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PaddleDetection
提交 ffafc5c9 编写于 作者: C caoying03
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fix the SubNestedSequenceLayer implementations.

上级 29fa73bc
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paddle/gserver/layers/SubNestedSequenceLayer.cpp
浏览文件 @ ffafc5c9
......@@ -31,16 +31,22 @@ public:
void backward(const UpdateCallback& callback = nullptr) override;
private:
void calSelectedCols(const MatrixPtr scores,
const int* seqStartPos,
const int* subSeqStartPos);
void reorganizeSeqInfo(const ICpuGpuVectorPtr seqStartPos,
const ICpuGpuVectorPtr subSeqStartPos);
void calSelectedCols(const MatrixPtr selectedIndices,
const std::vector<std::vector<int>> inputSeqInfo);
void buildOutputSeqInfo();
std::vector<int> outSeqStartInfo_;
std::vector<int> outSubSeqStartInfo_;
MatrixPtr scoreOverInputSeq_;
// if the second input of this layer is on GPU memory, copy it to CPU memory.
MatrixPtr selIdsCpu_;
// reorganize sequenceStartPositions and subSequenceStartPositions altogether
// into a 2d vector to facilitate the sequence selection process.
std::vector<std::vector<int>> inputSeqInfo_;
// the final seleted row indices in a batch,
// rowIdx_ and selectedRows_ actually share a same memory.
IVectorPtr rowIndice_;
std::vector<int> selectedRows_;
......@@ -57,12 +63,47 @@ bool SubNestedSequenceLayer::init(const LayerMap& layerMap,
return true;
}
void SubNestedSequenceLayer::calSelectedCols(const MatrixPtr selected_indices,
const int* seqStartPos,
const int* subSeqStartPos) {
void SubNestedSequenceLayer::reorganizeSeqInfo(
const ICpuGpuVectorPtr seqStartPos, const ICpuGpuVectorPtr subSeqStartPos) {
int* seqStarts = seqStartPos->getMutableData(false);
int* subSeqStarts = subSeqStartPos->getMutableData(false);
int seqNum = seqStartPos->getSize() - 1;
inputSeqInfo_.resize(seqNum, std::vector<int>());
int seqIdx = 0;
for (size_t i = 0; i < subSeqStartPos->getSize(); ++i) {
inputSeqInfo_[seqIdx].push_back(subSeqStarts[i]);
if (subSeqStarts[i] == seqStarts[seqIdx + 1]) {
seqIdx++;
if (seqIdx == seqNum) return;
inputSeqInfo_[seqIdx].push_back(subSeqStarts[i]);
}
}
}
void SubNestedSequenceLayer::calSelectedCols(
const MatrixPtr selectedIndices,
const std::vector<std::vector<int>> inputSeqInfo) {
selectedRows_.clear();
outSubSeqStartInfo_.resize(1, 0);
outSeqStartInfo_.resize(1, 0);
size_t seqNum = selectedIndices->getHeight();
size_t beamSize = selectedIndices->getWidth();
for (size_t i = 0; i < seqNum; ++i) {
for (size_t j = 0; j < beamSize; ++j) {
if (selectedIndices->getElement(i, j) == -1.) break;
int selSubSeqIdx = selectedIndices->getElement(i, j);
CHECK_GT(inputSeqInfo_[i].size() - 1, selSubSeqIdx);
size_t subSeqLen =
inputSeqInfo_[i][selSubSeqIdx + 1] - inputSeqInfo_[i][selSubSeqIdx];
for (size_t k = 0; k < subSeqLen; ++k)
selectedRows_.push_back(inputSeqInfo_[i][selSubSeqIdx] + k);
outSubSeqStartInfo_.push_back(outSubSeqStartInfo_.back() + subSeqLen);
}
outSeqStartInfo_.push_back(outSubSeqStartInfo_.back());
}
}
void SubNestedSequenceLayer::buildOutputSeqInfo() {
......@@ -83,17 +124,35 @@ void SubNestedSequenceLayer::forward(PassType passType) {
Layer::forward(passType);
const Argument& inputSeq = getInput(0);
const MatrixPtr selected_indices = getInputValue(1);
CHECK(inputSeq.hasSubseq()) << "The first input of SubNestSequence layer "
<< "must be a nested sequence.";
CHECK_EQ(inputSeq.getNumSequences(), selected_indices->getHeight());
calSelectedCols(selected_indices,
inputSeq.sequenceStartPositions->getMutableData(false),
inputSeq.subSequenceStartPositions->getMutableData(false));
const MatrixPtr selectedIndices = getInputValue(1);
CHECK_EQ(inputSeq.getNumSequences(), selectedIndices->getHeight());
if (dynamic_cast<GpuMatrix*>(selectedIndices.get())) {
/*
* Currently, the second input for this layer generated by
* kmax_sequence_score_layer whose output is always stored on CPU,
* or a data_layer which canbe on GPU.
*
* If the second input is on GPU, copy it to CPU memory, because this
* input always uses very few memory, and operations related to it are
* all logic control, not computations.
*/
Matrix::resizeOrCreate(selIdsCpu_,
selectedIndices->getHeight(),
selectedIndices->getWidth(),
false /* trans */,
false /* useGpu */);
selIdsCpu_->copyFrom(*selectedIndices);
} else {
selIdsCpu_ = selectedIndices;
}
reorganizeSeqInfo(inputSeq.sequenceStartPositions,
inputSeq.subSequenceStartPositions);
calSelectedCols(selIdsCpu_, inputSeqInfo_);
resetOutput(selectedRows_.size(), getSize());
buildOutputSeqInfo();
if (useGpu_) {
rowIndice_ = IVector::create(selectedRows_.size(), useGpu_);
......@@ -103,6 +162,7 @@ void SubNestedSequenceLayer::forward(PassType passType) {
IVector::create(selectedRows_.data(), selectedRows_.size(), useGpu_);
}
buildOutputSeqInfo();
getOutputValue()->selectRows(*getInputValue(0), *rowIndice_);
}
......
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @ ffafc5c9
......@@ -32,1887 +32,1872 @@ DECLARE_double(checkgrad_eps);
DECLARE_bool(thread_local_rand_use_global_seed);
DECLARE_bool(prev_batch_state);
// TEST(Operator, dot_mul) {
// TestConfig config;
// config.layerConfig.set_size(10);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs();
// operatorConf.set_type("dot_mul");
// operatorConf.set_dotmul_scale(-1);
//
// testOperatorGrad(config, operatorConf, 100, false, false);
// }
//
// TEST(Projection, context) {
// for (auto contextStart : {-5, -3, -1, 0, 3}) {
// for (auto contextLength : {1, 2, 5, 7}) {
// for (auto batchSize : {1, 2, 5, 20, 50}) {
// for (auto trainablePadding : {false, true}) {
// LOG(INFO) << " contextStart=" << contextStart
// << " contextLength=" << contextLength
// << " batchSize=" << batchSize
// << " trainablePadding=" << trainablePadding;
// ProjectionConfig conf;
// conf.set_type("context");
// conf.set_input_size(10);
// conf.set_context_start(contextStart);
// conf.set_context_length(contextLength);
// conf.set_trainable_padding(trainablePadding);
// conf.set_output_size(conf.context_length() * conf.input_size());
// int pad =
// std::max(0, -conf.context_start()) +
// std::max(0, conf.context_start() + conf.context_length() - 1);
// for (auto useGpu : {false, true}) {
// testProjectionGrad(
// conf,
// INPUT_SEQUENCE_DATA,
// trainablePadding ? conf.input_size() * pad : 0,
// batchSize,
// useGpu,
// contextStart + contextLength <= 1); // = testState
// }
// }
// }
// }
// }
// }
//
// TEST(Projection, trans_fc) {
// ProjectionConfig conf;
// conf.set_type("trans_fc");
// conf.set_input_size(50);
// conf.set_output_size(20);
// for (auto useGpu : {false, true}) {
// testProjectionGrad(conf,
// INPUT_DATA,
// /* parameterSize */ 1000,
// /* batchSize */ 100,
// useGpu);
// }
// }
//
// TEST(Projection, fc) {
// ProjectionConfig conf;
// conf.set_type("fc");
// conf.set_input_size(10);
// conf.set_output_size(20);
// for (auto useGpu : {false, true}) {
// testProjectionGrad(conf,
// INPUT_DATA,
// /* parameterSize */ 200,
// /* batchSize */ 100,
// useGpu);
// }
// }
//
// TEST(Projection, dot_mul) {
// ProjectionConfig conf;
// conf.set_type("dot_mul");
// conf.set_input_size(20);
// conf.set_output_size(20);
// for (auto useGpu : {false, true}) {
// testProjectionGrad(conf,
// INPUT_DATA,
// /* parameterSize */ 20,
// /* batchSize */ 100,
// useGpu);
// }
// }
//
// TEST(Projection, table) {
// ProjectionConfig conf;
// conf.set_type("table");
// conf.set_input_size(10);
// conf.set_output_size(20);
// for (auto useGpu : {false, true}) {
// testProjectionGrad(conf,
// INPUT_LABEL,
// /* parameterSize */ 200,
// /* batchSize */ 100,
// useGpu);
// }
// }
//
// TEST(Projection, identity) {
// ProjectionConfig conf;
// conf.set_type("identity");
// conf.set_input_size(10);
// conf.set_output_size(10);
// for (auto useGpu : {false, true}) {
// testProjectionGrad(conf,
// INPUT_DATA,
// /* parameterSize */ 0,
// /* batchSize */ 100,
// useGpu);
// }
// }
//
// TEST(Projection, slice) {
// ProjectionConfig conf;
// conf.set_type("slice");
// conf.set_input_size(100);
// SliceConfig& slice1 = *conf.add_slices();
// slice1.set_start(10);
// slice1.set_end(20);
// SliceConfig& slice2 = *conf.add_slices();
// slice2.set_start(50);
// slice2.set_end(70);
// conf.set_output_size(30);
// for (auto useGpu : {false, true}) {
// testProjectionGrad(conf,
// INPUT_DATA,
// /* parameterSize */ 0,
// /* batchSize */ 10,
// useGpu);
// }
// }
//
// TEST(Projection, scaling) {
// ProjectionConfig conf;
// conf.set_type("scaling");
// conf.set_input_size(10);
// conf.set_output_size(10);
// for (auto useGpu : {false}) {
// testProjectionGrad(conf,
// INPUT_DATA,
// /* parameterSize */ 1,
// /* batchSize */ 100,
// useGpu);
// }
// }
//
// void testProjectionConv(size_t groups, bool isDeconv) {
// const int NUM_FILTERS = 18;
// const int FILTER_SIZE = 2;
// const int FILTER_SIZE_Y = 4;
// const int CHANNELS = 3;
// const int IMAGE_SIZE = 16;
//
// ProjectionConfig conf;
// if (isDeconv) {
// conf.set_type("convt");
// } else {
// conf.set_type("conv");
// }
// conf.set_num_filters(NUM_FILTERS);
//
// ConvConfig* conv = conf.mutable_conv_conf();
// conv->set_filter_size(FILTER_SIZE);
// conv->set_filter_size_y(FILTER_SIZE_Y);
// conv->set_channels(CHANNELS);
// conv->set_padding(0);
// conv->set_padding_y(1);
// conv->set_stride(2);
// conv->set_stride_y(2);
// conv->set_groups(groups);
// if (isDeconv) {
// conv->set_filter_channels(NUM_FILTERS / conv->groups());
// } else {
// conv->set_filter_channels(conv->channels() / conv->groups());
// }
// conv->set_img_size(IMAGE_SIZE);
// int output_x = outputSize(conv->img_size(),
// conv->filter_size(),
// conv->padding(),
// conv->stride(),
// /* caffeMode */ true);
// int output_y = outputSize(conv->img_size(),
// conv->filter_size_y(),
// conv->padding_y(),
// conv->stride_y(),
// /* caffeMode */ true);
// conv->set_output_x(output_x);
// conv->set_output_y(output_y);
// if (isDeconv) {
// conf.set_input_size(output_x * output_y * CHANNELS);
// conf.set_output_size(IMAGE_SIZE * IMAGE_SIZE * NUM_FILTERS);
// } else {
// conf.set_input_size(IMAGE_SIZE * IMAGE_SIZE * CHANNELS);
// conf.set_output_size(output_x * output_y * NUM_FILTERS);
// }
//
// testProjectionGrad(conf,
// INPUT_DATA,
// /* parameterSize */ NUM_FILTERS * CHANNELS * FILTER_SIZE
// *
// FILTER_SIZE_Y / groups,
// /* batchSize */ 100,
// true,
// false,
// NUM_FILTERS,
// true);
// }
//
// #ifndef PADDLE_ONLY_CPU
// TEST(Projection, conv) {
// /// test ConvProjection
// testProjectionConv(1, false);
// testProjectionConv(3, false);
// /// test ConvTransProjection
// testProjectionConv(1, true);
// testProjectionConv(3, true);
// }
// #endif
//
// TEST(Layer, BilinearInterpLayer) {
// TestConfig config;
// config.layerConfig.set_type("bilinear_interp");
// config.biasSize = 0;
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0});
//
// LayerInputConfig* input = config.layerConfig.add_inputs();
// BilinearInterpConfig* bilinear = input->mutable_bilinear_interp_conf();
// ImageConfig* image = bilinear->mutable_image_conf();
// image->set_img_size(32);
// image->set_img_size_y(32);
// image->set_channels(4);
//
// for (auto useGpu : {false, true}) {
// for (auto outSize : {32, 64}) {
// bilinear->set_out_size_x(outSize);
// bilinear->set_out_size_y(outSize);
// testLayerGrad(config, "bilinear_interp", 10, false, useGpu);
// }
// }
// }
//
// TEST(Layer, concat) {
// TestConfig config;
// config.biasSize = 0;
// config.layerConfig.set_type("concat");
// config.layerConfig.set_size(15);
// config.layerConfig.set_active_type("sigmoid");
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0});
// config.layerConfig.add_inputs();
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "concat", 100, false, useGpu);
// }
// }
//
// TEST(Layer, AddtoLayer) {
// TestConfig config;
// config.biasSize = 0;
// config.layerConfig.set_type("addto");
// config.layerConfig.set_size(10);
// config.layerConfig.set_active_type("sigmoid");
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.layerConfig.add_inputs();
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "addto", 100, false, useGpu);
// }
// }
//
// TEST(Layer, CTCLayer) {
// TestConfig config;
// config.layerConfig.set_type("ctc");
// config.layerConfig.set_norm_by_times(false);
// config.layerConfig.set_size(10);
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0});
// config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config,
// "ctc",
// 100,
// /* trans */ false, /* useGpu */
// useGpu);
// }
// }
//
// TEST(Layer, cosSimLayer) {
// TestConfig config;
// config.layerConfig.set_type("cos");
// config.layerConfig.set_size(1);
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 50, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "cos", 100, false, useGpu);
// }
// }
//
// TEST(Layer, CosSimVecMatLayer) {
// TestConfig config;
// config.layerConfig.set_type("cos_vm");
// config.layerConfig.set_size(5); // output size
// config.layerConfig.set_cos_scale(2.0);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 0});
// config.layerConfig.add_inputs();
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "cos_vm", 100, false, useGpu);
// }
// }
//
// void testDepthwiseConvLayer(const string& type, bool useGpu) {
// TestConfig config;
// config.biasSize = 32;
// config.layerConfig.set_type(type);
// config.layerConfig.set_num_filters(32);
// config.layerConfig.set_partial_sum(1);
// config.layerConfig.set_shared_biases(true);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 2048, 192});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// ConvConfig* conv = input->mutable_conv_conf();
// conv->set_filter_size(2);
// conv->set_filter_size_y(3);
// conv->set_channels(16);
// conv->set_padding(0);
// conv->set_padding_y(1);
// conv->set_stride(2);
// conv->set_stride_y(2);
// conv->set_groups(16);
// conv->set_filter_channels(conv->channels() / conv->groups());
// conv->set_img_size(16);
// conv->set_img_size_y(8);
// conv->set_output_x(outputSize(conv->img_size(),
// conv->filter_size(),
// conv->padding(),
// conv->stride(),
// /* caffeMode */ true));
// conv->set_output_y(outputSize(conv->img_size_y(),
// conv->filter_size_y(),
// conv->padding_y(),
// conv->stride_y(),
// /* caffeMode */ true));
// config.layerConfig.set_size(conv->output_x() * conv->output_y() *
// config.layerConfig.num_filters());
//
// testLayerGrad(config, "depthwise_conv", 100, false, useGpu);
// // Use small batch_size and useWeight=true to test biasGrad
// testLayerGrad(config, "depthwise_conv", 2, false, useGpu, true, 0.02);
// }
//
// TEST(Layer, depthwiseConvLayer) {
// // 'depthwise_conv' is a sepecial case of 'exconv' whose
// // groups size equals to the input channels size.
// testDepthwiseConvLayer("exconv", /* useGpu= */ false);
// #ifndef PADDLE_ONLY_CPU
// testDepthwiseConvLayer("exconv", /* useGpu= */ true);
// #endif
// }
//
// void testConvLayer(const string& type, bool trans, bool useGpu) {
// TestConfig config;
// config.biasSize = 16;
// config.layerConfig.set_type(type);
// config.layerConfig.set_num_filters(16);
// config.layerConfig.set_partial_sum(1);
// config.layerConfig.set_shared_biases(true);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 384, 288});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// ConvConfig* conv = input->mutable_conv_conf();
// conv->set_filter_size(2);
// conv->set_filter_size_y(3);
// conv->set_channels(3);
// conv->set_padding(0);
// conv->set_padding_y(1);
// conv->set_stride(2);
// conv->set_stride_y(2);
// conv->set_groups(1);
// conv->set_filter_channels(conv->channels() / conv->groups());
// conv->set_img_size(16);
// conv->set_img_size_y(8);
// conv->set_output_x(outputSize(conv->img_size(),
// conv->filter_size(),
// conv->padding(),
// conv->stride(),
// /* caffeMode */ true));
// conv->set_output_y(outputSize(conv->img_size_y(),
// conv->filter_size_y(),
// conv->padding_y(),
// conv->stride_y(),
// /* caffeMode */ true));
// config.layerConfig.set_size(conv->output_x() * conv->output_y() *
// config.layerConfig.num_filters());
//
// 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
// 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;
// config.layerConfig.set_type(type);
// config.layerConfig.set_num_filters(3);
// config.layerConfig.set_partial_sum(1);
// config.layerConfig.set_shared_biases(true);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 384});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// ConvConfig* conv = input->mutable_conv_conf();
// conv->set_filter_size(2);
// conv->set_filter_size_y(4);
// conv->set_channels(16);
// conv->set_padding(0);
// conv->set_padding_y(1);
// conv->set_stride(2);
// conv->set_stride_y(2);
// conv->set_groups(1);
// conv->set_filter_channels(3 / conv->groups());
// conv->set_img_size(16);
// conv->set_output_x(outputSize(conv->img_size(),
// conv->filter_size(),
// conv->padding(),
// conv->stride(),
// /* caffeMode */ true));
//
// config.layerConfig.set_size(conv->img_size() * conv->img_size() *
// config.layerConfig.num_filters());
//
// 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);
// }
// #ifndef PADDLE_ONLY_CPU
// testConvTransLayer("cudnn_convt", /* trans= */ false, /* useGpu= */ true);
// #endif
// }
//
// TEST(Layer, blockExpandLayer) {
// TestConfig config;
// config.biasSize = 0;
// config.layerConfig.set_type("blockexpand");
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 6144, 0});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// BlockExpandConfig* blockExpand = input->mutable_block_expand_conf();
// blockExpand->set_img_size_x(64);
// blockExpand->set_img_size_y(32);
// blockExpand->set_channels(3);
// blockExpand->set_padding_x(0);
// blockExpand->set_padding_y(0);
// blockExpand->set_block_x(4);
// blockExpand->set_block_y(32);
// blockExpand->set_stride_x(2);
// blockExpand->set_stride_y(2);
// blockExpand->set_output_x(outputSize(blockExpand->img_size_x(),
// blockExpand->block_x(),
// blockExpand->padding_x(),
// blockExpand->stride_x(),
// /* caffeMode */ false));
// blockExpand->set_output_y(outputSize(blockExpand->img_size_y(),
// blockExpand->block_y(),
// blockExpand->padding_y(),
// blockExpand->stride_y(),
// /* caffeMode */ false));
// config.layerConfig.set_size(blockExpand->block_x() * blockExpand->block_y()
// *
// blockExpand->channels());
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "blockexpand", 100, false, useGpu);
// }
// }
//
// TEST(Layer, maxoutLayer) {
// TestConfig config;
// config.biasSize = 0;
// config.layerConfig.set_type("maxout");
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// 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_channels(4);
// maxout->set_groups(2);
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "maxout", 10, false, useGpu);
// }
// }
// void testFcLayer(string format, size_t nnz) {
// TestConfig config;
// config.biasSize = 4096;
// config.layerConfig.set_type("fc");
// config.layerConfig.set_size(4096);
// 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.layerConfig.add_inputs();
//
// LOG(INFO) << config.inputDefs[0].sparse.sparse << " "
// << config.inputDefs[0].sparse.format;
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config,
// "fc",
// 100,
// /* trans */ false,
// useGpu,
// /* weight */ true);
// }
// }
//
// TEST(Layer, fcLayer) {
// testFcLayer("", 4096 * 4096 * 2);
// testFcLayer("csc", 4096 * 40);
// testFcLayer("csr", 4096 * 40);
// }
//
// TEST(Layer, SelectiveFullyConnectedLayer) {
// TestConfig config;
// size_t nin = 16;
// size_t nout = 256;
// config.layerConfig.set_type("selective_fc");
// config.layerConfig.set_size(nout);
// config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_has_selected_colums(true);
// 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_SPARSE_NON_VALUE_DATA, "index", nout, 0, ParaSparse("csr",
// true)});
// config.layerConfig.add_inputs();
//
// testLayerGrad(config,
// "selective_fc",
// 100,
// /* trans= */ false,
// /* useGup= */ false,
// false);
// #ifndef PADDLE_ONLY_CPU
// testLayerGrad(config,
// "selective_fc",
// 100,
// /* trans= */ false,
// /* useGup= */ true,
// false);
// #endif
// }
//
// TEST(Layer, DataNormLayer) {
// TestConfig config;
// config.layerConfig.set_type("data_norm");
// config.layerConfig.set_size(20);
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 100});
// config.inputDefs.back().isStatic = true;
// config.layerConfig.add_inputs();
//
// for (auto strategy : {"z-score", "min-max", "decimal-scaling"}) {
// config.layerConfig.set_data_norm_strategy(strategy);
// // The parameters are static, so not support GPU now
// testLayerGrad(config,
// "data_norm",
// 200,
// /* trans */ false,
// /* useGpu */ false);
// }
// }
//
// TEST(Layer, hsigmoidLayer) {
// TestConfig config;
// config.layerConfig.set_type("hsigmoid");
// config.layerConfig.set_num_classes(5);
// config.layerConfig.set_size(1);
// config.biasSize = config.layerConfig.num_classes() - 1;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 200});
// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 5, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// // Not support GPU now
// testLayerGrad(config,
// "hsigmoid",
// 100,
// /* trans */ false, /* useGpu */
// false);
// }
//
// TEST(Layer, multi_cross) {
// TestConfig config;
// config.layerConfig.set_type("multi-class-cross-entropy");
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// 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;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
// config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50,
// 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config,
// "multi_binary_label_cross_entropy",
// 100,
// /* trans */ false,
// useGpu);
// }
// }
//
// TEST(layer, multi_binary_label_id) {
// TestConfig config;
// config.layerConfig.set_type("multi_binary_label_cross_entropy");
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config,
// "multi_binary_label_cross_entropy",
// 100,
// /* trans */ false,
// useGpu);
// }
// }
//
// TEST(Layer, multi_cross_with_selfnorm) {
// TestConfig config;
// config.layerConfig.set_type("multi_class_cross_entropy_with_selfnorm");
// config.layerConfig.set_softmax_selfnorm_alpha(0.1);
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// // Not support GPU now
// testLayerGrad(config,
// "multi_class_cross_entropy_with_selfnorm",
// 100,
// /* trans */ false,
// /* useGpu */ false);
// }
//
// TEST(Layer, multi_cross_soft) {
// TestConfig config;
// config.layerConfig.set_type("soft_binary_class_cross_entropy");
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config,
// "soft_binary_class_cross_entropy",
// 100,
// /* trans */ false,
// useGpu);
// }
// }
//
// TEST(Layer, square_error) {
// TestConfig config;
// config.layerConfig.set_type("square_error");
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// 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");
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
// config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50,
// 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// // "GpuSparseMatrix" as label is not supported
// testLayerGrad(config,
// "square_error",
// 100,
// /* trans */ false,
// /* useGpu */ false);
// }
//
// TEST(Layer, sparse_float_square_error) {
// TestConfig config;
// 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,
// 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// // "GpuSparseMatrix" as label is not supported
// testLayerGrad(config,
// "square_error",
// 100,
// /* trans */ false,
// /* useGpu */ false);
// }
//
// TEST(Layer, square_error_weighted) {
// TestConfig config;
// config.layerConfig.set_type("square_error");
// config.biasSize = 0;
// config.testAccumulate = false;
//
// 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_2", 1, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu);
// }
// }
//
// TEST(Layer, huber_two_class) {
// TestConfig config;
// config.layerConfig.set_type("huber");
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 2, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "huber", 100, /* trans */ false, useGpu);
// }
// }
//
// void testExpandLayer(string trans_type, bool hasSubseq) {
// TestConfig config;
// config.layerConfig.set_type("expand");
//
// config.inputDefs.push_back(
// {trans_type == "non-seq" ? INPUT_DENSE_DIM_DATA : INPUT_SEQUENCE_DATA,
// "layer_0",
// 10,
// 0});
// config.inputDefs.push_back(
// {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA,
// "layer_1",
// 10,
// 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// config.layerConfig.set_trans_type(trans_type);
// LOG(INFO) << " trans_type=" << trans_type << " hasSubseq=" << hasSubseq;
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "expand", 30, false, useGpu);
// }
// }
//
// TEST(Layer, ExpandLayer) {
// testExpandLayer("non-seq", false); // non-seq expand to seq
// testExpandLayer("non-seq", true); // non-seq expand to hasSubseq
// testExpandLayer("seq", true); // seq expand to hasSubseq
// }
//
// void testDegradeLayer(bool hasSubseq,
// string layer_type,
// string trans_type,
// int stride) {
// TestConfig config;
// config.layerConfig.set_type(layer_type);
// config.layerConfig.set_size(10);
// config.layerConfig.set_seq_pool_stride(stride);
// config.biasSize = 0;
//
// config.inputDefs.push_back(
// {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA,
// "layer_0",
// 10,
// 0});
// config.layerConfig.add_inputs();
// config.layerConfig.set_trans_type(trans_type);
//
// auto testDegradeLayerGrad = [](TestConfig& config, string layer_type) {
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, layer_type, 100, false, useGpu);
// }
// };
//
// if (layer_type == "average") {
// for (auto strategy : {"average", "sum", "squarerootn"}) {
// LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type
// << " average_strategy=" << strategy
// << " seq_pool_stride=" << stride;
// config.layerConfig.set_average_strategy(strategy);
// testDegradeLayerGrad(config, layer_type);
// }
// } else {
// LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type
// << " seq_pool_stride=" << stride;
// testDegradeLayerGrad(config, layer_type);
// }
// }
//
// TEST(Layer, MaxLayer) {
// testDegradeLayer(false, "max", "non-seq", -1); // seq max to non-seq
// testDegradeLayer(false,
// "max",
// "non-seq",
// 5); // seq max to a shorten seq, stride window = 5
// testDegradeLayer(true, "max", "non-seq", -1); // hasSubseq max to non-seq
// testDegradeLayer(true, "max", "seq", -1); // hasSubseq max to seq
// }
//
// TEST(Layer, SequenceLastInstanceLayer) {
// testDegradeLayer(false,
// "seqlastins",
// "non-seq",
// -1); // seq seqlastins to non-seq
// testDegradeLayer(false,
// "seqlastins",
// "non-seq",
// 5); // seq seqlastins to a shorten seq, stride window = 5
// testDegradeLayer(true,
// "seqlastins",
// "non-seq",
// -1); // hasSubseq seqlastins to non-seq
// testDegradeLayer(
// true, "seqlastins", "seq", -1); // hasSubseq seqlastins to seq
// }
//
// TEST(Layer, AverageLayer) {
// testDegradeLayer(false, "average", "non-seq", -1); // seq average to
// non-seq
// testDegradeLayer(false,
// "average",
// "non-seq",
// 5); // seq average to a shorten seq, stride window = 5
// testDegradeLayer(
// true, "average", "non-seq", -1); // hasSubseq average to
// non-seq
// testDegradeLayer(true, "average", "seq", -1); // hasSubseq average to seq
// }
//
// TEST(Layer, SequenceConcatLayer) {
// TestConfig config;
// config.layerConfig.set_type("seqconcat");
// config.layerConfig.set_size(10);
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0});
// config.layerConfig.add_inputs();
// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "seqconcat", 100, false, useGpu);
// }
// }
//
// TEST(Layer, SequenceReshapeLayer) {
// TestConfig config;
// config.layerConfig.set_type("seqreshape");
// config.layerConfig.set_size(10);
//
// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 100, 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "seqreshape", 100, false, useGpu);
// }
// }
//
// TEST(Layer, ConvShiftLayer) {
// TestConfig config;
// config.layerConfig.set_type("conv_shift");
// config.layerConfig.set_size(10);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 3, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// // Not support GPU now
// testLayerGrad(config, "conv_shift", 100, false, false);
// }
//
// TEST(Layer, PowerLayer) {
// TestConfig config;
// config.layerConfig.set_type("power");
// config.layerConfig.set_size(10);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "power", 100, false, useGpu);
// }
// }
//
// TEST(Layer, ConvexCombinationLayer) {
// TestConfig config;
// config.layerConfig.set_type("convex_comb");
// config.layerConfig.set_size(20);
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "convex_comb", 100, false, useGpu);
// }
// }
//
// TEST(Layer, InterpolationLayer) {
// TestConfig config;
// config.layerConfig.set_type("interpolation");
// config.layerConfig.set_size(10);
// config.biasSize = 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_2", 10, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "interpolation", 100, false, useGpu);
// }
// }
//
// TEST(Layer, OuterProdLayer) {
// TestConfig config;
// config.layerConfig.set_type("out_prod");
// config.layerConfig.set_size(100);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.layerConfig.add_inputs();
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "out_prod", 100, false, useGpu);
// }
// }
//
// TEST(Layer, SlopeInterceptLayer) {
// TestConfig config;
// config.layerConfig.set_type("slope_intercept");
// config.layerConfig.set_size(10);
// config.layerConfig.set_slope(1.0);
// config.layerConfig.set_intercept(0.1);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "slope_intercept", 100, false, useGpu);
// }
// }
//
// TEST(Layer, ScalingLayer) {
// TestConfig config;
// config.layerConfig.set_type("scaling");
// config.layerConfig.set_size(10);
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
// config.layerConfig.add_inputs();
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "scaling", 100, false, useGpu);
// }
// }
//
// void testNormLayer(const string& normType, bool trans, bool useGpu) {
// TestConfig config;
// config.layerConfig.set_type("norm");
// config.layerConfig.set_active_type("relu");
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1568, 0});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// NormConfig* norm = input->mutable_norm_conf();
// norm->set_norm_type(normType);
// norm->set_channels(16);
// norm->set_size(5);
// norm->set_scale(0.001);
// norm->set_pow(0.75);
// norm->set_blocked(0);
// norm->set_img_size(14);
// norm->set_img_size_y(7);
// norm->set_output_x(norm->img_size());
// norm->set_output_y(norm->img_size_y());
// if (norm->norm_type() == "cmrnorm" ||
// norm->norm_type() == "cmrnorm-projection") {
// norm->set_scale(norm->scale() / norm->size());
// } else {
// norm->set_scale(norm->scale() / (norm->size() * norm->size()));
// }
//
// config.layerConfig.set_size(norm->output_x() * norm->output_y() *
// norm->channels());
// config.biasSize = 0;
//
// testLayerGrad(config, "norm", 100, trans, useGpu);
// }
//
// TEST(Layer, NormLayer) {
// testNormLayer("cmrnorm-projection",
// /* trans= */ false, /* useGpu= */
// true);
// testNormLayer("cmrnorm-projection",
// /* trans= */ false, /* useGpu= */
// false);
// }
//
// void setPoolConfig(TestConfig* config,
// PoolConfig* pool,
// const string& poolType) {
// (*config).biasSize = 0;
// (*config).layerConfig.set_type("pool");
// (*config).layerConfig.set_num_filters(16);
//
// int kw = 3, kh = 3;
// int pw = 0, ph = 0;
// int sw = 2, sh = 2;
// pool->set_pool_type(poolType);
// pool->set_channels(16);
// pool->set_size_x(kw);
// pool->set_size_y(kh);
// pool->set_start(0);
// pool->set_padding(pw);
// pool->set_padding_y(ph);
// pool->set_stride(sw);
// pool->set_stride_y(sh);
//
// int ow = outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false);
// 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});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// PoolConfig* pool = input->mutable_pool_conf();
//
// pool->set_img_size(14);
// pool->set_img_size_y(14);
// setPoolConfig(&config, pool, poolType);
// config.layerConfig.set_size(pool->output_x() * pool->output_y() *
// pool->channels());
//
// testLayerGrad(config, "pool", 100, trans, useGpu);
// }
//
// #ifndef PADDLE_ONLY_CPU
// void testPoolLayer2(const string& poolType, bool trans, bool useGpu) {
// TestConfig config;
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// PoolConfig* pool = input->mutable_pool_conf();
//
// pool->set_size_y(4);
// pool->set_stride_y(3);
// pool->set_img_size(10);
// pool->set_img_size_y(20);
// setPoolConfig(&config, pool, poolType);
// pool->set_output_y((pool->img_size_y() - pool->start() - pool->size_y()) /
// ((float)pool->stride_y()) +
// 1.5);
// config.layerConfig.set_size(pool->output_x() * pool->output_y() *
// pool->channels());
//
// testLayerGrad(config, "pool", 100, trans, useGpu);
// }
// #endif
//
// TEST(Layer, PoolLayer) {
// testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ false);
// testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ false);
//
// #ifndef PADDLE_ONLY_CPU
// testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ true);
// testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ true);
// testPoolLayer("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true);
// testPoolLayer("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true);
// 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,
// bool useGpu) {
// TestConfig config;
// config.layerConfig.set_type("spp");
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// SppConfig* sppConfig = input->mutable_spp_conf();
// sppConfig->set_pool_type(poolType);
// sppConfig->set_pyramid_height(pyramidHeight);
// ImageConfig* imageConfig = sppConfig->mutable_image_conf();
// imageConfig->set_channels(16);
// imageConfig->set_img_size(10);
// imageConfig->set_img_size_y(20);
// 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}) {
// testSppLayer("avg-projection", pyramidHeight, false, useGpu);
// testSppLayer("max-projection", pyramidHeight, false, useGpu);
// }
// }
// }
//
// TEST(Layer, rankCostLayer) {
// TestConfig config;
// config.layerConfig.set_type("rank-cost");
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 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();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "rank-cost", 100, false, useGpu);
// }
// }
//
// TEST(Layer, sumCostLayer) {
// TestConfig config;
// config.layerConfig.set_type("sum_cost");
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "sum_cost", 100, false, useGpu);
// }
// }
//
// TEST(Layer, weightedRankCostLayer) {
// TestConfig config;
// config.layerConfig.set_type("rank-cost");
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 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_TARGET, "layer_3", 1, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "weighted-rank-cost", 100, false, useGpu);
// }
// }
//
// TEST(Layer, TensorLayer) {
// TestConfig config;
// config.layerConfig.set_type("tensor");
// config.layerConfig.set_size(10);
// config.layerConfig.set_active_type("sigmoid");
// config.biasSize = config.layerConfig.size();
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 250});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 5, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "tensor", 100, false, useGpu);
// }
// }
//
// TEST(Layer, RecurrentLayer) {
// TestConfig config;
// config.layerConfig.set_type("recurrent");
// config.layerConfig.set_size(4);
// config.layerConfig.set_active_type("tanh");
// config.biasSize = 4;
//
// config.inputDefs.push_back(
// {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 4, /* paraSize= */ 16});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// for (auto reversed : {false, true}) {
// 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");
// config.layerConfig.set_size(4);
// config.layerConfig.set_active_type("tanh");
// config.layerConfig.set_active_state_type("sigmoid");
// config.layerConfig.set_active_gate_type("sigmoid");
// config.biasSize = 28;
//
// config.inputDefs.push_back(
// {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 64});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// for (auto reversed : {false, true}) {
// 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);
// testLayerGrad(config, "lstmemory", 10, /* trans= */ false, useGpu);
// }
// }
//
// TEST(Layer, MDLstmLayer) {
// TestConfig config;
// config.layerConfig.set_type("mdlstmemory");
// config.layerConfig.set_size(4);
// config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_active_state_type("sigmoid");
// config.layerConfig.set_active_gate_type("sigmoid");
// config.biasSize = 4 * 9;
//
// config.inputDefs.push_back(
// {INPUT_SEQUENCE_MDIM_DATA, "layer_0", 4 * 5, 4 * 4 * 5});
// config.layerConfig.add_inputs();
// config.layerConfig.add_directions(true);
// config.layerConfig.add_directions(true);
//
// for (auto useGpu : {false, true}) {
// for (int i = 0; i < 2; i++) {
// for (int j = 0; j < 2; j++) {
// 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;
// config.layerConfig.set_type("prelu");
// config.inputDefs.push_back({INPUT_DATA, "layer_0", inputSize, channels});
// config.layerConfig.add_inputs();
// config.layerConfig.set_size(inputSize);
// config.layerConfig.set_partial_sum(inputSize /
// 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);
// }
//
// TEST(Layer, ResizeLayer) {
// TestConfig config;
// config.biasSize = 0;
// config.layerConfig.set_type("resize");
// config.layerConfig.set_size(64);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 16, 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "resize", 100, false, useGpu);
// }
// }
//
// TEST(Layer, RotateLayer) {
// TestConfig config;
// config.biasSize = 0;
// config.layerConfig.set_type("rotate");
// const int CHANNEL = 2;
// const int HEIGHT = 8;
// const int WIDTH = 4;
// const int INPUT_SIZE = HEIGHT * WIDTH * CHANNEL;
// config.layerConfig.set_size(INPUT_SIZE);
// config.layerConfig.set_height(HEIGHT);
// config.layerConfig.set_width(WIDTH);
// config.inputDefs.push_back({INPUT_DATA, "layer_0", INPUT_SIZE, 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "rotate", 100, false, useGpu);
// }
// }
//
// TEST(Layer, NCELayer) {
// TestConfig config;
// size_t numClasses = 4;
// config.layerConfig.set_type("nce");
// config.layerConfig.set_size(1);
// config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_num_classes(numClasses);
// config.biasSize = numClasses;
//
// config.inputDefs.push_back(
// {INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 16 *
// numClasses});
// config.inputDefs.push_back(
// {INPUT_LABEL, "label", /* dim= */ numClasses, /* paraSize= */ 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto withWeight : {false, true}) {
// if (withWeight) {
// config.inputDefs.push_back(
// {INPUT_DATA_TARGET, "weight", /* dim= */ 1, /* paraSize= */ 0});
// config.layerConfig.add_inputs();
// }
//
// for (auto isIdLabel : {false, true}) {
// config.inputDefs[1] = {
// isIdLabel ? INPUT_LABEL : INPUT_SPARSE_NON_VALUE_DATA,
// "label",
// /* dim= */ numClasses,
// /* paraSize= */ 0};
//
// for (auto withDist : {false, true}) {
// config.layerConfig.clear_neg_sampling_dist();
// if (withDist) {
// double sum = 0;
// for (size_t i = 0; i < numClasses; ++i) {
// real p = rand(); // NOLINT use rand_r
// 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;
// config.layerConfig.set_neg_sampling_dist(i, p);
// }
// }
// LOG(INFO) << "NCELayer "
// << " isIdLabel=" << isIdLabel << " withWeight=" <<
// withWeight
// << " withDist=" << withDist;
// // Not support GPU now
// testLayerGrad(config,
// "nce",
// 100,
// /* trans= */ false,
// /* useGpu */ false);
// }
// }
// }
// }
//
// TEST(Layer, GatedRecurrentLayer) {
// TestConfig config;
// config.layerConfig.set_type("gated_recurrent");
// config.layerConfig.set_size(4);
// config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_active_gate_type("sigmoid");
// config.biasSize = 12;
//
// config.inputDefs.push_back(
// {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// for (auto reversed : {false, true}) {
// config.layerConfig.set_reversed(reversed);
// config.testState = !reversed;
// testLayerGrad(config, "gated_recurrent", 100, /* trans= */ false,
// useGpu);
// }
// }
// }
//
// TEST(Layer, GruStepLayer) {
// TestConfig config;
// config.layerConfig.set_type("gru_step");
// config.layerConfig.set_size(4);
// config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_active_gate_type("sigmoid");
// config.biasSize = 12;
//
// config.inputDefs.push_back(
// {INPUT_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48});
// config.inputDefs.push_back(
// {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "gruStep", 100, /* trans= */ false, useGpu);
// }
// }
//
// TEST(Layer, LstmStepLayer) {
// TestConfig config;
// config.layerConfig.set_type("lstm_step");
// config.layerConfig.set_size(4);
// config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_active_state_type("sigmoid");
// config.layerConfig.set_active_gate_type("sigmoid");
// config.biasSize = 12;
// config.testAccumulate = false;
//
// config.inputDefs.push_back(
// {INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 0});
// config.inputDefs.push_back(
// {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "lstmStep", 100, /* trans= */ false, useGpu);
// }
// }
//
// void testBatchNormLayer(const string& type, bool trans, bool useGpu) {
// TestConfig config;
// const int CHANNELS = 10;
// const int IMG_SIZE = 16;
// const int IMG_SIZE_Y = 8;
// size_t size = CHANNELS * IMG_SIZE * IMG_SIZE_Y;
// config.layerConfig.set_type(type);
// config.layerConfig.set_size(size);
// config.layerConfig.set_active_type("sigmoid");
// config.biasSize = CHANNELS;
// config.inputDefs.push_back({INPUT_DATA,
// "layer_0",
// /* dim= */ size,
// /* paraSize= */ CHANNELS});
//
// config.inputDefs.push_back({INPUT_DATA, "layer_1_running_mean", 1,
// CHANNELS});
// config.inputDefs.back().isStatic = true;
// config.inputDefs.push_back({INPUT_DATA, "layer_2_running_var", 1,
// CHANNELS});
// config.inputDefs.back().isStatic = true;
//
// LayerInputConfig* input = config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// ImageConfig* img_conf = input->mutable_image_conf();
// img_conf->set_channels(CHANNELS);
// img_conf->set_img_size(IMG_SIZE);
// img_conf->set_img_size_y(IMG_SIZE_Y);
//
// testLayerGrad(config,
// "batch_norm",
// 64,
// /* trans= */ trans,
// useGpu,
// /* useWeight */ true);
// }
//
// TEST(Layer, BatchNormalizationLayer) {
// testBatchNormLayer("batch_norm", false, false);
// #ifndef PADDLE_ONLY_CPU
// testBatchNormLayer("batch_norm", false, true);
// if (hl_get_cudnn_lib_version() >= int(4000)) {
// testBatchNormLayer("cudnn_batch_norm", false, true);
// }
// #endif
// }
//
// void testConvOperator(bool isDeconv) {
// TestConfig config;
// const int NUM_FILTERS = 16;
// const int FILTER_SIZE = 2;
// const int FILTER_SIZE_Y = 3;
// const int CHANNELS = 3;
// const int IMAGE_SIZE = 16;
// const int IMAGE_SIZE_Y = 9;
// OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs();
// if (isDeconv) {
// operatorConf.set_type("convt");
// } else {
// operatorConf.set_type("conv");
// }
// ConvConfig* conv = operatorConf.mutable_conv_conf();
// operatorConf.set_num_filters(NUM_FILTERS);
// conv->set_filter_size(FILTER_SIZE);
// conv->set_filter_size_y(FILTER_SIZE_Y);
// conv->set_channels(CHANNELS);
// conv->set_padding(0);
// conv->set_padding_y(1);
// conv->set_stride(2);
// conv->set_stride_y(2);
// conv->set_groups(1);
// conv->set_img_size(IMAGE_SIZE);
// conv->set_img_size_y(IMAGE_SIZE_Y);
// conv->set_output_x(outputSize(conv->img_size(),
// conv->filter_size(),
// conv->padding(),
// conv->stride(),
// /* caffeMode */ true));
// conv->set_output_y(outputSize(conv->img_size_y(),
// conv->filter_size_y(),
// conv->padding_y(),
// conv->stride_y(),
// /* caffeMode */ true));
//
// if (isDeconv) {
// conv->set_filter_channels(NUM_FILTERS / conv->groups());
// config.inputDefs.push_back({INPUT_DATA,
// "layer_0",
// conv->output_x() * conv->output_y() *
// CHANNELS,
// 0});
// config.layerConfig.set_size(IMAGE_SIZE * IMAGE_SIZE_Y * NUM_FILTERS);
// } else {
// conv->set_filter_channels(conv->channels() / conv->groups());
// config.inputDefs.push_back(
// {INPUT_DATA, "layer_0", IMAGE_SIZE * IMAGE_SIZE_Y * CHANNELS, 0});
// config.layerConfig.set_size(conv->output_x() * conv->output_y() *
// NUM_FILTERS);
// }
//
// config.inputDefs.push_back(
// {INPUT_DATA,
// "layer_1",
// FILTER_SIZE * FILTER_SIZE_Y * CHANNELS * NUM_FILTERS,
// 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// testOperatorGrad(config, operatorConf, 100, /*useGpu*/ true, false);
// }
//
// TEST(Operator, conv) {
// testConvOperator(/*isDeconv*/ true);
// testConvOperator(/*isDeconv*/ false);
// }
//
// TEST(Layer, FeatureMapExpandLayer) {
// TestConfig config;
// config.layerConfig.set_type("featmap_expand");
// const int CHANNELS = 10;
// const int INPUT_SIZE = 100;
// config.layerConfig.set_size(INPUT_SIZE * CHANNELS);
// config.layerConfig.set_num_filters(CHANNELS);
// config.inputDefs.push_back({INPUT_SEQUENCE_DATA,
// "layer_0",
// /* dim= */ INPUT_SIZE,
// /* paraSize= */ 0});
// config.layerConfig.add_inputs();
// for (auto useGpu : {false, true}) {
// for (auto asRowVec : {false, true}) {
// config.layerConfig.set_user_arg(asRowVec ? "as_row_vec" :
// "as_col_vec");
// testLayerGrad(config,
// "featmap_expand",
// /*batch_size*/ 100,
// /* trans= */ false,
// useGpu,
// /* useWeight */ true);
// }
// }
// }
//
// TEST(Layer, MultiplexLayer) {
// TestConfig config;
// const int LAYER_SIZE = 100;
// config.layerConfig.set_type("multiplex");
// config.layerConfig.set_size(LAYER_SIZE);
//
// config.inputDefs.push_back({INPUT_LABEL, "layer_0", 2, 0});
// config.inputDefs.push_back(
// {INPUT_DATA, "layer_1", /* dim= */ LAYER_SIZE, /* paraSize= */ 0});
// config.inputDefs.push_back(
// {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}) {
// testLayerGrad(config, "multiplex", 512, /* trans= */ false, useGpu);
// }
// }
//
// TEST(Layer, PadLayer) {
// TestConfig config;
// config.biasSize = 0;
// config.layerConfig.set_type("pad");
//
// int c = 4;
// int h = 31;
// int w = 36;
// size_t size = c * h * w;
// config.inputDefs.push_back({INPUT_DATA, "layer_0", size, 0});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// PadConfig* pad = input->mutable_pad_conf();
// ImageConfig* image = pad->mutable_image_conf();
//
// image->set_channels(c);
// image->set_img_size(h);
// image->set_img_size_y(w);
// pad->add_pad_c(1);
// pad->add_pad_c(2);
// pad->add_pad_h(2);
// pad->add_pad_h(3);
// pad->add_pad_w(3);
// pad->add_pad_w(5);
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "pad", 10, false, useGpu);
// }
// }
//
// TEST(Layer, CrossChannelNormLayer) {
// TestConfig config;
// config.paramInitialMean = 1.;
// config.paramInitialStd = 0.;
// config.layerConfig.set_type("norm");
// config.layerConfig.set_size(100);
// LayerInputConfig* input = config.layerConfig.add_inputs();
// NormConfig* norm = input->mutable_norm_conf();
// norm->set_norm_type("cross-channel-norm");
// norm->set_channels(10);
// norm->set_size(100);
// norm->set_scale(0);
// norm->set_pow(0);
// norm->set_blocked(0);
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 100, 10});
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "cross-channel-norm", 10, false, useGpu, false);
// }
// }
//
// TEST(Layer, smooth_l1) {
// TestConfig config;
// config.layerConfig.set_type("smooth_l1");
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 200, 0});
// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 200, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "smooth_l1", 100, false, useGpu, false);
// }
// }
//
// TEST(Layer, multibox_loss) {
// TestConfig config;
// config.layerConfig.set_type("multibox_loss");
// config.biasSize = 0;
// LayerInputConfig* input = config.layerConfig.add_inputs();
// MultiBoxLossConfig* multiboxLoss = input->mutable_multibox_loss_conf();
// multiboxLoss->set_num_classes(21);
// multiboxLoss->set_input_num(1);
// multiboxLoss->set_overlap_threshold(0.5);
// multiboxLoss->set_neg_pos_ratio(3);
// multiboxLoss->set_neg_overlap(0.5);
// multiboxLoss->set_background_id(0);
// multiboxLoss->set_height(3);
// multiboxLoss->set_width(3);
//
// size_t gtNum = 1;
// MatrixPtr labelValue = Matrix::create(gtNum, 6, false, false);
// labelValue->randomizeUniform();
// labelValue->add(-0.5);
// labelValue->sigmoid(*labelValue);
// real* labelData = labelValue->getData();
// size_t labelWidth = labelValue->getWidth();
// for (size_t i = 0; i < gtNum; ++i) {
// *(labelData + i * labelWidth) = std::rand() % 20 + 1;
// *(labelData + i * labelWidth + 1) = 0.400259;
// *(labelData + i * labelWidth + 2) = 0.377857;
// *(labelData + i * labelWidth + 3) = 0.525712;
// *(labelData + i * labelWidth + 4) = 0.519368;
// }
// vector<int> seqStartPositions(gtNum + 1, 0);
// for (size_t i = 1; i <= gtNum; ++i) {
// seqStartPositions[i] = i;
// }
//
// // Ensure at lease one matched bbox
// MatrixPtr priorValue = Matrix::create(1, 72, false, false);
// priorValue->randomizeUniform();
// priorValue->add(-0.5);
// priorValue->sigmoid(*priorValue);
// real* priorData = priorValue->getData();
// *(priorData) = 0.424811;
// *(priorData + 1) = 0.397059;
// *(priorData + 2) = 0.538905;
// *(priorData + 3) = 0.447091;
// *(priorData + 4) = 0.425720;
// *(priorData + 5) = 0.515228;
// *(priorData + 6) = 0.519452;
// *(priorData + 7) = 0.591065;
//
// config.inputDefs.push_back(
// {INPUT_SELF_DEFINE_DATA, "priorbox", priorValue, {}});
// config.inputDefs.push_back(
// {INPUT_SELF_DEFINE_DATA, "label", labelValue, seqStartPositions});
// config.inputDefs.push_back({INPUT_DATA, "locPred", 36, 0});
// config.inputDefs.push_back({INPUT_DATA, "confPred", 189, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "multibox_loss", 1, false, useGpu, false);
// }
// }
//
// TEST(Layer, TransLayer) {
// TestConfig config;
// const int height = 128;
// const int width = 1028;
// config.layerConfig.set_type("trans");
// config.layerConfig.set_size(width);
//
// config.inputDefs.push_back(
// {INPUT_DATA, "layer_0", /* dim= */ height * width, /* paraSize= */ 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "trans", height, /* trans= */ false, useGpu);
// }
// }
//
// TEST(Layer, RowConvLayer) {
// const int context = 3;
// const int size = 512;
//
// TestConfig config;
// config.layerConfig.set_type("row_conv");
// config.layerConfig.set_size(size);
// config.layerConfig.set_active_type("sigmoid");
//
// config.inputDefs.push_back(
// {INPUT_SEQUENCE_DATA, "layer_0", size, context * size});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// RowConvConfig* conv = input->mutable_row_conv_conf();
// conv->set_context_length(context);
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "row_conv", 100, false, useGpu, false);
// }
// }
//
// TEST(Layer, CropLayer) {
// TestConfig config;
// // config input_0
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 0});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// ImageConfig* img = input->mutable_image_conf();
// img->set_channels(4);
// img->set_img_size(16);
// config.layerConfig.set_axis(2);
// config.layerConfig.add_offset(0);
// config.layerConfig.add_offset(0);
//
// // config input_1
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 128, 0});
// input = config.layerConfig.add_inputs();
// img = input->mutable_image_conf();
// img->set_channels(2);
// img->set_img_size(8);
//
// // config crop layer
// config.layerConfig.set_type("crop");
// config.layerConfig.set_name("cropLayer");
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "crop", 100, false, useGpu, false);
// }
// }
TEST(Operator, dot_mul) {
TestConfig config;
config.layerConfig.set_size(10);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs();
operatorConf.set_type("dot_mul");
operatorConf.set_dotmul_scale(-1);
testOperatorGrad(config, operatorConf, 100, false, false);
}
TEST(Projection, context) {
for (auto contextStart : {-5, -3, -1, 0, 3}) {
for (auto contextLength : {1, 2, 5, 7}) {
for (auto batchSize : {1, 2, 5, 20, 50}) {
for (auto trainablePadding : {false, true}) {
LOG(INFO) << " contextStart=" << contextStart
<< " contextLength=" << contextLength
<< " batchSize=" << batchSize
<< " trainablePadding=" << trainablePadding;
ProjectionConfig conf;
conf.set_type("context");
conf.set_input_size(10);
conf.set_context_start(contextStart);
conf.set_context_length(contextLength);
conf.set_trainable_padding(trainablePadding);
conf.set_output_size(conf.context_length() * conf.input_size());
int pad =
std::max(0, -conf.context_start()) +
std::max(0, conf.context_start() + conf.context_length() - 1);
for (auto useGpu : {false, true}) {
testProjectionGrad(
conf,
INPUT_SEQUENCE_DATA,
trainablePadding ? conf.input_size() * pad : 0,
batchSize,
useGpu,
contextStart + contextLength <= 1); // = testState
}
}
}
}
}
}
TEST(Projection, trans_fc) {
ProjectionConfig conf;
conf.set_type("trans_fc");
conf.set_input_size(50);
conf.set_output_size(20);
for (auto useGpu : {false, true}) {
testProjectionGrad(conf,
INPUT_DATA,
/* parameterSize */ 1000,
/* batchSize */ 100,
useGpu);
}
}
TEST(Projection, fc) {
ProjectionConfig conf;
conf.set_type("fc");
conf.set_input_size(10);
conf.set_output_size(20);
for (auto useGpu : {false, true}) {
testProjectionGrad(conf,
INPUT_DATA,
/* parameterSize */ 200,
/* batchSize */ 100,
useGpu);
}
}
TEST(Projection, dot_mul) {
ProjectionConfig conf;
conf.set_type("dot_mul");
conf.set_input_size(20);
conf.set_output_size(20);
for (auto useGpu : {false, true}) {
testProjectionGrad(conf,
INPUT_DATA,
/* parameterSize */ 20,
/* batchSize */ 100,
useGpu);
}
}
TEST(Projection, table) {
ProjectionConfig conf;
conf.set_type("table");
conf.set_input_size(10);
conf.set_output_size(20);
for (auto useGpu : {false, true}) {
testProjectionGrad(conf,
INPUT_LABEL,
/* parameterSize */ 200,
/* batchSize */ 100,
useGpu);
}
}
TEST(Projection, identity) {
ProjectionConfig conf;
conf.set_type("identity");
conf.set_input_size(10);
conf.set_output_size(10);
for (auto useGpu : {false, true}) {
testProjectionGrad(conf,
INPUT_DATA,
/* parameterSize */ 0,
/* batchSize */ 100,
useGpu);
}
}
TEST(Projection, slice) {
ProjectionConfig conf;
conf.set_type("slice");
conf.set_input_size(100);
SliceConfig& slice1 = *conf.add_slices();
slice1.set_start(10);
slice1.set_end(20);
SliceConfig& slice2 = *conf.add_slices();
slice2.set_start(50);
slice2.set_end(70);
conf.set_output_size(30);
for (auto useGpu : {false, true}) {
testProjectionGrad(conf,
INPUT_DATA,
/* parameterSize */ 0,
/* batchSize */ 10,
useGpu);
}
}
TEST(Projection, scaling) {
ProjectionConfig conf;
conf.set_type("scaling");
conf.set_input_size(10);
conf.set_output_size(10);
for (auto useGpu : {false}) {
testProjectionGrad(conf,
INPUT_DATA,
/* parameterSize */ 1,
/* batchSize */ 100,
useGpu);
}
}
void testProjectionConv(size_t groups, bool isDeconv) {
const int NUM_FILTERS = 18;
const int FILTER_SIZE = 2;
const int FILTER_SIZE_Y = 4;
const int CHANNELS = 3;
const int IMAGE_SIZE = 16;
ProjectionConfig conf;
if (isDeconv) {
conf.set_type("convt");
} else {
conf.set_type("conv");
}
conf.set_num_filters(NUM_FILTERS);
ConvConfig* conv = conf.mutable_conv_conf();
conv->set_filter_size(FILTER_SIZE);
conv->set_filter_size_y(FILTER_SIZE_Y);
conv->set_channels(CHANNELS);
conv->set_padding(0);
conv->set_padding_y(1);
conv->set_stride(2);
conv->set_stride_y(2);
conv->set_groups(groups);
if (isDeconv) {
conv->set_filter_channels(NUM_FILTERS / conv->groups());
} else {
conv->set_filter_channels(conv->channels() / conv->groups());
}
conv->set_img_size(IMAGE_SIZE);
int output_x = outputSize(conv->img_size(),
conv->filter_size(),
conv->padding(),
conv->stride(),
/* caffeMode */ true);
int output_y = outputSize(conv->img_size(),
conv->filter_size_y(),
conv->padding_y(),
conv->stride_y(),
/* caffeMode */ true);
conv->set_output_x(output_x);
conv->set_output_y(output_y);
if (isDeconv) {
conf.set_input_size(output_x * output_y * CHANNELS);
conf.set_output_size(IMAGE_SIZE * IMAGE_SIZE * NUM_FILTERS);
} else {
conf.set_input_size(IMAGE_SIZE * IMAGE_SIZE * CHANNELS);
conf.set_output_size(output_x * output_y * NUM_FILTERS);
}
testProjectionGrad(conf,
INPUT_DATA,
/* parameterSize */ NUM_FILTERS * CHANNELS * FILTER_SIZE *
FILTER_SIZE_Y / groups,
/* batchSize */ 100,
true,
false,
NUM_FILTERS,
true);
}
#ifndef PADDLE_ONLY_CPU
TEST(Projection, conv) {
/// test ConvProjection
testProjectionConv(1, false);
testProjectionConv(3, false);
/// test ConvTransProjection
testProjectionConv(1, true);
testProjectionConv(3, true);
}
#endif
TEST(Layer, BilinearInterpLayer) {
TestConfig config;
config.layerConfig.set_type("bilinear_interp");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
BilinearInterpConfig* bilinear = input->mutable_bilinear_interp_conf();
ImageConfig* image = bilinear->mutable_image_conf();
image->set_img_size(32);
image->set_img_size_y(32);
image->set_channels(4);
for (auto useGpu : {false, true}) {
for (auto outSize : {32, 64}) {
bilinear->set_out_size_x(outSize);
bilinear->set_out_size_y(outSize);
testLayerGrad(config, "bilinear_interp", 10, false, useGpu);
}
}
}
TEST(Layer, concat) {
TestConfig config;
config.biasSize = 0;
config.layerConfig.set_type("concat");
config.layerConfig.set_size(15);
config.layerConfig.set_active_type("sigmoid");
config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0});
config.layerConfig.add_inputs();
config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "concat", 100, false, useGpu);
}
}
TEST(Layer, AddtoLayer) {
TestConfig config;
config.biasSize = 0;
config.layerConfig.set_type("addto");
config.layerConfig.set_size(10);
config.layerConfig.set_active_type("sigmoid");
config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
config.layerConfig.add_inputs();
config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "addto", 100, false, useGpu);
}
}
TEST(Layer, CTCLayer) {
TestConfig config;
config.layerConfig.set_type("ctc");
config.layerConfig.set_norm_by_times(false);
config.layerConfig.set_size(10);
config.biasSize = 0;
config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0});
config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config,
"ctc",
100,
/* trans */ false, /* useGpu */
useGpu);
}
}
TEST(Layer, cosSimLayer) {
TestConfig config;
config.layerConfig.set_type("cos");
config.layerConfig.set_size(1);
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
config.inputDefs.push_back({INPUT_DATA, "layer_1", 50, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "cos", 100, false, useGpu);
}
}
TEST(Layer, CosSimVecMatLayer) {
TestConfig config;
config.layerConfig.set_type("cos_vm");
config.layerConfig.set_size(5); // output size
config.layerConfig.set_cos_scale(2.0);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 0});
config.layerConfig.add_inputs();
config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "cos_vm", 100, false, useGpu);
}
}
void testDepthwiseConvLayer(const string& type, bool useGpu) {
TestConfig config;
config.biasSize = 32;
config.layerConfig.set_type(type);
config.layerConfig.set_num_filters(32);
config.layerConfig.set_partial_sum(1);
config.layerConfig.set_shared_biases(true);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 2048, 192});
LayerInputConfig* input = config.layerConfig.add_inputs();
ConvConfig* conv = input->mutable_conv_conf();
conv->set_filter_size(2);
conv->set_filter_size_y(3);
conv->set_channels(16);
conv->set_padding(0);
conv->set_padding_y(1);
conv->set_stride(2);
conv->set_stride_y(2);
conv->set_groups(16);
conv->set_filter_channels(conv->channels() / conv->groups());
conv->set_img_size(16);
conv->set_img_size_y(8);
conv->set_output_x(outputSize(conv->img_size(),
conv->filter_size(),
conv->padding(),
conv->stride(),
/* caffeMode */ true));
conv->set_output_y(outputSize(conv->img_size_y(),
conv->filter_size_y(),
conv->padding_y(),
conv->stride_y(),
/* caffeMode */ true));
config.layerConfig.set_size(conv->output_x() * conv->output_y() *
config.layerConfig.num_filters());
testLayerGrad(config, "depthwise_conv", 100, false, useGpu);
// Use small batch_size and useWeight=true to test biasGrad
testLayerGrad(config, "depthwise_conv", 2, false, useGpu, true, 0.02);
}
TEST(Layer, depthwiseConvLayer) {
// 'depthwise_conv' is a sepecial case of 'exconv' whose
// groups size equals to the input channels size.
testDepthwiseConvLayer("exconv", /* useGpu= */ false);
#ifndef PADDLE_ONLY_CPU
testDepthwiseConvLayer("exconv", /* useGpu= */ true);
#endif
}
void testConvLayer(const string& type, bool trans, bool useGpu) {
TestConfig config;
config.biasSize = 16;
config.layerConfig.set_type(type);
config.layerConfig.set_num_filters(16);
config.layerConfig.set_partial_sum(1);
config.layerConfig.set_shared_biases(true);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 384, 288});
LayerInputConfig* input = config.layerConfig.add_inputs();
ConvConfig* conv = input->mutable_conv_conf();
conv->set_filter_size(2);
conv->set_filter_size_y(3);
conv->set_channels(3);
conv->set_padding(0);
conv->set_padding_y(1);
conv->set_stride(2);
conv->set_stride_y(2);
conv->set_groups(1);
conv->set_filter_channels(conv->channels() / conv->groups());
conv->set_img_size(16);
conv->set_img_size_y(8);
conv->set_output_x(outputSize(conv->img_size(),
conv->filter_size(),
conv->padding(),
conv->stride(),
/* caffeMode */ true));
conv->set_output_y(outputSize(conv->img_size_y(),
conv->filter_size_y(),
conv->padding_y(),
conv->stride_y(),
/* caffeMode */ true));
config.layerConfig.set_size(conv->output_x() * conv->output_y() *
config.layerConfig.num_filters());
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
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;
config.layerConfig.set_type(type);
config.layerConfig.set_num_filters(3);
config.layerConfig.set_partial_sum(1);
config.layerConfig.set_shared_biases(true);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 384});
LayerInputConfig* input = config.layerConfig.add_inputs();
ConvConfig* conv = input->mutable_conv_conf();
conv->set_filter_size(2);
conv->set_filter_size_y(4);
conv->set_channels(16);
conv->set_padding(0);
conv->set_padding_y(1);
conv->set_stride(2);
conv->set_stride_y(2);
conv->set_groups(1);
conv->set_filter_channels(3 / conv->groups());
conv->set_img_size(16);
conv->set_output_x(outputSize(conv->img_size(),
conv->filter_size(),
conv->padding(),
conv->stride(),
/* caffeMode */ true));
config.layerConfig.set_size(conv->img_size() * conv->img_size() *
config.layerConfig.num_filters());
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);
}
#ifndef PADDLE_ONLY_CPU
testConvTransLayer("cudnn_convt", /* trans= */ false, /* useGpu= */ true);
#endif
}
TEST(Layer, blockExpandLayer) {
TestConfig config;
config.biasSize = 0;
config.layerConfig.set_type("blockexpand");
config.inputDefs.push_back({INPUT_DATA, "layer_0", 6144, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
BlockExpandConfig* blockExpand = input->mutable_block_expand_conf();
blockExpand->set_img_size_x(64);
blockExpand->set_img_size_y(32);
blockExpand->set_channels(3);
blockExpand->set_padding_x(0);
blockExpand->set_padding_y(0);
blockExpand->set_block_x(4);
blockExpand->set_block_y(32);
blockExpand->set_stride_x(2);
blockExpand->set_stride_y(2);
blockExpand->set_output_x(outputSize(blockExpand->img_size_x(),
blockExpand->block_x(),
blockExpand->padding_x(),
blockExpand->stride_x(),
/* caffeMode */ false));
blockExpand->set_output_y(outputSize(blockExpand->img_size_y(),
blockExpand->block_y(),
blockExpand->padding_y(),
blockExpand->stride_y(),
/* caffeMode */ false));
config.layerConfig.set_size(blockExpand->block_x() * blockExpand->block_y() *
blockExpand->channels());
for (auto useGpu : {false, true}) {
testLayerGrad(config, "blockexpand", 100, false, useGpu);
}
}
TEST(Layer, maxoutLayer) {
TestConfig config;
config.biasSize = 0;
config.layerConfig.set_type("maxout");
config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
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_channels(4);
maxout->set_groups(2);
for (auto useGpu : {false, true}) {
testLayerGrad(config, "maxout", 10, false, useGpu);
}
}
void testFcLayer(string format, size_t nnz) {
TestConfig config;
config.biasSize = 4096;
config.layerConfig.set_type("fc");
config.layerConfig.set_size(4096);
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.layerConfig.add_inputs();
LOG(INFO) << config.inputDefs[0].sparse.sparse << " "
<< config.inputDefs[0].sparse.format;
for (auto useGpu : {false, true}) {
testLayerGrad(config,
"fc",
100,
/* trans */ false,
useGpu,
/* weight */ true);
}
}
TEST(Layer, fcLayer) {
testFcLayer("", 4096 * 4096 * 2);
testFcLayer("csc", 4096 * 40);
testFcLayer("csr", 4096 * 40);
}
TEST(Layer, SelectiveFullyConnectedLayer) {
TestConfig config;
size_t nin = 16;
size_t nout = 256;
config.layerConfig.set_type("selective_fc");
config.layerConfig.set_size(nout);
config.layerConfig.set_active_type("sigmoid");
config.layerConfig.set_has_selected_colums(true);
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_SPARSE_NON_VALUE_DATA, "index", nout, 0, ParaSparse("csr", true)});
config.layerConfig.add_inputs();
testLayerGrad(config,
"selective_fc",
100,
/* trans= */ false,
/* useGup= */ false,
false);
#ifndef PADDLE_ONLY_CPU
testLayerGrad(config,
"selective_fc",
100,
/* trans= */ false,
/* useGup= */ true,
false);
#endif
}
TEST(Layer, DataNormLayer) {
TestConfig config;
config.layerConfig.set_type("data_norm");
config.layerConfig.set_size(20);
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 100});
config.inputDefs.back().isStatic = true;
config.layerConfig.add_inputs();
for (auto strategy : {"z-score", "min-max", "decimal-scaling"}) {
config.layerConfig.set_data_norm_strategy(strategy);
// The parameters are static, so not support GPU now
testLayerGrad(config,
"data_norm",
200,
/* trans */ false,
/* useGpu */ false);
}
}
TEST(Layer, hsigmoidLayer) {
TestConfig config;
config.layerConfig.set_type("hsigmoid");
config.layerConfig.set_num_classes(5);
config.layerConfig.set_size(1);
config.biasSize = config.layerConfig.num_classes() - 1;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 200});
config.inputDefs.push_back({INPUT_LABEL, "layer_1", 5, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
// Not support GPU now
testLayerGrad(config,
"hsigmoid",
100,
/* trans */ false, /* useGpu */
false);
}
TEST(Layer, multi_cross) {
TestConfig config;
config.layerConfig.set_type("multi-class-cross-entropy");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
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;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config,
"multi_binary_label_cross_entropy",
100,
/* trans */ false,
useGpu);
}
}
TEST(layer, multi_binary_label_id) {
TestConfig config;
config.layerConfig.set_type("multi_binary_label_cross_entropy");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config,
"multi_binary_label_cross_entropy",
100,
/* trans */ false,
useGpu);
}
}
TEST(Layer, multi_cross_with_selfnorm) {
TestConfig config;
config.layerConfig.set_type("multi_class_cross_entropy_with_selfnorm");
config.layerConfig.set_softmax_selfnorm_alpha(0.1);
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
// Not support GPU now
testLayerGrad(config,
"multi_class_cross_entropy_with_selfnorm",
100,
/* trans */ false,
/* useGpu */ false);
}
TEST(Layer, multi_cross_soft) {
TestConfig config;
config.layerConfig.set_type("soft_binary_class_cross_entropy");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config,
"soft_binary_class_cross_entropy",
100,
/* trans */ false,
useGpu);
}
}
TEST(Layer, square_error) {
TestConfig config;
config.layerConfig.set_type("square_error");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
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");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
// "GpuSparseMatrix" as label is not supported
testLayerGrad(config,
"square_error",
100,
/* trans */ false,
/* useGpu */ false);
}
TEST(Layer, sparse_float_square_error) {
TestConfig config;
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, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
// "GpuSparseMatrix" as label is not supported
testLayerGrad(config,
"square_error",
100,
/* trans */ false,
/* useGpu */ false);
}
TEST(Layer, square_error_weighted) {
TestConfig config;
config.layerConfig.set_type("square_error");
config.biasSize = 0;
config.testAccumulate = false;
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_2", 1, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu);
}
}
TEST(Layer, huber_two_class) {
TestConfig config;
config.layerConfig.set_type("huber");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
config.inputDefs.push_back({INPUT_LABEL, "layer_1", 2, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "huber", 100, /* trans */ false, useGpu);
}
}
void testExpandLayer(string trans_type, bool hasSubseq) {
TestConfig config;
config.layerConfig.set_type("expand");
config.inputDefs.push_back(
{trans_type == "non-seq" ? INPUT_DENSE_DIM_DATA : INPUT_SEQUENCE_DATA,
"layer_0",
10,
0});
config.inputDefs.push_back(
{hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA,
"layer_1",
10,
0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
config.layerConfig.set_trans_type(trans_type);
LOG(INFO) << " trans_type=" << trans_type << " hasSubseq=" << hasSubseq;
for (auto useGpu : {false, true}) {
testLayerGrad(config, "expand", 30, false, useGpu);
}
}
TEST(Layer, ExpandLayer) {
testExpandLayer("non-seq", false); // non-seq expand to seq
testExpandLayer("non-seq", true); // non-seq expand to hasSubseq
testExpandLayer("seq", true); // seq expand to hasSubseq
}
void testDegradeLayer(bool hasSubseq,
string layer_type,
string trans_type,
int stride) {
TestConfig config;
config.layerConfig.set_type(layer_type);
config.layerConfig.set_size(10);
config.layerConfig.set_seq_pool_stride(stride);
config.biasSize = 0;
config.inputDefs.push_back(
{hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA,
"layer_0",
10,
0});
config.layerConfig.add_inputs();
config.layerConfig.set_trans_type(trans_type);
auto testDegradeLayerGrad = [](TestConfig& config, string layer_type) {
for (auto useGpu : {false, true}) {
testLayerGrad(config, layer_type, 100, false, useGpu);
}
};
if (layer_type == "average") {
for (auto strategy : {"average", "sum", "squarerootn"}) {
LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type
<< " average_strategy=" << strategy
<< " seq_pool_stride=" << stride;
config.layerConfig.set_average_strategy(strategy);
testDegradeLayerGrad(config, layer_type);
}
} else {
LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type
<< " seq_pool_stride=" << stride;
testDegradeLayerGrad(config, layer_type);
}
}
TEST(Layer, MaxLayer) {
testDegradeLayer(false, "max", "non-seq", -1); // seq max to non-seq
testDegradeLayer(false,
"max",
"non-seq",
5); // seq max to a shorten seq, stride window = 5
testDegradeLayer(true, "max", "non-seq", -1); // hasSubseq max to non-seq
testDegradeLayer(true, "max", "seq", -1); // hasSubseq max to seq
}
TEST(Layer, SequenceLastInstanceLayer) {
testDegradeLayer(false,
"seqlastins",
"non-seq",
-1); // seq seqlastins to non-seq
testDegradeLayer(false,
"seqlastins",
"non-seq",
5); // seq seqlastins to a shorten seq, stride window = 5
testDegradeLayer(true,
"seqlastins",
"non-seq",
-1); // hasSubseq seqlastins to non-seq
testDegradeLayer(
true, "seqlastins", "seq", -1); // hasSubseq seqlastins to seq
}
TEST(Layer, AverageLayer) {
testDegradeLayer(false, "average", "non-seq", -1); // seq average to non-seq
testDegradeLayer(false,
"average",
"non-seq",
5); // seq average to a shorten seq, stride window = 5
testDegradeLayer(
true, "average", "non-seq", -1); // hasSubseq average to non-seq
testDegradeLayer(true, "average", "seq", -1); // hasSubseq average to seq
}
TEST(Layer, SequenceConcatLayer) {
TestConfig config;
config.layerConfig.set_type("seqconcat");
config.layerConfig.set_size(10);
config.biasSize = 0;
config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0});
config.layerConfig.add_inputs();
config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_1", 10, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "seqconcat", 100, false, useGpu);
}
}
TEST(Layer, SequenceReshapeLayer) {
TestConfig config;
config.layerConfig.set_type("seqreshape");
config.layerConfig.set_size(10);
config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 100, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "seqreshape", 100, false, useGpu);
}
}
TEST(Layer, ConvShiftLayer) {
TestConfig config;
config.layerConfig.set_type("conv_shift");
config.layerConfig.set_size(10);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
config.inputDefs.push_back({INPUT_DATA, "layer_1", 3, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
// Not support GPU now
testLayerGrad(config, "conv_shift", 100, false, false);
}
TEST(Layer, PowerLayer) {
TestConfig config;
config.layerConfig.set_type("power");
config.layerConfig.set_size(10);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "power", 100, false, useGpu);
}
}
TEST(Layer, ConvexCombinationLayer) {
TestConfig config;
config.layerConfig.set_type("convex_comb");
config.layerConfig.set_size(20);
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0});
config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "convex_comb", 100, false, useGpu);
}
}
TEST(Layer, InterpolationLayer) {
TestConfig config;
config.layerConfig.set_type("interpolation");
config.layerConfig.set_size(10);
config.biasSize = 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_2", 10, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "interpolation", 100, false, useGpu);
}
}
TEST(Layer, OuterProdLayer) {
TestConfig config;
config.layerConfig.set_type("out_prod");
config.layerConfig.set_size(100);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
config.layerConfig.add_inputs();
config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "out_prod", 100, false, useGpu);
}
}
TEST(Layer, SlopeInterceptLayer) {
TestConfig config;
config.layerConfig.set_type("slope_intercept");
config.layerConfig.set_size(10);
config.layerConfig.set_slope(1.0);
config.layerConfig.set_intercept(0.1);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "slope_intercept", 100, false, useGpu);
}
}
TEST(Layer, ScalingLayer) {
TestConfig config;
config.layerConfig.set_type("scaling");
config.layerConfig.set_size(10);
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
config.layerConfig.add_inputs();
config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "scaling", 100, false, useGpu);
}
}
void testNormLayer(const string& normType, bool trans, bool useGpu) {
TestConfig config;
config.layerConfig.set_type("norm");
config.layerConfig.set_active_type("relu");
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1568, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
NormConfig* norm = input->mutable_norm_conf();
norm->set_norm_type(normType);
norm->set_channels(16);
norm->set_size(5);
norm->set_scale(0.001);
norm->set_pow(0.75);
norm->set_blocked(0);
norm->set_img_size(14);
norm->set_img_size_y(7);
norm->set_output_x(norm->img_size());
norm->set_output_y(norm->img_size_y());
if (norm->norm_type() == "cmrnorm" ||
norm->norm_type() == "cmrnorm-projection") {
norm->set_scale(norm->scale() / norm->size());
} else {
norm->set_scale(norm->scale() / (norm->size() * norm->size()));
}
config.layerConfig.set_size(norm->output_x() * norm->output_y() *
norm->channels());
config.biasSize = 0;
testLayerGrad(config, "norm", 100, trans, useGpu);
}
TEST(Layer, NormLayer) {
testNormLayer("cmrnorm-projection",
/* trans= */ false, /* useGpu= */
true);
testNormLayer("cmrnorm-projection",
/* trans= */ false, /* useGpu= */
false);
}
void setPoolConfig(TestConfig* config,
PoolConfig* pool,
const string& poolType) {
(*config).biasSize = 0;
(*config).layerConfig.set_type("pool");
(*config).layerConfig.set_num_filters(16);
int kw = 3, kh = 3;
int pw = 0, ph = 0;
int sw = 2, sh = 2;
pool->set_pool_type(poolType);
pool->set_channels(16);
pool->set_size_x(kw);
pool->set_size_y(kh);
pool->set_start(0);
pool->set_padding(pw);
pool->set_padding_y(ph);
pool->set_stride(sw);
pool->set_stride_y(sh);
int ow = outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false);
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});
LayerInputConfig* input = config.layerConfig.add_inputs();
PoolConfig* pool = input->mutable_pool_conf();
pool->set_img_size(14);
pool->set_img_size_y(14);
setPoolConfig(&config, pool, poolType);
config.layerConfig.set_size(pool->output_x() * pool->output_y() *
pool->channels());
testLayerGrad(config, "pool", 100, trans, useGpu);
}
#ifndef PADDLE_ONLY_CPU
void testPoolLayer2(const string& poolType, bool trans, bool useGpu) {
TestConfig config;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
PoolConfig* pool = input->mutable_pool_conf();
pool->set_size_y(4);
pool->set_stride_y(3);
pool->set_img_size(10);
pool->set_img_size_y(20);
setPoolConfig(&config, pool, poolType);
pool->set_output_y((pool->img_size_y() - pool->start() - pool->size_y()) /
((float)pool->stride_y()) +
1.5);
config.layerConfig.set_size(pool->output_x() * pool->output_y() *
pool->channels());
testLayerGrad(config, "pool", 100, trans, useGpu);
}
#endif
TEST(Layer, PoolLayer) {
testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ false);
testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ false);
#ifndef PADDLE_ONLY_CPU
testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ true);
testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ true);
testPoolLayer("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true);
testPoolLayer("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true);
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,
bool useGpu) {
TestConfig config;
config.layerConfig.set_type("spp");
config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
SppConfig* sppConfig = input->mutable_spp_conf();
sppConfig->set_pool_type(poolType);
sppConfig->set_pyramid_height(pyramidHeight);
ImageConfig* imageConfig = sppConfig->mutable_image_conf();
imageConfig->set_channels(16);
imageConfig->set_img_size(10);
imageConfig->set_img_size_y(20);
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}) {
testSppLayer("avg-projection", pyramidHeight, false, useGpu);
testSppLayer("max-projection", pyramidHeight, false, useGpu);
}
}
}
TEST(Layer, rankCostLayer) {
TestConfig config;
config.layerConfig.set_type("rank-cost");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
config.inputDefs.push_back({INPUT_DATA, "layer_1", 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();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "rank-cost", 100, false, useGpu);
}
}
TEST(Layer, sumCostLayer) {
TestConfig config;
config.layerConfig.set_type("sum_cost");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "sum_cost", 100, false, useGpu);
}
}
TEST(Layer, weightedRankCostLayer) {
TestConfig config;
config.layerConfig.set_type("rank-cost");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 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_TARGET, "layer_3", 1, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "weighted-rank-cost", 100, false, useGpu);
}
}
TEST(Layer, TensorLayer) {
TestConfig config;
config.layerConfig.set_type("tensor");
config.layerConfig.set_size(10);
config.layerConfig.set_active_type("sigmoid");
config.biasSize = config.layerConfig.size();
config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 250});
config.inputDefs.push_back({INPUT_DATA, "layer_1", 5, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "tensor", 100, false, useGpu);
}
}
TEST(Layer, RecurrentLayer) {
TestConfig config;
config.layerConfig.set_type("recurrent");
config.layerConfig.set_size(4);
config.layerConfig.set_active_type("tanh");
config.biasSize = 4;
config.inputDefs.push_back(
{INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 4, /* paraSize= */ 16});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
for (auto reversed : {false, true}) {
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");
config.layerConfig.set_size(4);
config.layerConfig.set_active_type("tanh");
config.layerConfig.set_active_state_type("sigmoid");
config.layerConfig.set_active_gate_type("sigmoid");
config.biasSize = 28;
config.inputDefs.push_back(
{INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 64});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
for (auto reversed : {false, true}) {
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);
testLayerGrad(config, "lstmemory", 10, /* trans= */ false, useGpu);
}
}
TEST(Layer, MDLstmLayer) {
TestConfig config;
config.layerConfig.set_type("mdlstmemory");
config.layerConfig.set_size(4);
config.layerConfig.set_active_type("sigmoid");
config.layerConfig.set_active_state_type("sigmoid");
config.layerConfig.set_active_gate_type("sigmoid");
config.biasSize = 4 * 9;
config.inputDefs.push_back(
{INPUT_SEQUENCE_MDIM_DATA, "layer_0", 4 * 5, 4 * 4 * 5});
config.layerConfig.add_inputs();
config.layerConfig.add_directions(true);
config.layerConfig.add_directions(true);
for (auto useGpu : {false, true}) {
for (int i = 0; i < 2; i++) {
for (int j = 0; j < 2; j++) {
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;
config.layerConfig.set_type("prelu");
config.inputDefs.push_back({INPUT_DATA, "layer_0", inputSize, channels});
config.layerConfig.add_inputs();
config.layerConfig.set_size(inputSize);
config.layerConfig.set_partial_sum(inputSize /
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);
}
TEST(Layer, ResizeLayer) {
TestConfig config;
config.biasSize = 0;
config.layerConfig.set_type("resize");
config.layerConfig.set_size(64);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 16, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "resize", 100, false, useGpu);
}
}
TEST(Layer, RotateLayer) {
TestConfig config;
config.biasSize = 0;
config.layerConfig.set_type("rotate");
const int CHANNEL = 2;
const int HEIGHT = 8;
const int WIDTH = 4;
const int INPUT_SIZE = HEIGHT * WIDTH * CHANNEL;
config.layerConfig.set_size(INPUT_SIZE);
config.layerConfig.set_height(HEIGHT);
config.layerConfig.set_width(WIDTH);
config.inputDefs.push_back({INPUT_DATA, "layer_0", INPUT_SIZE, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "rotate", 100, false, useGpu);
}
}
TEST(Layer, NCELayer) {
TestConfig config;
size_t numClasses = 4;
config.layerConfig.set_type("nce");
config.layerConfig.set_size(1);
config.layerConfig.set_active_type("sigmoid");
config.layerConfig.set_num_classes(numClasses);
config.biasSize = numClasses;
config.inputDefs.push_back(
{INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 16 * numClasses});
config.inputDefs.push_back(
{INPUT_LABEL, "label", /* dim= */ numClasses, /* paraSize= */ 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto withWeight : {false, true}) {
if (withWeight) {
config.inputDefs.push_back(
{INPUT_DATA_TARGET, "weight", /* dim= */ 1, /* paraSize= */ 0});
config.layerConfig.add_inputs();
}
for (auto isIdLabel : {false, true}) {
config.inputDefs[1] = {
isIdLabel ? INPUT_LABEL : INPUT_SPARSE_NON_VALUE_DATA,
"label",
/* dim= */ numClasses,
/* paraSize= */ 0};
for (auto withDist : {false, true}) {
config.layerConfig.clear_neg_sampling_dist();
if (withDist) {
double sum = 0;
for (size_t i = 0; i < numClasses; ++i) {
real p = rand(); // NOLINT use rand_r
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;
config.layerConfig.set_neg_sampling_dist(i, p);
}
}
LOG(INFO) << "NCELayer "
<< " isIdLabel=" << isIdLabel << " withWeight=" << withWeight
<< " withDist=" << withDist;
// Not support GPU now
testLayerGrad(config,
"nce",
100,
/* trans= */ false,
/* useGpu */ false);
}
}
}
}
TEST(Layer, GatedRecurrentLayer) {
TestConfig config;
config.layerConfig.set_type("gated_recurrent");
config.layerConfig.set_size(4);
config.layerConfig.set_active_type("sigmoid");
config.layerConfig.set_active_gate_type("sigmoid");
config.biasSize = 12;
config.inputDefs.push_back(
{INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
for (auto reversed : {false, true}) {
config.layerConfig.set_reversed(reversed);
config.testState = !reversed;
testLayerGrad(config, "gated_recurrent", 100, /* trans= */ false, useGpu);
}
}
}
TEST(Layer, GruStepLayer) {
TestConfig config;
config.layerConfig.set_type("gru_step");
config.layerConfig.set_size(4);
config.layerConfig.set_active_type("sigmoid");
config.layerConfig.set_active_gate_type("sigmoid");
config.biasSize = 12;
config.inputDefs.push_back(
{INPUT_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48});
config.inputDefs.push_back(
{INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "gruStep", 100, /* trans= */ false, useGpu);
}
}
TEST(Layer, LstmStepLayer) {
TestConfig config;
config.layerConfig.set_type("lstm_step");
config.layerConfig.set_size(4);
config.layerConfig.set_active_type("sigmoid");
config.layerConfig.set_active_state_type("sigmoid");
config.layerConfig.set_active_gate_type("sigmoid");
config.biasSize = 12;
config.testAccumulate = false;
config.inputDefs.push_back(
{INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 0});
config.inputDefs.push_back(
{INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "lstmStep", 100, /* trans= */ false, useGpu);
}
}
void testBatchNormLayer(const string& type, bool trans, bool useGpu) {
TestConfig config;
const int CHANNELS = 10;
const int IMG_SIZE = 16;
const int IMG_SIZE_Y = 8;
size_t size = CHANNELS * IMG_SIZE * IMG_SIZE_Y;
config.layerConfig.set_type(type);
config.layerConfig.set_size(size);
config.layerConfig.set_active_type("sigmoid");
config.biasSize = CHANNELS;
config.inputDefs.push_back({INPUT_DATA,
"layer_0",
/* dim= */ size,
/* paraSize= */ CHANNELS});
config.inputDefs.push_back({INPUT_DATA, "layer_1_running_mean", 1, CHANNELS});
config.inputDefs.back().isStatic = true;
config.inputDefs.push_back({INPUT_DATA, "layer_2_running_var", 1, CHANNELS});
config.inputDefs.back().isStatic = true;
LayerInputConfig* input = config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
ImageConfig* img_conf = input->mutable_image_conf();
img_conf->set_channels(CHANNELS);
img_conf->set_img_size(IMG_SIZE);
img_conf->set_img_size_y(IMG_SIZE_Y);
testLayerGrad(config,
"batch_norm",
64,
/* trans= */ trans,
useGpu,
/* useWeight */ true);
}
TEST(Layer, BatchNormalizationLayer) {
testBatchNormLayer("batch_norm", false, false);
#ifndef PADDLE_ONLY_CPU
testBatchNormLayer("batch_norm", false, true);
if (hl_get_cudnn_lib_version() >= int(4000)) {
testBatchNormLayer("cudnn_batch_norm", false, true);
}
#endif
}
void testConvOperator(bool isDeconv) {
TestConfig config;
const int NUM_FILTERS = 16;
const int FILTER_SIZE = 2;
const int FILTER_SIZE_Y = 3;
const int CHANNELS = 3;
const int IMAGE_SIZE = 16;
const int IMAGE_SIZE_Y = 9;
OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs();
if (isDeconv) {
operatorConf.set_type("convt");
} else {
operatorConf.set_type("conv");
}
ConvConfig* conv = operatorConf.mutable_conv_conf();
operatorConf.set_num_filters(NUM_FILTERS);
conv->set_filter_size(FILTER_SIZE);
conv->set_filter_size_y(FILTER_SIZE_Y);
conv->set_channels(CHANNELS);
conv->set_padding(0);
conv->set_padding_y(1);
conv->set_stride(2);
conv->set_stride_y(2);
conv->set_groups(1);
conv->set_img_size(IMAGE_SIZE);
conv->set_img_size_y(IMAGE_SIZE_Y);
conv->set_output_x(outputSize(conv->img_size(),
conv->filter_size(),
conv->padding(),
conv->stride(),
/* caffeMode */ true));
conv->set_output_y(outputSize(conv->img_size_y(),
conv->filter_size_y(),
conv->padding_y(),
conv->stride_y(),
/* caffeMode */ true));
if (isDeconv) {
conv->set_filter_channels(NUM_FILTERS / conv->groups());
config.inputDefs.push_back({INPUT_DATA,
"layer_0",
conv->output_x() * conv->output_y() * CHANNELS,
0});
config.layerConfig.set_size(IMAGE_SIZE * IMAGE_SIZE_Y * NUM_FILTERS);
} else {
conv->set_filter_channels(conv->channels() / conv->groups());
config.inputDefs.push_back(
{INPUT_DATA, "layer_0", IMAGE_SIZE * IMAGE_SIZE_Y * CHANNELS, 0});
config.layerConfig.set_size(conv->output_x() * conv->output_y() *
NUM_FILTERS);
}
config.inputDefs.push_back(
{INPUT_DATA,
"layer_1",
FILTER_SIZE * FILTER_SIZE_Y * CHANNELS * NUM_FILTERS,
0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
testOperatorGrad(config, operatorConf, 100, /*useGpu*/ true, false);
}
TEST(Operator, conv) {
testConvOperator(/*isDeconv*/ true);
testConvOperator(/*isDeconv*/ false);
}
TEST(Layer, FeatureMapExpandLayer) {
TestConfig config;
config.layerConfig.set_type("featmap_expand");
const int CHANNELS = 10;
const int INPUT_SIZE = 100;
config.layerConfig.set_size(INPUT_SIZE * CHANNELS);
config.layerConfig.set_num_filters(CHANNELS);
config.inputDefs.push_back({INPUT_SEQUENCE_DATA,
"layer_0",
/* dim= */ INPUT_SIZE,
/* paraSize= */ 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
for (auto asRowVec : {false, true}) {
config.layerConfig.set_user_arg(asRowVec ? "as_row_vec" : "as_col_vec");
testLayerGrad(config,
"featmap_expand",
/*batch_size*/ 100,
/* trans= */ false,
useGpu,
/* useWeight */ true);
}
}
}
TEST(Layer, MultiplexLayer) {
TestConfig config;
const int LAYER_SIZE = 100;
config.layerConfig.set_type("multiplex");
config.layerConfig.set_size(LAYER_SIZE);
config.inputDefs.push_back({INPUT_LABEL, "layer_0", 2, 0});
config.inputDefs.push_back(
{INPUT_DATA, "layer_1", /* dim= */ LAYER_SIZE, /* paraSize= */ 0});
config.inputDefs.push_back(
{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}) {
testLayerGrad(config, "multiplex", 512, /* trans= */ false, useGpu);
}
}
TEST(Layer, PadLayer) {
TestConfig config;
config.biasSize = 0;
config.layerConfig.set_type("pad");
int c = 4;
int h = 31;
int w = 36;
size_t size = c * h * w;
config.inputDefs.push_back({INPUT_DATA, "layer_0", size, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
PadConfig* pad = input->mutable_pad_conf();
ImageConfig* image = pad->mutable_image_conf();
image->set_channels(c);
image->set_img_size(h);
image->set_img_size_y(w);
pad->add_pad_c(1);
pad->add_pad_c(2);
pad->add_pad_h(2);
pad->add_pad_h(3);
pad->add_pad_w(3);
pad->add_pad_w(5);
for (auto useGpu : {false, true}) {
testLayerGrad(config, "pad", 10, false, useGpu);
}
}
TEST(Layer, CrossChannelNormLayer) {
TestConfig config;
config.paramInitialMean = 1.;
config.paramInitialStd = 0.;
config.layerConfig.set_type("norm");
config.layerConfig.set_size(100);
LayerInputConfig* input = config.layerConfig.add_inputs();
NormConfig* norm = input->mutable_norm_conf();
norm->set_norm_type("cross-channel-norm");
norm->set_channels(10);
norm->set_size(100);
norm->set_scale(0);
norm->set_pow(0);
norm->set_blocked(0);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 100, 10});
for (auto useGpu : {false, true}) {
testLayerGrad(config, "cross-channel-norm", 10, false, useGpu, false);
}
}
TEST(Layer, smooth_l1) {
TestConfig config;
config.layerConfig.set_type("smooth_l1");
config.inputDefs.push_back({INPUT_DATA, "layer_0", 200, 0});
config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 200, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "smooth_l1", 100, false, useGpu, false);
}
}
TEST(Layer, multibox_loss) {
TestConfig config;
config.layerConfig.set_type("multibox_loss");
config.biasSize = 0;
LayerInputConfig* input = config.layerConfig.add_inputs();
MultiBoxLossConfig* multiboxLoss = input->mutable_multibox_loss_conf();
multiboxLoss->set_num_classes(21);
multiboxLoss->set_input_num(1);
multiboxLoss->set_overlap_threshold(0.5);
multiboxLoss->set_neg_pos_ratio(3);
multiboxLoss->set_neg_overlap(0.5);
multiboxLoss->set_background_id(0);
multiboxLoss->set_height(3);
multiboxLoss->set_width(3);
size_t gtNum = 1;
MatrixPtr labelValue = Matrix::create(gtNum, 6, false, false);
labelValue->randomizeUniform();
labelValue->add(-0.5);
labelValue->sigmoid(*labelValue);
real* labelData = labelValue->getData();
size_t labelWidth = labelValue->getWidth();
for (size_t i = 0; i < gtNum; ++i) {
*(labelData + i * labelWidth) = std::rand() % 20 + 1;
*(labelData + i * labelWidth + 1) = 0.400259;
*(labelData + i * labelWidth + 2) = 0.377857;
*(labelData + i * labelWidth + 3) = 0.525712;
*(labelData + i * labelWidth + 4) = 0.519368;
}
vector<int> seqStartPositions(gtNum + 1, 0);
for (size_t i = 1; i <= gtNum; ++i) {
seqStartPositions[i] = i;
}
// Ensure at lease one matched bbox
MatrixPtr priorValue = Matrix::create(1, 72, false, false);
priorValue->randomizeUniform();
priorValue->add(-0.5);
priorValue->sigmoid(*priorValue);
real* priorData = priorValue->getData();
*(priorData) = 0.424811;
*(priorData + 1) = 0.397059;
*(priorData + 2) = 0.538905;
*(priorData + 3) = 0.447091;
*(priorData + 4) = 0.425720;
*(priorData + 5) = 0.515228;
*(priorData + 6) = 0.519452;
*(priorData + 7) = 0.591065;
config.inputDefs.push_back(
{INPUT_SELF_DEFINE_DATA, "priorbox", priorValue, {}});
config.inputDefs.push_back(
{INPUT_SELF_DEFINE_DATA, "label", labelValue, seqStartPositions});
config.inputDefs.push_back({INPUT_DATA, "locPred", 36, 0});
config.inputDefs.push_back({INPUT_DATA, "confPred", 189, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "multibox_loss", 1, false, useGpu, false);
}
}
TEST(Layer, TransLayer) {
TestConfig config;
const int height = 128;
const int width = 1028;
config.layerConfig.set_type("trans");
config.layerConfig.set_size(width);
config.inputDefs.push_back(
{INPUT_DATA, "layer_0", /* dim= */ height * width, /* paraSize= */ 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "trans", height, /* trans= */ false, useGpu);
}
}
TEST(Layer, RowConvLayer) {
const int context = 3;
const int size = 512;
TestConfig config;
config.layerConfig.set_type("row_conv");
config.layerConfig.set_size(size);
config.layerConfig.set_active_type("sigmoid");
config.inputDefs.push_back(
{INPUT_SEQUENCE_DATA, "layer_0", size, context * size});
LayerInputConfig* input = config.layerConfig.add_inputs();
RowConvConfig* conv = input->mutable_row_conv_conf();
conv->set_context_length(context);
for (auto useGpu : {false, true}) {
testLayerGrad(config, "row_conv", 100, false, useGpu, false);
}
}
TEST(Layer, CropLayer) {
TestConfig config;
// config input_0
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
ImageConfig* img = input->mutable_image_conf();
img->set_channels(4);
img->set_img_size(16);
config.layerConfig.set_axis(2);
config.layerConfig.add_offset(0);
config.layerConfig.add_offset(0);
// config input_1
config.inputDefs.push_back({INPUT_DATA, "layer_1", 128, 0});
input = config.layerConfig.add_inputs();
img = input->mutable_image_conf();
img->set_channels(2);
img->set_img_size(8);
// config crop layer
config.layerConfig.set_type("crop");
config.layerConfig.set_name("cropLayer");
for (auto useGpu : {false, true}) {
testLayerGrad(config, "crop", 100, false, useGpu, false);
}
}
vector<real> randSampling(real range, int n) {
CHECK_GE(range, n);
......@@ -1929,18 +1914,20 @@ vector<real> randSampling(real range, int n) {
TEST(Layer, SubNestedSequenceLayer) {
// layer size is not crutial for this layer,
// so use a small layer size in unittest
const int layerSize = 8;
const int maxSeqNum = 5;
const int maxSeqLen = 5;
const int beamSize = 3;
const int layerSize = 4;
const int maxSeqNum = 50;
const int maxSeqLen = 50;
const int maxBeamSize = 32;
srand((size_t)(time(NULL)));
int beamSize = 1 + (rand() % maxBeamSize);
TestConfig config;
config.layerConfig.set_type("sub_nested_seq");
config.layerConfig.set_name("sub_nested_seq_layer");
config.layerConfig.set_size(layerSize);
// srand((size_t)(time(NULL)));
srand(1);
int seqNum = 1 + (rand() % maxSeqNum);
// sequence information for the first input, it is a nested sequence
......@@ -1969,6 +1956,7 @@ TEST(Layer, SubNestedSequenceLayer) {
MatrixPtr seqInputPtr =
Matrix::create(seqStartPos.back(), layerSize, false, false);
seqInputPtr->randomizeUniform();
config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA,
"nested_seq_input",
seqInputPtr,
......@@ -1989,35 +1977,35 @@ TEST(Layer, SubNestedSequenceLayer) {
}
}
// TEST(Layer, ClipLayer) {
// const size_t batchSize = 128;
// const size_t size = 512;
// TestConfig config;
// config.layerConfig.set_type("clip");
// config.inputDefs.push_back({INPUT_DATA, "input", size, 0});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// ClipConfig* layerConf = input->mutable_clip_conf();
// double p1 = std::rand() / (double)RAND_MAX;
// double p2 = std::rand() / (double)RAND_MAX;
// layerConf->set_min(std::min(p1, p2));
// layerConf->set_max(std::max(p1, p2));
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "clip", batchSize, false, useGpu, false);
// }
// }
//
// TEST(Layer, RowL2NormLayer) {
// const size_t batchSize = 128;
// const size_t size = 512;
// TestConfig config;
// config.layerConfig.set_type("row_l2_norm");
// config.layerConfig.set_size(size);
// config.inputDefs.push_back({INPUT_DATA, "input", size, 0});
// config.layerConfig.add_inputs();
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "row_l2_norm", batchSize, false, useGpu, false);
// }
// }
TEST(Layer, ClipLayer) {
const size_t batchSize = 128;
const size_t size = 512;
TestConfig config;
config.layerConfig.set_type("clip");
config.inputDefs.push_back({INPUT_DATA, "input", size, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
ClipConfig* layerConf = input->mutable_clip_conf();
double p1 = std::rand() / (double)RAND_MAX;
double p2 = std::rand() / (double)RAND_MAX;
layerConf->set_min(std::min(p1, p2));
layerConf->set_max(std::max(p1, p2));
for (auto useGpu : {false, true}) {
testLayerGrad(config, "clip", batchSize, false, useGpu, false);
}
}
TEST(Layer, RowL2NormLayer) {
const size_t batchSize = 128;
const size_t size = 512;
TestConfig config;
config.layerConfig.set_type("row_l2_norm");
config.layerConfig.set_size(size);
config.inputDefs.push_back({INPUT_DATA, "input", size, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "row_l2_norm", batchSize, false, useGpu, false);
}
}
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
......
python/paddle/trainer_config_helpers/layers.py
浏览文件 @ ffafc5c9
......@@ -6097,9 +6097,11 @@ def sub_nested_seq_layer(input, selected_indices, name=None):
The sub_nested_seq_layer accepts two inputs: the first one is a nested
sequence; the second one is a set of selceted indices in the nested sequence.
Then sub_nest_seq_layer trims the first nested sequence input according to
the selected indices to form a new output.
This layer is useful in beam training.
Then sub_nest_seq_layer selects trims the first input according to the
selected indices to give a new output. This layer is used in beam training.
The example usage is:
......
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