Comments (7)
Sorry, but I am still confused about the speed, in model zoo , AOTS has around 40 FPS and AOTT 50 FPS in DAVIS-VOS as marked in the following picture.
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@xinyeCH I update the code about result saving. At present, the result saving happens after the inference of each sequence instead of each frame.
The speed should be faster on your device.
from aot-benchmark.
@xinyeCH One more update. Currently, the evaluator call torch.cuda.synchronize() only once. After the inference of each sequence, I synchronize all the frame timers (start
and end
). The influence of slow CPU and storage devices should be negligible now.
I evaluated the new code on a V100 device with slow storage, maybe like your device, the speed results are slightly faster than the reported results now (AOTS: 41fps now, 33fps before).
from aot-benchmark.
@z-x-yang Thanks for your reply.
I updated the evaluator.py according to your newest commit and modified some hyperparameters of dataloader such as worker numbers. FPS of AOTS is about 34 now and AOTT 45.
I think the difference between my testing result and paper maybe comes from computer environment.
from aot-benchmark.
@xinyeCH Could try to use a single GPU in the inference?
from aot-benchmark.
@z-x-yang Sorry for the late reply. Yes, I use only one V100 during inference. Using 2 gpus gets worser performance.
from aot-benchmark.
OK. If your cpu is strong enough, using multiple gpus should not make speed slower.
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Related Issues (20)
- [Reproducing quantitative numbers of AOT and DeAOT] HOT 7
- YoutubeVOS quantitative numbers
- Would it help if I had more "ground truth" masks? HOT 2
- Object merging in tracking HOT 3
- Recreating the general examples HOT 2
- Optimizing Model Performance: Exploring ONNX Export and Engine Integration with TensorRT and OpenVino HOT 4
- Work Around for Pytorch-Correlation-extension HOT 5
- Demo.py with more then one GPU HOT 2
- package specified for utils HOT 1
- AUX loss WEIGHT and RATIO HOT 3
- [PAOT] swinv2b
- Evaluator Memory
- F.interpolate upsample gradient with amp HOT 1
- SDPA Attention
- How to train aot? Where is aot's train.py?
- Static image
- INT_MAX `local2global`
- How to reproduce the pre_dav_ytb result?
- To feed truth values into the model during the testing phase? HOT 4
- Does this model convert to onnx to run on edge device? HOT 1
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