# 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 .search_space_base import SearchSpaceBase from .base_layer import conv_bn_layer from .search_space_registry import SEARCHSPACE from .utils import compute_downsample_num, check_points, get_random_tokens __all__ = ["InceptionABlockSpace", "InceptionCBlockSpace"] ### TODO add asymmetric kernel of conv when paddle-lite support ### inceptionB is same as inceptionA if asymmetric kernel is not support @SEARCHSPACE.register class InceptionABlockSpace(SearchSpaceBase): def __init__(self, input_size, output_size, block_num, block_mask): super(InceptionABlockSpace, self).__init__(input_size, output_size, block_num, block_mask) if self.block_mask == None: # use input_size and output_size to compute self.downsample_num self.downsample_num = compute_downsample_num(self.input_size, self.output_size) if self.block_num != None: assert self.downsample_num <= self.block_num, 'downsample numeber must be LESS THAN OR EQUAL TO block_num, but NOW: downsample numeber is {}, block_num is {}'.format( self.downsample_num, self.block_num) ### self.filter_num means filter nums self.filter_num = np.array([ 3, 4, 8, 12, 16, 24, 32, 48, 64, 80, 96, 128, 144, 160, 192, 224, 256, 320, 384, 448, 480, 512, 1024 ]) ### self.k_size means kernel_size self.k_size = np.array([3, 5]) ### self.pool_type means pool type, 0 means avg, 1 means max self.pool_type = np.array([0, 1]) ### self.repeat means repeat of 1x1 conv in branch of inception ### self.repeat = np.array([0,1]) def init_tokens(self): """ The initial token. """ return get_random_tokens(self.range_table()) def range_table(self): """ Get range table of current search space, constrains the range of tokens. """ range_table_base = [] if self.block_mask != None: range_table_length = len(self.block_mask) else: range_table_length = self.block_num for i in range(range_table_length): range_table_base.append(len(self.filter_num)) range_table_base.append(len(self.filter_num)) range_table_base.append(len(self.filter_num)) range_table_base.append(len(self.filter_num)) range_table_base.append(len(self.filter_num)) range_table_base.append(len(self.filter_num)) range_table_base.append(len(self.filter_num)) range_table_base.append(len(self.k_size)) range_table_base.append(len(self.pool_type)) return range_table_base def token2arch(self, tokens=None): """ return net_arch function """ #assert self.block_num if tokens is None: tokens = self.init_tokens() self.bottleneck_params_list = [] if self.block_mask != None: for i in range(len(self.block_mask)): self.bottleneck_params_list.append( (self.filter_num[tokens[i * 9]], self.filter_num[tokens[i * 9 + 1]], self.filter_num[tokens[i * 9 + 2]], self.filter_num[tokens[i * 9 + 3]], self.filter_num[tokens[i * 9 + 4]], self.filter_num[tokens[i * 9 + 5]], self.filter_num[tokens[i * 9 + 6]], self.k_size[tokens[i * 9 + 7]], 2 if self.block_mask == 1 else 1, self.pool_type[tokens[i * 9 + 8]])) else: repeat_num = int(self.block_num / self.downsample_num) num_minus = self.block_num % self.downsample_num ### if block_num > downsample_num, add stride=1 block at last (block_num-downsample_num) layers for i in range(self.downsample_num): self.bottleneck_params_list.append( (self.filter_num[tokens[i * 9]], self.filter_num[tokens[i * 9 + 1]], self.filter_num[tokens[i * 9 + 2]], self.filter_num[tokens[i * 9 + 3]], self.filter_num[tokens[i * 9 + 4]], self.filter_num[tokens[i * 9 + 5]], self.filter_num[tokens[i * 9 + 6]], self.k_size[tokens[i * 9 + 7]], 2, self.pool_type[tokens[i * 9 + 8]])) ### if block_num / downsample_num > 1, add (block_num / downsample_num) times stride=1 block for k in range(repeat_num - 1): kk = k * self.downsample_num + i self.bottleneck_params_list.append( (self.filter_num[tokens[kk * 9]], self.filter_num[tokens[kk * 9 + 1]], self.filter_num[tokens[kk * 9 + 2]], self.filter_num[tokens[kk * 9 + 3]], self.filter_num[tokens[kk * 9 + 4]], self.filter_num[tokens[kk * 9 + 5]], self.filter_num[tokens[kk * 9 + 6]], self.k_size[tokens[kk * 9 + 7]], 1, self.pool_type[tokens[kk * 9 + 8]])) if self.downsample_num - i <= num_minus: j = self.downsample_num * (repeat_num - 1) + i self.bottleneck_params_list.append( (self.filter_num[tokens[j * 9]], self.filter_num[tokens[j * 9 + 1]], self.filter_num[tokens[j * 9 + 2]], self.filter_num[tokens[j * 9 + 3]], self.filter_num[tokens[j * 9 + 4]], self.filter_num[tokens[j * 9 + 5]], self.filter_num[tokens[j * 9 + 6]], self.k_size[tokens[j * 9 + 7]], 1, self.pool_type[tokens[j * 9 + 8]])) if self.downsample_num == 0 and self.block_num != 0: for i in range(len(self.block_num)): self.bottleneck_params_list.append( (self.filter_num[tokens[i * 9]], self.filter_num[tokens[i * 9 + 1]], self.filter_num[tokens[i * 9 + 2]], self.filter_num[tokens[i * 9 + 3]], self.filter_num[tokens[i * 9 + 4]], self.filter_num[tokens[i * 9 + 5]], self.filter_num[tokens[i * 9 + 6]], self.k_size[tokens[i * 9 + 7]], 1, self.pool_type[tokens[i * 9 + 8]])) def net_arch(input, return_mid_layer=False, return_block=None): layer_count = 0 mid_layer = dict() for i, layer_setting in enumerate(self.bottleneck_params_list): filter_nums = layer_setting[0:7] filter_size = layer_setting[7] stride = layer_setting[8] pool_type = 'avg' if layer_setting[9] == 0 else 'max' if stride == 2: layer_count += 1 if check_points((layer_count - 1), return_block): mid_layer[layer_count - 1] = input input = self._inceptionA( input, A_tokens=filter_nums, filter_size=int(filter_size), stride=stride, pool_type=pool_type, name='inceptionA_{}'.format(i + 1)) if return_mid_layer: return input, mid_layer else: return input, return net_arch def _inceptionA(self, data, A_tokens, filter_size, stride, pool_type, name=None): pool1 = fluid.layers.pool2d( input=data, pool_size=filter_size, pool_padding='SAME', pool_type=pool_type, name=name + '_pool2d') conv1 = conv_bn_layer( input=pool1, filter_size=1, num_filters=A_tokens[0], stride=stride, act='relu', name=name + '_conv1') conv2 = conv_bn_layer( input=data, filter_size=1, num_filters=A_tokens[1], stride=stride, act='relu', name=name + '_conv2') conv3 = conv_bn_layer( input=data, filter_size=1, num_filters=A_tokens[2], stride=1, act='relu', name=name + '_conv3_1') conv3 = conv_bn_layer( input=conv3, filter_size=filter_size, num_filters=A_tokens[3], stride=stride, act='relu', name=name + '_conv3_2') conv4 = conv_bn_layer( input=data, filter_size=1, num_filters=A_tokens[4], stride=1, act='relu', name=name + '_conv4_1') conv4 = conv_bn_layer( input=conv4, filter_size=filter_size, num_filters=A_tokens[5], stride=1, act='relu', name=name + '_conv4_2') conv4 = conv_bn_layer( input=conv4, filter_size=filter_size, num_filters=A_tokens[6], stride=stride, act='relu', name=name + '_conv4_3') concat = fluid.layers.concat( [conv1, conv2, conv3, conv4], axis=1, name=name + '_concat') return concat @SEARCHSPACE.register class InceptionCBlockSpace(SearchSpaceBase): def __init__(self, input_size, output_size, block_num, block_mask): super(InceptionCBlockSpace, self).__init__(input_size, output_size, block_num, block_mask) if self.block_mask == None: # use input_size and output_size to compute self.downsample_num self.downsample_num = compute_downsample_num(self.input_size, self.output_size) if self.block_num != None: assert self.downsample_num <= self.block_num, 'downsample numeber must be LESS THAN OR EQUAL TO block_num, but NOW: downsample numeber is {}, block_num is {}'.format( self.downsample_num, self.block_num) ### self.filter_num means filter nums self.filter_num = np.array([ 3, 4, 8, 12, 16, 24, 32, 48, 64, 80, 96, 128, 144, 160, 192, 224, 256, 320, 384, 448, 480, 512, 1024 ]) ### self.k_size means kernel_size self.k_size = np.array([3, 5]) ### self.pool_type means pool type, 0 means avg, 1 means max self.pool_type = np.array([0, 1]) ### self.repeat means repeat of 1x1 conv in branch of inception ### self.repeat = np.array([0,1]) def init_tokens(self): """ The initial token. """ return get_random_tokens(self.range_table()) def range_table(self): """ Get range table of current search space, constrains the range of tokens. """ range_table_base = [] if self.block_mask != None: range_table_length = len(self.block_mask) else: range_table_length = self.block_num for i in range(range_table_length): range_table_base.append(len(self.filter_num)) range_table_base.append(len(self.filter_num)) range_table_base.append(len(self.filter_num)) range_table_base.append(len(self.filter_num)) range_table_base.append(len(self.filter_num)) range_table_base.append(len(self.filter_num)) range_table_base.append(len(self.filter_num)) range_table_base.append(len(self.k_size)) range_table_base.append(len(self.pool_type)) return range_table_base def token2arch(self, tokens=None): """ return net_arch function """ #assert self.block_num if tokens is None: tokens = self.init_tokens() self.bottleneck_params_list = [] if self.block_mask != None: for i in range(len(self.block_mask)): self.bottleneck_params_list.append( (self.filter_num[tokens[i * 11]], self.filter_num[tokens[i * 11 + 1]], self.filter_num[tokens[i * 11 + 2]], self.filter_num[tokens[i * 11 + 3]], self.filter_num[tokens[i * 11 + 4]], self.filter_num[tokens[i * 11 + 5]], self.filter_num[tokens[i * 11 + 6]], self.filter_num[tokens[i * 11 + 7]], self.filter_num[tokens[i * 11 + 8]], self.k_size[tokens[i * 11 + 9]], 2 if self.block_mask == 1 else 1, self.pool_type[tokens[i * 11 + 10]])) else: repeat_num = int(self.block_num / self.downsample_num) num_minus = self.block_num % self.downsample_num ### if block_num > downsample_num, add stride=1 block at last (block_num-downsample_num) layers for i in range(self.downsample_num): self.bottleneck_params_list.append( (self.filter_num[tokens[i * 11]], self.filter_num[tokens[i * 11 + 1]], self.filter_num[tokens[i * 11 + 2]], self.filter_num[tokens[i * 11 + 3]], self.filter_num[tokens[i * 11 + 4]], self.filter_num[tokens[i * 11 + 5]], self.filter_num[tokens[i * 11 + 6]], self.filter_num[tokens[i * 11 + 7]], self.filter_num[tokens[i * 11 + 8]], self.k_size[tokens[i * 11 + 9]], 2, self.pool_type[tokens[i * 11 + 10]])) ### if block_num / downsample_num > 1, add (block_num / downsample_num) times stride=1 block for k in range(repeat_num - 1): kk = k * self.downsample_num + i self.bottleneck_params_list.append( (self.filter_num[tokens[kk * 11]], self.filter_num[tokens[kk * 11 + 1]], self.filter_num[tokens[kk * 11 + 2]], self.filter_num[tokens[kk * 11 + 3]], self.filter_num[tokens[kk * 11 + 4]], self.filter_num[tokens[kk * 11 + 5]], self.filter_num[tokens[kk * 11 + 6]], self.filter_num[tokens[kk * 11 + 7]], self.filter_num[tokens[kk * 11 + 8]], self.k_size[tokens[kk * 11 + 9]], 1, self.pool_type[tokens[kk * 11 + 10]])) if self.downsample_num - i <= num_minus: j = self.downsample_num * (repeat_num - 1) + i self.bottleneck_params_list.append( (self.filter_num[tokens[j * 11]], self.filter_num[tokens[j * 11 + 1]], self.filter_num[tokens[j * 11 + 2]], self.filter_num[tokens[j * 11 + 3]], self.filter_num[tokens[j * 11 + 4]], self.filter_num[tokens[j * 11 + 5]], self.filter_num[tokens[j * 11 + 6]], self.filter_num[tokens[j * 11 + 7]], self.filter_num[tokens[j * 11 + 8]], self.k_size[tokens[j * 11 + 9]], 1, self.pool_type[tokens[j * 11 + 10]])) if self.downsample_num == 0 and self.block_num != 0: for i in range(len(self.block_num)): self.bottleneck_params_list.append( (self.filter_num[tokens[i * 11]], self.filter_num[tokens[i * 11 + 1]], self.filter_num[tokens[i * 11 + 2]], self.filter_num[tokens[i * 11 + 3]], self.filter_num[tokens[i * 11 + 4]], self.filter_num[tokens[i * 11 + 5]], self.filter_num[tokens[i * 11 + 6]], self.filter_num[tokens[i * 11 + 7]], self.filter_num[tokens[i * 11 + 8]], self.k_size[tokens[i * 11 + 9]], 1, self.pool_type[tokens[i * 11 + 10]])) def net_arch(input, return_mid_layer=False, return_block=None): layer_count = 0 mid_layer = dict() for i, layer_setting in enumerate(self.bottleneck_params_list): filter_nums = layer_setting[0:9] filter_size = layer_setting[9] stride = layer_setting[10] pool_type = 'avg' if layer_setting[11] == 0 else 'max' if stride == 2: layer_count += 1 if check_points((layer_count - 1), return_block): mid_layer[layer_count - 1] = input input = self._inceptionC( input, C_tokens=filter_nums, filter_size=int(filter_size), stride=stride, pool_type=pool_type, name='inceptionC_{}'.format(i + 1)) if return_mid_layer: return input, mid_layer else: return input, return net_arch def _inceptionC(self, data, C_tokens, filter_size, stride, pool_type, name=None): pool1 = fluid.layers.pool2d( input=data, pool_size=filter_size, pool_padding='SAME', pool_type=pool_type, name=name + '_pool2d') conv1 = conv_bn_layer( input=pool1, filter_size=1, num_filters=C_tokens[0], stride=stride, act='relu', name=name + '_conv1') conv2 = conv_bn_layer( input=data, filter_size=1, num_filters=C_tokens[1], stride=stride, act='relu', name=name + '_conv2') conv3 = conv_bn_layer( input=data, filter_size=1, num_filters=C_tokens[2], stride=1, act='relu', name=name + '_conv3_1') conv3_1 = conv_bn_layer( input=conv3, filter_size=filter_size, num_filters=C_tokens[3], stride=stride, act='relu', name=name + '_conv3_2_1') conv3_2 = conv_bn_layer( input=conv3, filter_size=filter_size, num_filters=C_tokens[4], stride=stride, act='relu', name=name + '_conv3_2_2') conv4 = conv_bn_layer( input=data, filter_size=1, num_filters=C_tokens[5], stride=1, act='relu', name=name + '_conv4_1') conv4 = conv_bn_layer( input=conv4, filter_size=filter_size, num_filters=C_tokens[6], stride=1, act='relu', name=name + '_conv4_2') conv4_1 = conv_bn_layer( input=conv4, filter_size=filter_size, num_filters=C_tokens[7], stride=stride, act='relu', name=name + '_conv4_3_1') conv4_2 = conv_bn_layer( input=conv4, filter_size=filter_size, num_filters=C_tokens[8], stride=stride, act='relu', name=name + '_conv4_3_2') concat = fluid.layers.concat( [conv1, conv2, conv3_1, conv3_2, conv4_1, conv4_2], axis=1, name=name + '_concat') return concat