Comments (2)
Yes, it can achieve actual acceleration on ResNet. We plan to report detailed latency comparison on ResNet.
Please note that if practical acceleration on batch inference is wanted, the dynamic gate should be disabled, as dynamic mode only support sequential inference, i.e., one input at a time.
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Edited.
Seems the default cfg is "mobilenet", which can't get acceleration on cuda.
Previous question:
@changlin31
Dear author,
I just tried model, however, I don't receive actual acceleration.
I test DSNet with "largest" and "smallest", 30 warm-up and 30 test. The input data is (1, 3, 224, 224)
The latency is almost the same.
My machine: GPU3060 + CUDA 11.2 + torch 1.12.
Note that I find one line in dyn_slim_ops/DSConv2d: self.channel_choice = -1, which can prohibit the inference of mode "smallest", so I temporarily comment it. Otherwise, the smallest model can only run once.
I have tested the intermediate feature shape, my "smallest" model actually gets a smaller feature shape, however, no actual acceleration is found.
Can you give me some hints? Am I misunderstanding?
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Related Issues (19)
- The Approximate Date for Stage II training code HOT 6
- The usage of gumbel softmax in DS-Net HOT 7
- Dynamic path for DS-mobilenet HOT 1
- 运行问题 HOT 1
- DS-Net for object detection HOT 8
- MAdds of Pretrained Supernet HOT 2
- why not set ensemble_ib to True? HOT 2
- Can we futher improve autoalim without gate? HOT 3
- Softmax twice for SGS loss?
- Commands to perfrom Inference
- Question about calculating MAdds of dynamic network in the paper HOT 3
- Pretrained models HOT 2
- Object Detection HOT 2
- project environment HOT 1
- Why the num_choice in different yml is different? HOT 2
- Some issues about the gradients of slimNet HOT 6
- UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum. HOT 3
- Error of change the num_choice in mobilenetv1_bn_uniform_reset_bn.yml HOT 2
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