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提交 ca5642c8 编写于 作者: W Wojciech Uss 提交者： Tao Luo
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unify FP32 vs. INT8 comparison tests output (#18111) 

test=develop
上级 c26130f3
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Showing with 91 addition and 46 deletion
paddle/fluid/inference/tests/api/tester_helper.h
浏览文件 @ ca5642c8
......@@ -320,7 +320,8 @@ void PredictionRun(PaddlePredictor *predictor,
const std::vector<std::vector<PaddleTensor>> &inputs,
std::vector<std::vector<PaddleTensor>> *outputs,
int num_threads, int tid,
const VarType::Type data_type = VarType::FP32) {
const VarType::Type data_type = VarType::FP32,
float *sample_latency = nullptr) {
int num_times = FLAGS_repeat;
int iterations = inputs.size(); // process the whole dataset ...
if (FLAGS_iterations > 0 &&
......@@ -360,6 +361,10 @@ void PredictionRun(PaddlePredictor *predictor,
auto batch_latency = elapsed_time / (iterations * num_times);
PrintTime(FLAGS_batch_size, num_times, num_threads, tid, batch_latency,
iterations, data_type);
if (sample_latency != nullptr)
*sample_latency = batch_latency / FLAGS_batch_size;
if (FLAGS_record_benchmark) {
Benchmark benchmark;
benchmark.SetName(FLAGS_model_name);
......@@ -373,12 +378,14 @@ void TestOneThreadPrediction(
const PaddlePredictor::Config *config,
const std::vector<std::vector<PaddleTensor>> &inputs,
std::vector<std::vector<PaddleTensor>> *outputs, bool use_analysis = true,
const VarType::Type data_type = VarType::FP32) {
const VarType::Type data_type = VarType::FP32,
float *sample_latency = nullptr) {
auto predictor = CreateTestPredictor(config, use_analysis);
if (FLAGS_warmup) {
PredictionWarmUp(predictor.get(), inputs, outputs, 1, 0, data_type);
}
PredictionRun(predictor.get(), inputs, outputs, 1, 0, data_type);
PredictionRun(predictor.get(), inputs, outputs, 1, 0, data_type,
sample_latency);
}
void TestMultiThreadPrediction(
......@@ -430,6 +437,31 @@ void TestPrediction(const PaddlePredictor::Config *config,
}
}
void SummarizeAccuracy(float avg_acc1_fp32, float avg_acc1_int8) {
LOG(INFO) << "--- Accuracy summary --- ";
LOG(INFO) << "Accepted top1 accuracy drop threshold: "
<< FLAGS_quantized_accuracy
<< ". (condition: (FP32_top1_acc - INT8_top1_acc) <= threshold)";
LOG(INFO) << "FP32: avg top1 accuracy: " << std::fixed << std::setw(6)
<< std::setprecision(4) << avg_acc1_fp32;
LOG(INFO) << "INT8: avg top1 accuracy: " << std::fixed << std::setw(6)
<< std::setprecision(4) << avg_acc1_int8;
}
void SummarizePerformance(float sample_latency_fp32,
float sample_latency_int8) {
// sample latency in ms
auto throughput_fp32 = 1000.0 / sample_latency_fp32;
auto throughput_int8 = 1000.0 / sample_latency_int8;
LOG(INFO) << "--- Performance summary --- ";
LOG(INFO) << "FP32: avg fps: " << std::fixed << std::setw(6)
<< std::setprecision(4) << throughput_fp32
<< ", avg latency: " << sample_latency_fp32 << " ms";
LOG(INFO) << "INT8: avg fps: " << std::fixed << std::setw(6)
<< std::setprecision(4) << throughput_int8
<< ", avg latency: " << sample_latency_int8 << " ms";
}
void CompareTopAccuracy(
const std::vector<std::vector<PaddleTensor>> &output_slots_quant,
const std::vector<std::vector<PaddleTensor>> &output_slots_ref) {
......@@ -459,12 +491,10 @@ void CompareTopAccuracy(
float avg_acc1_quant = total_accs1_quant / output_slots_quant.size();
float avg_acc1_ref = total_accs1_ref / output_slots_ref.size();
LOG(INFO) << "Avg top1 INT8 accuracy: " << std::fixed << std::setw(6)
<< std::setprecision(4) << avg_acc1_quant;
LOG(INFO) << "Avg top1 FP32 accuracy: " << std::fixed << std::setw(6)
<< std::setprecision(4) << avg_acc1_ref;
LOG(INFO) << "Accepted accuracy drop threshold: " << FLAGS_quantized_accuracy;
CHECK_LE(std::abs(avg_acc1_quant - avg_acc1_ref), FLAGS_quantized_accuracy);
SummarizeAccuracy(avg_acc1_ref, avg_acc1_quant);
CHECK_GT(avg_acc1_ref, 0.0);
CHECK_GT(avg_acc1_quant, 0.0);
CHECK_LE(avg_acc1_ref - avg_acc1_quant, FLAGS_quantized_accuracy);
}
void CompareDeterministic(
......@@ -510,16 +540,19 @@ void CompareQuantizedAndAnalysis(
auto *cfg = reinterpret_cast<const PaddlePredictor::Config *>(config);
PrintConfig(cfg, true);
std::vector<std::vector<PaddleTensor>> analysis_outputs;
TestOneThreadPrediction(cfg, inputs, &analysis_outputs, true, VarType::FP32);
float sample_latency_fp32{-1};
TestOneThreadPrediction(cfg, inputs, &analysis_outputs, true, VarType::FP32,
&sample_latency_fp32);
LOG(INFO) << "--- INT8 prediction start ---";
auto *qcfg = reinterpret_cast<const PaddlePredictor::Config *>(qconfig);
PrintConfig(qcfg, true);
std::vector<std::vector<PaddleTensor>> quantized_outputs;
TestOneThreadPrediction(qcfg, inputs, &quantized_outputs, true,
VarType::INT8);
float sample_latency_int8{-1};
TestOneThreadPrediction(qcfg, inputs, &quantized_outputs, true, VarType::INT8,
&sample_latency_int8);
LOG(INFO) << "--- comparing outputs --- ";
SummarizePerformance(sample_latency_fp32, sample_latency_int8);
CompareTopAccuracy(quantized_outputs, analysis_outputs);
}
......
python/paddle/fluid/contrib/slim/tests/qat_int8_comparison.py
浏览文件 @ ca5642c8
......@@ -83,8 +83,8 @@ class TestQatInt8Comparison(unittest.TestCase):
while step < num:
fp.seek(imgs_offset + img_size * step)
img = fp.read(img_size)
img = struct.unpack_from('{}f'.format(img_ch * img_w *
img_h), img)
img = struct.unpack_from(
'{}f'.format(img_ch * img_w * img_h), img)
img = np.array(img)
img.shape = (img_ch, img_w, img_h)
fp.seek(labels_offset + label_size * step)
......@@ -147,6 +147,7 @@ class TestQatInt8Comparison(unittest.TestCase):
def _predict(self,
test_reader=None,
model_path=None,
batch_size=1,
batch_num=1,
skip_batch_num=0,
transform_to_int8=False):
......@@ -199,7 +200,7 @@ class TestQatInt8Comparison(unittest.TestCase):
out = exe.run(inference_program,
feed={feed_target_names[0]: images},
fetch_list=fetch_targets)
batch_time = time.time() - start
batch_time = (time.time() - start) * 1000 # in miliseconds
outputs.append(out[0])
batch_acc1, batch_acc5 = self._get_batch_accuracy(out[0],
labels)
......@@ -212,14 +213,15 @@ class TestQatInt8Comparison(unittest.TestCase):
fpses.append(fps)
iters += 1
appx = ' (warm-up)' if iters <= skip_batch_num else ''
_logger.info(
'batch {0}{5}, acc1: {1:.4f}, acc5: {2:.4f}, '
'batch latency: {3:.4f} s, batch fps: {4:.2f}'.format(
iters, batch_acc1, batch_acc5, batch_time, fps, appx))
_logger.info('batch {0}{5}, acc1: {1:.4f}, acc5: {2:.4f}, '
'latency: {3:.4f} ms, fps: {4:.2f}'.format(
iters, batch_acc1, batch_acc5, batch_time /
batch_size, fps, appx))
# Postprocess benchmark data
latencies = batch_times[skip_batch_num:]
latency_avg = np.average(latencies)
batch_latencies = batch_times[skip_batch_num:]
batch_latency_avg = np.average(batch_latencies)
latency_avg = batch_latency_avg / batch_size
fpses = fpses[skip_batch_num:]
fps_avg = np.average(fpses)
infer_total_time = time.time() - infer_start_time
......@@ -230,13 +232,25 @@ class TestQatInt8Comparison(unittest.TestCase):
return outputs, acc1_avg, acc5_avg, fps_avg, latency_avg
def _summarize_performance(self, fp32_fps, fp32_lat, int8_fps, int8_lat):
_logger.info('--- Performance summary ---')
_logger.info('FP32: avg fps: {0:.2f}, avg latency: {1:.4f} ms'.format(
fp32_fps, fp32_lat))
_logger.info('INT8: avg fps: {0:.2f}, avg latency: {1:.4f} ms'.format(
int8_fps, int8_lat))
def _compare_accuracy(self, fp32_acc1, fp32_acc5, int8_acc1, int8_acc5,
threshold):
_logger.info('Accepted acc1 diff threshold: {0}'.format(threshold))
_logger.info('FP32: avg acc1: {0:.4f}, avg acc5: {1:.4f}'.format(
fp32_acc1, fp32_acc5))
_logger.info('INT8: avg acc1: {0:.4f}, avg acc5: {1:.4f}'.format(
int8_acc1, int8_acc5))
_logger.info('--- Accuracy summary ---')
_logger.info(
'Accepted top1 accuracy drop threshold: {0}. (condition: (FP32_top1_acc - IN8_top1_acc) <= threshold)'
.format(threshold))
_logger.info(
'FP32: avg top1 accuracy: {0:.4f}, avg top5 accuracy: {1:.4f}'.
format(fp32_acc1, fp32_acc5))
_logger.info(
'INT8: avg top1 accuracy: {0:.4f}, avg top5 accuracy: {1:.4f}'.
format(int8_acc1, int8_acc5))
assert fp32_acc1 > 0.0
assert int8_acc1 > 0.0
assert fp32_acc1 - int8_acc1 <= threshold
......@@ -257,9 +271,7 @@ class TestQatInt8Comparison(unittest.TestCase):
_logger.info('Dataset: {0}'.format(data_path))
_logger.info('Batch size: {0}'.format(batch_size))
_logger.info('Batch number: {0}'.format(batch_num))
_logger.info('Accuracy diff threshold: {0}. '
'(condition: (fp32_acc - int8_acc) <= threshold)'
.format(acc_diff_threshold))
_logger.info('Accuracy drop threshold: {0}.'.format(acc_diff_threshold))
_logger.info('--- QAT FP32 prediction start ---')
val_reader = paddle.batch(
......@@ -267,6 +279,7 @@ class TestQatInt8Comparison(unittest.TestCase):
fp32_output, fp32_acc1, fp32_acc5, fp32_fps, fp32_lat = self._predict(
val_reader,
qat_model_path,
batch_size,
batch_num,
skip_batch_num,
transform_to_int8=False)
......@@ -277,17 +290,12 @@ class TestQatInt8Comparison(unittest.TestCase):
int8_output, int8_acc1, int8_acc5, int8_fps, int8_lat = self._predict(
val_reader,
qat_model_path,
batch_size,
batch_num,
skip_batch_num,
transform_to_int8=True)
_logger.info('--- Performance summary ---')
_logger.info('FP32: avg fps: {0:.2f}, avg latency: {1:.4f} s'.format(
fp32_fps, fp32_lat))
_logger.info('INT8: avg fps: {0:.2f}, avg latency: {1:.4f} s'.format(
int8_fps, int8_lat))
_logger.info('--- Comparing accuracy ---')
self._summarize_performance(fp32_fps, fp32_lat, int8_fps, int8_lat)
self._compare_accuracy(fp32_acc1, fp32_acc5, int8_acc1, int8_acc5,
acc_diff_threshold)
......
python/paddle/fluid/contrib/slim/tests/test_mkldnn_int8_quantization_strategy.py
浏览文件 @ ca5642c8
......@@ -172,6 +172,17 @@ class TestMKLDNNPostTrainingQuantStrategy(unittest.TestCase):
com_pass.config(config_path)
com_pass.run()
def _compare_accuracy(self, fp32_acc1, int8_acc1, threshold):
_logger.info('--- Accuracy summary ---')
_logger.info(
'Accepted top1 accuracy drop threshold: {0}. (condition: (FP32_top1_acc - IN8_top1_acc) <= threshold)'
.format(threshold))
_logger.info('FP32: avg top1 accuracy: {0:.4f}'.format(fp32_acc1))
_logger.info('INT8: avg top1 accuracy: {0:.4f}'.format(int8_acc1))
assert fp32_acc1 > 0.0
assert int8_acc1 > 0.0
assert fp32_acc1 - int8_acc1 <= threshold
def test_compression(self):
if not fluid.core.is_compiled_with_mkldnn():
return
......@@ -204,15 +215,8 @@ class TestMKLDNNPostTrainingQuantStrategy(unittest.TestCase):
self._reader_creator(data_path, False), batch_size=batch_size)
fp32_model_result = self._predict(val_reader, fp32_model_path)
_logger.info('--- comparing outputs ---')
_logger.info('Avg top1 INT8 accuracy: {0:.4f}'.format(int8_model_result[
0]))
_logger.info('Avg top1 FP32 accuracy: {0:.4f}'.format(fp32_model_result[
0]))
_logger.info('Accepted accuracy drop threshold: {0}'.format(
accuracy_diff_threshold))
assert fp32_model_result[0] - int8_model_result[
0] <= accuracy_diff_threshold
self._compare_accuracy(fp32_model_result[0], int8_model_result[0],
accuracy_diff_threshold)
if __name__ == '__main__':
......
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