Is it possible to run inference on GPU with 1GB DRAM? about flownet2 HOT 1 CLOSED

Hi nikkou,

the straightforward way to use less memory is to feed in smaller images, but that is probably not what you want. It's possible to reduce the raw memory requirements of the larger networks, but not very easy.

• You could run the network layer-by-layer instead of the full network at once. This will require some familiarity with Caffe, and some script coding (setup tiny one-layer network, read this layer's weights, feed input, save output to disk, continue with next layer.. rinse and repeat). It will also be slower.

• You could recompile Caffe to use a lower-precision float representation (e.g. FP16). Accuracy of the results will suffer, but possibly not by much. Note that this is certainly more work than the first approach. It might not save enough memory for the full FlowNet2.

Best,
Nikolaus

from flownet2.