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[minor] checkpoint #2

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Merged
merged 1 commit into from
Oct 29, 2021
Merged

[minor] checkpoint #2

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29 changes: 15 additions & 14 deletions demo/demo_without_detection_timecheck.py
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Original file line number Diff line number Diff line change
Expand Up @@ -147,13 +147,13 @@ def get_pose_estimation_prediction(pose_model, image, center, scale):
time_start = time.time()
##############################################################################
output = pose_model(model_input)
# ##############################################################################
##############################################################################
torch.cuda.current_stream().synchronize()
time_end = time.time()
time_processing = time_end - time_start
global time_processing_total
time_processing_total += time_processing
# ##############################################################################
##############################################################################

preds, _ = get_final_preds(
cfg,
Expand Down Expand Up @@ -231,23 +231,24 @@ def parse_args():
model_ = get_model(args)
print('Total ', sum(p.numel() for p in model_.parameters()), ' Parameters')


image_ = cv2.imread(args.image)

count_iter = 1000
count_iter = 100
print('Timecheck Start!')

# ##############################################################################
# torch.cuda.current_stream().synchronize()
# time_start = time.time()
# ##############################################################################
for i in range(count_iter):
image_ = np.clip((image_ + (i % 10) -5), 0, 255)
# ##############################################################################
# torch.cuda.current_stream().synchronize()
# time_start = time.time()
# ##############################################################################

keypoints = inference(model_, image_)
# ##############################################################################
# torch.cuda.current_stream().synchronize()
# time_end = time.time()
# time_processing = time_end - time_start
# time_processing_total += time_processing
# ##############################################################################
##############################################################################
# torch.cuda.current_stream().synchronize()
# time_end = time.time()
# time_processing = time_end - time_start
# time_processing_total += time_processing
##############################################################################
print(time_processing_total / count_iter)