提交 8e271896 编写于 作者: T tensor-tang

add test data for seqpool1

上级 ce70229b
......@@ -90,6 +90,11 @@ set(SEQ_CONV1_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/seq_conv1")
download_model_and_data(${SEQ_CONV1_INSTALL_DIR} "seq_conv1_model.tar.gz" "seq_conv1_data.txt.tar.gz")
inference_analysis_api_test(test_analyzer_seq_conv1 ${SEQ_CONV1_INSTALL_DIR} analyzer_seq_conv1_tester.cc)
# seq_pool1
set(SEQ_POOL1_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/seq_pool1")
download_model_and_data(${SEQ_POOL1_INSTALL_DIR} "seq_pool1_model.tar.gz" "seq_pool1_data.txt.tar.gz")
inference_analysis_api_test(test_analyzer_seq_pool1 ${SEQ_POOL1_INSTALL_DIR} analyzer_seq_pool1_tester.cc)
# ocr
set(OCR_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/ocr")
if (NOT EXISTS ${OCR_INSTALL_DIR})
......@@ -108,10 +113,6 @@ inference_analysis_api_test_with_refer_result(test_analyzer_mobilenet_transpose
inference_analysis_api_test_with_fake_data(test_analyzer_resnet50
"${INFERENCE_DEMO_INSTALL_DIR}/resnet50" analyzer_resnet50_tester.cc "resnet50_model.tar.gz")
# seq_pool1
inference_analysis_api_test_with_fake_data(test_analyzer_seq_pool1
"${INFERENCE_DEMO_INSTALL_DIR}/seq_pool1" analyzer_seq_pool1_tester.cc "seq_pool1.tar.gz")
# mobilenet with depthwise_conv op
inference_analysis_api_test_with_fake_data(test_analyzer_mobilenet_depthwise_conv
"${INFERENCE_DEMO_INSTALL_DIR}/mobilenet_depthwise_conv" analyzer_resnet50_tester.cc "mobilenet_model.tar.gz")
......
......@@ -12,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <algorithm>
#include <fstream>
#include <iostream>
#include "paddle/fluid/inference/tests/api/tester_helper.h"
......@@ -20,6 +21,106 @@ namespace paddle {
namespace inference {
namespace analysis {
struct OneSlotInBatch {
std::string name;
std::vector<std::vector<float>> data;
std::vector<int> shape;
std::vector<size_t> lod;
};
struct DataRecord {
std::vector<std::vector<OneSlotInBatch>> batched_data;
std::map<std::string, std::vector<std::vector<float>>> datasets;
size_t batch_iter{0}, num_samples; // total number of samples
DataRecord() = default;
explicit DataRecord(const std::string &path, int batch_size = 1) {
Load(path);
Prepare(batch_size);
}
void Load(const std::string &path) {
std::ifstream file(path);
constexpr int num_slots = 154;
std::string line;
int num_lines = 0;
while (std::getline(file, line)) {
num_lines++;
std::vector<std::string> data;
split(line, '\t', &data);
std::vector<float> slot_data;
split_to_float(data[1], ' ', &slot_data);
std::string name = data[0];
PADDLE_ENFORCE_EQ(slot_data.size() % 11, 0,
"line %d, %s should be divisible", num_lines, name);
datasets[name].emplace_back(std::move(slot_data));
}
num_samples = num_lines / num_slots;
PADDLE_ENFORCE_EQ(num_samples * num_slots, static_cast<size_t>(num_lines),
"num samples should be divisible");
PADDLE_ENFORCE_GT(num_samples, 0);
}
void Prepare(int bs) {
for (auto it = datasets.begin(); it != datasets.end(); ++it) {
PADDLE_ENFORCE_EQ(it->second.size(), num_samples,
"size of each slot should be equal");
}
size_t num_batches = num_samples / bs;
EXPECT_GT(num_batches, 0);
batched_data.resize(num_batches);
for (auto &one_batch : batched_data) {
one_batch.resize(datasets.size());
size_t i = 0;
for (auto it = datasets.begin(); it != datasets.end(); ++it) {
auto &slot = one_batch[i];
slot.name = it->first;
slot.data.resize(bs);
slot.lod.resize(bs + 1);
slot.lod[0] = 0;
auto &lod = slot.lod;
auto &datas = it->second;
for (int k = 0; k < bs; ++k) {
size_t id = k + batch_iter * bs;
std::copy(datas[id].begin(), datas[id].end(),
std::back_inserter(slot.data[k]));
size_t len = datas[id].size() / 11;
PADDLE_ENFORCE_EQ(len * 11, datas[id].size(),
"%s %d size should be divisible", slot.name, id);
lod[k + 1] = lod[k] + len;
}
slot.shape.assign({static_cast<int>(lod[bs]), 11});
i++;
}
}
}
const std::vector<OneSlotInBatch> &NextBatch() {
if (batch_iter >= batched_data.size() - 1) {
batch_iter = -1;
}
return batched_data[++batch_iter];
}
};
static void TensorAssignSlot(PaddleTensor *tensor, const OneSlotInBatch &slot) {
tensor->name = slot.name + "_embed";
tensor->shape = slot.shape;
tensor->dtype = PaddleDType::FLOAT32;
tensor->lod.clear();
tensor->lod.emplace_back(slot.lod);
TensorAssignData(tensor, slot.data);
}
void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data) {
const auto &one_batch = data->NextBatch();
input_slots->resize(one_batch.size());
for (size_t i = 0; i < one_batch.size(); ++i) {
auto &slot = one_batch[i];
TensorAssignSlot(&((*input_slots)[i]), slot);
}
}
void SetConfig(AnalysisConfig *cfg) {
cfg->param_file = FLAGS_infer_model + "/params";
cfg->prog_file = FLAGS_infer_model + "/model";
......@@ -27,62 +128,22 @@ void SetConfig(AnalysisConfig *cfg) {
cfg->device = 0;
cfg->enable_ir_optim = true;
cfg->specify_input_name = true;
cfg->pass_builder()->TurnOnDebug();
cfg->SetCpuMathLibraryNumThreads(FLAGS_paddle_num_threads);
}
void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
std::vector<std::string> feed_names = {
"slot10000_embed", "slot10001_embed", "slot10004_embed",
"slot10005_embed", "slot10008_embed", "slot10009_embed",
"slot10012_embed", "slot10013_embed", "slot10108_embed",
"slot13324_embed", "slot13325_embed", "slot13326_embed",
"slot13327_embed", "slot13328_embed", "slot13329_embed",
"slot13330_embed", "slot13331_embed", "slot15501_embed",
"slot15502_embed", "slot15503_embed", "slot15504_embed",
"slot15505_embed", "slot15506_embed", "slot15507_embed",
"slot15508_embed", "slot15516_embed", "slot15519_embed",
"slot15523_embed", "slot15531_embed", "slot15533_embed",
"slot15548_embed", "slot15564_embed", "slot15565_embed",
"slot15566_embed", "slot15570_embed", "slot15571_embed",
"slot15572_embed", "slot15573_embed", "slot15574_embed",
"slot15575_embed", "slot15576_embed", "slot15577_embed",
"slot15579_embed", "slot15581_embed", "slot15582_embed",
"slot15583_embed", "slot15584_embed", "slot5016_embed",
"slot5021_embed", "slot6002_embed", "slot6003_embed",
"slot6004_embed", "slot6005_embed", "slot6006_embed",
"slot6007_embed", "slot6008_embed", "slot6009_embed",
"slot6011_embed", "slot6014_embed", "slot6015_embed",
"slot6023_embed", "slot6024_embed", "slot6025_embed",
"slot6027_embed", "slot6029_embed", "slot6031_embed",
"slot6034_embed", "slot6035_embed", "slot6036_embed",
"slot6037_embed", "slot6039_embed", "slot6048_embed",
"slot6050_embed", "slot6058_embed", "slot6059_embed",
"slot6060_embed", "slot6066_embed", "slot6067_embed",
"slot6068_embed", "slot6069_embed", "slot6070_embed",
"slot6071_embed", "slot6072_embed", "slot6073_embed",
"slot6182_embed", "slot6183_embed", "slot6184_embed",
"slot6185_embed", "slot6186_embed", "slot6188_embed",
"slot6189_embed", "slot6190_embed", "slot6201_embed",
"slot6202_embed", "slot6203_embed", "slot6247_embed",
"slot6248_embed", "slot6250_embed", "slot6251_embed",
"slot6807_embed", "slot6808_embed", "slot6809_embed",
"slot6810_embed", "slot6811_embed", "slot6812_embed",
"slot6813_embed", "slot6814_embed", "slot6815_embed",
"slot6816_embed", "slot6817_embed", "slot6818_embed",
"slot6819_embed", "slot6820_embed", "slot6822_embed",
"slot6823_embed", "slot6826_embed", "slot7002_embed",
"slot7003_embed", "slot7004_embed", "slot7005_embed",
"slot7006_embed", "slot7008_embed", "slot7009_embed",
"slot7010_embed", "slot7011_embed", "slot7013_embed",
"slot7014_embed", "slot7015_embed", "slot7016_embed",
"slot7017_embed", "slot7019_embed", "slot7100_embed",
"slot7506_embed", "slot7507_embed", "slot7514_embed",
"slot7515_embed", "slot7516_embed"};
SetFakeImageInput(inputs, FLAGS_infer_model, true, "model", "params",
&feed_names);
DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
std::vector<PaddleTensor> input_slots;
int epoch = FLAGS_test_all_data ? data.batched_data.size() : 1;
LOG(INFO) << "number of samples: "
<< data.batched_data.size() * FLAGS_batch_size;
for (int bid = 0; bid < epoch; ++bid) {
PrepareInputs(&input_slots, &data);
(*inputs).emplace_back(input_slots);
}
}
// Easy for profiling independently.
void profile(bool use_mkldnn = false) {
AnalysisConfig cfg;
SetConfig(&cfg);
......@@ -100,6 +161,17 @@ void profile(bool use_mkldnn = false) {
TEST(Analyzer_seq_pool1, profile) { profile(); }
// Compare result of NativeConfig and AnalysisConfig
TEST(Analyzer_seq_pool1, compare) {
AnalysisConfig cfg;
SetConfig(&cfg);
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
CompareNativeAndAnalysis(
reinterpret_cast<const PaddlePredictor::Config *>(&cfg), input_slots_all);
}
// Check the fuse status
TEST(Analyzer_seq_pool1, fuse_statis) {
AnalysisConfig cfg;
......
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