We lifted 13 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| bestfleer/RepNAS | canonical | 3 of 13 |
| Function | Status | Where it lives |
|---|---|---|
| mixup_target | Ran | bestfleer/RepNAS/utils/Mixup.py code served (permissive licence) · get_code("8733e0d87f232d11") |
| one_hot | Ran | bestfleer/RepNAS/utils/Mixup.py code served (permissive licence) · get_code("4728dc2025227bba") |
| shear_y | Ran | bestfleer/RepNAS/utils/auto_augment.py code served (permissive licence) · get_code("2d7b973a4b160d73") |
| accuracy | Not yet run | bestfleer/RepNAS/cnn/utils.py code served (permissive licence) · get_code("e93d9ddbfeb73ced") |
| dist_reduce_tensor | Not yet run | bestfleer/RepNAS/distributed.py code served (permissive licence) · get_code("4be37bd74adfab6e") |
| init_dist | Not yet run | bestfleer/RepNAS/distributed.py code served (permissive licence) · get_code("c94c1dbbf94b5928") |
| master_only | Not yet run | bestfleer/RepNAS/distributed.py code served (permissive licence) · get_code("028e1a08f69df3e7") |
| rand_bbox | Not yet run | bestfleer/RepNAS/utils/Mixup.py code served (permissive licence) · get_code("45e8fbb4a42814eb") |
| shear_x | Not yet run | bestfleer/RepNAS/utils/auto_augment.py code served (permissive licence) · get_code("b31e84df5541213a") |
| transIII_1x1_kxk | Not yet run | bestfleer/RepNAS/cnn/ddb_transforms.py code served (permissive licence) · get_code("a1bfd1b41b138e66") |
| transII_addbranch | Not yet run | bestfleer/RepNAS/cnn/ddb_transforms.py code served (permissive licence) · get_code("788ed0b33aadf578") |
| transI_fusebn | Not yet run | bestfleer/RepNAS/cnn/ddb_transforms.py code served (permissive licence) · get_code("079d0a85a0bfbb47") |
| translate_x_rel | Not yet run | bestfleer/RepNAS/utils/auto_augment.py code served (permissive licence) · get_code("61f2a3e08a11e1f5") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In the past years, significant improvements in the field of neural architecture search(NAS) have been made. However, it is still challenging to search for efficient networks due to the gap between the searched constraint and real inference time exists. To search for a high-performance network with low inference time, several previous works set a computational complexity constraint for the search algorithm. However, many factors affect the speed of inference(e.g., FLOPs, MACs). The correlation between a single indicator and the latency is not strong. Currently, some re-parameterization(Rep) techniques are proposed to convert multi-branch to single-path architecture which is inference-friendly. Nevertheless, multi-branch architectures are still human-defined and inefficient. In this work, we propose a new search space that is suitable for structural re-parameterization techniques. RepNAS, a one-stage NAS approach, is present to efficiently search the optimal diverse branch block(ODBB) for each layer under the branch number constraint. Our experimental results show the searched ODBB can easily surpass the manual diverse branch block(DBB) with efficient training.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2109.03508")
get_code_for_paper("2109.03508")
have("2109.03508")
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