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# regnetx_006
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])]
```
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