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@ -17,8 +17,6 @@ limitations under the License. */
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#include "paddle/fluid/inference/api/paddle_analysis_config.h"
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#include "paddle/fluid/inference/tests/api/tester_helper.h"
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DEFINE_int32(iterations, 0, "Number of iterations");
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namespace paddle {
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namespace inference {
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namespace analysis {
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@ -30,8 +28,13 @@ void SetConfig(AnalysisConfig *cfg) {
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cfg->SwitchIrOptim();
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cfg->SwitchSpecifyInputNames(false);
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cfg->SetCpuMathLibraryNumThreads(FLAGS_paddle_num_threads);
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cfg->EnableMKLDNN();
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cfg->pass_builder()->SetPasses(
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{"infer_clean_graph_pass", "mkldnn_placement_pass",
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"depthwise_conv_mkldnn_pass", "conv_bn_fuse_pass",
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"conv_eltwiseadd_bn_fuse_pass", "conv_bias_mkldnn_fuse_pass",
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"conv_elementwise_add_mkldnn_fuse_pass", "conv_relu_mkldnn_fuse_pass",
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"fc_fuse_pass", "is_test_pass"});
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}
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template <typename T>
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@ -40,8 +43,8 @@ class TensorReader {
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TensorReader(std::ifstream &file, size_t beginning_offset,
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std::vector<int> shape, std::string name)
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: file_(file), position(beginning_offset), shape_(shape), name_(name) {
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numel =
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std::accumulate(shape_.begin(), shape_.end(), 1, std::multiplies<T>());
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numel = std::accumulate(shape_.begin(), shape_.end(), size_t{1},
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std::multiplies<size_t>());
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}
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PaddleTensor NextBatch() {
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@ -71,10 +74,14 @@ class TensorReader {
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};
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std::shared_ptr<std::vector<PaddleTensor>> GetWarmupData(
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const std::vector<std::vector<PaddleTensor>> &test_data, int num_images) {
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const std::vector<std::vector<PaddleTensor>> &test_data,
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int num_images = FLAGS_warmup_batch_size) {
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int test_data_batch_size = test_data[0][0].shape[0];
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CHECK_LE(static_cast<size_t>(num_images),
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test_data.size() * test_data_batch_size);
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auto iterations_max = test_data.size();
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PADDLE_ENFORCE(
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static_cast<size_t>(num_images) <= iterations_max * test_data_batch_size,
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"The requested quantization warmup data size " +
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std::to_string(num_images) + " is bigger than all test data size.");
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PaddleTensor images;
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images.name = "input";
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@ -120,20 +127,17 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs,
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std::vector<int> image_batch_shape{batch_size, 3, 224, 224};
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std::vector<int> label_batch_shape{batch_size, 1};
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auto images_offset_in_file = static_cast<size_t>(file.tellg());
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auto labels_offset_in_file =
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static_cast<size_t>(file.tellg()) +
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sizeof(float) * total_images *
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std::accumulate(image_batch_shape.begin() + 1,
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image_batch_shape.end(), 1, std::multiplies<int>());
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images_offset_in_file + sizeof(float) * total_images * 3 * 224 * 224;
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TensorReader<float> image_reader(file, 0, image_batch_shape, "input");
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TensorReader<float> image_reader(file, images_offset_in_file,
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image_batch_shape, "input");
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TensorReader<int64_t> label_reader(file, labels_offset_in_file,
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label_batch_shape, "label");
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auto iterations = total_images / batch_size;
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if (FLAGS_iterations > 0 && FLAGS_iterations < iterations)
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iterations = FLAGS_iterations;
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for (auto i = 0; i < iterations; i++) {
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auto iterations_max = total_images / batch_size;
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for (auto i = 0; i < iterations_max; i++) {
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auto images = image_reader.NextBatch();
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auto labels = label_reader.NextBatch();
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inputs->emplace_back(
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@ -148,20 +152,21 @@ TEST(Analyzer_int8_resnet50, quantization) {
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AnalysisConfig q_cfg;
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SetConfig(&q_cfg);
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// read data from file and prepare batches with test data
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std::vector<std::vector<PaddleTensor>> input_slots_all;
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SetInput(&input_slots_all, 100);
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SetInput(&input_slots_all);
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// prepare warmup batch from input data read earlier
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// warmup batch size can be different than batch size
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std::shared_ptr<std::vector<PaddleTensor>> warmup_data =
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GetWarmupData(input_slots_all, 100);
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GetWarmupData(input_slots_all);
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// configure quantizer
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q_cfg.EnableMkldnnQuantizer();
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q_cfg.mkldnn_quantizer_config()->SetWarmupData(warmup_data);
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q_cfg.mkldnn_quantizer_config()->SetWarmupBatchSize(100);
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q_cfg.mkldnn_quantizer_config()->SetWarmupBatchSize(FLAGS_warmup_batch_size);
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CompareQuantizedAndAnalysis(
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reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
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reinterpret_cast<const PaddlePredictor::Config *>(&q_cfg),
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input_slots_all);
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CompareQuantizedAndAnalysis(&cfg, &q_cfg, input_slots_all);
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}
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} // namespace analysis
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lujun
6 years ago