| # regnety_002 |
| Implementation of RegNet proposed in [Designing Network Design |
| Spaces](https://arxiv.org/abs/2003.13678) |
| |
| The main idea is to start with a high dimensional search space and |
| iteratively reduce the search space by empirically apply constrains |
| based on the best performing models sampled by the current search |
| space. |
| |
| The resulting models are light, accurate, and faster than |
| EfficientNets (up to 5x times!) |
| |
| For example, to go from $AnyNet_A$ to $AnyNet_B$ they fixed the |
| bottleneck ratio $b_i$ for all stage $i$. The following table shows |
| all the restrictions applied from one search space to the next one. |
|
|
|  |
|
|
| The paper is really well written and very interesting, I highly |
| recommended read it. |
|
|
| ``` python |
| ResNet.regnetx_002() |
| ResNet.regnetx_004() |
| ResNet.regnetx_006() |
| ResNet.regnetx_008() |
| ResNet.regnetx_016() |
| ResNet.regnetx_040() |
| ResNet.regnetx_064() |
| ResNet.regnetx_080() |
| ResNet.regnetx_120() |
| ResNet.regnetx_160() |
| ResNet.regnetx_320() |
| # Y variants (with SE) |
| ResNet.regnety_002() |
| # ... |
| ResNet.regnetx_320() |
| |
| You can easily customize your model |
| ``` |
|
|
| Examples: |
|
|
| ``` python |
| # change activation |
| RegNet.regnetx_004(activation = nn.SELU) |
| # change number of classes (default is 1000 ) |
| RegNet.regnetx_004(n_classes=100) |
| # pass a different block |
| RegNet.regnetx_004(block=RegNetYBotteneckBlock) |
| # change the steam |
| model = RegNet.regnetx_004(stem=ResNetStemC) |
| change shortcut |
| model = RegNet.regnetx_004(block=partial(RegNetYBotteneckBlock, shortcut=ResNetShorcutD)) |
| # store each feature |
| x = torch.rand((1, 3, 224, 224)) |
| # get features |
| model = RegNet.regnetx_004() |
| # first call .features, this will activate the forward hooks and tells the model you'll like to get the features |
| model.encoder.features |
| model(torch.randn((1,3,224,224))) |
| # get the features from the encoder |
| features = model.encoder.features |
| print([x.shape for x in features]) |
| #[torch.Size([1, 32, 112, 112]), torch.Size([1, 32, 56, 56]), torch.Size([1, 64, 28, 28]), torch.Size([1, 160, 14, 14])] |
| ``` |
|
|
| |
