Skip to content

P
Paddle

PaddlePaddle / Paddle
大约 2 年 前同步成功

通知 2325
Star 20933
Fork 5424
P
Paddle
提交 6b61a096 编写于 作者: D dangqingqing
浏览文件
操作

Optional padding mode, namely ceil or floor, ceil by default.

上级 c8817a19
隐藏空白更改
内联 并排
Showing with 1614 addition and 1600 deletion
paddle/function/PadOp.cpp
浏览文件 @ 6b61a096
...@@ -73,10 +73,6 @@ void PadGrad<DEVICE_TYPE_CPU>(real* inGrad, ...@@ -73,10 +73,6 @@ void PadGrad<DEVICE_TYPE_CPU>(real* inGrad,
} }
} }
/**
* \param inputs[0] input value.
* \param outputs[0] output value.
*/
template <DeviceType Device> template <DeviceType Device>
class PadFunc : public FunctionBase { class PadFunc : public FunctionBase {
public: public:
...@@ -89,6 +85,10 @@ public: ...@@ -89,6 +85,10 @@ public:
padw1_ = config.get<int>("padw1"); padw1_ = config.get<int>("padw1");
} }
/**
* \param inputs[0] input value.
* \param outputs[0] output value.
*/
void calc(const Arguments& inputs, void calc(const Arguments& inputs,
const Arguments& outputs, const Arguments& outputs,
const Arguments& inouts) override { const Arguments& inouts) override {
...@@ -124,10 +124,6 @@ private: ...@@ -124,10 +124,6 @@ private:
int padw1_; int padw1_;
}; };
/**
* \param inputs[0] input grad.
* \param outputs[0] output grad.
*/
template <DeviceType Device> template <DeviceType Device>
class PadGradFunc : public FunctionBase { class PadGradFunc : public FunctionBase {
public: public:
...@@ -140,6 +136,10 @@ public: ...@@ -140,6 +136,10 @@ public:
padw1_ = config.get<int>("padw1"); padw1_ = config.get<int>("padw1");
} }
/**
* \param inputs[0] output grad.
* \param inouts[0] input grad.
*/
void calc(const Arguments& inputs, void calc(const Arguments& inputs,
const Arguments& outputs, const Arguments& outputs,
const Arguments& inouts) override { const Arguments& inouts) override {
......
paddle/function/PadOpTest.cpp
浏览文件 @ 6b61a096
...@@ -43,28 +43,30 @@ TEST(Pad, real) { ...@@ -43,28 +43,30 @@ TEST(Pad, real) {
} }
} }
// TEST(PadGrad, real) { TEST(PadGrad, real) {
// for (size_t numSamples : {5, 32}) { for (size_t numSamples : {5, 32}) {
// for (size_t channels : {1, 5, 32}) { for (size_t channels : {1, 5, 32}) {
// for (size_t imgSizeH : {5, 33, 100}) { for (size_t imgSizeH : {5, 33, 100}) {
// for (size_t imgSizeW : {5, 32, 96}) { for (size_t imgSizeW : {5, 32, 96}) {
// VLOG(3) << " numSamples=" << numSamples << " channels=" << channels VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
// << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW; << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW;
//
// FunctionCompare compare("PadGrad", FunctionCompare compare("PadGrad",
// FuncConfig() FuncConfig()
// .set("padc0", 2).set("padc1", 3) .set("padc0", 2)
// .set("padh0", 1).set("padh1", 2) .set("padc1", 3)
// .set("padw0", 3).set("padw1", 2)); .set("padh0", 1)
// Dims inDims{numSamples, channels, imgSizeH, imgSizeW}; .set("padh1", 2)
// Dims outDims{numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5}; .set("padw0", 3)
// compare.cmpWithArg({Tensor(nullptr, inDims)}, .set("padw1", 2));
// {Tensor(nullptr, outDims)}, Dims inDims{numSamples, channels, imgSizeH, imgSizeW};
// {}); Dims outDims{numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5};
// } compare.cmpWithArg(
// } {Tensor(nullptr, inDims)}, {}, {Tensor(nullptr, outDims)});
// } }
// } }
//} }
}
}
} // namespace paddle } // namespace paddle
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @ 6b61a096
...@@ -32,1554 +32,1551 @@ DECLARE_double(checkgrad_eps); ...@@ -32,1554 +32,1551 @@ DECLARE_double(checkgrad_eps);
DECLARE_bool(thread_local_rand_use_global_seed); DECLARE_bool(thread_local_rand_use_global_seed);
DECLARE_bool(prev_batch_state); DECLARE_bool(prev_batch_state);
// TEST(Operator, dot_mul) { TEST(Operator, dot_mul) {
// TestConfig config; TestConfig config;
// config.layerConfig.set_size(10); config.layerConfig.set_size(10);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
//
// OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs(); OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs();
// operatorConf.set_type("dot_mul"); operatorConf.set_type("dot_mul");
// operatorConf.set_dotmul_scale(-1); operatorConf.set_dotmul_scale(-1);
//
// testOperatorGrad(config, operatorConf, 100, false, false); testOperatorGrad(config, operatorConf, 100, false, false);
// } }
//
// TEST(Projection, context) { TEST(Projection, context) {
// for (auto contextStart : {-5, -3, -1, 0, 3}) { for (auto contextStart : {-5, -3, -1, 0, 3}) {
// for (auto contextLength : {1, 2, 5, 7}) { for (auto contextLength : {1, 2, 5, 7}) {
// for (auto batchSize : {1, 2, 5, 20, 50}) { for (auto batchSize : {1, 2, 5, 20, 50}) {
// for (auto trainablePadding : {false, true}) { for (auto trainablePadding : {false, true}) {
// LOG(INFO) << " contextStart=" << contextStart LOG(INFO) << " contextStart=" << contextStart
// << " contextLength=" << contextLength << " contextLength=" << contextLength
// << " batchSize=" << batchSize << " batchSize=" << batchSize
// << " trainablePadding=" << trainablePadding; << " trainablePadding=" << trainablePadding;
// ProjectionConfig conf; ProjectionConfig conf;
// conf.set_type("context"); conf.set_type("context");
// conf.set_input_size(10); conf.set_input_size(10);
// conf.set_context_start(contextStart); conf.set_context_start(contextStart);
// conf.set_context_length(contextLength); conf.set_context_length(contextLength);
// conf.set_trainable_padding(trainablePadding); conf.set_trainable_padding(trainablePadding);
// conf.set_output_size(conf.context_length() * conf.input_size()); conf.set_output_size(conf.context_length() * conf.input_size());
// int pad = int pad =
// std::max(0, -conf.context_start()) + std::max(0, -conf.context_start()) +
// std::max(0, conf.context_start() + conf.context_length() - 1); std::max(0, conf.context_start() + conf.context_length() - 1);
// for (auto useGpu : {false, true}) { for (auto useGpu : {false, true}) {
// testProjectionGrad( testProjectionGrad(
// conf, conf,
// INPUT_SEQUENCE_DATA, INPUT_SEQUENCE_DATA,
// trainablePadding ? conf.input_size() * pad : 0, trainablePadding ? conf.input_size() * pad : 0,
// batchSize, batchSize,
// useGpu, useGpu,
// contextStart + contextLength <= 1); // = testState contextStart + contextLength <= 1); // = testState
// } }
// } }
// } }
// } }
// } }
// } }
//
// TEST(Projection, trans_fc) { TEST(Projection, trans_fc) {
// ProjectionConfig conf; ProjectionConfig conf;
// conf.set_type("trans_fc"); conf.set_type("trans_fc");
// conf.set_input_size(50); conf.set_input_size(50);
// conf.set_output_size(20); conf.set_output_size(20);
// for (auto useGpu : {false, true}) { for (auto useGpu : {false, true}) {
// testProjectionGrad(conf, testProjectionGrad(conf,
// INPUT_DATA, INPUT_DATA,
// /* parameterSize */ 1000, /* parameterSize */ 1000,
// /* batchSize */ 100, /* batchSize */ 100,
// useGpu); useGpu);
// } }
// } }
//
// TEST(Projection, fc) { TEST(Projection, fc) {
// ProjectionConfig conf; ProjectionConfig conf;
// conf.set_type("fc"); conf.set_type("fc");
// conf.set_input_size(10); conf.set_input_size(10);
// conf.set_output_size(20); conf.set_output_size(20);
// for (auto useGpu : {false, true}) { for (auto useGpu : {false, true}) {
// testProjectionGrad(conf, testProjectionGrad(conf,
// INPUT_DATA, INPUT_DATA,
// /* parameterSize */ 200, /* parameterSize */ 200,
// /* batchSize */ 100, /* batchSize */ 100,
// useGpu); useGpu);
// } }
// } }
//
// TEST(Projection, dot_mul) { TEST(Projection, dot_mul) {
// ProjectionConfig conf; ProjectionConfig conf;
// conf.set_type("dot_mul"); conf.set_type("dot_mul");
// conf.set_input_size(20); conf.set_input_size(20);
// conf.set_output_size(20); conf.set_output_size(20);
// for (auto useGpu : {false, true}) { for (auto useGpu : {false, true}) {
// testProjectionGrad(conf, testProjectionGrad(conf,
// INPUT_DATA, INPUT_DATA,
// /* parameterSize */ 20, /* parameterSize */ 20,
// /* batchSize */ 100, /* batchSize */ 100,
// useGpu); useGpu);
// } }
// } }
//
// TEST(Projection, table) { TEST(Projection, table) {
// ProjectionConfig conf; ProjectionConfig conf;
// conf.set_type("table"); conf.set_type("table");
// conf.set_input_size(10); conf.set_input_size(10);
// conf.set_output_size(20); conf.set_output_size(20);
// for (auto useGpu : {false, true}) { for (auto useGpu : {false, true}) {
// testProjectionGrad(conf, testProjectionGrad(conf,
// INPUT_LABEL, INPUT_LABEL,
// /* parameterSize */ 200, /* parameterSize */ 200,
// /* batchSize */ 100, /* batchSize */ 100,
// useGpu); useGpu);
// } }
// } }
//
// TEST(Projection, identity) { TEST(Projection, identity) {
// ProjectionConfig conf; ProjectionConfig conf;
// conf.set_type("identity"); conf.set_type("identity");
// conf.set_input_size(10); conf.set_input_size(10);
// conf.set_output_size(10); conf.set_output_size(10);
// for (auto useGpu : {false, true}) { for (auto useGpu : {false, true}) {
// testProjectionGrad(conf, testProjectionGrad(conf,
// INPUT_DATA, INPUT_DATA,
// /* parameterSize */ 0, /* parameterSize */ 0,
// /* batchSize */ 100, /* batchSize */ 100,
// useGpu); useGpu);
// } }
// } }
//
// TEST(Projection, scaling) { TEST(Projection, scaling) {
// ProjectionConfig conf; ProjectionConfig conf;
// conf.set_type("scaling"); conf.set_type("scaling");
// conf.set_input_size(10); conf.set_input_size(10);
// conf.set_output_size(10); conf.set_output_size(10);
// for (auto useGpu : {false}) { for (auto useGpu : {false}) {
// testProjectionGrad(conf, testProjectionGrad(conf,
// INPUT_DATA, INPUT_DATA,
// /* parameterSize */ 1, /* parameterSize */ 1,
// /* batchSize */ 100, /* batchSize */ 100,
// useGpu); useGpu);
// } }
// } }
//
// void testProjectionConv(size_t groups) { void testProjectionConv(size_t groups) {
// const int NUM_FILTERS = 18; const int NUM_FILTERS = 18;
// const int FILTER_SIZE = 2; const int FILTER_SIZE = 2;
// const int FILTER_SIZE_Y = 3; const int FILTER_SIZE_Y = 3;
// const int CHANNELS = 3; const int CHANNELS = 3;
// const int IMAGE_SIZE = 16; const int IMAGE_SIZE = 16;
//
// ProjectionConfig conf; ProjectionConfig conf;
// conf.set_type("conv"); conf.set_type("conv");
// conf.set_num_filters(NUM_FILTERS); conf.set_num_filters(NUM_FILTERS);
//
// ConvConfig* conv = conf.mutable_conv_conf(); ConvConfig* conv = conf.mutable_conv_conf();
// conv->set_filter_size(FILTER_SIZE); conv->set_filter_size(FILTER_SIZE);
// conv->set_filter_size_y(FILTER_SIZE_Y); conv->set_filter_size_y(FILTER_SIZE_Y);
// conv->set_channels(CHANNELS); conv->set_channels(CHANNELS);
// conv->set_padding(0); conv->set_padding(0);
// conv->set_padding_y(1); conv->set_padding_y(1);
// conv->set_stride(2); conv->set_stride(2);
// conv->set_stride_y(2); conv->set_stride_y(2);
// conv->set_groups(groups); conv->set_groups(groups);
// conv->set_filter_channels(conv->channels() / conv->groups()); conv->set_filter_channels(conv->channels() / conv->groups());
// conv->set_img_size(IMAGE_SIZE); conv->set_img_size(IMAGE_SIZE);
// int output_x = outputSize(conv->img_size(), int output_x = outputSize(conv->img_size(),
// conv->filter_size(), conv->filter_size(),
// conv->padding(), conv->padding(),
// conv->stride(), conv->stride(),
// /* caffeMode */ true); /* caffeMode */ true);
// int output_y = outputSize(conv->img_size(), int output_y = outputSize(conv->img_size(),
// conv->filter_size_y(), conv->filter_size_y(),
// conv->padding_y(), conv->padding_y(),
// conv->stride_y(), conv->stride_y(),
// /* caffeMode */ true); /* caffeMode */ true);
// conv->set_output_x(output_x); conv->set_output_x(output_x);
// conf.set_input_size(IMAGE_SIZE * IMAGE_SIZE * CHANNELS); conf.set_input_size(IMAGE_SIZE * IMAGE_SIZE * CHANNELS);
// conf.set_output_size(output_x * output_y * NUM_FILTERS); conf.set_output_size(output_x * output_y * NUM_FILTERS);
//
// testProjectionGrad(conf, testProjectionGrad(conf,
// INPUT_DATA, INPUT_DATA,
// /* parameterSize */ NUM_FILTERS * CHANNELS * FILTER_SIZE /* parameterSize */ NUM_FILTERS * CHANNELS * FILTER_SIZE *
// * FILTER_SIZE_Y / groups,
// FILTER_SIZE_Y / groups, /* batchSize */ 100,
// /* batchSize */ 100, true,
// true, false,
// false, NUM_FILTERS,
// NUM_FILTERS, true);
// true); }
// }
// #ifndef PADDLE_ONLY_CPU
// #ifndef PADDLE_ONLY_CPU TEST(Projection, conv) {
// TEST(Projection, conv) { testProjectionConv(1);
// testProjectionConv(1); testProjectionConv(3);
// testProjectionConv(3); }
// } #endif
// #endif
// TEST(Layer, BilinearInterpLayer) {
// TEST(Layer, BilinearInterpLayer) { TestConfig config;
// TestConfig config; config.layerConfig.set_type("bilinear_interp");
// config.layerConfig.set_type("bilinear_interp"); config.biasSize = 0;
// config.biasSize = 0; config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// LayerInputConfig* input = config.layerConfig.add_inputs(); BilinearInterpConfig* bilinear = input->mutable_bilinear_interp_conf();
// BilinearInterpConfig* bilinear = input->mutable_bilinear_interp_conf(); ImageConfig* image = bilinear->mutable_image_conf();
// ImageConfig* image = bilinear->mutable_image_conf(); image->set_img_size(32);
// image->set_img_size(32); image->set_img_size_y(32);
// image->set_img_size_y(32); image->set_channels(4);
// image->set_channels(4);
// for (auto useGpu : {false, true}) {
// for (auto useGpu : {false, true}) { for (auto outSize : {32, 64}) {
// for (auto outSize : {32, 64}) { bilinear->set_out_size_x(outSize);
// bilinear->set_out_size_x(outSize); bilinear->set_out_size_y(outSize);
// bilinear->set_out_size_y(outSize); testLayerGrad(config, "bilinear_interp", 10, false, useGpu);
// testLayerGrad(config, "bilinear_interp", 10, false, useGpu); }
// } }
// } }
// }
// TEST(Layer, concat) {
// TEST(Layer, concat) { TestConfig config;
// TestConfig config; config.biasSize = 0;
// config.biasSize = 0; config.layerConfig.set_type("concat");
// config.layerConfig.set_type("concat"); config.layerConfig.set_size(15);
// config.layerConfig.set_size(15); config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_active_type("sigmoid");
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0}); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// for (auto useGpu : {false, true}) {
// for (auto useGpu : {false, true}) { testLayerGrad(config, "concat", 100, false, useGpu);
// testLayerGrad(config, "concat", 100, false, useGpu); }
// } }
// }
// TEST(Layer, AddtoLayer) {
// TEST(Layer, AddtoLayer) { TestConfig config;
// TestConfig config; config.biasSize = 0;
// config.biasSize = 0; config.layerConfig.set_type("addto");
// config.layerConfig.set_type("addto"); config.layerConfig.set_size(10);
// config.layerConfig.set_size(10); config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_active_type("sigmoid");
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// for (auto useGpu : {false, true}) {
// for (auto useGpu : {false, true}) { testLayerGrad(config, "addto", 100, false, useGpu);
// testLayerGrad(config, "addto", 100, false, useGpu); }
// } }
// }
// TEST(Layer, CRFLayer) {
// TEST(Layer, CRFLayer) { TestConfig config;
// TestConfig config; config.layerConfig.set_type("crf");
// config.layerConfig.set_type("crf"); config.layerConfig.set_size(10);
// config.layerConfig.set_size(10); config.biasSize = 0;
// config.biasSize = 0;
// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 120});
// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 120}); config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0});
// config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0}); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// // Not support GPU now
// // Not support GPU now testLayerGrad(config,
// testLayerGrad(config, "crf",
// "crf", 100,
// 100, /* trans */ false,
// /* trans */ false, /* useGpu */ false,
// /* useGpu */ false, false /*useWeight*/,
// false /*useWeight*/, 0.03 /*epsilon*/);
// 0.03 /*epsilon*/); }
// }
// TEST(Layer, CTCLayer) {
// TEST(Layer, CTCLayer) { TestConfig config;
// TestConfig config; config.layerConfig.set_type("ctc");
// config.layerConfig.set_type("ctc"); config.layerConfig.set_norm_by_times(false);
// config.layerConfig.set_norm_by_times(false); config.layerConfig.set_size(10);
// config.layerConfig.set_size(10); config.biasSize = 0;
// config.biasSize = 0;
// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0});
// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0}); config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0});
// config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0}); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// for (auto useGpu : {false, true}) {
// for (auto useGpu : {false, true}) { testLayerGrad(config,
// testLayerGrad(config, "ctc", 100, /* trans */ false, /* useGpu */ "ctc",
// useGpu); 100,
// } /* trans */ false, /* useGpu */
// } useGpu);
// }
// TEST(Layer, cosSimLayer) { }
// TestConfig config;
// config.layerConfig.set_type("cos"); TEST(Layer, cosSimLayer) {
// config.layerConfig.set_size(1); TestConfig config;
// config.biasSize = 0; config.layerConfig.set_type("cos");
// config.layerConfig.set_size(1);
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); config.biasSize = 0;
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 50, 0});
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_DATA, "layer_1", 50, 0});
// config.layerConfig.add_inputs();
// for (auto useGpu : {false, true}) { config.layerConfig.add_inputs();
// testLayerGrad(config, "cos", 100, false, useGpu);
// } for (auto useGpu : {false, true}) {
// } testLayerGrad(config, "cos", 100, false, useGpu);
// }
// TEST(Layer, CosSimVecMatLayer) { }
// TestConfig config;
// config.layerConfig.set_type("cos_vm"); TEST(Layer, CosSimVecMatLayer) {
// config.layerConfig.set_size(5); // output size TestConfig config;
// config.layerConfig.set_cos_scale(2.0); config.layerConfig.set_type("cos_vm");
// config.layerConfig.set_size(5); // output size
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 0}); config.layerConfig.set_cos_scale(2.0);
// config.layerConfig.add_inputs();
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0});
// for (auto useGpu : {false, true}) { config.layerConfig.add_inputs();
// testLayerGrad(config, "cos_vm", 100, false, useGpu);
// } for (auto useGpu : {false, true}) {
// } testLayerGrad(config, "cos_vm", 100, false, useGpu);
// }
// void testConvLayer(const string& type, bool trans, bool useGpu) { }
// TestConfig config;
// config.biasSize = 16; void testConvLayer(const string& type, bool trans, bool useGpu) {
// config.layerConfig.set_type(type); TestConfig config;
// config.layerConfig.set_num_filters(16); config.biasSize = 16;
// config.layerConfig.set_partial_sum(1); config.layerConfig.set_type(type);
// config.layerConfig.set_shared_biases(true); config.layerConfig.set_num_filters(16);
// config.layerConfig.set_partial_sum(1);
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 384, 288}); config.layerConfig.set_shared_biases(true);
// LayerInputConfig* input = config.layerConfig.add_inputs();
// ConvConfig* conv = input->mutable_conv_conf(); config.inputDefs.push_back({INPUT_DATA, "layer_0", 384, 288});
// conv->set_filter_size(2); LayerInputConfig* input = config.layerConfig.add_inputs();
// conv->set_filter_size_y(3); ConvConfig* conv = input->mutable_conv_conf();
// conv->set_channels(3); conv->set_filter_size(2);
// conv->set_padding(0); conv->set_filter_size_y(3);
// conv->set_padding_y(1); conv->set_channels(3);
// conv->set_stride(2); conv->set_padding(0);
// conv->set_stride_y(2); conv->set_padding_y(1);
// conv->set_groups(1); conv->set_stride(2);
// conv->set_filter_channels(conv->channels() / conv->groups()); conv->set_stride_y(2);
// conv->set_img_size(16); conv->set_groups(1);
// conv->set_img_size_y(8); conv->set_filter_channels(conv->channels() / conv->groups());
// conv->set_output_x(outputSize(conv->img_size(), conv->set_img_size(16);
// conv->filter_size(), conv->set_img_size_y(8);
// conv->padding(), conv->set_output_x(outputSize(conv->img_size(),
// conv->stride(), conv->filter_size(),
// /* caffeMode */ true)); conv->padding(),
// conv->set_output_y(outputSize(conv->img_size_y(), conv->stride(),
// conv->filter_size_y(), /* caffeMode */ true));
// conv->padding_y(), conv->set_output_y(outputSize(conv->img_size_y(),
// conv->stride_y(), conv->filter_size_y(),
// /* caffeMode */ true)); conv->padding_y(),
// config.layerConfig.set_size(conv->output_x() * conv->output_y() * conv->stride_y(),
// config.layerConfig.num_filters()); /* caffeMode */ true));
// config.layerConfig.set_size(conv->output_x() * conv->output_y() *
// testLayerGrad(config, "conv", 100, trans, useGpu); config.layerConfig.num_filters());
// // Use small batch_size and useWeight=true to test biasGrad
// testLayerGrad(config, "conv", 2, trans, useGpu, true, 0.02); testLayerGrad(config, "conv", 100, trans, useGpu);
// } // Use small batch_size and useWeight=true to test biasGrad
// testLayerGrad(config, "conv", 2, trans, useGpu, true, 0.02);
// TEST(Layer, convLayer) { }
// testConvLayer("exconv", /* trans= */ false, /* useGpu= */ false);
// #ifndef PADDLE_ONLY_CPU TEST(Layer, convLayer) {
// testConvLayer("exconv", /* trans= */ false, /* useGpu= */ true); testConvLayer("exconv", /* trans= */ false, /* useGpu= */ false);
// testConvLayer("cudnn_conv", /* trans= */ false, /* useGpu= */ true); #ifndef PADDLE_ONLY_CPU
// #endif testConvLayer("exconv", /* trans= */ false, /* useGpu= */ true);
// } testConvLayer("cudnn_conv", /* trans= */ false, /* useGpu= */ true);
// #endif
// void testConvTransLayer(const string& type, bool trans, bool useGpu) { }
// TestConfig config;
// config.biasSize = 3; void testConvTransLayer(const string& type, bool trans, bool useGpu) {
// config.layerConfig.set_type(type); TestConfig config;
// config.layerConfig.set_num_filters(3); config.biasSize = 3;
// config.layerConfig.set_partial_sum(1); config.layerConfig.set_type(type);
// config.layerConfig.set_shared_biases(true); config.layerConfig.set_num_filters(3);
// config.layerConfig.set_partial_sum(1);
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 288}); config.layerConfig.set_shared_biases(true);
// LayerInputConfig* input = config.layerConfig.add_inputs();
// ConvConfig* conv = input->mutable_conv_conf(); config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 288});
// conv->set_filter_size(2); LayerInputConfig* input = config.layerConfig.add_inputs();
// conv->set_filter_size_y(3); ConvConfig* conv = input->mutable_conv_conf();
// conv->set_channels(16); conv->set_filter_size(2);
// conv->set_padding(0); conv->set_filter_size_y(3);
// conv->set_padding_y(1); conv->set_channels(16);
// conv->set_stride(2); conv->set_padding(0);
// conv->set_stride_y(2); conv->set_padding_y(1);
// conv->set_groups(1); conv->set_stride(2);
// conv->set_filter_channels(3 / conv->groups()); conv->set_stride_y(2);
// conv->set_img_size(16); conv->set_groups(1);
// conv->set_output_x(outputSize(conv->img_size(), conv->set_filter_channels(3 / conv->groups());
// conv->filter_size(), conv->set_img_size(16);
// conv->padding(), conv->set_output_x(outputSize(conv->img_size(),
// conv->stride(), conv->filter_size(),
// /* caffeMode */ true)); conv->padding(),
// conv->stride(),
// config.layerConfig.set_size(conv->img_size() * conv->img_size() * /* caffeMode */ true));
// config.layerConfig.num_filters());
// config.layerConfig.set_size(conv->img_size() * conv->img_size() *
// testLayerGrad(config, "convTrans", 100, trans, useGpu); config.layerConfig.num_filters());
// // Use small batch_size and useWeight=true to test biasGrad
// testLayerGrad(config, "convTrans", 2, trans, useGpu, true, 0.02); testLayerGrad(config, "convTrans", 100, trans, useGpu);
// } // Use small batch_size and useWeight=true to test biasGrad
// testLayerGrad(config, "convTrans", 2, trans, useGpu, true, 0.02);
// TEST(Layer, convTransLayer) { }
// for (auto useGpu : {false, true}) {
// testConvTransLayer("exconvt", /* trans= */ false, /* useGpu= */ useGpu); TEST(Layer, convTransLayer) {
// } for (auto useGpu : {false, true}) {
// } testConvTransLayer("exconvt", /* trans= */ false, /* useGpu= */ useGpu);
// }
// TEST(Layer, blockExpandLayer) { }
// TestConfig config;
// config.biasSize = 0; TEST(Layer, blockExpandLayer) {
// config.layerConfig.set_type("blockexpand"); TestConfig config;
// config.biasSize = 0;
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 6144, 0}); config.layerConfig.set_type("blockexpand");
// LayerInputConfig* input = config.layerConfig.add_inputs();
// BlockExpandConfig* blockExpand = input->mutable_block_expand_conf(); config.inputDefs.push_back({INPUT_DATA, "layer_0", 6144, 0});
// blockExpand->set_img_size_x(64); LayerInputConfig* input = config.layerConfig.add_inputs();
// blockExpand->set_img_size_y(32); BlockExpandConfig* blockExpand = input->mutable_block_expand_conf();
// blockExpand->set_channels(3); blockExpand->set_img_size_x(64);
// blockExpand->set_padding_x(0); blockExpand->set_img_size_y(32);
// blockExpand->set_padding_y(0); blockExpand->set_channels(3);
// blockExpand->set_block_x(4); blockExpand->set_padding_x(0);
// blockExpand->set_block_y(32); blockExpand->set_padding_y(0);
// blockExpand->set_stride_x(2); blockExpand->set_block_x(4);
// blockExpand->set_stride_y(2); blockExpand->set_block_y(32);
// blockExpand->set_output_x(outputSize(blockExpand->img_size_x(), blockExpand->set_stride_x(2);
// blockExpand->block_x(), blockExpand->set_stride_y(2);
// blockExpand->padding_x(), blockExpand->set_output_x(outputSize(blockExpand->img_size_x(),
// blockExpand->stride_x(), blockExpand->block_x(),
// /* caffeMode */ false)); blockExpand->padding_x(),
// blockExpand->set_output_y(outputSize(blockExpand->img_size_y(), blockExpand->stride_x(),
// blockExpand->block_y(), /* caffeMode */ false));
// blockExpand->padding_y(), blockExpand->set_output_y(outputSize(blockExpand->img_size_y(),
// blockExpand->stride_y(), blockExpand->block_y(),
// /* caffeMode */ false)); blockExpand->padding_y(),
// config.layerConfig.set_size(blockExpand->block_x() * blockExpand->block_y() blockExpand->stride_y(),
// * /* caffeMode */ false));
// blockExpand->channels()); config.layerConfig.set_size(blockExpand->block_x() * blockExpand->block_y() *
// blockExpand->channels());
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "blockexpand", 100, false, useGpu); for (auto useGpu : {false, true}) {
// } testLayerGrad(config, "blockexpand", 100, false, useGpu);
// } }
// }
// TEST(Layer, maxoutLayer) {
// TestConfig config; TEST(Layer, maxoutLayer) {
// config.biasSize = 0; TestConfig config;
// config.layerConfig.set_type("maxout"); config.biasSize = 0;
// config.layerConfig.set_type("maxout");
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0});
// LayerInputConfig* input = config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0});
// MaxOutConfig* maxout = input->mutable_maxout_conf(); LayerInputConfig* input = config.layerConfig.add_inputs();
// ImageConfig* image = maxout->mutable_image_conf(); MaxOutConfig* maxout = input->mutable_maxout_conf();
// ImageConfig* image = maxout->mutable_image_conf();
// image->set_img_size(32);
// image->set_img_size_y(32); image->set_img_size(32);
// image->set_channels(4); image->set_img_size_y(32);
// maxout->set_groups(2); image->set_channels(4);
// maxout->set_groups(2);
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "maxout", 10, false, useGpu); for (auto useGpu : {false, true}) {
// } testLayerGrad(config, "maxout", 10, false, useGpu);
// } }
// void testFcLayer(string format, size_t nnz) { }
// TestConfig config; void testFcLayer(string format, size_t nnz) {
// config.biasSize = 4096; TestConfig config;
// config.layerConfig.set_type("fc"); config.biasSize = 4096;
// config.layerConfig.set_size(4096); config.layerConfig.set_type("fc");
// config.layerConfig.set_active_type("sigmoid"); config.layerConfig.set_size(4096);
// config.layerConfig.set_drop_rate(0.1); config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_drop_rate(0.1);
// config.inputDefs.push_back(
// {INPUT_DATA, "layer_0", 8192, nnz, ParaSparse(format)}); config.inputDefs.push_back(
// config.layerConfig.add_inputs(); {INPUT_DATA, "layer_0", 8192, nnz, ParaSparse(format)});
// config.layerConfig.add_inputs();
// LOG(INFO) << config.inputDefs[0].sparse.sparse << " "
// << config.inputDefs[0].sparse.format; LOG(INFO) << config.inputDefs[0].sparse.sparse << " "
// << config.inputDefs[0].sparse.format;
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, for (auto useGpu : {false, true}) {
// "fc", testLayerGrad(config,
// 100, "fc",
// /* trans */ false, 100,
// useGpu, /* trans */ false,
// /* weight */ true); useGpu,
// } /* weight */ true);
// } }
// }
// TEST(Layer, fcLayer) {
// testFcLayer("", 4096 * 4096 * 2); TEST(Layer, fcLayer) {
// testFcLayer("csc", 4096 * 40); testFcLayer("", 4096 * 4096 * 2);
// testFcLayer("csr", 4096 * 40); testFcLayer("csc", 4096 * 40);
// } testFcLayer("csr", 4096 * 40);
// }
// TEST(Layer, SelectiveFullyConnectedLayer) {
// TestConfig config; TEST(Layer, SelectiveFullyConnectedLayer) {
// size_t nin = 16; TestConfig config;
// size_t nout = 256; size_t nin = 16;
// config.layerConfig.set_type("selective_fc"); size_t nout = 256;
// config.layerConfig.set_size(nout); config.layerConfig.set_type("selective_fc");
// config.layerConfig.set_active_type("sigmoid"); config.layerConfig.set_size(nout);
// config.layerConfig.set_has_selected_colums(true); config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_selective_fc_pass_generation(false); config.layerConfig.set_has_selected_colums(true);
// config.biasSize = nout; config.layerConfig.set_selective_fc_pass_generation(false);
// config.biasSize = nout;
// config.inputDefs.push_back({INPUT_DATA, "input0", nin, nin * nout});
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_DATA, "input0", nin, nin * nout});
// config.inputDefs.push_back( config.layerConfig.add_inputs();
// {INPUT_SPARSE_NON_VALUE_DATA, "index", nout, 0, ParaSparse("csr", config.inputDefs.push_back(
// true)}); {INPUT_SPARSE_NON_VALUE_DATA, "index", nout, 0, ParaSparse("csr", true)});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
//
// testLayerGrad(config, testLayerGrad(config,
// "selective_fc", "selective_fc",
// 100, 100,
// /* trans= */ false, /* trans= */ false,
// /* useGup= */ false, /* useGup= */ false,
// false); false);
// #ifndef PADDLE_ONLY_CPU #ifndef PADDLE_ONLY_CPU
// testLayerGrad(config, testLayerGrad(config,
// "selective_fc", "selective_fc",
// 100, 100,
// /* trans= */ false, /* trans= */ false,
// /* useGup= */ true, /* useGup= */ true,
// false); false);
// #endif #endif
// } }
//
// TEST(Layer, DataNormLayer) { TEST(Layer, DataNormLayer) {
// TestConfig config; TestConfig config;
// config.layerConfig.set_type("data_norm"); config.layerConfig.set_type("data_norm");
// config.layerConfig.set_size(20); config.layerConfig.set_size(20);
// config.biasSize = 0; config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 100}); config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 100});
// config.inputDefs.back().isStatic = true; config.inputDefs.back().isStatic = true;
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
//
// for (auto strategy : {"z-score", "min-max", "decimal-scaling"}) { for (auto strategy : {"z-score", "min-max", "decimal-scaling"}) {
// config.layerConfig.set_data_norm_strategy(strategy); config.layerConfig.set_data_norm_strategy(strategy);
// // The parameters are static, so not support GPU now // The parameters are static, so not support GPU now
// testLayerGrad(config, testLayerGrad(config,
// "data_norm", "data_norm",
// 200, 200,
// /* trans */ false, /* trans */ false,
// /* useGpu */ false); /* useGpu */ false);
// } }
// } }
//
// TEST(Layer, hsigmoidLayer) { TEST(Layer, hsigmoidLayer) {
// TestConfig config; TestConfig config;
// config.layerConfig.set_type("hsigmoid"); config.layerConfig.set_type("hsigmoid");
// config.layerConfig.set_num_classes(5); config.layerConfig.set_num_classes(5);
// config.layerConfig.set_size(1); config.layerConfig.set_size(1);
// config.biasSize = config.layerConfig.num_classes() - 1; config.biasSize = config.layerConfig.num_classes() - 1;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 200}); config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 200});
// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 5, 0}); config.inputDefs.push_back({INPUT_LABEL, "layer_1", 5, 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
//
// // Not support GPU now // Not support GPU now
// testLayerGrad(config, "hsigmoid", 100, /* trans */ false, /* useGpu */ testLayerGrad(config,
// false); "hsigmoid",
// } 100,
// /* trans */ false, /* useGpu */
// TEST(Layer, multi_cross) { false);
// TestConfig config; }
// config.layerConfig.set_type("multi-class-cross-entropy");
// config.biasSize = 0; TEST(Layer, multi_cross) {
// TestConfig config;
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); config.layerConfig.set_type("multi-class-cross-entropy");
// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0}); config.biasSize = 0;
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
// for (auto useGpu : {false, true}) { config.layerConfig.add_inputs();
// testLayerGrad( config.layerConfig.add_inputs();
// config, "multi-class-cross-entropy", 100, /* trans */ false, useGpu);
// } for (auto useGpu : {false, true}) {
// } testLayerGrad(
// config, "multi-class-cross-entropy", 100, /* trans */ false, useGpu);
// TEST(Layer, multi_binary_label_sparse_mat) { }
// TestConfig config; }
// config.layerConfig.set_type("multi_binary_label_cross_entropy");
// config.biasSize = 0; TEST(Layer, multi_binary_label_sparse_mat) {
// TestConfig config;
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); config.layerConfig.set_type("multi_binary_label_cross_entropy");
// config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50, config.biasSize = 0;
// 0});
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50, 0});
// config.layerConfig.add_inputs();
// for (auto useGpu : {false, true}) { config.layerConfig.add_inputs();
// testLayerGrad(config,
// "multi_binary_label_cross_entropy", for (auto useGpu : {false, true}) {
// 100, testLayerGrad(config,
// /* trans */ false, "multi_binary_label_cross_entropy",
// useGpu); 100,
// } /* trans */ false,
// } useGpu);
// }
// TEST(layer, multi_binary_label_id) { }
// TestConfig config;
// config.layerConfig.set_type("multi_binary_label_cross_entropy"); TEST(layer, multi_binary_label_id) {
// config.biasSize = 0; TestConfig config;
// config.layerConfig.set_type("multi_binary_label_cross_entropy");
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); config.biasSize = 0;
// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// for (auto useGpu : {false, true}) { config.layerConfig.add_inputs();
// testLayerGrad(config,
// "multi_binary_label_cross_entropy", for (auto useGpu : {false, true}) {
// 100, testLayerGrad(config,
// /* trans */ false, "multi_binary_label_cross_entropy",
// useGpu); 100,
// } /* trans */ false,
// } useGpu);
// }
// TEST(Layer, multi_cross_with_selfnorm) { }
// TestConfig config;
// config.layerConfig.set_type("multi_class_cross_entropy_with_selfnorm"); TEST(Layer, multi_cross_with_selfnorm) {
// config.layerConfig.set_softmax_selfnorm_alpha(0.1); TestConfig config;
// config.biasSize = 0; config.layerConfig.set_type("multi_class_cross_entropy_with_selfnorm");
// config.layerConfig.set_softmax_selfnorm_alpha(0.1);
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); config.biasSize = 0;
// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// // Not support GPU now config.layerConfig.add_inputs();
// testLayerGrad(config,
// "multi_class_cross_entropy_with_selfnorm", // Not support GPU now
// 100, testLayerGrad(config,
// /* trans */ false, "multi_class_cross_entropy_with_selfnorm",
// /* useGpu */ false); 100,
// } /* trans */ false,
// /* useGpu */ false);
// TEST(Layer, multi_cross_soft) { }
// TestConfig config;
// config.layerConfig.set_type("soft_binary_class_cross_entropy"); TEST(Layer, multi_cross_soft) {
// config.biasSize = 0; TestConfig config;
// config.layerConfig.set_type("soft_binary_class_cross_entropy");
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); config.biasSize = 0;
// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// for (auto useGpu : {false, true}) { config.layerConfig.add_inputs();
// testLayerGrad(config,
// "soft_binary_class_cross_entropy", for (auto useGpu : {false, true}) {
// 100, testLayerGrad(config,
// /* trans */ false, "soft_binary_class_cross_entropy",
// useGpu); 100,
// } /* trans */ false,
// } useGpu);
// }
// TEST(Layer, square_error) { }
// TestConfig config;
// config.layerConfig.set_type("square_error"); TEST(Layer, square_error) {
// config.biasSize = 0; TestConfig config;
// config.layerConfig.set_type("square_error");
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); config.biasSize = 0;
// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// for (auto useGpu : {false, true}) { config.layerConfig.add_inputs();
// testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu);
// } for (auto useGpu : {false, true}) {
// } testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu);
// }
// TEST(Layer, sparse_square_error) { }
// TestConfig config;
// config.layerConfig.set_type("square_error"); TEST(Layer, sparse_square_error) {
// config.biasSize = 0; TestConfig config;
// config.layerConfig.set_type("square_error");
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); config.biasSize = 0;
// config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50,
// 0}); config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50, 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// // "GpuSparseMatrix" as label is not supported
// testLayerGrad(config, // "GpuSparseMatrix" as label is not supported
// "square_error", testLayerGrad(config,
// 100, "square_error",
// /* trans */ false, 100,
// /* useGpu */ false); /* trans */ false,
// } /* useGpu */ false);
// }
// TEST(Layer, sparse_float_square_error) {
// TestConfig config; TEST(Layer, sparse_float_square_error) {
// config.layerConfig.set_type("square_error"); TestConfig config;
// config.biasSize = 0; config.layerConfig.set_type("square_error");
// config.biasSize = 0;
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
// config.inputDefs.push_back({INPUT_SPARSE_FLOAT_VALUE_DATA, "layer_1", 50, config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
// 0}); config.inputDefs.push_back({INPUT_SPARSE_FLOAT_VALUE_DATA, "layer_1", 50, 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
//
// // "GpuSparseMatrix" as label is not supported // "GpuSparseMatrix" as label is not supported
// testLayerGrad(config, testLayerGrad(config,
// "square_error", "square_error",
// 100, 100,
// /* trans */ false, /* trans */ false,
// /* useGpu */ false); /* useGpu */ false);
// } }
//
// TEST(Layer, square_error_weighted) { TEST(Layer, square_error_weighted) {
// TestConfig config; TestConfig config;
// config.layerConfig.set_type("square_error"); config.layerConfig.set_type("square_error");
// config.biasSize = 0; config.biasSize = 0;
// config.testAccumulate = false; config.testAccumulate = false;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0}); config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0}); config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) { for (auto useGpu : {false, true}) {
// testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu); testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu);
// } }
// } }
//
// TEST(Layer, huber_two_class) { TEST(Layer, huber_two_class) {
// TestConfig config; TestConfig config;
// config.layerConfig.set_type("huber"); config.layerConfig.set_type("huber");
// config.biasSize = 0; config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 2, 0}); config.inputDefs.push_back({INPUT_LABEL, "layer_1", 2, 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) { for (auto useGpu : {false, true}) {
// testLayerGrad(config, "huber", 100, /* trans */ false, useGpu); testLayerGrad(config, "huber", 100, /* trans */ false, useGpu);
// } }
// } }
//
// void testExpandLayer(string trans_type, bool hasSubseq) { void testExpandLayer(string trans_type, bool hasSubseq) {
// TestConfig config; TestConfig config;
// config.layerConfig.set_type("expand"); config.layerConfig.set_type("expand");
//
// config.inputDefs.push_back( config.inputDefs.push_back(
// {trans_type == "non-seq" ? INPUT_DENSE_DIM_DATA : INPUT_SEQUENCE_DATA, {trans_type == "non-seq" ? INPUT_DENSE_DIM_DATA : INPUT_SEQUENCE_DATA,
// "layer_0", "layer_0",
// 10, 10,
// 0}); 0});
// config.inputDefs.push_back( config.inputDefs.push_back(
// {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA, {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA,
// "layer_1", "layer_1",
// 10, 10,
// 0}); 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.set_trans_type(trans_type); config.layerConfig.set_trans_type(trans_type);
// LOG(INFO) << " trans_type=" << trans_type << " hasSubseq=" << hasSubseq; LOG(INFO) << " trans_type=" << trans_type << " hasSubseq=" << hasSubseq;
//
// for (auto useGpu : {false, true}) { for (auto useGpu : {false, true}) {
// testLayerGrad(config, "expand", 30, false, useGpu); testLayerGrad(config, "expand", 30, false, useGpu);
// } }
// } }
//
// TEST(Layer, ExpandLayer) { TEST(Layer, ExpandLayer) {
// testExpandLayer("non-seq", false); // non-seq expand to seq testExpandLayer("non-seq", false); // non-seq expand to seq
// testExpandLayer("non-seq", true); // non-seq expand to hasSubseq testExpandLayer("non-seq", true); // non-seq expand to hasSubseq
// testExpandLayer("seq", true); // seq expand to hasSubseq testExpandLayer("seq", true); // seq expand to hasSubseq
// } }
//
// void testDegradeLayer(bool hasSubseq, string layer_type, string trans_type) { void testDegradeLayer(bool hasSubseq, string layer_type, string trans_type) {
// TestConfig config; TestConfig config;
// config.layerConfig.set_type(layer_type); config.layerConfig.set_type(layer_type);
// config.layerConfig.set_size(10); config.layerConfig.set_size(10);
// config.biasSize = 0; config.biasSize = 0;
//
// config.inputDefs.push_back( config.inputDefs.push_back(
// {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA, {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA,
// "layer_0", "layer_0",
// 10, 10,
// 0}); 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.set_trans_type(trans_type); config.layerConfig.set_trans_type(trans_type);
//
// auto testDegradeLayerGrad = [](TestConfig& config, string layer_type) { auto testDegradeLayerGrad = [](TestConfig& config, string layer_type) {
// for (auto useGpu : {false, true}) { for (auto useGpu : {false, true}) {
// testLayerGrad(config, layer_type, 100, false, useGpu); testLayerGrad(config, layer_type, 100, false, useGpu);
// } }
// }; };
//
// if (layer_type == "average") { if (layer_type == "average") {
// for (auto strategy : {"average", "sum", "squarerootn"}) { for (auto strategy : {"average", "sum", "squarerootn"}) {
// LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type
// << " average_strategy=" << strategy; << " average_strategy=" << strategy;
// config.layerConfig.set_average_strategy(strategy); config.layerConfig.set_average_strategy(strategy);
// testDegradeLayerGrad(config, layer_type); testDegradeLayerGrad(config, layer_type);
// } }
// } else { } else {
// LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type; LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type;
// testDegradeLayerGrad(config, layer_type); testDegradeLayerGrad(config, layer_type);
// } }
// } }
//
// TEST(Layer, MaxLayer) { TEST(Layer, MaxLayer) {
// testDegradeLayer(false, "max", "non-seq"); // seq max to non-seq testDegradeLayer(false, "max", "non-seq"); // seq max to non-seq
// testDegradeLayer(true, "max", "non-seq"); // hasSubseq max to non-seq testDegradeLayer(true, "max", "non-seq"); // hasSubseq max to non-seq
// testDegradeLayer(true, "max", "seq"); // hasSubseq max to seq testDegradeLayer(true, "max", "seq"); // hasSubseq max to seq
// } }
//
// TEST(Layer, SequenceLastInstanceLayer) { TEST(Layer, SequenceLastInstanceLayer) {
// testDegradeLayer(false, testDegradeLayer(false,
// "seqlastins", "seqlastins",
// "non-seq"); // seq seqlastins to non-seq "non-seq"); // seq seqlastins to non-seq
// testDegradeLayer(true, testDegradeLayer(true,
// "seqlastins", "seqlastins",
// "non-seq"); // hasSubseq seqlastins to non-seq "non-seq"); // hasSubseq seqlastins to non-seq
// testDegradeLayer(true, "seqlastins", "seq"); // hasSubseq seqlastins to testDegradeLayer(true, "seqlastins", "seq"); // hasSubseq seqlastins to
// seq seq
// } }
//
// TEST(Layer, AverageLayer) { TEST(Layer, AverageLayer) {
// testDegradeLayer(false, "average", "non-seq"); // seq average to non-seq testDegradeLayer(false, "average", "non-seq"); // seq average to non-seq
// testDegradeLayer(true, "average", "non-seq"); // hasSubseq average to testDegradeLayer(true, "average", "non-seq"); // hasSubseq average to
// non-seq non -
// testDegradeLayer(true, "average", "seq"); // hasSubseq average to seq seq testDegradeLayer(true, "average", "seq"); // hasSubseq average to seq
// } }
//
// TEST(Layer, SequenceConcatLayer) { TEST(Layer, SequenceConcatLayer) {
// TestConfig config; TestConfig config;
// config.layerConfig.set_type("seqconcat"); config.layerConfig.set_type("seqconcat");
// config.layerConfig.set_size(10); config.layerConfig.set_size(10);
// config.biasSize = 0; config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0}); config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_1", 10, 0}); config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_1", 10, 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) { for (auto useGpu : {false, true}) {
// testLayerGrad(config, "seqconcat", 100, false, useGpu); testLayerGrad(config, "seqconcat", 100, false, useGpu);
// } }
// } }
//
// TEST(Layer, SequenceReshapeLayer) { TEST(Layer, SequenceReshapeLayer) {
// TestConfig config; TestConfig config;
// config.layerConfig.set_type("seqreshape"); config.layerConfig.set_type("seqreshape");
// config.layerConfig.set_size(10); config.layerConfig.set_size(10);
//
// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 100, 0}); config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 100, 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) { for (auto useGpu : {false, true}) {
// testLayerGrad(config, "seqreshape", 100, false, useGpu); testLayerGrad(config, "seqreshape", 100, false, useGpu);
// } }
// } }
//
// TEST(Layer, ConvShiftLayer) { TEST(Layer, ConvShiftLayer) {
// TestConfig config; TestConfig config;
// config.layerConfig.set_type("conv_shift"); config.layerConfig.set_type("conv_shift");
// config.layerConfig.set_size(10); config.layerConfig.set_size(10);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 3, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_1", 3, 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
//
// // Not support GPU now // Not support GPU now
// testLayerGrad(config, "conv_shift", 100, false, false); testLayerGrad(config, "conv_shift", 100, false, false);
// } }
//
// TEST(Layer, PowerLayer) { TEST(Layer, PowerLayer) {
// TestConfig config; TestConfig config;
// config.layerConfig.set_type("power"); config.layerConfig.set_type("power");
// config.layerConfig.set_size(10); config.layerConfig.set_size(10);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) { for (auto useGpu : {false, true}) {
// testLayerGrad(config, "power", 100, false, useGpu); testLayerGrad(config, "power", 100, false, useGpu);
// } }
// } }
//
// TEST(Layer, ConvexCombinationLayer) { TEST(Layer, ConvexCombinationLayer) {
// TestConfig config; TestConfig config;
// config.layerConfig.set_type("convex_comb"); config.layerConfig.set_type("convex_comb");
// config.layerConfig.set_size(20); config.layerConfig.set_size(20);
// config.biasSize = 0; config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) { for (auto useGpu : {false, true}) {
// testLayerGrad(config, "convex_comb", 100, false, useGpu); testLayerGrad(config, "convex_comb", 100, false, useGpu);
// } }
// } }
//
// TEST(Layer, InterpolationLayer) { TEST(Layer, InterpolationLayer) {
// TestConfig config; TestConfig config;
// config.layerConfig.set_type("interpolation"); config.layerConfig.set_type("interpolation");
// config.layerConfig.set_size(10); config.layerConfig.set_size(10);
// config.biasSize = 0; config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_2", 10, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_2", 10, 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) { for (auto useGpu : {false, true}) {
// testLayerGrad(config, "interpolation", 100, false, useGpu); testLayerGrad(config, "interpolation", 100, false, useGpu);
// } }
// } }
//
// TEST(Layer, OuterProdLayer) { TEST(Layer, OuterProdLayer) {
// TestConfig config; TestConfig config;
// config.layerConfig.set_type("out_prod"); config.layerConfig.set_type("out_prod");
// config.layerConfig.set_size(100); config.layerConfig.set_size(100);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) { for (auto useGpu : {false, true}) {
// testLayerGrad(config, "out_prod", 100, false, useGpu); testLayerGrad(config, "out_prod", 100, false, useGpu);
// } }
// } }
//
// TEST(Layer, SlopeInterceptLayer) { TEST(Layer, SlopeInterceptLayer) {
// TestConfig config; TestConfig config;
// config.layerConfig.set_type("slope_intercept"); config.layerConfig.set_type("slope_intercept");
// config.layerConfig.set_size(10); config.layerConfig.set_size(10);
// config.layerConfig.set_slope(1.0); config.layerConfig.set_slope(1.0);
// config.layerConfig.set_intercept(0.1); config.layerConfig.set_intercept(0.1);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) { for (auto useGpu : {false, true}) {
// testLayerGrad(config, "slope_intercept", 100, false, useGpu); testLayerGrad(config, "slope_intercept", 100, false, useGpu);
// } }
// } }
//
// TEST(Layer, ScalingLayer) { TEST(Layer, ScalingLayer) {
// TestConfig config; TestConfig config;
// config.layerConfig.set_type("scaling"); config.layerConfig.set_type("scaling");
// config.layerConfig.set_size(10); config.layerConfig.set_size(10);
// config.biasSize = 0; config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) { for (auto useGpu : {false, true}) {
// testLayerGrad(config, "scaling", 100, false, useGpu); testLayerGrad(config, "scaling", 100, false, useGpu);
// } }
// } }
//
// void testNormLayer(const string& normType, bool trans, bool useGpu) { void testNormLayer(const string& normType, bool trans, bool useGpu) {
// TestConfig config; TestConfig config;
// config.layerConfig.set_type("norm"); config.layerConfig.set_type("norm");
// config.layerConfig.set_active_type("relu"); config.layerConfig.set_active_type("relu");
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1568, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_0", 1568, 0});
// LayerInputConfig* input = config.layerConfig.add_inputs(); LayerInputConfig* input = config.layerConfig.add_inputs();
// NormConfig* norm = input->mutable_norm_conf(); NormConfig* norm = input->mutable_norm_conf();
// norm->set_norm_type(normType); norm->set_norm_type(normType);
// norm->set_channels(16); norm->set_channels(16);
// norm->set_size(5); norm->set_size(5);
// norm->set_scale(0.001); norm->set_scale(0.001);
// norm->set_pow(0.75); norm->set_pow(0.75);
// norm->set_blocked(0); norm->set_blocked(0);
// norm->set_img_size(14); norm->set_img_size(14);
// norm->set_img_size_y(7); norm->set_img_size_y(7);
// norm->set_output_x(norm->img_size()); norm->set_output_x(norm->img_size());
// norm->set_output_y(norm->img_size_y()); norm->set_output_y(norm->img_size_y());
// if (norm->norm_type() == "cmrnorm" || if (norm->norm_type() == "cmrnorm" ||
// norm->norm_type() == "cmrnorm-projection") { norm->norm_type() == "cmrnorm-projection") {
// norm->set_scale(norm->scale() / norm->size()); norm->set_scale(norm->scale() / norm->size());
// } else { } else {
// norm->set_scale(norm->scale() / (norm->size() * norm->size())); norm->set_scale(norm->scale() / (norm->size() * norm->size()));
// } }
//
// config.layerConfig.set_size(norm->output_x() * norm->output_y() * config.layerConfig.set_size(norm->output_x() * norm->output_y() *
// norm->channels()); norm->channels());
// config.biasSize = 0; config.biasSize = 0;
//
// testLayerGrad(config, "norm", 100, trans, useGpu); testLayerGrad(config, "norm", 100, trans, useGpu);
// } }
//
// TEST(Layer, NormLayer) { TEST(Layer, NormLayer) {
// testNormLayer("cmrnorm-projection", /* trans= */ false, /* useGpu= */ testNormLayer("cmrnorm-projection",
// true); /* trans= */ false, /* useGpu= */
// testNormLayer("cmrnorm-projection", /* trans= */ false, /* useGpu= */ true);
// false); testNormLayer("cmrnorm-projection",
// } /* trans= */ false, /* useGpu= */
// false);
// void setPoolConfig(TestConfig* config, }
// PoolConfig* pool,
// const string& poolType) { void setPoolConfig(TestConfig* config,
// (*config).biasSize = 0; PoolConfig* pool,
// (*config).layerConfig.set_type("pool"); const string& poolType) {
// (*config).layerConfig.set_num_filters(16); (*config).biasSize = 0;
// (*config).layerConfig.set_type("pool");
// int kw = 3, kh = 3; (*config).layerConfig.set_num_filters(16);
// int pw = 0, ph = 0;
// int sw = 2, sh = 2; int kw = 3, kh = 3;
// pool->set_pool_type(poolType); int pw = 0, ph = 0;
// pool->set_channels(16); int sw = 2, sh = 2;
// pool->set_size_x(kw); pool->set_pool_type(poolType);
// pool->set_size_y(kh); pool->set_channels(16);
// pool->set_start(0); pool->set_size_x(kw);
// pool->set_padding(pw); pool->set_size_y(kh);
// pool->set_padding_y(ph); pool->set_start(0);
// pool->set_stride(sw); pool->set_padding(pw);
// pool->set_stride_y(sh); pool->set_padding_y(ph);
// pool->set_stride(sw);
// int ow = outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false); pool->set_stride_y(sh);
// int oh = outputSize(pool->img_size_y(), kh, ph, sh, /* caffeMode */ false);
// pool->set_output_x(ow); int ow = outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false);
// pool->set_output_y(oh); int oh = outputSize(pool->img_size_y(), kh, ph, sh, /* caffeMode */ false);
// } pool->set_output_x(ow);
// pool->set_output_y(oh);
// void testPoolLayer(const string& poolType, bool trans, bool useGpu) { }
// TestConfig config;
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 3136, 0}); void testPoolLayer(const string& poolType, bool trans, bool useGpu) {
// LayerInputConfig* input = config.layerConfig.add_inputs(); TestConfig config;
// PoolConfig* pool = input->mutable_pool_conf(); config.inputDefs.push_back({INPUT_DATA, "layer_0", 3136, 0});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// pool->set_img_size(14); PoolConfig* pool = input->mutable_pool_conf();
// pool->set_img_size_y(14);
// setPoolConfig(&config, pool, poolType); pool->set_img_size(14);
// config.layerConfig.set_size(pool->output_x() * pool->output_y() * pool->set_img_size_y(14);
// pool->channels()); setPoolConfig(&config, pool, poolType);
// config.layerConfig.set_size(pool->output_x() * pool->output_y() *
// testLayerGrad(config, "pool", 100, trans, useGpu); pool->channels());
// }
// testLayerGrad(config, "pool", 100, trans, useGpu);
// #ifndef PADDLE_ONLY_CPU }
// void testPoolLayer2(const string& poolType, bool trans, bool useGpu) {
// TestConfig config; #ifndef PADDLE_ONLY_CPU
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0}); void testPoolLayer2(const string& poolType, bool trans, bool useGpu) {
// LayerInputConfig* input = config.layerConfig.add_inputs(); TestConfig config;
// PoolConfig* pool = input->mutable_pool_conf(); config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// pool->set_size_y(4); PoolConfig* pool = input->mutable_pool_conf();
// pool->set_stride_y(3);
// pool->set_img_size(10); pool->set_size_y(4);
// pool->set_img_size_y(20); pool->set_stride_y(3);
// setPoolConfig(&config, pool, poolType); pool->set_img_size(10);
// pool->set_output_y((pool->img_size_y() - pool->start() - pool->size_y()) / pool->set_img_size_y(20);
// ((float)pool->stride_y()) + setPoolConfig(&config, pool, poolType);
// 1.5); pool->set_output_y((pool->img_size_y() - pool->start() - pool->size_y()) /
// config.layerConfig.set_size(pool->output_x() * pool->output_y() * ((float)pool->stride_y()) +
// pool->channels()); 1.5);
// config.layerConfig.set_size(pool->output_x() * pool->output_y() *
// testLayerGrad(config, "pool", 100, trans, useGpu); pool->channels());
// }
// #endif testLayerGrad(config, "pool", 100, trans, useGpu);
// }
// TEST(Layer, PoolLayer) { #endif
// testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ false);
// testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ false); TEST(Layer, PoolLayer) {
// testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ false);
// #ifndef PADDLE_ONLY_CPU testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ false);
// testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ true);
// testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ true); #ifndef PADDLE_ONLY_CPU
// testPoolLayer("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true); testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ true);
// testPoolLayer("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true); testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ true);
// testPoolLayer2("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true); testPoolLayer("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true);
// testPoolLayer2("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true); testPoolLayer("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true);
// #endif testPoolLayer2("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true);
// } testPoolLayer2("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true);
// #endif
// void testSppLayer(const string& poolType, }
// const int pyramidHeight,
// bool trans, void testSppLayer(const string& poolType,
// bool useGpu) { const int pyramidHeight,
// TestConfig config; bool trans,
// config.layerConfig.set_type("spp"); bool useGpu) {
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0}); TestConfig config;
// LayerInputConfig* input = config.layerConfig.add_inputs(); config.layerConfig.set_type("spp");
// SppConfig* sppConfig = input->mutable_spp_conf(); config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0});
// sppConfig->set_pool_type(poolType); LayerInputConfig* input = config.layerConfig.add_inputs();
// sppConfig->set_pyramid_height(pyramidHeight); SppConfig* sppConfig = input->mutable_spp_conf();
// ImageConfig* imageConfig = sppConfig->mutable_image_conf(); sppConfig->set_pool_type(poolType);
// imageConfig->set_channels(16); sppConfig->set_pyramid_height(pyramidHeight);
// imageConfig->set_img_size(10); ImageConfig* imageConfig = sppConfig->mutable_image_conf();
// imageConfig->set_img_size_y(20); imageConfig->set_channels(16);
// int outputSize = (std::pow(4, sppConfig->pyramid_height()) - 1) / (4 - 1); imageConfig->set_img_size(10);
// config.layerConfig.set_size(outputSize * imageConfig->channels()); imageConfig->set_img_size_y(20);
// testLayerGrad(config, "spp", 100, trans, useGpu); int outputSize = (std::pow(4, sppConfig->pyramid_height()) - 1) / (4 - 1);
// } config.layerConfig.set_size(outputSize * imageConfig->channels());
// testLayerGrad(config, "spp", 100, trans, useGpu);
// TEST(Layer, SpatialPyramidPoolLayer) { }
// for (auto useGpu : {false, true}) {
// for (auto pyramidHeight : {1, 2, 3}) { TEST(Layer, SpatialPyramidPoolLayer) {
// testSppLayer("avg-projection", pyramidHeight, false, useGpu); for (auto useGpu : {false, true}) {
// testSppLayer("max-projection", pyramidHeight, false, useGpu); for (auto pyramidHeight : {1, 2, 3}) {
// } testSppLayer("avg-projection", pyramidHeight, false, useGpu);
// } testSppLayer("max-projection", pyramidHeight, false, useGpu);
// } }
// }
// TEST(Layer, rankCostLayer) { }
// TestConfig config;
// config.layerConfig.set_type("rank-cost"); TEST(Layer, rankCostLayer) {
// config.biasSize = 0; TestConfig config;
// config.layerConfig.set_type("rank-cost");
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); config.biasSize = 0;
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 1, 0});
// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_DATA, "layer_1", 1, 0});
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// for (auto useGpu : {false, true}) { config.layerConfig.add_inputs();
// testLayerGrad(config, "rank-cost", 100, false, useGpu);
// } for (auto useGpu : {false, true}) {
// } testLayerGrad(config, "rank-cost", 100, false, useGpu);
// }
// TEST(Layer, sumCostLayer) { }
// TestConfig config;
// config.layerConfig.set_type("sum_cost"); TEST(Layer, sumCostLayer) {
// config.biasSize = 0; TestConfig config;
// config.layerConfig.set_type("sum_cost");
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); config.biasSize = 0;
// config.layerConfig.add_inputs();
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
// for (auto useGpu : {false, true}) { config.layerConfig.add_inputs();
// testLayerGrad(config, "sum_cost", 100, false, useGpu);
// } for (auto useGpu : {false, true}) {
// } testLayerGrad(config, "sum_cost", 100, false, useGpu);
// }
// TEST(Layer, weightedRankCostLayer) { }
// TestConfig config;
// config.layerConfig.set_type("rank-cost"); TEST(Layer, weightedRankCostLayer) {
// config.biasSize = 0; TestConfig config;
// config.layerConfig.set_type("rank-cost");
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); config.biasSize = 0;
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 1, 0});
// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_3", 1, 0}); config.inputDefs.push_back({INPUT_DATA, "layer_1", 1, 0});
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0});
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_3", 1, 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// for (auto useGpu : {false, true}) { config.layerConfig.add_inputs();
// testLayerGrad(config, "weighted-rank-cost", 100, false, useGpu);
// } for (auto useGpu : {false, true}) {
// } testLayerGrad(config, "weighted-rank-cost", 100, false, useGpu);
// }
// TEST(Layer, TensorLayer) { }
// TestConfig config;
// config.layerConfig.set_type("tensor"); TEST(Layer, TensorLayer) {
// config.layerConfig.set_size(10); TestConfig config;
// config.layerConfig.set_active_type("sigmoid"); config.layerConfig.set_type("tensor");
// config.biasSize = config.layerConfig.size(); config.layerConfig.set_size(10);
// config.layerConfig.set_active_type("sigmoid");
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 250}); config.biasSize = config.layerConfig.size();
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 5, 0});
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 250});
// config.layerConfig.add_inputs(); config.inputDefs.push_back({INPUT_DATA, "layer_1", 5, 0});
// config.layerConfig.add_inputs();
// for (auto useGpu : {false, true}) { config.layerConfig.add_inputs();
// testLayerGrad(config, "tensor", 100, false, useGpu);
// } for (auto useGpu : {false, true}) {
// } testLayerGrad(config, "tensor", 100, false, useGpu);
// }
// TEST(Layer, RecurrentLayer) { }
// TestConfig config;
// config.layerConfig.set_type("recurrent"); TEST(Layer, RecurrentLayer) {
// config.layerConfig.set_size(4); TestConfig config;
// config.layerConfig.set_active_type("tanh"); config.layerConfig.set_type("recurrent");
// config.biasSize = 4; config.layerConfig.set_size(4);
// config.layerConfig.set_active_type("tanh");
// config.inputDefs.push_back( config.biasSize = 4;
// {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 4, /* paraSize= */ 16});
// config.layerConfig.add_inputs(); config.inputDefs.push_back(
// {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 4, /* paraSize= */ 16});
// for (auto useGpu : {false, true}) { config.layerConfig.add_inputs();
// for (auto reversed : {false, true}) {
// config.layerConfig.set_reversed(reversed); for (auto useGpu : {false, true}) {
// config.testState = !reversed; for (auto reversed : {false, true}) {
// testLayerGrad(config, "recurrent", 50, /* trans= */ false, useGpu); config.layerConfig.set_reversed(reversed);
// } config.testState = !reversed;
// } testLayerGrad(config, "recurrent", 50, /* trans= */ false, useGpu);
// } }
// }
// TEST(Layer, LstmLayer) { }
// TestConfig config;
// config.layerConfig.set_type("lstmemory"); TEST(Layer, LstmLayer) {
// config.layerConfig.set_size(4); TestConfig config;
// config.layerConfig.set_active_type("tanh"); config.layerConfig.set_type("lstmemory");
// config.layerConfig.set_active_state_type("sigmoid"); config.layerConfig.set_size(4);
// config.layerConfig.set_active_gate_type("sigmoid"); config.layerConfig.set_active_type("tanh");
// config.biasSize = 28; config.layerConfig.set_active_state_type("sigmoid");
// config.layerConfig.set_active_gate_type("sigmoid");
// config.inputDefs.push_back( config.biasSize = 28;
// {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 64});
// config.layerConfig.add_inputs(); config.inputDefs.push_back(
// {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 64});
// for (auto useGpu : {false, true}) { config.layerConfig.add_inputs();
// for (auto reversed : {false, true}) {
// config.layerConfig.set_reversed(reversed); for (auto useGpu : {false, true}) {
// config.testState = !reversed; for (auto reversed : {false, true}) {
// testLayerGrad(config, "lstmemory", 100, /* trans= */ false, useGpu); config.layerConfig.set_reversed(reversed);
// } config.testState = !reversed;
// } testLayerGrad(config, "lstmemory", 100, /* trans= */ false, useGpu);
// for (auto useGpu : {true}) { }
// config.testBatchState = true; }
// config.layerConfig.set_reversed(false); for (auto useGpu : {true}) {
// testLayerGrad(config, "lstmemory", 10, /* trans= */ false, useGpu); config.testBatchState = true;
// } config.layerConfig.set_reversed(false);
// } testLayerGrad(config, "lstmemory", 10, /* trans= */ false, useGpu);
// }
// TEST(Layer, MDLstmLayer) { }
// TestConfig config;
// config.layerConfig.set_type("mdlstmemory"); TEST(Layer, MDLstmLayer) {
// config.layerConfig.set_size(4); TestConfig config;
// config.layerConfig.set_active_type("sigmoid"); config.layerConfig.set_type("mdlstmemory");
// config.layerConfig.set_active_state_type("sigmoid"); config.layerConfig.set_size(4);
// config.layerConfig.set_active_gate_type("sigmoid"); config.layerConfig.set_active_type("sigmoid");
// config.biasSize = 4 * 9; config.layerConfig.set_active_state_type("sigmoid");
// config.layerConfig.set_active_gate_type("sigmoid");
// config.inputDefs.push_back( config.biasSize = 4 * 9;
// {INPUT_SEQUENCE_MDIM_DATA, "layer_0", 4 * 5, 4 * 4 * 5});
// config.layerConfig.add_inputs(); config.inputDefs.push_back(
// config.layerConfig.add_directions(true); {INPUT_SEQUENCE_MDIM_DATA, "layer_0", 4 * 5, 4 * 4 * 5});
// config.layerConfig.add_directions(true); config.layerConfig.add_inputs();
// config.layerConfig.add_directions(true);
// for (auto useGpu : {false, true}) { config.layerConfig.add_directions(true);
// for (int i = 0; i < 2; i++) {
// for (int j = 0; j < 2; j++) { for (auto useGpu : {false, true}) {
// config.layerConfig.set_directions(0, bool(i)); for (int i = 0; i < 2; i++) {
// config.layerConfig.set_directions(1, bool(j)); for (int j = 0; j < 2; j++) {
// testLayerGrad(config, "mdlstmemory", 100, false, useGpu); config.layerConfig.set_directions(0, bool(i));
// } config.layerConfig.set_directions(1, bool(j));
// } testLayerGrad(config, "mdlstmemory", 100, false, useGpu);
// } }
// } }
// }
// TEST(Layer, ParameterReluLayer) { }
// auto testParameterReluLayer = [&](size_t inputSize, size_t channels) {
// TestConfig config; TEST(Layer, ParameterReluLayer) {
// config.layerConfig.set_type("prelu"); auto testParameterReluLayer = [&](size_t inputSize, size_t channels) {
// config.inputDefs.push_back({INPUT_DATA, "layer_0", inputSize, channels}); TestConfig config;
// config.layerConfig.add_inputs(); config.layerConfig.set_type("prelu");
// config.layerConfig.set_size(inputSize); config.inputDefs.push_back({INPUT_DATA, "layer_0", inputSize, channels});
// config.layerConfig.set_partial_sum(inputSize / config.layerConfig.add_inputs();
// channels); // size of feature map config.layerConfig.set_size(inputSize);
// for (auto useGpu : {false, true}) { config.layerConfig.set_partial_sum(inputSize /
// testLayerGrad(config, "prelu", 100, false, useGpu); channels); // size of feature map
// } for (auto useGpu : {false, true}) {
// }; testLayerGrad(config, "prelu", 100, false, useGpu);
// }
// testParameterReluLayer(192, 1); };
// testParameterReluLayer(192, 3);
// testParameterReluLayer(192, 192); testParameterReluLayer(192, 1);
// } testParameterReluLayer(192, 3);
// testParameterReluLayer(192, 192);
// TEST(Layer, ResizeLayer) { }
// TestConfig config;
// config.biasSize = 0; TEST(Layer, ResizeLayer) {
// config.layerConfig.set_type("resize"); TestConfig config;
// config.layerConfig.set_size(64); config.biasSize = 0;
// config.layerConfig.set_type("resize");
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 16, 0}); config.layerConfig.set_size(64);
// config.layerConfig.add_inputs();
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 16, 0});
// for (auto useGpu : {false, true}) { config.layerConfig.add_inputs();
// testLayerGrad(config, "resize", 100, false, useGpu);
// } for (auto useGpu : {false, true}) {
// } testLayerGrad(config, "resize", 100, false, useGpu);
// }
// TEST(Layer, NCELayer) { }
// TestConfig config;
// size_t numClasses = 4; TEST(Layer, NCELayer) {
// config.layerConfig.set_type("nce"); TestConfig config;
// config.layerConfig.set_size(1); size_t numClasses = 4;
// config.layerConfig.set_active_type("sigmoid"); config.layerConfig.set_type("nce");
// config.layerConfig.set_num_classes(numClasses); config.layerConfig.set_size(1);
// config.biasSize = numClasses; config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_num_classes(numClasses);
// config.inputDefs.push_back( config.biasSize = numClasses;
// {INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 16 *
// numClasses}); config.inputDefs.push_back(
// config.inputDefs.push_back( {INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 16 * numClasses});
// {INPUT_LABEL, "label", /* dim= */ numClasses, /* paraSize= */ 0}); config.inputDefs.push_back(
// config.layerConfig.add_inputs(); {INPUT_LABEL, "label", /* dim= */ numClasses, /* paraSize= */ 0});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// for (auto withWeight : {false, true}) {
// if (withWeight) { for (auto withWeight : {false, true}) {
// config.inputDefs.push_back( if (withWeight) {
// {INPUT_DATA_TARGET, "weight", /* dim= */ 1, /* paraSize= */ 0}); config.inputDefs.push_back(
// config.layerConfig.add_inputs(); {INPUT_DATA_TARGET, "weight", /* dim= */ 1, /* paraSize= */ 0});
// } config.layerConfig.add_inputs();
// }
// for (auto isIdLabel : {false, true}) {
// config.inputDefs[1] = { for (auto isIdLabel : {false, true}) {
// isIdLabel ? INPUT_LABEL : INPUT_SPARSE_NON_VALUE_DATA, config.inputDefs[1] = {
// "label", isIdLabel ? INPUT_LABEL : INPUT_SPARSE_NON_VALUE_DATA,
// /* dim= */ numClasses, "label",
// /* paraSize= */ 0}; /* dim= */ numClasses,
// /* paraSize= */ 0};
// for (auto withDist : {false, true}) {
// config.layerConfig.clear_neg_sampling_dist(); for (auto withDist : {false, true}) {
// if (withDist) { config.layerConfig.clear_neg_sampling_dist();
// double sum = 0; if (withDist) {
// for (size_t i = 0; i < numClasses; ++i) { double sum = 0;
// real p = rand(); // NOLINT use rand_r for (size_t i = 0; i < numClasses; ++i) {
// config.layerConfig.add_neg_sampling_dist(p); real p = rand(); // NOLINT use rand_r
// sum += p; config.layerConfig.add_neg_sampling_dist(p);
// } sum += p;
// for (size_t i = 0; i < numClasses; ++i) { }
// real p = config.layerConfig.neg_sampling_dist(i) / sum; for (size_t i = 0; i < numClasses; ++i) {
// config.layerConfig.set_neg_sampling_dist(i, p); real p = config.layerConfig.neg_sampling_dist(i) / sum;
// } config.layerConfig.set_neg_sampling_dist(i, p);
// } }
// LOG(INFO) << "NCELayer " }
// << " isIdLabel=" << isIdLabel << " withWeight=" << LOG(INFO) << "NCELayer "
// withWeight << " isIdLabel=" << isIdLabel << " withWeight=" << withWeight
// << " withDist=" << withDist; << " withDist=" << withDist;
// // Not support GPU now // Not support GPU now
// testLayerGrad(config, testLayerGrad(config,
// "nce", "nce",
// 100, 100,
// /* trans= */ false, /* trans= */ false,
// /* useGpu */ false); /* useGpu */ false);
// } }
// } }
// } }
// } }
//
// TEST(Layer, GatedRecurrentLayer) { TEST(Layer, GatedRecurrentLayer) {
// TestConfig config; TestConfig config;
// config.layerConfig.set_type("gated_recurrent"); config.layerConfig.set_type("gated_recurrent");
// config.layerConfig.set_size(4); config.layerConfig.set_size(4);
// config.layerConfig.set_active_type("sigmoid"); config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_active_gate_type("sigmoid"); config.layerConfig.set_active_gate_type("sigmoid");
// config.biasSize = 12; config.biasSize = 12;
//
// config.inputDefs.push_back( config.inputDefs.push_back(
// {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48}); {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48});
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) { for (auto useGpu : {false, true}) {
// for (auto reversed : {false, true}) { for (auto reversed : {false, true}) {
// config.layerConfig.set_reversed(reversed); config.layerConfig.set_reversed(reversed);
// config.testState = !reversed; config.testState = !reversed;
// testLayerGrad(config, "gated_recurrent", 100, /* trans= */ false, testLayerGrad(config, "gated_recurrent", 100, /* trans= */ false, useGpu);
// useGpu); }
// } }
// } }
// }
// TEST(Layer, GruStepLayer) {
// TEST(Layer, GruStepLayer) { TestConfig config;
// TestConfig config; config.layerConfig.set_type("gru_step");
// config.layerConfig.set_type("gru_step"); config.layerConfig.set_size(4);
// config.layerConfig.set_size(4); config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_active_type("sigmoid"); config.layerConfig.set_active_gate_type("sigmoid");
// config.layerConfig.set_active_gate_type("sigmoid"); config.biasSize = 12;
// config.biasSize = 12;
// config.inputDefs.push_back(
// config.inputDefs.push_back( {INPUT_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48});
// {INPUT_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48}); config.inputDefs.push_back(
// config.inputDefs.push_back( {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0});
// {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0}); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// for (auto useGpu : {false, true}) {
// for (auto useGpu : {false, true}) { testLayerGrad(config, "gruStep", 100, /* trans= */ false, useGpu);
// testLayerGrad(config, "gruStep", 100, /* trans= */ false, useGpu); }
// } }
// }
// TEST(Layer, LstmStepLayer) {
// TEST(Layer, LstmStepLayer) { TestConfig config;
// TestConfig config; config.layerConfig.set_type("lstm_step");
// config.layerConfig.set_type("lstm_step"); config.layerConfig.set_size(4);
// config.layerConfig.set_size(4); config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_active_type("sigmoid"); config.layerConfig.set_active_state_type("sigmoid");
// config.layerConfig.set_active_state_type("sigmoid"); config.layerConfig.set_active_gate_type("sigmoid");
// config.layerConfig.set_active_gate_type("sigmoid"); config.biasSize = 12;
// config.biasSize = 12; config.testAccumulate = false;
// config.testAccumulate = false;
// config.inputDefs.push_back(
// config.inputDefs.push_back( {INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 0});
// {INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 0}); config.inputDefs.push_back(
// config.inputDefs.push_back( {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0});
// {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0}); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// for (auto useGpu : {false, true}) {
// for (auto useGpu : {false, true}) { testLayerGrad(config, "lstmStep", 100, /* trans= */ false, useGpu);
// testLayerGrad(config, "lstmStep", 100, /* trans= */ false, useGpu); }
// } }
// }
// void testBatchNormLayer(const string& type, bool trans, bool useGpu) {
// void testBatchNormLayer(const string& type, bool trans, bool useGpu) { TestConfig config;
// TestConfig config; const int CHANNELS = 10;
// const int CHANNELS = 10; const int IMG_SIZE = 16;
// const int IMG_SIZE = 16; const int IMG_SIZE_Y = 8;
// const int IMG_SIZE_Y = 8; size_t size = CHANNELS * IMG_SIZE * IMG_SIZE_Y;
// size_t size = CHANNELS * IMG_SIZE * IMG_SIZE_Y; config.layerConfig.set_type(type);
// config.layerConfig.set_type(type); config.layerConfig.set_size(size);
// config.layerConfig.set_size(size); config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_active_type("sigmoid"); config.biasSize = CHANNELS;
// config.biasSize = CHANNELS; config.inputDefs.push_back({INPUT_DATA,
// config.inputDefs.push_back({INPUT_DATA, "layer_0",
// "layer_0", /* dim= */ size,
// /* dim= */ size, /* paraSize= */ CHANNELS});
// /* paraSize= */ CHANNELS});
// config.inputDefs.push_back({INPUT_DATA, "layer_1_running_mean", 1, CHANNELS});
// config.inputDefs.push_back({INPUT_DATA, "layer_1_running_mean", 1, config.inputDefs.back().isStatic = true;
// CHANNELS}); config.inputDefs.push_back({INPUT_DATA, "layer_2_running_var", 1, CHANNELS});
// config.inputDefs.back().isStatic = true; config.inputDefs.back().isStatic = true;
// config.inputDefs.push_back({INPUT_DATA, "layer_2_running_var", 1,
// CHANNELS}); LayerInputConfig* input = config.layerConfig.add_inputs();
// config.inputDefs.back().isStatic = true; config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// LayerInputConfig* input = config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); ImageConfig* img_conf = input->mutable_image_conf();
// config.layerConfig.add_inputs(); img_conf->set_channels(CHANNELS);
// img_conf->set_img_size(IMG_SIZE);
// ImageConfig* img_conf = input->mutable_image_conf(); img_conf->set_img_size_y(IMG_SIZE_Y);
// img_conf->set_channels(CHANNELS);
// img_conf->set_img_size(IMG_SIZE); testLayerGrad(config,
// img_conf->set_img_size_y(IMG_SIZE_Y); "batch_norm",
// 64,
// testLayerGrad(config, /* trans= */ trans,
// "batch_norm", useGpu,
// 64, /* useWeight */ true);
// /* trans= */ trans, }
// useGpu,
// /* useWeight */ true); TEST(Layer, BatchNormalizationLayer) {
// } testBatchNormLayer("batch_norm", false, false);
// #ifndef PADDLE_ONLY_CPU
// TEST(Layer, BatchNormalizationLayer) { testBatchNormLayer("batch_norm", false, true);
// testBatchNormLayer("batch_norm", false, false); if (hl_get_cudnn_lib_version() >= int(4000)) {
// #ifndef PADDLE_ONLY_CPU testBatchNormLayer("cudnn_batch_norm", false, true);
// testBatchNormLayer("batch_norm", false, true); }
// if (hl_get_cudnn_lib_version() >= int(4000)) { #endif
// testBatchNormLayer("cudnn_batch_norm", false, true); }
// }
// #endif TEST(Operator, conv) {
// } TestConfig config;
// const int NUM_FILTERS = 16;
// TEST(Operator, conv) { const int FILTER_SIZE = 2;
// TestConfig config; const int FILTER_SIZE_Y = 3;
// const int NUM_FILTERS = 16; const int CHANNELS = 3;
// const int FILTER_SIZE = 2; const int IMAGE_SIZE = 16;
// const int FILTER_SIZE_Y = 3; const int IMAGE_SIZE_Y = 8;
// const int CHANNELS = 3; OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs();
// const int IMAGE_SIZE = 16; operatorConf.set_type("conv");
// const int IMAGE_SIZE_Y = 8; ConvConfig* conv = operatorConf.mutable_conv_conf();
// OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs(); operatorConf.set_num_filters(NUM_FILTERS);
// operatorConf.set_type("conv"); conv->set_filter_size(FILTER_SIZE);
// ConvConfig* conv = operatorConf.mutable_conv_conf(); conv->set_filter_size_y(FILTER_SIZE_Y);
// operatorConf.set_num_filters(NUM_FILTERS); conv->set_channels(CHANNELS);
// conv->set_filter_size(FILTER_SIZE); conv->set_padding(0);
// conv->set_filter_size_y(FILTER_SIZE_Y); conv->set_padding_y(1);
// conv->set_channels(CHANNELS); conv->set_stride(2);
// conv->set_padding(0); conv->set_stride_y(2);
// conv->set_padding_y(1); conv->set_groups(1);
// conv->set_stride(2); conv->set_filter_channels(conv->channels() / conv->groups());
// conv->set_stride_y(2); conv->set_img_size(IMAGE_SIZE);
// conv->set_groups(1); conv->set_img_size_y(IMAGE_SIZE_Y);
// conv->set_filter_channels(conv->channels() / conv->groups()); conv->set_output_x(outputSize(conv->img_size(),
// conv->set_img_size(IMAGE_SIZE); conv->filter_size(),
// conv->set_img_size_y(IMAGE_SIZE_Y); conv->padding(),
// conv->set_output_x(outputSize(conv->img_size(), conv->stride(),
// conv->filter_size(), /* caffeMode */ true));
// conv->padding(), conv->set_output_y(outputSize(conv->img_size_y(),
// conv->stride(), conv->filter_size_y(),
// /* caffeMode */ true)); conv->padding_y(),
// conv->set_output_y(outputSize(conv->img_size_y(), conv->stride_y(),
// conv->filter_size_y(), /* caffeMode */ true));
// conv->padding_y(), config.layerConfig.set_size(conv->output_x() * conv->output_y() *
// conv->stride_y(), NUM_FILTERS);
// /* caffeMode */ true));
// config.layerConfig.set_size(conv->output_x() * conv->output_y() * config.inputDefs.push_back(
// NUM_FILTERS); {INPUT_DATA, "layer_0", IMAGE_SIZE * IMAGE_SIZE_Y * CHANNELS, 0});
// config.inputDefs.push_back(
// config.inputDefs.push_back( {INPUT_DATA,
// {INPUT_DATA, "layer_0", IMAGE_SIZE * IMAGE_SIZE_Y * CHANNELS, 0}); "layer_1",
// config.inputDefs.push_back( FILTER_SIZE * FILTER_SIZE_Y * CHANNELS * NUM_FILTERS,
// {INPUT_DATA, 0});
// "layer_1", config.layerConfig.add_inputs();
// FILTER_SIZE * FILTER_SIZE_Y * CHANNELS * NUM_FILTERS, config.layerConfig.add_inputs();
// 0});
// config.layerConfig.add_inputs(); testOperatorGrad(config, operatorConf, 100, /*useGpu*/ true, false);
// config.layerConfig.add_inputs(); }
//
// testOperatorGrad(config, operatorConf, 100, /*useGpu*/ true, false); TEST(Layer, FeatureMapExpandLayer) {
// } TestConfig config;
// config.layerConfig.set_type("featmap_expand");
// TEST(Layer, FeatureMapExpandLayer) { const int CHANNELS = 10;
// TestConfig config; const int INPUT_SIZE = 100;
// config.layerConfig.set_type("featmap_expand"); config.layerConfig.set_size(INPUT_SIZE * CHANNELS);
// const int CHANNELS = 10; config.layerConfig.set_num_filters(CHANNELS);
// const int INPUT_SIZE = 100; config.inputDefs.push_back({INPUT_SEQUENCE_DATA,
// config.layerConfig.set_size(INPUT_SIZE * CHANNELS); "layer_0",
// config.layerConfig.set_num_filters(CHANNELS); /* dim= */ INPUT_SIZE,
// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, /* paraSize= */ 0});
// "layer_0", config.layerConfig.add_inputs();
// /* dim= */ INPUT_SIZE, for (auto useGpu : {false, true}) {
// /* paraSize= */ 0}); testLayerGrad(config,
// config.layerConfig.add_inputs(); "featmap_expand",
// for (auto useGpu : {false, true}) { /*batch_size*/ 100,
// testLayerGrad(config, /* trans= */ false,
// "featmap_expand", useGpu,
// /*batch_size*/ 100, /* useWeight */ true);
// /* trans= */ false, }
// useGpu, }
// /* useWeight */ true);
// } TEST(Layer, MultiplexLayer) {
// } TestConfig config;
// const int LAYER_SIZE = 100;
// TEST(Layer, MultiplexLayer) { config.layerConfig.set_type("multiplex");
// TestConfig config; config.layerConfig.set_size(LAYER_SIZE);
// const int LAYER_SIZE = 100;
// config.layerConfig.set_type("multiplex"); config.inputDefs.push_back({INPUT_LABEL, "layer_0", 2, 0});
// config.layerConfig.set_size(LAYER_SIZE); config.inputDefs.push_back(
// {INPUT_DATA, "layer_1", /* dim= */ LAYER_SIZE, /* paraSize= */ 0});
// config.inputDefs.push_back({INPUT_LABEL, "layer_0", 2, 0}); config.inputDefs.push_back(
// config.inputDefs.push_back( {INPUT_DATA, "layer_2", /* dim= */ LAYER_SIZE, /* paraSize= */ 0});
// {INPUT_DATA, "layer_1", /* dim= */ LAYER_SIZE, /* paraSize= */ 0}); config.layerConfig.add_inputs();
// config.inputDefs.push_back( config.layerConfig.add_inputs();
// {INPUT_DATA, "layer_2", /* dim= */ LAYER_SIZE, /* paraSize= */ 0}); config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs(); for (auto useGpu : {false, true}) {
// config.layerConfig.add_inputs(); testLayerGrad(config, "multiplex", 512, /* trans= */ false, useGpu);
// }
// for (auto useGpu : {false, true}) { }
// testLayerGrad(config, "multiplex", 512, /* trans= */ false, useGpu);
// }
// }
//
TEST(Layer, PadLayer) { TEST(Layer, PadLayer) {
TestConfig config; TestConfig config;
config.biasSize = 0; config.biasSize = 0;
......
python/paddle/trainer/config_parser.py
浏览文件 @ 6b61a096
...@@ -1109,7 +1109,7 @@ def parse_bilinear(bilinear, input_layer_name, bilinear_conf): ...@@ -1109,7 +1109,7 @@ def parse_bilinear(bilinear, input_layer_name, bilinear_conf):
bilinear_conf.out_size_y = bilinear.out_size_y bilinear_conf.out_size_y = bilinear.out_size_y
def parse_pool(pool, input_layer_name, pool_conf): def parse_pool(pool, input_layer_name, pool_conf, ceil_mode):
pool_conf.pool_type = pool.pool_type pool_conf.pool_type = pool.pool_type
config_assert(pool.pool_type in [ config_assert(pool.pool_type in [
'max-projection', 'avg-projection', 'cudnn-max-pool', 'cudnn-avg-pool' 'max-projection', 'avg-projection', 'cudnn-max-pool', 'cudnn-avg-pool'
...@@ -1134,10 +1134,10 @@ def parse_pool(pool, input_layer_name, pool_conf): ...@@ -1134,10 +1134,10 @@ def parse_pool(pool, input_layer_name, pool_conf):
pool_conf.padding_y = default(pool.padding_y, pool_conf.padding) pool_conf.padding_y = default(pool.padding_y, pool_conf.padding)
pool_conf.output_x = cnn_output_size(pool_conf.img_size, pool_conf.size_x, pool_conf.output_x = cnn_output_size(pool_conf.img_size, pool_conf.size_x,
pool_conf.padding, pool_conf.stride, pool_conf.padding, pool_conf.stride,
False) not ceil_mode)
pool_conf.output_y = cnn_output_size(pool_conf.img_size_y, pool_conf.size_y, pool_conf.output_y = cnn_output_size(pool_conf.img_size_y, pool_conf.size_y,
pool_conf.padding_y, pool_conf.padding_y,
pool_conf.stride_y, False) pool_conf.stride_y, not ceil_mode)
def parse_spp(spp, input_layer_name, spp_conf): def parse_spp(spp, input_layer_name, spp_conf):
...@@ -1810,9 +1810,8 @@ class ConvTransLayer(ConvTransLayerBase): ...@@ -1810,9 +1810,8 @@ class ConvTransLayer(ConvTransLayerBase):
@config_layer('norm') @config_layer('norm')
class NormLayer(LayerBase): class NormLayer(LayerBase):
def __init__(self, name, inputs, device=None): def __init__(self, name, inputs, **xargs):
super(NormLayer, self).__init__( super(NormLayer, self).__init__(name, 'norm', 0, inputs=inputs, **xargs)
name, 'norm', 0, inputs=inputs, device=device)
for input_index in xrange(len(self.inputs)): for input_index in xrange(len(self.inputs)):
input_layer = self.get_input_layer(input_index) input_layer = self.get_input_layer(input_index)
norm_conf = self.config.inputs[input_index].norm_conf norm_conf = self.config.inputs[input_index].norm_conf
...@@ -1824,23 +1823,22 @@ class NormLayer(LayerBase): ...@@ -1824,23 +1823,22 @@ class NormLayer(LayerBase):
@config_layer('pool') @config_layer('pool')
class PoolLayer(LayerBase): class PoolLayer(LayerBase):
def __init__(self, name, inputs, device=None): def __init__(self, name, inputs, ceil_mode=True, **xargs):
super(PoolLayer, self).__init__( super(PoolLayer, self).__init__(name, 'pool', 0, inputs=inputs, **xargs)
name, 'pool', 0, inputs=inputs, device=device)
for input_index in xrange(len(self.inputs)): for input_index in xrange(len(self.inputs)):
input_layer = self.get_input_layer(input_index) input_layer = self.get_input_layer(input_index)
pool_conf = self.config.inputs[input_index].pool_conf pool_conf = self.config.inputs[input_index].pool_conf
parse_pool(self.inputs[input_index].pool, input_layer.name, parse_pool(self.inputs[input_index].pool, input_layer.name,
pool_conf) pool_conf, ceil_mode)
self.set_cnn_layer(name, pool_conf.output_y, pool_conf.output_x, self.set_cnn_layer(name, pool_conf.output_y, pool_conf.output_x,
pool_conf.channels) pool_conf.channels)
@config_layer('spp') @config_layer('spp')
class SpatialPyramidPoolLayer(LayerBase): class SpatialPyramidPoolLayer(LayerBase):
def __init__(self, name, inputs, device=None): def __init__(self, name, inputs, **xargs):
super(SpatialPyramidPoolLayer, self).__init__( super(SpatialPyramidPoolLayer, self).__init__(
name, 'spp', 0, inputs=inputs, device=device) name, 'spp', 0, inputs=inputs, **xargs)
for input_index in xrange(len(self.inputs)): for input_index in xrange(len(self.inputs)):
input_layer = self.get_input_layer(input_index) input_layer = self.get_input_layer(input_index)
spp_conf = self.config.inputs[input_index].spp_conf spp_conf = self.config.inputs[input_index].spp_conf
...@@ -1877,7 +1875,6 @@ class BatchNormLayer(LayerBase): ...@@ -1877,7 +1875,6 @@ class BatchNormLayer(LayerBase):
inputs, inputs,
active_type="linear", active_type="linear",
bias=True, bias=True,
device=None,
use_global_stats=True, use_global_stats=True,
moving_average_fraction=0.9, moving_average_fraction=0.9,
batch_norm_type=None, batch_norm_type=None,
...@@ -1919,7 +1916,6 @@ class BatchNormLayer(LayerBase): ...@@ -1919,7 +1916,6 @@ class BatchNormLayer(LayerBase):
0, 0,
active_type=active_type, active_type=active_type,
inputs=inputs, inputs=inputs,
device=device,
**xargs) **xargs)
if use_global_stats is not None: if use_global_stats is not None:
...@@ -1953,9 +1949,9 @@ class BatchNormLayer(LayerBase): ...@@ -1953,9 +1949,9 @@ class BatchNormLayer(LayerBase):
@config_layer('trans') @config_layer('trans')
class TransLayer(LayerBase): class TransLayer(LayerBase):
def __init__(self, name, inputs, device=None): def __init__(self, name, inputs, **xargs):
super(TransLayer, self).__init__( super(TransLayer, self).__init__(
name, 'trans', 0, inputs=inputs, device=device) name, 'trans', 0, inputs=inputs, **xargs)
config_assert( config_assert(
len(self.inputs) == 1, len(self.inputs) == 1,
'TransLayer must have one and only one input') 'TransLayer must have one and only one input')
...@@ -1964,9 +1960,9 @@ class TransLayer(LayerBase): ...@@ -1964,9 +1960,9 @@ class TransLayer(LayerBase):
@config_layer('resize') @config_layer('resize')
class ResizeLayer(LayerBase): class ResizeLayer(LayerBase):
def __init__(self, name, size, inputs, device=None): def __init__(self, name, size, inputs, **xargs):
super(ResizeLayer, self).__init__( super(ResizeLayer, self).__init__(
name, 'resize', size=size, inputs=inputs, device=device) name, 'resize', size=size, inputs=inputs, **xargs)
config_assert( config_assert(
len(self.inputs) == 1, len(self.inputs) == 1,
'ResizeLayer must have one and only one input') 'ResizeLayer must have one and only one input')
...@@ -1974,9 +1970,9 @@ class ResizeLayer(LayerBase): ...@@ -1974,9 +1970,9 @@ class ResizeLayer(LayerBase):
@config_layer('blockexpand') @config_layer('blockexpand')
class BlockExpandLayer(LayerBase): class BlockExpandLayer(LayerBase):
def __init__(self, name, inputs, device=None): def __init__(self, name, inputs, **xargs):
super(BlockExpandLayer, self).__init__( super(BlockExpandLayer, self).__init__(
name, 'blockexpand', 0, inputs=inputs, device=device) name, 'blockexpand', 0, inputs=inputs, **xargs)
for input_index in xrange(len(self.inputs)): for input_index in xrange(len(self.inputs)):
input_layer = self.get_input_layer(input_index) input_layer = self.get_input_layer(input_index)
parse_block_expand( parse_block_expand(
......
python/paddle/trainer_config_helpers/layers.py
浏览文件 @ 6b61a096
...@@ -1980,7 +1980,8 @@ def img_pool_layer(input, ...@@ -1980,7 +1980,8 @@ def img_pool_layer(input,
layer_attr=None, layer_attr=None,
pool_size_y=None, pool_size_y=None,
stride_y=None, stride_y=None,
padding_y=None): padding_y=None,
ceil_mode=True):
""" """
Image pooling Layer. Image pooling Layer.
...@@ -2011,6 +2012,23 @@ def img_pool_layer(input, ...@@ -2011,6 +2012,23 @@ def img_pool_layer(input,
:type stride_y: int|None :type stride_y: int|None
:param layer_attr: Extra Layer attribute. :param layer_attr: Extra Layer attribute.
:type layer_attr: ExtraLayerAttribute :type layer_attr: ExtraLayerAttribute
:param ceil_mode: Wether to use ceil mode to calculate output height and with.
Defalut is True. If set false, Otherwise use floor.
- ceil_mode=True:
.. math::
w = 1 + int(ceil(input_width + 2 * padding - pool_size) / float(stride))
h = 1 + int(ceil(input_height + 2 * padding_y - pool_size_y) / float(stride_y))
- ceil_mode=False:
.. math::
w = 1 + int(floor(input_width + 2 * padding - pool_size) / float(stride))
h = 1 + int(floor(input_height + 2 * padding_y - pool_size_y) / float(stride_y))
:type ceil_mode: bool
:return: LayerOutput object. :return: LayerOutput object.
:rtype: LayerOutput :rtype: LayerOutput
""" """
...@@ -2048,6 +2066,7 @@ def img_pool_layer(input, ...@@ -2048,6 +2066,7 @@ def img_pool_layer(input,
stride_y=stride_y, stride_y=stride_y,
padding_y=padding_y)) padding_y=padding_y))
], ],
ceil_mode=ceil_mode,
**ExtraLayerAttribute.to_kwargs(layer_attr)) **ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput( return LayerOutput(
name, name,
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或