search space
========
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
-------

Based on origin model architecture:
1. MobileNetV2Space<br>
&emsp; 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)

2. MobileNetV1Space<br>
&emsp; MobilNetV1's architecture can reference: [code](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/models/mobilenet_v1.py#L29), [paper](https://arxiv.org/abs/1704.04861)

3. ResNetSpace<br>
&emsp; 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:
1. MobileNetV1BlockSpace<br>
&emsp; MobileNetV1Block's architecture can reference: [code](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/models/mobilenet_v1.py#L173)

2. MobileNetV2BlockSpace<br>
&emsp; MobileNetV2Block's architecture can reference: [code](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/models/mobilenet_v2.py#L174)

3. ResNetBlockSpace<br>
&emsp; ResNetBlock's architecture can reference: [code](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/models/resnet.py#L148)

4. InceptionABlockSpace<br>
&emsp; InceptionABlock's architecture can reference: [code](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/models/inception_v4.py#L140)

5. InceptionCBlockSpace<br>
&emsp; 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
--------
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
--------
If you want to write yourself search space, you need to inherit base class named SearchSpaceBase and overwrite following functions:<br>
&emsp; 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].
&emsp; 2. Function about the length of every token in tokens(function `range_table`), range of every token in tokens.
&emsp; 3. Function to get model architecture according to tokens(function `token2arch`), get model architecture according to tokens in the search process.

For example, how to add a search space with resnet block. New search space can NOT has the same name with existing search space.

```python
### import necessary head file
from .search_space_base import SearchSpaceBase
from .search_space_registry import SEARCHSPACE
import numpy as np

### use decorator SEARCHSPACE.register to register yourself search space to search space NameSpace
@SEARCHSPACE.register
### define a search space class inherit the base class SearchSpaceBase
class ResNetBlockSpace2(SearchSpaceBase):
    def __init__(self, input_size, output_size, block_num, block_mask):
        ### define the iterm you want to search, such as the numeber of channel, the number of convolution repeat, the size of kernel.
        ### self.filter_num represents the search list about the numeber of channel.
        self.filter_num = np.array([8, 16, 32, 40, 64, 128, 256, 512])

    ### define initial tokens, the length of initial tokens according to block_num or block_mask.
    def init_tokens(self):
        return [0] * 3 * len(self.block_mask)

    ### define the range of index in tokens.
    def range_table(self):
        return [len(self.filter_num)] * 3 * len(self.block_mask)

    ### transform tokens to model architecture.
    def token2arch(self, tokens=None):
        if tokens == None:
            tokens = self.init_tokens()

        self.bottleneck_params_list = []
        for i in range(len(self.block_mask)):
            self.bottleneck_params_list.append(self.filter_num[tokens[i * 3 + 0]],
                                               self.filter_num[tokens[i * 3 + 1]],
                                               self.filter_num[tokens[i * 3 + 2]],
                                               2 if self.block_mask[i] == 1 else 1)

        def net_arch(input):
            for i, layer_setting in enumerate(self.bottleneck_params_list):
                channel_num, stride = layer_setting[:-1], layer_setting[-1]
                input = self._resnet_block(input, channel_num, stride, name='resnet_layer{}'.format(i+1))

            return input

        return net_arch

    ### code to get block.
    def _resnet_block(self, input, channel_num, stride, name=None):
        shortcut_conv = self._shortcut(input, channel_num[2], stride, name=name)
        input = self._conv_bn_layer(input=input, num_filters=channel_num[0], filter_size=1, act='relu', name=name + '_conv0')
        input = self._conv_bn_layer(input=input, num_filters=channel_num[1], filter_size=3, stride=stride, act='relu', name=name + '_conv1')
        input = self._conv_bn_layer(input=input, num_filters=channel_num[2], filter_size=1, name=name + '_conv2')
        return fluid.layers.elementwise_add(x=shortcut_conv, y=input, axis=0, name=name+'_elementwise_add')

    def _shortcut(self, input, channel_num, stride, name=None):
        channel_in = input.shape[1]
        if channel_in != channel_num or stride != 1:
            return self.conv_bn_layer(input, num_filters=channel_num, filter_size=1, stride=stride, name=name+'_shortcut')
        else:
            return input

    def _conv_bn_layer(self, input, num_filters, filter_size, stride=1, padding='SAME', act=None, name=None):
        conv = fluid.layers.conv2d(input, num_filters, filter_size, stride, name=name+'_conv')
        bn = fluid.layers.batch_norm(conv, act=act, name=name+'_bn')
        return bn
```
