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P
Paddle
提交 6b61a096 编写于 作者: D dangqingqing
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Optional padding mode, namely ceil or floor, ceil by default.

上级 c8817a19
隐藏空白更改
内联 并排
Showing with 1614 addition and 1600 deletion
paddle/function/PadOp.cpp
浏览文件 @ 6b61a096
......@@ -73,10 +73,6 @@ void PadGrad<DEVICE_TYPE_CPU>(real* inGrad,
}
}
/**
* \param inputs[0] input value.
* \param outputs[0] output value.
*/
template <DeviceType Device>
class PadFunc : public FunctionBase {
public:
......@@ -89,6 +85,10 @@ public:
padw1_ = config.get<int>("padw1");
}
/**
* \param inputs[0] input value.
* \param outputs[0] output value.
*/
void calc(const Arguments& inputs,
const Arguments& outputs,
const Arguments& inouts) override {
......@@ -124,10 +124,6 @@ private:
int padw1_;
};
/**
* \param inputs[0] input grad.
* \param outputs[0] output grad.
*/
template <DeviceType Device>
class PadGradFunc : public FunctionBase {
public:
......@@ -140,6 +136,10 @@ public:
padw1_ = config.get<int>("padw1");
}
/**
* \param inputs[0] output grad.
* \param inouts[0] input grad.
*/
void calc(const Arguments& inputs,
const Arguments& outputs,
const Arguments& inouts) override {
......
paddle/function/PadOpTest.cpp
浏览文件 @ 6b61a096
......@@ -43,28 +43,30 @@ TEST(Pad, real) {
}
}
// TEST(PadGrad, real) {
// for (size_t numSamples : {5, 32}) {
// for (size_t channels : {1, 5, 32}) {
// for (size_t imgSizeH : {5, 33, 100}) {
// for (size_t imgSizeW : {5, 32, 96}) {
// VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
// << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW;
//
// FunctionCompare compare("PadGrad",
// FuncConfig()
// .set("padc0", 2).set("padc1", 3)
// .set("padh0", 1).set("padh1", 2)
// .set("padw0", 3).set("padw1", 2));
// Dims inDims{numSamples, channels, imgSizeH, imgSizeW};
// Dims outDims{numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5};
// compare.cmpWithArg({Tensor(nullptr, inDims)},
// {Tensor(nullptr, outDims)},
// {});
// }
// }
// }
// }
//}
TEST(PadGrad, real) {
for (size_t numSamples : {5, 32}) {
for (size_t channels : {1, 5, 32}) {
for (size_t imgSizeH : {5, 33, 100}) {
for (size_t imgSizeW : {5, 32, 96}) {
VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
<< " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW;
FunctionCompare compare("PadGrad",
FuncConfig()
.set("padc0", 2)
.set("padc1", 3)
.set("padh0", 1)
.set("padh1", 2)
.set("padw0", 3)
.set("padw1", 2));
Dims inDims{numSamples, channels, imgSizeH, imgSizeW};
Dims outDims{numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5};
compare.cmpWithArg(
{Tensor(nullptr, inDims)}, {}, {Tensor(nullptr, outDims)});
}
}
}
}
}
} // namespace paddle
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @ 6b61a096
......@@ -32,1554 +32,1551 @@ 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, 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) {
// const int NUM_FILTERS = 18;
// const int FILTER_SIZE = 2;
// const int FILTER_SIZE_Y = 3;
// const int CHANNELS = 3;
// const int IMAGE_SIZE = 16;
//
// ProjectionConfig conf;
// 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);
// 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);
// 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) {
// testProjectionConv(1);
// testProjectionConv(3);
// }
// #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, CRFLayer) {
// TestConfig config;
// config.layerConfig.set_type("crf");
// config.layerConfig.set_size(10);
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 120});
// config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// // Not support GPU now
// testLayerGrad(config,
// "crf",
// 100,
// /* trans */ false,
// /* useGpu */ false,
// false /*useWeight*/,
// 0.03 /*epsilon*/);
// }
//
// 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 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, 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(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);
// }
// }
//
// 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) {
// TestConfig config;
// config.layerConfig.set_type(layer_type);
// config.layerConfig.set_size(10);
// 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;
// config.layerConfig.set_average_strategy(strategy);
// testDegradeLayerGrad(config, layer_type);
// }
// } else {
// LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type;
// testDegradeLayerGrad(config, layer_type);
// }
// }
//
// TEST(Layer, MaxLayer) {
// testDegradeLayer(false, "max", "non-seq"); // seq max to non-seq
// testDegradeLayer(true, "max", "non-seq"); // hasSubseq max to non-seq
// testDegradeLayer(true, "max", "seq"); // hasSubseq max to seq
// }
//
// TEST(Layer, SequenceLastInstanceLayer) {
// testDegradeLayer(false,
// "seqlastins",
// "non-seq"); // seq seqlastins to non-seq
// testDegradeLayer(true,
// "seqlastins",
// "non-seq"); // hasSubseq seqlastins to non-seq
// testDegradeLayer(true, "seqlastins", "seq"); // hasSubseq seqlastins to
// seq
// }
//
// TEST(Layer, AverageLayer) {
// testDegradeLayer(false, "average", "non-seq"); // seq average to non-seq
// testDegradeLayer(true, "average", "non-seq"); // hasSubseq average to
// non-seq
// testDegradeLayer(true, "average", "seq"); // 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, 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
// }
//
// TEST(Operator, conv) {
// 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 = 8;
// OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs();
// 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_filter_channels(conv->channels() / conv->groups());
// 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));
// config.layerConfig.set_size(conv->output_x() * conv->output_y() *
// NUM_FILTERS);
//
// config.inputDefs.push_back(
// {INPUT_DATA, "layer_0", IMAGE_SIZE * IMAGE_SIZE_Y * CHANNELS, 0});
// 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(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}) {
// 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(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, 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) {
const int NUM_FILTERS = 18;
const int FILTER_SIZE = 2;
const int FILTER_SIZE_Y = 3;
const int CHANNELS = 3;
const int IMAGE_SIZE = 16;
ProjectionConfig conf;
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);
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);
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) {
testProjectionConv(1);
testProjectionConv(3);
}
#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, CRFLayer) {
TestConfig config;
config.layerConfig.set_type("crf");
config.layerConfig.set_size(10);
config.biasSize = 0;
config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 120});
config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
// Not support GPU now
testLayerGrad(config,
"crf",
100,
/* trans */ false,
/* useGpu */ false,
false /*useWeight*/,
0.03 /*epsilon*/);
}
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 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, 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(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);
}
}
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) {
TestConfig config;
config.layerConfig.set_type(layer_type);
config.layerConfig.set_size(10);
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;
config.layerConfig.set_average_strategy(strategy);
testDegradeLayerGrad(config, layer_type);
}
} else {
LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type;
testDegradeLayerGrad(config, layer_type);
}
}
TEST(Layer, MaxLayer) {
testDegradeLayer(false, "max", "non-seq"); // seq max to non-seq
testDegradeLayer(true, "max", "non-seq"); // hasSubseq max to non-seq
testDegradeLayer(true, "max", "seq"); // hasSubseq max to seq
}
TEST(Layer, SequenceLastInstanceLayer) {
testDegradeLayer(false,
"seqlastins",
"non-seq"); // seq seqlastins to non-seq
testDegradeLayer(true,
"seqlastins",
"non-seq"); // hasSubseq seqlastins to non-seq
testDegradeLayer(true, "seqlastins", "seq"); // hasSubseq seqlastins to
seq
}
TEST(Layer, AverageLayer) {
testDegradeLayer(false, "average", "non-seq"); // seq average to non-seq
testDegradeLayer(true, "average", "non-seq"); // hasSubseq average to
non -
seq testDegradeLayer(true, "average", "seq"); // 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, 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
}
TEST(Operator, conv) {
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 = 8;
OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs();
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_filter_channels(conv->channels() / conv->groups());
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));
config.layerConfig.set_size(conv->output_x() * conv->output_y() *
NUM_FILTERS);
config.inputDefs.push_back(
{INPUT_DATA, "layer_0", IMAGE_SIZE * IMAGE_SIZE_Y * CHANNELS, 0});
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(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}) {
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;
......
python/paddle/trainer/config_parser.py
浏览文件 @ 6b61a096
......@@ -1109,7 +1109,7 @@ def parse_bilinear(bilinear, input_layer_name, bilinear_conf):
bilinear_conf.out_size_y = bilinear.out_size_y
def parse_pool(pool, input_layer_name, pool_conf):
def parse_pool(pool, input_layer_name, pool_conf, ceil_mode):
pool_conf.pool_type = pool.pool_type
config_assert(pool.pool_type in [
'max-projection', 'avg-projection', 'cudnn-max-pool', 'cudnn-avg-pool'
......@@ -1134,10 +1134,10 @@ def parse_pool(pool, input_layer_name, pool_conf):
pool_conf.padding_y = default(pool.padding_y, pool_conf.padding)
pool_conf.output_x = cnn_output_size(pool_conf.img_size, pool_conf.size_x,
pool_conf.padding, pool_conf.stride,
False)
not ceil_mode)
pool_conf.output_y = cnn_output_size(pool_conf.img_size_y, pool_conf.size_y,
pool_conf.padding_y,
pool_conf.stride_y, False)
pool_conf.stride_y, not ceil_mode)
def parse_spp(spp, input_layer_name, spp_conf):
......@@ -1810,9 +1810,8 @@ class ConvTransLayer(ConvTransLayerBase):
@config_layer('norm')
class NormLayer(LayerBase):
def __init__(self, name, inputs, device=None):
super(NormLayer, self).__init__(
name, 'norm', 0, inputs=inputs, device=device)
def __init__(self, name, inputs, **xargs):
super(NormLayer, self).__init__(name, 'norm', 0, inputs=inputs, **xargs)
for input_index in xrange(len(self.inputs)):
input_layer = self.get_input_layer(input_index)
norm_conf = self.config.inputs[input_index].norm_conf
......@@ -1824,23 +1823,22 @@ class NormLayer(LayerBase):
@config_layer('pool')
class PoolLayer(LayerBase):
def __init__(self, name, inputs, device=None):
super(PoolLayer, self).__init__(
name, 'pool', 0, inputs=inputs, device=device)
def __init__(self, name, inputs, ceil_mode=True, **xargs):
super(PoolLayer, self).__init__(name, 'pool', 0, inputs=inputs, **xargs)
for input_index in xrange(len(self.inputs)):
input_layer = self.get_input_layer(input_index)
pool_conf = self.config.inputs[input_index].pool_conf
parse_pool(self.inputs[input_index].pool, input_layer.name,
pool_conf)
pool_conf, ceil_mode)
self.set_cnn_layer(name, pool_conf.output_y, pool_conf.output_x,
pool_conf.channels)
@config_layer('spp')
class SpatialPyramidPoolLayer(LayerBase):
def __init__(self, name, inputs, device=None):
def __init__(self, name, inputs, **xargs):
super(SpatialPyramidPoolLayer, self).__init__(
name, 'spp', 0, inputs=inputs, device=device)
name, 'spp', 0, inputs=inputs, **xargs)
for input_index in xrange(len(self.inputs)):
input_layer = self.get_input_layer(input_index)
spp_conf = self.config.inputs[input_index].spp_conf
......@@ -1877,7 +1875,6 @@ class BatchNormLayer(LayerBase):
inputs,
active_type="linear",
bias=True,
device=None,
use_global_stats=True,
moving_average_fraction=0.9,
batch_norm_type=None,
......@@ -1919,7 +1916,6 @@ class BatchNormLayer(LayerBase):
0,
active_type=active_type,
inputs=inputs,
device=device,
**xargs)
if use_global_stats is not None:
......@@ -1953,9 +1949,9 @@ class BatchNormLayer(LayerBase):
@config_layer('trans')
class TransLayer(LayerBase):
def __init__(self, name, inputs, device=None):
def __init__(self, name, inputs, **xargs):
super(TransLayer, self).__init__(
name, 'trans', 0, inputs=inputs, device=device)
name, 'trans', 0, inputs=inputs, **xargs)
config_assert(
len(self.inputs) == 1,
'TransLayer must have one and only one input')
......@@ -1964,9 +1960,9 @@ class TransLayer(LayerBase):
@config_layer('resize')
class ResizeLayer(LayerBase):
def __init__(self, name, size, inputs, device=None):
def __init__(self, name, size, inputs, **xargs):
super(ResizeLayer, self).__init__(
name, 'resize', size=size, inputs=inputs, device=device)
name, 'resize', size=size, inputs=inputs, **xargs)
config_assert(
len(self.inputs) == 1,
'ResizeLayer must have one and only one input')
......@@ -1974,9 +1970,9 @@ class ResizeLayer(LayerBase):
@config_layer('blockexpand')
class BlockExpandLayer(LayerBase):
def __init__(self, name, inputs, device=None):
def __init__(self, name, inputs, **xargs):
super(BlockExpandLayer, self).__init__(
name, 'blockexpand', 0, inputs=inputs, device=device)
name, 'blockexpand', 0, inputs=inputs, **xargs)
for input_index in xrange(len(self.inputs)):
input_layer = self.get_input_layer(input_index)
parse_block_expand(
......
python/paddle/trainer_config_helpers/layers.py
浏览文件 @ 6b61a096
......@@ -1980,7 +1980,8 @@ def img_pool_layer(input,
layer_attr=None,
pool_size_y=None,
stride_y=None,
padding_y=None):
padding_y=None,
ceil_mode=True):
"""
Image pooling Layer.
......@@ -2011,6 +2012,23 @@ def img_pool_layer(input,
:type stride_y: int|None
:param layer_attr: Extra Layer attribute.
:type layer_attr: ExtraLayerAttribute
:param ceil_mode: Wether to use ceil mode to calculate output height and with.
Defalut is True. If set false, Otherwise use floor.
- ceil_mode=True:
.. math::
w = 1 + int(ceil(input_width + 2 * padding - pool_size) / float(stride))
h = 1 + int(ceil(input_height + 2 * padding_y - pool_size_y) / float(stride_y))
- ceil_mode=False:
.. math::
w = 1 + int(floor(input_width + 2 * padding - pool_size) / float(stride))
h = 1 + int(floor(input_height + 2 * padding_y - pool_size_y) / float(stride_y))
:type ceil_mode: bool
:return: LayerOutput object.
:rtype: LayerOutput
"""
......@@ -2048,6 +2066,7 @@ def img_pool_layer(input,
stride_y=stride_y,
padding_y=padding_y))
],
ceil_mode=ceil_mode,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
......
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