mobilenetv2_space.py

# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import sys
import numpy as np
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from .search_space_base import SearchSpaceBase
from .base_layer import conv_bn_layer
from .search_space_registry import SEARCHSPACE


@SEARCHSPACE.register
class MobileNetV2Space(SearchSpaceBase):
    def __init__(self,
                 input_size,
                 output_size,
                 block_num,
                 scale=1.0,
                 class_dim=1000):
        super(MobileNetV2Space, self).__init__(input_size, output_size,
                                               block_num)
        self.head_num = np.array([3, 4, 8, 12, 16, 24, 32])  #7
        self.filter_num1 = np.array([3, 4, 8, 12, 16, 24, 32, 48])  #8
        self.filter_num2 = np.array([8, 12, 16, 24, 32, 48, 64, 80])  #8
        self.filter_num3 = np.array([16, 24, 32, 48, 64, 80, 96, 128])  #8
        self.filter_num4 = np.array(
            [24, 32, 48, 64, 80, 96, 128, 144, 160, 192])  #10
        self.filter_num5 = np.array(
            [32, 48, 64, 80, 96, 128, 144, 160, 192, 224])  #10
        self.filter_num6 = np.array(
            [64, 80, 96, 128, 144, 160, 192, 224, 256, 320, 384, 512])  #12
        self.k_size = np.array([3, 5])  #2
        self.multiply = np.array([1, 2, 3, 4, 6])  #5
        self.repeat = np.array([1, 2, 3, 4, 5, 6])  #6
        self.scale = scale
        self.class_dim = class_dim

    def init_tokens(self):
        """
        The initial token send to controller.
        The first one is the index of the first layers' channel in self.head_num,
        each line in the following represent the index of the [expansion_factor, filter_num, repeat_num, kernel_size]
        """
        # original MobileNetV2
        return [
            4,  # 1, 16, 1
            4,
            5,
            1,
            0,  # 6, 24, 1
            4,
            5,
            1,
            0,  # 6, 24, 2
            4,
            4,
            2,
            0,  # 6, 32, 3
            4,
            4,
            3,
            0,  # 6, 64, 4
            4,
            5,
            2,
            0,  # 6, 96, 3
            4,
            7,
            2,
            0,  # 6, 160, 3
            4,
            9,
            0,
            0
        ]  # 6, 320, 1

    def range_table(self):
        """
        get range table of current search space 
        """
        # head_num + 7 * [multiple(expansion_factor), filter_num, repeat, kernel_size]
        return [
            7, 5, 8, 6, 2, 5, 8, 6, 2, 5, 8, 6, 2, 5, 8, 6, 2, 5, 10, 6, 2, 5,
            10, 6, 2, 5, 12, 6, 2
        ]

    def token2arch(self, tokens=None):
        """
        return net_arch function
        """
        if tokens is None:
            tokens = self.init_tokens()

        base_bottleneck_params_list = [
            (1, self.head_num[tokens[0]], 1, 1, 3),
            (self.multiply[tokens[1]], self.filter_num1[tokens[2]],
             self.repeat[tokens[3]], 2, self.k_size[tokens[4]]),
            (self.multiply[tokens[5]], self.filter_num1[tokens[6]],
             self.repeat[tokens[7]], 2, self.k_size[tokens[8]]),
            (self.multiply[tokens[9]], self.filter_num2[tokens[10]],
             self.repeat[tokens[11]], 2, self.k_size[tokens[12]]),
            (self.multiply[tokens[13]], self.filter_num3[tokens[14]],
             self.repeat[tokens[15]], 2, self.k_size[tokens[16]]),
            (self.multiply[tokens[17]], self.filter_num3[tokens[18]],
             self.repeat[tokens[19]], 1, self.k_size[tokens[20]]),
            (self.multiply[tokens[21]], self.filter_num5[tokens[22]],
             self.repeat[tokens[23]], 2, self.k_size[tokens[24]]),
            (self.multiply[tokens[25]], self.filter_num6[tokens[26]],
             self.repeat[tokens[27]], 1, self.k_size[tokens[28]]),
        ]

        assert self.block_num < 7, 'block number must less than 7, but receive block number is {}'.format(
            self.block_num)

        # the stride = 2 means downsample feature map in the convolution, so only when stride=2, block_num minus 1,
        # otherwise, add layers to params_list directly.
        bottleneck_params_list = []
        for param_list in base_bottleneck_params_list:
            if param_list[3] == 1:
                bottleneck_params_list.append(param_list)
            else:
                if self.block_num > 1:
                    bottleneck_params_list.append(param_list)
                    self.block_num -= 1
                else:
                    break

        def net_arch(input):
            #conv1
            # all padding is 'SAME' in the conv2d, can compute the actual padding automatic. 
            input = conv_bn_layer(
                input,
                num_filters=int(32 * self.scale),
                filter_size=3,
                stride=2,
                padding='SAME',
                act='relu6',
                name='conv1_1')

            # bottleneck sequences
            i = 1
            in_c = int(32 * self.scale)
            for layer_setting in bottleneck_params_list:
                t, c, n, s, k = layer_setting
                i += 1
                input = self.invresi_blocks(
                    input=input,
                    in_c=in_c,
                    t=t,
                    c=int(c * self.scale),
                    n=n,
                    s=s,
                    k=k,
                    name='conv' + str(i))
                in_c = int(c * self.scale)

            # if output_size is 1, add fc layer in the end
            if self.output_size == 1:
                input = fluid.layers.fc(
                    input=input,
                    size=self.class_dim,
                    param_attr=ParamAttr(name='fc10_weights'),
                    bias_attr=ParamAttr(name='fc10_offset'))
            else:
                assert self.output_size == input.shape[2], \
                          ("output_size must EQUAL to input_size / (2^block_num)."
                          "But receive input_size={}, output_size={}, block_num={}".format(
                          self.input_size, self.output_size, self.block_num))

            return input

        return net_arch

    def shortcut(self, input, data_residual):
        """Build shortcut layer.
        Args:
            input(Variable): input.
            data_residual(Variable): residual layer.
        Returns:
            Variable, layer output.
        """
        return fluid.layers.elementwise_add(input, data_residual)

    def inverted_residual_unit(self,
                               input,
                               num_in_filter,
                               num_filters,
                               ifshortcut,
                               stride,
                               filter_size,
                               expansion_factor,
                               reduction_ratio=4,
                               name=None):
        """Build inverted residual unit.
        Args:
            input(Variable), input.
            num_in_filter(int), number of in filters.
            num_filters(int), number of filters.
            ifshortcut(bool), whether using shortcut.
            stride(int), stride.
            filter_size(int), filter size.
            padding(str|int|list), padding.
            expansion_factor(float), expansion factor.
            name(str), name.
        Returns:
            Variable, layers output.
        """
        num_expfilter = int(round(num_in_filter * expansion_factor))
        channel_expand = conv_bn_layer(
            input=input,
            num_filters=num_expfilter,
            filter_size=1,
            stride=1,
            padding='SAME',
            num_groups=1,
            act='relu6',
            name=name + '_expand')

        bottleneck_conv = conv_bn_layer(
            input=channel_expand,
            num_filters=num_expfilter,
            filter_size=filter_size,
            stride=stride,
            padding='SAME',
            num_groups=num_expfilter,
            act='relu6',
            name=name + '_dwise',
            use_cudnn=False)

        linear_out = conv_bn_layer(
            input=bottleneck_conv,
            num_filters=num_filters,
            filter_size=1,
            stride=1,
            padding='SAME',
            num_groups=1,
            act=None,
            name=name + '_linear')
        out = linear_out
        if ifshortcut:
            out = self.shortcut(input=input, data_residual=out)
        return out

    def invresi_blocks(self, input, in_c, t, c, n, s, k, name=None):
        """Build inverted residual blocks.
        Args:
            input: Variable, input.
            in_c: int, number of in filters.
            t: float, expansion factor.
            c: int, number of filters.
            n: int, number of layers.
            s: int, stride.
            k: int, filter size.
            name: str, name.
        Returns:
            Variable, layers output.
        """
        first_block = self.inverted_residual_unit(
            input=input,
            num_in_filter=in_c,
            num_filters=c,
            ifshortcut=False,
            stride=s,
            filter_size=k,
            expansion_factor=t,
            name=name + '_1')

        last_residual_block = first_block
        last_c = c

        for i in range(1, n):
            last_residual_block = self.inverted_residual_unit(
                input=last_residual_block,
                num_in_filter=last_c,
                num_filters=c,
                ifshortcut=True,
                stride=1,
                filter_size=k,
                expansion_factor=t,
                name=name + '_' + str(i + 1))
        return last_residual_block