How to compute the FLOPS reported in the paper
YellowPig-zp opened this issue · 2 comments
Hi Bowen, thank you for such a great work! I just have a small question about how to get the reported FLOPs in the paper. Is there a script or public repo that has this functionality? Thanks!
https://github.com/facebookresearch/detectron2/blob/main/tools/analyze_model.py
Hi Bowen. I calculate the flop and params with the scirpt, but the result is not the same with your paper.
The maskformer_swin_small_bs16_160k.yaml is 63M Params and 111G Flops. In your paper is 63M Params and 79G Flops. Is there any problems with my calculation?
python3 analyze_model.py --config-file ./configs/ade20k-150/swin/maskformer_swin_small_bs16_160k.yaml --tasks flop
Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(512, 512), max_size=2048, sample_style='choice')]
[11/15 13:41:29 detectron2]: Flops table computed from only one input sample:
| module | #parameters or shape | #flops |
|---|---|---|
| model | 63.075M | 80.909G |
| backbone | 48.839M | 49.38G |
| backbone.patch_embed | 4.896K | 83.362M |
| backbone.patch_embed.proj | 4.704K | 75.497M |
| backbone.patch_embed.norm | 0.192K | 7.864M |
| backbone.layers | 48.831M | 49.282G |
| backbone.layers.0 | 0.299M | 4.394G |
| backbone.layers.1 | 1.188M | 4.367G |
| backbone.layers.2 | 33.16M | 35.953G |
| backbone.layers.3.blocks | 14.184M | 4.567G |
| backbone.norm0 | 0.192K | 7.864M |
| backbone.norm0.weight | (96,) | |
| backbone.norm0.bias | (96,) | |
| backbone.norm1 | 0.384K | 3.932M |
| backbone.norm1.weight | (192,) | |
| backbone.norm1.bias | (192,) | |
| backbone.norm2 | 0.768K | 1.966M |
| backbone.norm2.weight | (384,) | |
| backbone.norm2.bias | (384,) | |
| backbone.norm3 | 1.536K | 0.983M |
| backbone.norm3.weight | (768,) | |
| backbone.norm3.bias | (768,) | |
| sem_seg_head | 14.236M | 27.453G |
| sem_seg_head.pixel_decoder | 4.305M | 23.56G |
| sem_seg_head.pixel_decoder.adapter_1 | 25.088K | 0.424G |
| sem_seg_head.pixel_decoder.layer_1 | 0.59M | 9.685G |
| sem_seg_head.pixel_decoder.adapter_2 | 49.664K | 0.207G |
| sem_seg_head.pixel_decoder.layer_2 | 0.59M | 2.421G |
| sem_seg_head.pixel_decoder.adapter_3 | 98.816K | 0.102G |
| sem_seg_head.pixel_decoder.layer_3 | 0.59M | 0.605G |
| sem_seg_head.pixel_decoder.layer_4 | 1.77M | 0.453G |
| sem_seg_head.pixel_decoder.mask_features | 0.59M | 9.664G |
| sem_seg_head.predictor | 9.932M | 3.887G |
| sem_seg_head.predictor.transformer.decoder | 9.473M | 1.179G |
| sem_seg_head.predictor.query_embed | 25.6K | |
| sem_seg_head.predictor.input_proj | 0.197M | 50.332M |
| sem_seg_head.predictor.class_embed | 38.807K | 23.194M |
| sem_seg_head.predictor.mask_embed.layers | 0.197M | 0.118G |
| [11/15 13:41:29 detectron2]: Average GFlops for each type of operators: | ||
| [('conv', 32.83191595008), ('layer_norm', 0.22296760319999998), ('linear', 67.07614236672), ('matmul', 1.92566500224), ('group_norm', 0.0769406976), ('upsample_nearest2d', 0.00764854272), ('bmm', 0.139984896), ('einsum', 8.959275), ('upsample_bilinear2d', 0.29302461)] | ||
| [11/15 13:41:29 detectron2]: Total GFlops: 111.5±12.8 |