# 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 import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddleslim.nas.search_space.search_space_base import SearchSpaceBase from paddleslim.nas.search_space.base_layer import conv_bn_layer from paddleslim.nas.search_space.search_space_registry import SEARCHSPACE from paddleslim.nas.search_space.utils import check_points __all__ = ["MobileNetV2SpaceSeg"] @SEARCHSPACE.register class MobileNetV2SpaceSeg(SearchSpaceBase): def __init__(self, input_size, output_size, block_num, block_mask=None): super(MobileNetV2SpaceSeg, self).__init__(input_size, output_size, block_num, block_mask) # self.head_num means the first convolution channel self.head_num = np.array([3, 4, 8, 12, 16, 24, 32]) #7 # self.filter_num1 ~ self.filter_num6 means following convlution channel 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 means kernel size self.k_size = np.array([3, 5]) #2 # self.multiply means expansion_factor of each _inverted_residual_unit self.multiply = np.array([1, 2, 3, 4, 6]) #5 # self.repeat means repeat_num _inverted_residual_unit in each _invresi_blocks self.repeat = np.array([1, 2, 3, 4, 5, 6]) #6 def init_tokens(self): """ The initial token. 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 # yapf: disable init_token_base = [4, # 1, 16, 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 # yapf: enable return init_token_base def range_table(self): """ Get range table of current search space, constrains the range of tokens. """ # head_num + 6 * [multiple(expansion_factor), filter_num, repeat, kernel_size] # yapf: disable range_table_base = [len(self.head_num), len(self.multiply), len(self.filter_num1), len(self.repeat), len(self.k_size), len(self.multiply), len(self.filter_num2), len(self.repeat), len(self.k_size), len(self.multiply), len(self.filter_num3), len(self.repeat), len(self.k_size), len(self.multiply), len(self.filter_num4), len(self.repeat), len(self.k_size), len(self.multiply), len(self.filter_num5), len(self.repeat), len(self.k_size), len(self.multiply), len(self.filter_num6), len(self.repeat), len(self.k_size)] # yapf: enable return range_table_base def token2arch(self, tokens=None): """ return net_arch function """ if tokens is None: tokens = self.init_tokens() self.bottleneck_params_list = [] self.bottleneck_params_list.append( (1, self.head_num[tokens[0]], 1, 1, 3)) self.bottleneck_params_list.append( (self.multiply[tokens[1]], self.filter_num1[tokens[2]], self.repeat[tokens[3]], 2, self.k_size[tokens[4]])) self.bottleneck_params_list.append( (self.multiply[tokens[5]], self.filter_num2[tokens[6]], self.repeat[tokens[7]], 2, self.k_size[tokens[8]])) self.bottleneck_params_list.append( (self.multiply[tokens[9]], self.filter_num3[tokens[10]], self.repeat[tokens[11]], 2, self.k_size[tokens[12]])) self.bottleneck_params_list.append( (self.multiply[tokens[13]], self.filter_num4[tokens[14]], self.repeat[tokens[15]], 1, self.k_size[tokens[16]])) self.bottleneck_params_list.append( (self.multiply[tokens[17]], self.filter_num5[tokens[18]], self.repeat[tokens[19]], 2, self.k_size[tokens[20]])) self.bottleneck_params_list.append( (self.multiply[tokens[21]], self.filter_num6[tokens[22]], self.repeat[tokens[23]], 1, self.k_size[tokens[24]])) def _modify_bottle_params(output_stride=None): if output_stride is not None and output_stride % 2 != 0: raise Exception("output stride must to be even number") if output_stride is None: return else: stride = 2 for i, layer_setting in enumerate(self.bottleneck_params_list): t, c, n, s, ks = layer_setting stride = stride * s if stride > output_stride: s = 1 self.bottleneck_params_list[i] = (t, c, n, s, ks) def net_arch(input, scale=1.0, return_block=None, end_points=None, output_stride=None): self.scale = scale _modify_bottle_params(output_stride) decode_ends = dict() def check_points(count, points): if points is None: return False else: if isinstance(points, list): return (True if count in points else False) else: return (True if count == points else False) #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='mobilenetv2_conv1') layer_count = 1 depthwise_output = None # bottleneck sequences in_c = int(32 * self.scale) for i, layer_setting in enumerate(self.bottleneck_params_list): t, c, n, s, k = layer_setting layer_count += 1 ### return_block and end_points means block num if check_points((layer_count - 1), return_block): decode_ends[layer_count - 1] = depthwise_output if check_points((layer_count - 1), end_points): return input, decode_ends input, depthwise_output = self._invresi_blocks( input=input, in_c=in_c, t=t, c=int(c * self.scale), n=n, s=s, k=k, name='mobilenetv2_conv' + str(i)) in_c = int(c * self.scale) ### return_block and end_points means block num if check_points(layer_count, return_block): decode_ends[layer_count] = depthwise_output if check_points(layer_count, end_points): return input, decode_ends # last conv input = conv_bn_layer( input=input, num_filters=int(1280 * self.scale) if self.scale > 1.0 else 1280, filter_size=1, stride=1, padding='SAME', act='relu6', name='mobilenetv2_conv' + str(i + 1)) input = fluid.layers.pool2d( input=input, pool_type='avg', global_pooling=True, name='mobilenetv2_last_pool') 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) depthwise_output = bottleneck_conv 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, depthwise_output 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, depthwise_output = 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, depthwise_output = 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, depthwise_output