Search Space used in neural architecture search. Search Space is a collection of model architecture, the purpose of SANAS is to get a model which FLOPs or latency is smaller or percision is higher.
search space which paddleslim.nas provided
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## search space which paddleslim.nas provided
Based on origin model architecture:
#### Based on origin model architecture:
1. MobileNetV2Space<br>
  MobileNetV2's architecture can reference: [code](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/models/mobilenet_v2.py#L29), [paper](https://arxiv.org/abs/1801.04381)
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@@ -16,7 +14,7 @@ Based on origin model architecture:
  ResNetSpace's architecture can reference: [code](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/models/resnet.py#L30), [paper](https://arxiv.org/pdf/1512.03385.pdf)
Based on block from different model:
#### Based on block from different model:
1. MobileNetV1BlockSpace<br>
  MobileNetV1Block's architecture can reference: [code](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/models/mobilenet_v1.py#L173)
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@@ -33,15 +31,13 @@ Based on block from different model:
  InceptionCBlock's architecture can reference: [code](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/models/inception_v4.py#L291)
How to use search space
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## How to use search space
1. Only need to specify the name of search space if use the space based on origin model architecture, such as configs for class SANAS is [('MobileNetV2Space')] if you want to use origin MobileNetV2 as search space.
2. Use search space paddleslim.nas provided based on block:<br>
2.1 Use `input_size`, `output_size` and `block_num` to construct search space, such as configs for class SANAS is ('MobileNetV2BlockSpace', {'input_size': 224, 'output_size': 32, 'block_num': 10})].<br>
2.2 Use `block_mask` to construct search space, such as configs for class SANAS is [('MobileNetV2BlockSpace', {'block_mask': [0, 1, 1, 1, 1, 0, 1, 0]})].
How to write yourself search space
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## How to write yourself search space
If you want to write yourself search space, you need to inherit base class named SearchSpaceBase and overwrite following functions:<br>
  1. Function to get initial tokens(function `init_tokens`), set the initial tokens which you want, every token in tokens means index of search list, such as if tokens=[0, 3, 5], it means the list of channel of current model architecture is [8, 40, 128].
  2. Function about the length of every token in tokens(function `range_table`), range of every token in tokens.
第二种是基于FSP的蒸馏方法（参考论文：[A Gift from Knowledge Distillation:
Fast Optimization, Network Minimization and Transfer Learning](http://openaccess.thecvf.com/content_cvpr_2017/papers/Yim_A_Gift_From_CVPR_2017_paper.pdf)）
第二种是基于FSP的蒸馏方法（参考论文：[A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning](http://openaccess.thecvf.com/content_cvpr_2017/papers/Yim_A_Gift_From_CVPR_2017_paper.pdf)）
