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update benchmark

pull/1097/head
dongshuilong 2021-08-04 02:49:11 +00:00
parent 7ac12a9403
commit 227f4fdd62
1 changed files with 7 additions and 6 deletions
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13
deploy/python/predict_cls.py
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@ -47,7 +47,7 @@ class ClsPredictor(Predictor):
import auto_log import auto_log
import os import os
pid = os.getpid() pid = os.getpid()
self.auto_log = auto_log.AutoLogger( self.auto_logger = auto_log.AutoLogger(
model_name=config["Global"].get("model_name", "cls"), model_name=config["Global"].get("model_name", "cls"),
model_precision='fp16' model_precision='fp16'
if config["Global"]["use_fp16"] else 'fp32', if config["Global"]["use_fp16"] else 'fp32',
@ -73,7 +73,7 @@ class ClsPredictor(Predictor):
0]) 0])
if self.benchmark: if self.benchmark:
self.auto_log.times.start() self.auto_logger.times.start()
if not isinstance(images, (list, )): if not isinstance(images, (list, )):
images = [images] images = [images]
for idx in range(len(images)): for idx in range(len(images)):
@ -81,17 +81,17 @@ class ClsPredictor(Predictor):
images[idx] = ops(images[idx]) images[idx] = ops(images[idx])
image = np.array(images) image = np.array(images)
if self.benchmark: if self.benchmark:
self.auto_log.times.stamp() self.auto_logger.times.stamp()
input_tensor.copy_from_cpu(image) input_tensor.copy_from_cpu(image)
self.paddle_predictor.run() self.paddle_predictor.run()
batch_output = output_tensor.copy_to_cpu() batch_output = output_tensor.copy_to_cpu()
if self.benchmark: if self.benchmark:
self.auto_log.times.stamp() self.auto_logger.times.stamp()
if self.postprocess is not None: if self.postprocess is not None:
batch_output = self.postprocess(batch_output) batch_output = self.postprocess(batch_output)
if self.benchmark: if self.benchmark:
self.auto_log.times.end(stamp=True) self.auto_logger.times.end(stamp=True)
return batch_output return batch_output
@ -131,7 +131,8 @@ def main(config):
format(filename, clas_ids, scores_str, label_names)) format(filename, clas_ids, scores_str, label_names))
batch_imgs = [] batch_imgs = []
batch_names = [] batch_names = []
cls_predictor.auto_log.report() if cls_predictor.benchmark:
cls_predictor.auto_logger.report()
return return