct1_0_split_0' " " top: 'innerproduct1_innerproduct1_0_split_1' " @@ -632,7 +632,7 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) { "} " "layers: { " " name: 'innerproduct2' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 10 " " bias_term: false " @@ -647,7 +647,7 @@ TEST_F(SplitLayerInsertionTest, TestLossInsertion) { "} " "layers: { " " name: 'loss' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'innerproduct1_innerproduct1_0_split_1' " " bottom: 'innerproduct2' " "} "; @@ -659,37 +659,37 @@ TEST_F(SplitLayerInsertionTest, TestInsertion) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod1' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod1' " "} " "layers: { " " name: 'innerprod2' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod2' " "} " "layers: { " " name: 'innerprod3' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod3' " "} " "layers: { " " name: 'loss1' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'innerprod1' " " bottom: 'innerprod2' " "} " "layers: { " " name: 'loss2' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'innerprod2' " " bottom: 'innerprod3' " "} "; @@ -697,13 +697,13 @@ TEST_F(SplitLayerInsertionTest, TestInsertion) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'data_data_0_split' " - " type: SPLIT " + " type: 'Split' " " bottom: 'data' " " top: 'data_data_0_split_0' " " top: 'data_data_0_split_1' " @@ -711,38 +711,38 @@ TEST_F(SplitLayerInsertionTest, TestInsertion) { "} " "layers: { " " name: 'innerprod1' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data_data_0_split_0' " " top: 'innerprod1' " "} " "layers: { " " name: 'innerprod2' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data_data_0_split_1' " " top: 'innerprod2' " "} " "layers: { " " name: 'innerprod2_innerprod2_0_split' " - " type: SPLIT " + " type: 'Split' " " bottom: 'innerprod2' " " top: 'innerprod2_innerprod2_0_split_0' " " top: 'innerprod2_innerprod2_0_split_1' " "} " "layers: { " " name: 'innerprod3' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data_data_0_split_2' " " top: 'innerprod3' " "} " "layers: { " " name: 'loss1' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'innerprod1' " " bottom: 'innerprod2_innerprod2_0_split_0' " "} " "layers: { " " name: 'loss2' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'innerprod2_innerprod2_0_split_1' " " bottom: 'innerprod3' " "} "; @@ -754,43 +754,43 @@ TEST_F(SplitLayerInsertionTest, TestInsertionTwoTop) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod1' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod1' " "} " "layers: { " " name: 'innerprod2' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'label' " " top: 'innerprod2' " "} " "layers: { " " name: 'innerprod3' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod3' " "} " "layers: { " " name: 'innerprod4' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'label' " " top: 'innerprod4' " "} " "layers: { " " name: 'loss1' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'innerprod1' " " bottom: 'innerprod3' " "} " "layers: { " " name: 'loss2' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'innerprod2' " " bottom: 'innerprod4' " "} "; @@ -798,57 +798,57 @@ TEST_F(SplitLayerInsertionTest, TestInsertionTwoTop) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'data_data_0_split' " - " type: SPLIT " + " type: 'Split' " " bottom: 'data' " " top: 'data_data_0_split_0' " " top: 'data_data_0_split_1' " "} " "layers: { " " name: 'label_data_1_split' " - " type: SPLIT " + " type: 'Split' " " bottom: 'label' " " top: 'label_data_1_split_0' " " top: 'label_data_1_split_1' " "} " "layers: { " " name: 'innerprod1' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data_data_0_split_0' " " top: 'innerprod1' " "} " "layers: { " " name: 'innerprod2' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'label_data_1_split_0' " " top: 'innerprod2' " "} " "layers: { " " name: 'innerprod3' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data_data_0_split_1' " " top: 'innerprod3' " "} " "layers: { " " name: 'innerprod4' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'label_data_1_split_1' " " top: 'innerprod4' " "} " "layers: { " " name: 'loss1' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'innerprod1' " " bottom: 'innerprod3' " "} " "layers: { " " name: 'loss2' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'innerprod2' " " bottom: 'innerprod4' " "} "; @@ -865,19 +865,19 @@ TEST_F(SplitLayerInsertionTest, TestInputInsertion) { "input_dim: 227 " "layers: { " " name: 'innerprod1' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod1' " "} " "layers: { " " name: 'innerprod2' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod2' " "} " "layers: { " " name: 'loss' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'innerprod1' " " bottom: 'innerprod2' " "} "; @@ -890,26 +890,26 @@ TEST_F(SplitLayerInsertionTest, TestInputInsertion) { "input_dim: 227 " "layers: { " " name: 'data_input_0_split' " - " type: SPLIT " + " type: 'Split' " " bottom: 'data' " " top: 'data_input_0_split_0' " " top: 'data_input_0_split_1' " "} " "layers: { " " name: 'innerprod1' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data_input_0_split_0' " " top: 'innerprod1' " "} " "layers: { " " name: 'innerprod2' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data_input_0_split_1' " " top: 'innerprod2' " "} " "layers: { " " name: 'loss' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'innerprod1' " " bottom: 'innerprod2' " "} "; @@ -921,37 +921,37 @@ TEST_F(SplitLayerInsertionTest, TestWithInPlace) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'innerprod1' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data' " " top: 'innerprod1' " "} " "layers: { " " name: 'relu1' " - " type: RELU " + " type: 'ReLU' " " bottom: 'innerprod1' " " top: 'innerprod1' " "} " "layers: { " " name: 'innerprod2' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'innerprod1' " " top: 'innerprod2' " "} " "layers: { " " name: 'loss1' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'innerprod1' " " bottom: 'label' " "} " "layers: { " " name: 'loss2' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'innerprod2' " " bottom: 'data' " "} "; @@ -959,51 +959,51 @@ TEST_F(SplitLayerInsertionTest, TestWithInPlace) { "name: 'TestNetwork' " "layers: { " " name: 'data' " - " type: DATA " + " type: 'Data' " " top: 'data' " " top: 'label' " "} " "layers: { " " name: 'data_data_0_split' " - " type: SPLIT " + " type: 'Split' " " bottom: 'data' " " top: 'data_data_0_split_0' " " top: 'data_data_0_split_1' " "} " "layers: { " " name: 'innerprod1' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'data_data_0_split_0' " " top: 'innerprod1' " "} " "layers: { " " name: 'relu1' " - " type: RELU " + " type: 'ReLU' " " bottom: 'innerprod1' " " top: 'innerprod1' " "} " "layers: { " " name: 'innerprod1_relu1_0_split' " - " type: SPLIT " + " type: 'Split' " " bottom: 'innerprod1' " " top: 'innerprod1_relu1_0_split_0' " " top: 'innerprod1_relu1_0_split_1' " "} " "layers: { " " name: 'innerprod2' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " bottom: 'innerprod1_relu1_0_split_0' " " top: 'innerprod2' " "} " "layers: { " " name: 'loss1' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'innerprod1_relu1_0_split_1' " " bottom: 'label' " "} " "layers: { " " name: 'loss2' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " " bottom: 'innerprod2' " " bottom: 'data_data_0_split_1' " "} "; diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp index 52e7f1f9..e520b85f 100644 --- a/src/caffe/test/test_upgrade_proto.cpp +++ b/src/caffe/test/test_upgrade_proto.cpp @@ -1184,7 +1184,7 @@ TEST_F(V0UpgradeTest, TestSimple) { "name: 'CaffeNet' " "layers { " " name: 'data' " - " type: DATA " + " type: 'Data' " " data_param { " " source: '/home/jiayq/Data/ILSVRC12/train-leveldb' " " batch_size: 256 " @@ -1199,7 +1199,7 @@ TEST_F(V0UpgradeTest, TestSimple) { "} " "layers { " " name: 'conv1' " - " type: CONVOLUTION " + " type: 'Convolution' " " convolution_param { " " num_output: 96 " " kernel_size: 11 " @@ -1223,7 +1223,7 @@ TEST_F(V0UpgradeTest, TestSimple) { "} " "layers { " " name: 'fc8' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 1000 " " weight_filler { " @@ -1244,7 +1244,7 @@ TEST_F(V0UpgradeTest, TestSimple) { "} " "layers { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'fc8' " " bottom: 'label' " "} "; @@ -1520,7 +1520,7 @@ TEST_F(V0UpgradeTest, TestAllParams) { "input_dim: 32 " "layers { " " name: 'data' " - " type: DATA " + " type: 'Data' " " data_param { " " source: '/home/jiayq/Data/ILSVRC12/train-leveldb' " " batch_size: 256 " @@ -1537,7 +1537,7 @@ TEST_F(V0UpgradeTest, TestAllParams) { "} " "layers { " " name: 'images' " - " type: IMAGE_DATA " + " type: 'ImageData' " " image_data_param { " " source: '/home/jiayq/Data/ILSVRC12/train-images' " " batch_size: 256 " @@ -1557,7 +1557,7 @@ TEST_F(V0UpgradeTest, TestAllParams) { "} " "layers { " " name: 'window_data' " - " type: WINDOW_DATA " + " type: 'WindowData' " " window_data_param { " " source: '/home/jiayq/Data/ILSVRC12/train-leveldb' " " batch_size: 256 " @@ -1577,7 +1577,7 @@ TEST_F(V0UpgradeTest, TestAllParams) { "} " "layers { " " name: 'hdf5data' " - " type: HDF5_DATA " + " type: 'HDF5Data' " " hdf5_data_param { " " source: '/my/hdf5/data' " " batch_size: 256 " @@ -1586,7 +1586,7 @@ TEST_F(V0UpgradeTest, TestAllParams) { "} " "layers { " " name: 'conv1' " - " type: CONVOLUTION " + " type: 'Convolution' " " convolution_param { " " num_output: 96 " " bias_term: false " @@ -1611,7 +1611,7 @@ TEST_F(V0UpgradeTest, TestAllParams) { "} " "layers { " " name: 'pool1ave' " - " type: POOLING " + " type: 'Pooling' " " pooling_param { " " pool: AVE " " kernel_size: 3 " @@ -1622,7 +1622,7 @@ TEST_F(V0UpgradeTest, TestAllParams) { "} " "layers { " " name: 'pool1stoch' " - " type: POOLING " + " type: 'Pooling' " " pooling_param { " " pool: STOCHASTIC " " kernel_size: 4 " @@ -1633,7 +1633,7 @@ TEST_F(V0UpgradeTest, TestAllParams) { "} " "layers { " " name: 'concat' " - " type: CONCAT " + " type: 'Concat' " " concat_param { " " concat_dim: 2 " " } " @@ -1643,7 +1643,7 @@ TEST_F(V0UpgradeTest, TestAllParams) { "} " "layers { " " name: 'norm1' " - " type: LRN " + " type: 'LRN' " " lrn_param { " " local_size: 5 " " alpha: 0.0001 " @@ -1654,7 +1654,7 @@ TEST_F(V0UpgradeTest, TestAllParams) { "} " "layers { " " name: 'fc6' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 4096 " " bias_term: false " @@ -1676,13 +1676,13 @@ TEST_F(V0UpgradeTest, TestAllParams) { "} " "layers { " " name: 'relu6' " - " type: RELU " + " type: 'ReLU' " " bottom: 'fc6' " " top: 'fc6' " "} " "layers { " " name: 'drop6' " - " type: DROPOUT " + " type: 'Dropout' " " dropout_param { " " dropout_ratio: 0.2 " " } " @@ -1691,7 +1691,7 @@ TEST_F(V0UpgradeTest, TestAllParams) { "} " "layers { " " name: 'loss' " - " type: INFOGAIN_LOSS " + " type: 'InfogainLoss' " " infogain_loss_param { " " source: '/my/infogain/matrix' " " } " @@ -1700,54 +1700,54 @@ TEST_F(V0UpgradeTest, TestAllParams) { "} " "layers { " " name: 'accuracy' " - " type: ACCURACY " + " type: 'Accuracy' " "} " "layers { " " name: 'bnll' " - " type: BNLL " + " type: 'BNLL' " "} " "layers { " " name: 'euclidean_loss' " - " type: EUCLIDEAN_LOSS " + " type: 'EuclideanLoss' " "} " "layers { " " name: 'flatten' " - " type: FLATTEN " + " type: 'Flatten' " "} " "layers { " " name: 'hdf5_output' " - " type: HDF5_OUTPUT " + " type: 'HDF5Output' " " hdf5_output_param { " " file_name: '/my/hdf5/output/file' " " } " "} " "layers { " " name: 'im2col' " - " type: IM2COL " + " type: 'Im2Col' " "} " "layers { " " name: 'images' " - " type: IMAGE_DATA " + " type: 'ImageData' " "} " "layers { " " name: 'multinomial_logistic_loss' " - " type: MULTINOMIAL_LOGISTIC_LOSS " + " type: 'MultinomialLogisticLoss' " "} " "layers { " " name: 'sigmoid' " - " type: SIGMOID " + " type: 'Sigmoid' " "} " "layers { " " name: 'softmax' " - " type: SOFTMAX " + " type: 'Softmax' " "} " "layers { " " name: 'split' " - " type: SPLIT " + " type: 'Split' " "} " "layers { " " name: 'tanh' " - " type: TANH " + " type: 'TanH' " "} "; this->RunV0UpgradeTest(input_proto, expected_output_proto); } @@ -2122,7 +2122,7 @@ TEST_F(V0UpgradeTest, TestImageNet) { "name: 'CaffeNet' " "layers { " " name: 'data' " - " type: DATA " + " type: 'Data' " " data_param { " " source: '/home/jiayq/Data/ILSVRC12/train-leveldb' " " batch_size: 256 " @@ -2137,7 +2137,7 @@ TEST_F(V0UpgradeTest, TestImageNet) { "} " "layers { " " name: 'conv1' " - " type: CONVOLUTION " + " type: 'Convolution' " " convolution_param { " " num_output: 96 " " kernel_size: 11 " @@ -2160,13 +2160,13 @@ TEST_F(V0UpgradeTest, TestImageNet) { "} " "layers { " " name: 'relu1' " - " type: RELU " + " type: 'ReLU' " " bottom: 'conv1' " " top: 'conv1' " "} " "layers { " " name: 'pool1' " - " type: POOLING " + " type: 'Pooling' " " pooling_param { " " pool: MAX " " kernel_size: 3 " @@ -2177,7 +2177,7 @@ TEST_F(V0UpgradeTest, TestImageNet) { "} " "layers { " " name: 'norm1' " - " type: LRN " + " type: 'LRN' " " lrn_param { " " local_size: 5 " " alpha: 0.0001 " @@ -2188,7 +2188,7 @@ TEST_F(V0UpgradeTest, TestImageNet) { "} " "layers { " " name: 'conv2' " - " type: CONVOLUTION " + " type: 'Convolution' " " convolution_param { " " num_output: 256 " " group: 2 " @@ -2212,13 +2212,13 @@ TEST_F(V0UpgradeTest, TestImageNet) { "} " "layers { " " name: 'relu2' " - " type: RELU " + " type: 'ReLU' " " bottom: 'conv2' " " top: 'conv2' " "} " "layers { " " name: 'pool2' " - " type: POOLING " + " type: 'Pooling' " " pooling_param { " " pool: MAX " " kernel_size: 3 " @@ -2229,7 +2229,7 @@ TEST_F(V0UpgradeTest, TestImageNet) { "} " "layers { " " name: 'norm2' " - " type: LRN " + " type: 'LRN' " " lrn_param { " " local_size: 5 " " alpha: 0.0001 " @@ -2240,7 +2240,7 @@ TEST_F(V0UpgradeTest, TestImageNet) { "} " "layers { " " name: 'conv3' " - " type: CONVOLUTION " + " type: 'Convolution' " " convolution_param { " " num_output: 384 " " kernel_size: 3 " @@ -2263,13 +2263,13 @@ TEST_F(V0UpgradeTest, TestImageNet) { "} " "layers { " " name: 'relu3' " - " type: RELU " + " type: 'ReLU' " " bottom: 'conv3' " " top: 'conv3' " "} " "layers { " " name: 'conv4' " - " type: CONVOLUTION " + " type: 'Convolution' " " convolution_param { " " num_output: 384 " " group: 2 " @@ -2293,13 +2293,13 @@ TEST_F(V0UpgradeTest, TestImageNet) { "} " "layers { " " name: 'relu4' " - " type: RELU " + " type: 'ReLU' " " bottom: 'conv4' " " top: 'conv4' " "} " "layers { " " name: 'conv5' " - " type: CONVOLUTION " + " type: 'Convolution' " " convolution_param { " " num_output: 256 " " group: 2 " @@ -2323,13 +2323,13 @@ TEST_F(V0UpgradeTest, TestImageNet) { "} " "layers { " " name: 'relu5' " - " type: RELU " + " type: 'ReLU' " " bottom: 'conv5' " " top: 'conv5' " "} " "layers { " " name: 'pool5' " - " type: POOLING " + " type: 'Pooling' " " pooling_param { " " kernel_size: 3 " " pool: MAX " @@ -2340,7 +2340,7 @@ TEST_F(V0UpgradeTest, TestImageNet) { "} " "layers { " " name: 'fc6' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 4096 " " weight_filler { " @@ -2361,13 +2361,13 @@ TEST_F(V0UpgradeTest, TestImageNet) { "} " "layers { " " name: 'relu6' " - " type: RELU " + " type: 'ReLU' " " bottom: 'fc6' " " top: 'fc6' " "} " "layers { " " name: 'drop6' " - " type: DROPOUT " + " type: 'Dropout' " " dropout_param { " " dropout_ratio: 0.5 " " } " @@ -2376,7 +2376,7 @@ TEST_F(V0UpgradeTest, TestImageNet) { "} " "layers { " " name: 'fc7' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 4096 " " weight_filler { " @@ -2397,13 +2397,13 @@ TEST_F(V0UpgradeTest, TestImageNet) { "} " "layers { " " name: 'relu7' " - " type: RELU " + " type: 'ReLU' " " bottom: 'fc7' " " top: 'fc7' " "} " "layers { " " name: 'drop7' " - " type: DROPOUT " + " type: 'Dropout' " " dropout_param { " " dropout_ratio: 0.5 " " } " @@ -2412,7 +2412,7 @@ TEST_F(V0UpgradeTest, TestImageNet) { "} " "layers { " " name: 'fc8' " - " type: INNER_PRODUCT " + " type: 'InnerProduct' " " inner_product_param { " " num_output: 1000 " " weight_filler { " @@ -2433,7 +2433,7 @@ TEST_F(V0UpgradeTest, TestImageNet) { "} " "layers { " " name: 'loss' " - " type: SOFTMAX_LOSS " + " type: 'SoftmaxWithLoss' " " bottom: 'fc8' " " bottom: 'label' " "} "; diff --git a/src/caffe/util/insert_splits.cpp b/src/caffe/util/insert_splits.cpp index f20efdae..8a0ad53e 100644 --- a/src/caffe/util/insert_splits.cpp +++ b/src/caffe/util/insert_splits.cpp @@ -111,7 +111,7 @@ void ConfigureSplitLayer(const string& layer_name, const string& blob_name, split_layer_param->Clear(); split_layer_param->add_bottom(blob_name); split_layer_param->set_name(SplitLayerName(layer_name, blob_name, blob_idx)); - split_layer_param->set_type(LayerParameter_LayerType_SPLIT); + split_layer_param->set_type("Split"); for (int k = 0; k < split_count; ++k) { split_layer_param->add_top( SplitBlobName(layer_name, blob_name, blob_idx, k)); diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp index cbd6003c..31e7d4db 100644 --- a/src/caffe/util/upgrade_proto.cpp +++ b/src/caffe/util/upgrade_proto.cpp @@ -459,78 +459,78 @@ bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection, return is_fully_compatible; } -LayerParameter_LayerType UpgradeV0LayerType(const string& type) { +const char* UpgradeV0LayerType(const string& type) { if (type == "accuracy") { - return LayerParameter_LayerType_ACCURACY; + return "Accuracy"; } else if (type == "bnll") { - return LayerParameter_LayerType_BNLL; + return "BNLL"; } else if (type == "concat") { - return LayerParameter_LayerType_CONCAT; + return "Concat"; } else if (type == "conv") { - return LayerParameter_LayerType_CONVOLUTION; + return "Convolution"; } else if (type == "data") { - return LayerParameter_LayerType_DATA; + return "Data"; } else if (type == "dropout") { - return LayerParameter_LayerType_DROPOUT; + return "Dropout"; } else if (type == "euclidean_loss") { - return LayerParameter_LayerType_EUCLIDEAN_LOSS; + return "EuclideanLoss"; } else if (type == "flatten") { - return LayerParameter_LayerType_FLATTEN; + return "Flatten"; } else if (type == "hdf5_data") { - return LayerParameter_LayerType_HDF5_DATA; + return "HDF5Data"; } else if (type == "hdf5_output") { - return LayerParameter_LayerType_HDF5_OUTPUT; + return "HDF5Output"; } else if (type == "im2col") { - return LayerParameter_LayerType_IM2COL; + return "Im2Col"; } else if (type == "images") { - return LayerParameter_LayerType_IMAGE_DATA; + return "ImageData"; } else if (type == "infogain_loss") { - return LayerParameter_LayerType_INFOGAIN_LOSS; + return "InfogainLoss"; } else if (type == "innerproduct") { - return LayerParameter_LayerType_INNER_PRODUCT; + return "InnerProduct"; } else if (type == "lrn") { - return LayerParameter_LayerType_LRN; + return "LRN"; } else if (type == "multinomial_logistic_loss") { - return LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS; + return "MultinomialLogisticLoss"; } else if (type == "pool") { - return LayerParameter_LayerType_POOLING; + return "Pooling"; } else if (type == "relu") { - return LayerParameter_LayerType_RELU; + return "ReLU"; } else if (type == "sigmoid") { - return LayerParameter_LayerType_SIGMOID; + return "Sigmoid"; } else if (type == "softmax") { - return LayerParameter_LayerType_SOFTMAX; + return "Softmax"; } else if (type == "softmax_loss") { - return LayerParameter_LayerType_SOFTMAX_LOSS; + return "SoftmaxWithLoss"; } else if (type == "split") { - return LayerParameter_LayerType_SPLIT; + return "Split"; } else if (type == "tanh") { - return LayerParameter_LayerType_TANH; + return "TanH"; } else if (type == "window_data") { - return LayerParameter_LayerType_WINDOW_DATA; + return "WindowData"; } else { - LOG(FATAL) << "Unknown layer name: " << type; - return LayerParameter_LayerType_NONE; + LOG(FATAL) << "Unknown layer type: " << type; + return ""; } } bool NetNeedsDataUpgrade(const NetParameter& net_param) { for (int i = 0; i < net_param.layers_size(); ++i) { - if (net_param.layers(i).type() == LayerParameter_LayerType_DATA) { + if (net_param.layers(i).type() == "Data") { DataParameter layer_param = net_param.layers(i).data_param(); if (layer_param.has_scale()) { return true; } if (layer_param.has_mean_file()) { return true; } if (layer_param.has_crop_size()) { return true; } if (layer_param.has_mirror()) { return true; } } - if (net_param.layers(i).type() == LayerParameter_LayerType_IMAGE_DATA) { + if (net_param.layers(i).type() == "ImageData") { ImageDataParameter layer_param = net_param.layers(i).image_data_param(); if (layer_param.has_scale()) { return true; } if (layer_param.has_mean_file()) { return true; } if (layer_param.has_crop_size()) { return true; } if (layer_param.has_mirror()) { return true; } } - if (net_param.layers(i).type() == LayerParameter_LayerType_WINDOW_DATA) { + if (net_param.layers(i).type() == "WindowData") { WindowDataParameter layer_param = net_param.layers(i).window_data_param(); if (layer_param.has_scale()) { return true; } if (layer_param.has_mean_file()) { return true; } @@ -541,11 +541,11 @@ bool NetNeedsDataUpgrade(const NetParameter& net_param) { return false; } -#define CONVERT_LAYER_TRANSFORM_PARAM(TYPE, Name, param_name) \ +#define CONVERT_LAYER_TRANSFORM_PARAM(TYPE_NAME, PARAM_NAME) \ do { \ - if (net_param->layers(i).type() == LayerParameter_LayerType_##TYPE) { \ - Name##Parameter* layer_param = \ - net_param->mutable_layers(i)->mutable_##param_name##_param(); \ + if (net_param->layers(i).type() == #TYPE_NAME) { \ + TYPE_NAME##Parameter* layer_param = \ + net_param->mutable_layers(i)->mutable_##PARAM_NAME##_param(); \ TransformationParameter* transform_param = \ net_param->mutable_layers(i)->mutable_transform_param(); \ if (layer_param->has_scale()) { \ @@ -569,9 +569,9 @@ bool NetNeedsDataUpgrade(const NetParameter& net_param) { void UpgradeNetDataTransformation(NetParameter* net_param) { for (int i = 0; i < net_param->layers_size(); ++i) { - CONVERT_LAYER_TRANSFORM_PARAM(DATA, Data, data); - CONVERT_LAYER_TRANSFORM_PARAM(IMAGE_DATA, ImageData, image_data); - CONVERT_LAYER_TRANSFORM_PARAM(WINDOW_DATA, WindowData, window_data); + CONVERT_LAYER_TRANSFORM_PARAM(Data, data); + CONVERT_LAYER_TRANSFORM_PARAM(ImageData, image_data); + CONVERT_LAYER_TRANSFORM_PARAM(WindowData, window_data); } } |