Comments (4)
Hello @haikuoyao , caffe version does not because Caffe does not support enough computational graph style optimizations for shareMemory. However, my implementation avoids the "2-sidedness" of Caffe, which means within DenseBlock's data memory I didn't use Blob, instead they are pointers. Another issue is Caffe, in multi-gpu case, don't have good load-balance for memory across GPUs.
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Thank you, Tongcheng.
It works well on CIFAR data.
While I tried to train on my own dataset, batch size has to be set 4. If I set it bigger, I got a out of memory
error.
I also tried https://github.com/liuzhuang13/DenseNetCaffe yesterday. It's the same.
I wonder should I change network to adapt my dataset?
Thanks a million.
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Hello @haikuoyao,
what is your input size? CIFAR image is 32x32. If your image is much larger, you may want to do a downsampling through a conv with stride 2, before feeding the image into the first dense block, to reduce the memory consumption.
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Thanks @liuzhuang13 .
yeah, you are right. My images are 224 * 224 which is used for Resnet.
Thanks a lot. It's so helpful.
Gonna close this issue.
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Related Issues (20)
- Covolution before entering the first dense block for imagenet dataset HOT 1
- DenseNet on Pascal VOC HOT 2
- results on cifar100 HOT 1
- I tried to reproduce Wide-DenseNet-BC results on cifar10, but got 0.5% more than your error HOT 4
- Why is composite function BN-ReLU-Conv3x3 ? HOT 1
- Pretrained weights for the 0.8M parameters config HOT 1
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- cifar validation loss decrease than increase after learning rate change HOT 4
- Question on channel before entering the first block HOT 2
- Question on impede information flow HOT 1
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- Densenet on CIFAR training from scratch
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