enhance auto_log
@@ -34,6 +34,7 @@ from ppocr.postprocess import build_post_process
# import tools.infer.benchmark_utils as benchmark_utils
logger = get_logger()
+import auto_log
class TextDetector(object):
@@ -100,6 +101,22 @@ class TextDetector(object):
self.predictor, self.input_tensor, self.output_tensors, self.config = utility.create_predictor(
args, 'det', logger)
+ pid = os.getpid()
+ self.autolog = auto_log.AutoLogger(
+ model_name="det",
+ model_precision="fp32",
+ batch_size=1,
+ data_shape="dynamic",
+ save_path="./output/auto_log.lpg",
+ inference_config=self.config,
+ pids=pid,
+ process_name=None,
+ gpu_ids=0,
+ time_keys=[
+ 'preprocess_time', 'inference_time', 'postprocess_time'
+ ],
+ warmup=10)
+
def order_points_clockwise(self, pts):
"""
reference from: https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
@@ -158,6 +175,9 @@ class TextDetector(object):
data = {'image': img}
st = time.time()
+
+ self.autolog.times.start()
+
data = transform(data, self.preprocess_op)
img, shape_list = data
if img is None:
@@ -166,6 +186,8 @@ class TextDetector(object):
shape_list = np.expand_dims(shape_list, axis=0)
img = img.copy()
+ self.autolog.times.stamp()
+
self.input_tensor.copy_from_cpu(img)
self.predictor.run()
outputs = []
@@ -173,6 +195,8 @@ class TextDetector(object):
output = output_tensor.copy_to_cpu()
outputs.append(output)
+ self.autolog.times.stamp()
+
preds = {}
if self.det_algorithm == "EAST":
preds['f_geo'] = outputs[0]
@@ -195,7 +219,9 @@ class TextDetector(object):
else:
dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
+ self.autolog.times.end(stamp=True)
et = time.time()
+ self.autolog.get_avg_mem_mb()
return dt_boxes, et - st
@@ -212,7 +238,7 @@ if __name__ == "__main__":
for i in range(10):
res = text_detector(img)
- cpu_mem, gpu_mem, gpu_util = 0, 0, 0
+ text_detector.autolog.mem_info._start_subprocess()
if not os.path.exists(draw_img_save):
os.makedirs(draw_img_save)
@@ -237,3 +263,5 @@ if __name__ == "__main__":
"det_res_{}".format(img_name_pure))
logger.info("The visualized image saved in {}".format(img_path))
+
+ text_detector.autolog.report()
