PulseFocusPlatform/static/slim/nas/search_space/blazefacespace_nas.py

114 lines
4.6 KiB
Python

# 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 numpy as np
from paddleslim.nas.search_space.search_space_base import SearchSpaceBase
from paddleslim.nas.search_space.search_space_registry import SEARCHSPACE
from ppdet.modeling.backbones.blazenet import BlazeNet
from ppdet.modeling.architectures.blazeface import BlazeFace
@SEARCHSPACE.register
class BlazeFaceNasSpace(SearchSpaceBase):
def __init__(self, input_size, output_size, block_num, block_mask):
super(BlazeFaceNasSpace, self).__init__(input_size, output_size,
block_num, block_mask)
self.blaze_filter_num1 = np.array([4, 8, 12, 16, 20, 24, 32])
self.blaze_filter_num2 = np.array([8, 12, 16, 20, 24, 32, 40, 48, 64])
self.mid_filter_num = np.array([8, 12, 16, 20, 24, 32])
self.double_filter_num = np.array(
[8, 12, 16, 24, 32, 40, 48, 64, 72, 80, 88, 96])
self.use_5x5kernel = np.array([0, 1])
def init_tokens(self):
return [2, 1, 3, 8, 2, 1, 2, 1, 1]
def range_table(self):
return [
len(self.blaze_filter_num1), len(self.blaze_filter_num2),
len(self.double_filter_num), len(self.double_filter_num),
len(self.mid_filter_num), len(self.mid_filter_num),
len(self.mid_filter_num), len(self.mid_filter_num),
len(self.use_5x5kernel)
]
def get_nas_cnf(self, tokens=None):
if tokens is None:
tokens = self.init_tokens()
blaze_filters = [[
self.blaze_filter_num1[tokens[0]], self.blaze_filter_num1[tokens[0]]
], [
self.blaze_filter_num1[tokens[0]],
self.blaze_filter_num2[tokens[1]], 2
], [
self.blaze_filter_num2[tokens[1]], self.blaze_filter_num2[tokens[1]]
]]
double_blaze_filters = [[
self.blaze_filter_num2[tokens[1]], self.mid_filter_num[tokens[4]],
self.double_filter_num[tokens[2]], 2
], [
self.double_filter_num[tokens[2]], self.mid_filter_num[tokens[5]],
self.double_filter_num[tokens[2]]
], [
self.double_filter_num[tokens[2]], self.mid_filter_num[tokens[6]],
self.double_filter_num[tokens[3]], 2
], [
self.double_filter_num[tokens[3]], self.mid_filter_num[tokens[7]],
self.double_filter_num[tokens[3]]
]]
is_5x5kernel = True if self.use_5x5kernel[tokens[8]] else False
return blaze_filters, double_blaze_filters, is_5x5kernel
def token2arch(self, tokens=None):
blaze_filters, double_blaze_filters, is_5x5kernel = self.get_nas_cnf(
tokens)
self.print_nas_structure(tokens)
def net_arch(input, mode, cfg):
self.output_decoder = cfg.BlazeFace['output_decoder']
self.min_sizes = cfg.BlazeFace['min_sizes']
self.use_density_prior_box = cfg.BlazeFace['use_density_prior_box']
my_backbone = BlazeNet(
blaze_filters=blaze_filters,
double_blaze_filters=double_blaze_filters,
use_5x5kernel=is_5x5kernel)
my_blazeface = BlazeFace(
my_backbone,
output_decoder=self.output_decoder,
min_sizes=self.min_sizes,
use_density_prior_box=self.use_density_prior_box)
return my_blazeface.build(input, mode=mode)
return net_arch
def print_nas_structure(self, tokens=None):
blaze_filters, double_filters, is_5x5kernel = self.get_nas_cnf(tokens)
print('---------->>> BlazeFace-NAS structure start: <<<------------')
print('BlazeNet:')
print(' blaze_filters: {}'.format(blaze_filters))
print(' double_blaze_filters: {}'.format(double_filters))
print(' use_5x5kernel: {}'.format(is_5x5kernel))
print(' with_extra_blocks: true')
print(' lite_edition: false')
print('---------->>> BlazeFace-NAS structure end! <<<------------')