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some fixes for int8 mobilenet_ssd tester (#18112)

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* some fixes for int8 mobilenet_ssd tester
test=develop

* change wrong data file name
test=develop

* change test images bin file from 200 images to 100 images

* change directory existence to file existence during downloading
test=develop

* reuse download_data
test=develop

* run full dataset when iterations=0
test=develop
nan-debug-tool
lidanqing 6 years ago committed by Tao Luo
parent c2efdfd5bc
commit 5fd68ac154
3 changed files with 39 additions and 34 deletions
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14
paddle/fluid/inference/tests/api/CMakeLists.txt
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@ -5,22 +5,20 @@ if(WITH_GPU AND TENSORRT_FOUND)
endif()
function(download_data install_dir data_file)
if (NOT EXISTS ${install_dir})
if (NOT EXISTS ${install_dir}/${data_file})
inference_download_and_uncompress(${install_dir} ${INFERENCE_URL} ${data_file})
endif()
endfunction()
function(download_int8_data install_dir data_file)
if (NOT EXISTS ${install_dir})
if (NOT EXISTS ${install_dir}/${data_file})
inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8 ${data_file})
endif()
endfunction()
function(download_model_and_data install_dir model_name data_name)
if (NOT EXISTS ${install_dir})
inference_download_and_uncompress(${install_dir} ${INFERENCE_URL} ${model_name})
inference_download_and_uncompress(${install_dir} ${INFERENCE_URL} ${data_name})
endif()
download_data(${install_dir} ${model_name})
download_data(${install_dir} ${data_name})
endfunction()
function(inference_analysis_api_test target install_dir filename)
@ -234,12 +232,12 @@ if(WITH_MKLDNN)
inference_analysis_api_int8_test_run(test_analyzer_int8_googlenet ${INT8_IMG_CLASS_TEST_APP} ${INT8_GOOGLENET_MODEL_DIR} ${IMAGENET_DATA_PATH})
### Object detection models
set(PASCALVOC_DATA_PATH "${INT8_DATA_DIR}/pascalvoc_data.bin")
set(PASCALVOC_DATA_PATH "${INT8_DATA_DIR}/pascalvoc_val_head_100.bin")
set(INT8_OBJ_DETECT_TEST_APP "test_analyzer_int8_object_detection")
set(INT8_OBJ_DETECT_TEST_APP_SRC "analyzer_int8_object_detection_tester.cc")
# download dataset if necessary
download_int8_data(${INT8_DATA_DIR} "pascalvoc_val_100_head.tar.gz")
download_int8_data(${INT8_DATA_DIR} "pascalvoc_val_head_100.tar.gz")
# build test binary to be used in subsequent tests
inference_analysis_api_int8_test_build(${INT8_OBJ_DETECT_TEST_APP} ${INT8_OBJ_DETECT_TEST_APP_SRC})

10
paddle/fluid/inference/tests/api/analyzer_int8_image_classification_tester.cc
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@ -70,9 +70,9 @@ std::shared_ptr<std::vector<PaddleTensor>> GetWarmupData(
const std::vector<std::vector<PaddleTensor>> &test_data,
int num_images = FLAGS_warmup_batch_size) {
int test_data_batch_size = test_data[0][0].shape[0];
auto iterations_max = test_data.size();
auto iterations = test_data.size();
PADDLE_ENFORCE(
static_cast<size_t>(num_images) <= iterations_max * test_data_batch_size,
static_cast<size_t>(num_images) <= iterations * test_data_batch_size,
"The requested quantization warmup data size " +
std::to_string(num_images) + " is bigger than all test data size.");
@ -130,7 +130,11 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs,
label_batch_shape, "label");
auto iterations_max = total_images / batch_size;
for (auto i = 0; i < iterations_max; i++) {
auto iterations = iterations_max;
if (FLAGS_iterations > 0 && FLAGS_iterations < iterations_max) {
iterations = FLAGS_iterations;
}
for (auto i = 0; i < iterations; i++) {
auto images = image_reader.NextBatch();
auto labels = label_reader.NextBatch();
inputs->emplace_back(

49
paddle/fluid/inference/tests/api/analyzer_int8_object_detection_tester.cc
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@ -79,7 +79,7 @@ class TensorReader {
};
void SetInput(std::vector<std::vector<PaddleTensor>> *inputs,
int32_t batch_size = FLAGS_batch_size, int process_images = 0) {
int32_t batch_size = FLAGS_batch_size) {
std::ifstream file(FLAGS_infer_data, std::ios::binary);
if (!file) {
FAIL() << "Couldn't open file: " << FLAGS_infer_data;
@ -110,9 +110,12 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs,
TensorReader<float> bbox_reader(file, bbox_beginning_offset, "gt_bbox");
TensorReader<int64_t> difficult_reader(file, difficult_beginning_offset,
"gt_difficult");
if (process_images == 0) process_images = total_images;
auto iterations_max = process_images / batch_size;
for (auto i = 0; i < iterations_max; i++) {
auto iterations_max = total_images / batch_size;
auto iterations = iterations_max;
if (FLAGS_iterations > 0 && FLAGS_iterations < iterations_max) {
iterations = FLAGS_iterations;
}
for (auto i = 0; i < iterations; i++) {
auto images_tensor = image_reader.NextBatch({batch_size, 3, 300, 300}, {});
std::vector<size_t> batch_lod(lod_full.begin() + i * batch_size,
lod_full.begin() + batch_size * (i + 1));
@ -139,9 +142,9 @@ std::shared_ptr<std::vector<PaddleTensor>> GetWarmupData(
const std::vector<std::vector<PaddleTensor>> &test_data,
int32_t num_images = FLAGS_warmup_batch_size) {
int test_data_batch_size = test_data[0][0].shape[0];
auto iterations_max = test_data.size();
auto iterations = test_data.size();
PADDLE_ENFORCE(
static_cast<int32_t>(num_images) <= iterations_max * test_data_batch_size,
static_cast<int32_t>(num_images) <= iterations * test_data_batch_size,
"The requested quantization warmup data size " +
std::to_string(num_images) + " is bigger than all test data size.");
@ -214,23 +217,23 @@ std::shared_ptr<std::vector<PaddleTensor>> GetWarmupData(
static_cast<int64_t *>(difficult.data.data()) + objects_accum);
objects_accum = objects_accum + objects_in_batch;
}
size_t objects_remain = test_data[batches][1].lod[0][batch_remain];
std::copy_n(
static_cast<float *>(test_data[batches][0].data.data()),
batch_remain * 3 * 300 * 300,
static_cast<float *>(images.data.data()) + objects_accum * 3 * 300 * 300);
std::copy_n(static_cast<int64_t *>(test_data[batches][1].data.data()),
objects_remain,
static_cast<int64_t *>(labels.data.data()) + objects_accum);
std::copy_n(static_cast<float *>(test_data[batches][2].data.data()),
objects_remain * 4,
static_cast<float *>(bbox.data.data()) + objects_accum * 4);
std::copy_n(static_cast<int64_t *>(test_data[batches][3].data.data()),
objects_remain,
static_cast<int64_t *>(difficult.data.data()) + objects_accum);
objects_accum = objects_accum + objects_remain;
if (batch_remain > 0) {
size_t objects_remain = test_data[batches][1].lod[0][batch_remain];
std::copy_n(static_cast<float *>(test_data[batches][0].data.data()),
batch_remain * 3 * 300 * 300,
static_cast<float *>(images.data.data()) +
objects_accum * 3 * 300 * 300);
std::copy_n(static_cast<int64_t *>(test_data[batches][1].data.data()),
objects_remain,
static_cast<int64_t *>(labels.data.data()) + objects_accum);
std::copy_n(static_cast<float *>(test_data[batches][2].data.data()),
objects_remain * 4,
static_cast<float *>(bbox.data.data()) + objects_accum * 4);
std::copy_n(static_cast<int64_t *>(test_data[batches][3].data.data()),
objects_remain,
static_cast<int64_t *>(difficult.data.data()) + objects_accum);
objects_accum = objects_accum + objects_remain;
}
PADDLE_ENFORCE(
static_cast<size_t>(num_objects) == static_cast<size_t>(objects_accum),
"The requested num of objects " + std::to_string(num_objects) +

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