提交 f7d58cc4 编写于 作者: L Liufang Sang 提交者: Bai Yifan

[PaddleSlim] add infer time (#3688)

* add infer time test=release/1.6

* fix model name in doc  test=release/1.6
上级 8c2e9593
......@@ -19,6 +19,7 @@ from __future__ import print_function
import os
import sys
import glob
import time
import numpy as np
from PIL import Image
......@@ -166,8 +167,29 @@ def main():
imid2path = reader.imid2path
keys = ['bbox']
infer_time = True
for iter_id, data in enumerate(reader()):
feed_data = [[d[0], d[1]] for d in data]
# for infer time
if infer_time:
warmup_times = 10
repeats_time = 30
feed_data_dict = feeder.feed(feed_data);
for i in range(warmup_times):
exe.run(infer_prog,
feed=feed_data_dict,
fetch_list=fetch_list,
return_numpy=False)
start_time = time.time()
for i in range(repeats_time):
exe.run(infer_prog,
feed=feed_data_dict,
fetch_list=fetch_list,
return_numpy=False)
print("infer time: {} ms/sample".format((time.time()-start_time) * 1000 / repeats_time))
infer_time = False
outs = exe.run(infer_prog,
feed=feeder.feed(feed_data),
fetch_list=fetch_list,
......
......@@ -228,7 +228,7 @@ FP32模型可使用PaddleLite进行加载预测,可参见教程[Paddle-Lite如
>当前release的结果并非超参调优后的最好结果,仅做示例参考,后续我们会优化当前结果。
### MobileNetV1
### MobileNetV1-YOLO-V3
| weight量化方式 | activation量化方式| Box ap |Paddle Fluid inference time(ms)| Paddle Lite inference time(ms)|
|---|---|---|---|---|
......@@ -237,8 +237,6 @@ FP32模型可使用PaddleLite进行加载预测,可参见教程[Paddle-Lite如
|abs_max|moving_average_abs_max|- |- |-|
|channel_wise_abs_max|abs_max|- |- |-|
>训练超参:
## FAQ
......
......@@ -17,6 +17,7 @@ import sys
import numpy as np
import argparse
import functools
import time
import paddle
import paddle.fluid as fluid
......@@ -47,8 +48,25 @@ def infer(args):
feeder = fluid.DataFeeder(place=place, feed_list=feed_target_names, program=test_program)
results=[]
#for infer time, if you don't need, please change infer_time to False
infer_time = True
for batch_id, data in enumerate(test_reader()):
# for infer time
if infer_time:
warmup_times = 10
repeats_time = 30
feed_data = feeder.feed(data)
for i in range(warmup_times):
exe.run(test_program,
feed=feed_data,
fetch_list=fetch_targets)
start_time = time.time()
for i in range(repeats_time):
exe.run(test_program,
feed=feed_data,
fetch_list=fetch_targets)
print("infer time: {} ms/sample".format((time.time()-start_time) * 1000 / repeats_time))
infer_time = False
# top1_acc, top5_acc
result = exe.run(test_program,
feed=feeder.feed(data),
......
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