Comments (1)
Inference time is exactly the same. The efficient operations are only necessary for training. The efficient operations reduce the number of feature maps that are stored in memory. During inference, this is not an issue because feature maps are not stored during inference.
For more description, please read the tech report.
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Related Issues (20)
- Can we test using the trained model. HOT 1
- New adaptive pooling layer. HOT 1
- Unable to run demo.
- How about the version of the torchvision, project killer and pyhon-fire? HOT 1
- Question: why use bn_function on 1x1 conv, not on 3x3 conv HOT 1
- dropout not in 3x3 convolutional layer
- What is bn_size? HOT 3
- 网络内存消耗?
- Is it possible to provide ImageNet pre-trained models? HOT 2
- Is this really memory efficient? HOT 1
- Is the normalizatin values for CIFAR-10 correct? HOT 1
- test error interpretation HOT 1
- How can I apply this to my own model? HOT 1
- AttributeError: module 'fire' has no attribute 'Fire' HOT 8
- The function received no value for the required argument: data HOT 2
- The BN running mean&var with torch.utils.checkpoint.checkpoint HOT 2
- will the inference memory reduced too? HOT 1
- Question about the place of checkpoint (shared memory allocation) HOT 1
- Excuse me, what is the cause of this problem? HOT 1
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