Refine lib -> xautodl
This commit is contained in:
parent
5b9a028e60
commit
bd407ac4dc
@ -1,6 +1,6 @@
|
|||||||
# [Searching for A Robust Neural Architecture in Four GPU Hours](https://arxiv.org/abs/1910.04465)
|
# [Searching for A Robust Neural Architecture in Four GPU Hours](https://arxiv.org/abs/1910.04465)
|
||||||
|
|
||||||
<img align="right" src="https://d-x-y.github.com/resources/paper-icon/CVPR-2019-GDAS.png" width="300">
|
<img align="right" src="http://xuanyidong.com/resources/paper-icon/CVPR-2019-GDAS.png" width="300">
|
||||||
|
|
||||||
Searching for A Robust Neural Architecture in Four GPU Hours is accepted at CVPR 2019.
|
Searching for A Robust Neural Architecture in Four GPU Hours is accepted at CVPR 2019.
|
||||||
In this paper, we proposed a Gradient-based searching algorithm using Differentiable Architecture Sampling (GDAS).
|
In this paper, we proposed a Gradient-based searching algorithm using Differentiable Architecture Sampling (GDAS).
|
||||||
|
@ -1,6 +1,6 @@
|
|||||||
# [One-Shot Neural Architecture Search via Self-Evaluated Template Network](https://arxiv.org/abs/1910.05733)
|
# [One-Shot Neural Architecture Search via Self-Evaluated Template Network](https://arxiv.org/abs/1910.05733)
|
||||||
|
|
||||||
<img align="right" src="https://d-x-y.github.com/resources/paper-icon/ICCV-2019-SETN.png" width="450">
|
<img align="right" src="http://xuanyidong.com/resources/paper-icon/ICCV-2019-SETN.png" width="450">
|
||||||
|
|
||||||
<strong>Highlight</strong>: we equip one-shot NAS with an architecture sampler and train network weights using uniformly sampling.
|
<strong>Highlight</strong>: we equip one-shot NAS with an architecture sampler and train network weights using uniformly sampling.
|
||||||
|
|
||||||
|
@ -10,9 +10,9 @@ def count_parameters_in_MB(model):
|
|||||||
def count_parameters(model_or_parameters, unit="mb"):
|
def count_parameters(model_or_parameters, unit="mb"):
|
||||||
if isinstance(model_or_parameters, nn.Module):
|
if isinstance(model_or_parameters, nn.Module):
|
||||||
counts = sum(np.prod(v.size()) for v in model_or_parameters.parameters())
|
counts = sum(np.prod(v.size()) for v in model_or_parameters.parameters())
|
||||||
elif isinstance(models_or_parameters, nn.Parameter):
|
elif isinstance(model_or_parameters, nn.Parameter):
|
||||||
counts = models_or_parameters.numel()
|
counts = models_or_parameters.numel()
|
||||||
elif isinstance(models_or_parameters, (list, tuple)):
|
elif isinstance(model_or_parameters, (list, tuple)):
|
||||||
counts = sum(count_parameters(x, None) for x in models_or_parameters)
|
counts = sum(count_parameters(x, None) for x in models_or_parameters)
|
||||||
else:
|
else:
|
||||||
counts = sum(np.prod(v.size()) for v in model_or_parameters)
|
counts = sum(np.prod(v.size()) for v in model_or_parameters)
|
||||||
|
Loading…
Reference in New Issue
Block a user