Comments (2)
On GPUs, when FLOPS is small, the main constrain of speed is the bandwidth. Depthwise conv is not so fast on GPUs but super fast on CPUs. That is why MobileNet and GhostNet mainly test speed on ARM/CPU.
If you are familar to Chinese, you could refer to https://zhuanlan.zhihu.com/p/122943688 and https://www.zhihu.com/question/339909499.
from ghostnet.pytorch.
In addition, your code for testing GPU time is not correct. You should add torch.cuda.synchronize()
after forward. Please refer to https://discuss.pytorch.org/t/measuring-gpu-tensor-operation-speed/2513
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Related Issues (20)
- cheap_operation HOT 2
- 请问,可否使用您提供的预训练模型进行finuting,可否提供下您训练时使用的预处理
- the activation fn of depthwiseconv in res connectionis different from that of tensorflow version?? HOT 6
- Custom Weight Initialization HOT 3
- Stride=1 cfg Kernel Sizes Never Used? HOT 2
- kernet_size in ghost_net.py line 101, it should be kernel_size HOT 2
- About the accuracy of Ghost-ResNet-50 (s=2) on Imagenet HOT 1
- Robustness to linear quantization HOT 1
- kernet is kernel HOT 1
- Can not run the sample HOT 6
- Params/ FLOPs counter HOT 1
- The neural network slowed down with only replace the Conv2d in my net with GhostModule. HOT 11
- 为什么我在替换nn.Conv2d的时候,初始化权重会报错? HOT 5
- How to set the channels? HOT 4
- 用ghostnet做anchorfree目标检测的主干网络 HOT 5
- ghostnet用于细粒度图像分类 HOT 3
- 用GhostModule替换残差50的Bottleneck中的nn.Conv2d报错 HOT 2
- 我将Ghostnet用在resnet中,还需要对网络进行训练吗? HOT 2
- 关于卷积核尺寸的疑问 HOT 1
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