# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. # # 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 paddle from paddle import ParamAttr import paddle.nn as nn import paddle.nn.functional as F from paddle.nn import Conv2D, BatchNorm, Linear, Dropout from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D from paddle.nn.initializer import Uniform import math from ppcls.arch.backbone.base.theseus_layer import TheseusLayer __all__ = ["InceptionV3"] NET_CONFIG = { 'inception_a':[[192, 256, 288], [32, 64, 64]], 'inception_b':[288], 'inception_c':[[768, 768, 768, 768], [128, 160, 160, 192]], 'inception_d':[768], 'inception_e':[1280,2048] } def InceptionV3(**kwargs): model = Inception_V3(NET_CONFIG, **kwargs) return model class ConvBNLayer(TheseusLayer): def __init__(self, num_channels, num_filters, filter_size, stride=1, padding=0, groups=1, act="relu", name=None): super(ConvBNLayer, self).__init__() self.conv = Conv2D( in_channels=num_channels, out_channels=num_filters, kernel_size=filter_size, stride=stride, padding=padding, groups=groups, weight_attr=ParamAttr(name=name+"_weights"), bias_attr=False) self.batch_norm = BatchNorm( num_filters, act=act, param_attr=ParamAttr(name=name+"_bn_scale"), bias_attr=ParamAttr(name=name+"_bn_offset"), moving_mean_name=name+"_bn_mean", moving_variance_name=name+"_bn_variance") def forward(self, inputs): y = self.conv(inputs) y = self.batch_norm(y) return y class InceptionStem(TheseusLayer): def __init__(self): super(InceptionStem, self).__init__() self.conv_1a_3x3 = ConvBNLayer(num_channels=3, num_filters=32, filter_size=3, stride=2, act="relu", name="conv_1a_3x3") self.conv_2a_3x3 = ConvBNLayer(num_channels=32, num_filters=32, filter_size=3, stride=1, act="relu", name="conv_2a_3x3") self.conv_2b_3x3 = ConvBNLayer(num_channels=32, num_filters=64, filter_size=3, padding=1, act="relu", name="conv_2b_3x3") self.maxpool = MaxPool2D(kernel_size=3, stride=2, padding=0) self.conv_3b_1x1 = ConvBNLayer(num_channels=64, num_filters=80, filter_size=1, act="relu", name="conv_3b_1x1") self.conv_4a_3x3 = ConvBNLayer(num_channels=80, num_filters=192, filter_size=3, act="relu", name="conv_4a_3x3") def forward(self, x): y = self.conv_1a_3x3(x) y = self.conv_2a_3x3(y) y = self.conv_2b_3x3(y) y = self.maxpool(y) y = self.conv_3b_1x1(y) y = self.conv_4a_3x3(y) y = self.maxpool(y) return y class InceptionA(TheseusLayer): def __init__(self, num_channels, pool_features, name=None): super(InceptionA, self).__init__() self.branch1x1 = ConvBNLayer(num_channels=num_channels, num_filters=64, filter_size=1, act="relu", name="inception_a_branch1x1_"+name) self.branch5x5_1 = ConvBNLayer(num_channels=num_channels, num_filters=48, filter_size=1, act="relu", name="inception_a_branch5x5_1_"+name) self.branch5x5_2 = ConvBNLayer(num_channels=48, num_filters=64, filter_size=5, padding=2, act="relu", name="inception_a_branch5x5_2_"+name) self.branch3x3dbl_1 = ConvBNLayer(num_channels=num_channels, num_filters=64, filter_size=1, act="relu", name="inception_a_branch3x3dbl_1_"+name) self.branch3x3dbl_2 = ConvBNLayer(num_channels=64, num_filters=96, filter_size=3, padding=1, act="relu", name="inception_a_branch3x3dbl_2_"+name) self.branch3x3dbl_3 = ConvBNLayer(num_channels=96, num_filters=96, filter_size=3, padding=1, act="relu", name="inception_a_branch3x3dbl_3_"+name) self.branch_pool = AvgPool2D(kernel_size=3, stride=1, padding=1, exclusive=False) self.branch_pool_conv = ConvBNLayer(num_channels=num_channels, num_filters=pool_features, filter_size=1, act="relu", name="inception_a_branch_pool_"+name) def forward(self, x): branch1x1 = self.branch1x1(x) branch5x5 = self.branch5x5_1(x) branch5x5 = self.branch5x5_2(branch5x5) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) branch_pool = self.branch_pool(x) branch_pool = self.branch_pool_conv(branch_pool) outputs = paddle.concat([branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1) return outputs class InceptionB(TheseusLayer): def __init__(self, num_channels, name=None): super(InceptionB, self).__init__() self.branch3x3 = ConvBNLayer(num_channels=num_channels, num_filters=384, filter_size=3, stride=2, act="relu", name="inception_b_branch3x3_"+name) self.branch3x3dbl_1 = ConvBNLayer(num_channels=num_channels, num_filters=64, filter_size=1, act="relu", name="inception_b_branch3x3dbl_1_"+name) self.branch3x3dbl_2 = ConvBNLayer(num_channels=64, num_filters=96, filter_size=3, padding=1, act="relu", name="inception_b_branch3x3dbl_2_"+name) self.branch3x3dbl_3 = ConvBNLayer(num_channels=96, num_filters=96, filter_size=3, stride=2, act="relu", name="inception_b_branch3x3dbl_3_"+name) self.branch_pool = MaxPool2D(kernel_size=3, stride=2) def forward(self, x): branch3x3 = self.branch3x3(x) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) branch_pool = self.branch_pool(x) outputs = paddle.concat([branch3x3, branch3x3dbl, branch_pool], axis=1) return outputs class InceptionC(TheseusLayer): def __init__(self, num_channels, channels_7x7, name=None): super(InceptionC, self).__init__() self.branch1x1 = ConvBNLayer(num_channels=num_channels, num_filters=192, filter_size=1, act="relu", name="inception_c_branch1x1_"+name) self.branch7x7_1 = ConvBNLayer(num_channels=num_channels, num_filters=channels_7x7, filter_size=1, stride=1, act="relu", name="inception_c_branch7x7_1_"+name) self.branch7x7_2 = ConvBNLayer(num_channels=channels_7x7, num_filters=channels_7x7, filter_size=(1, 7), stride=1, padding=(0, 3), act="relu", name="inception_c_branch7x7_2_"+name) self.branch7x7_3 = ConvBNLayer(num_channels=channels_7x7, num_filters=192, filter_size=(7, 1), stride=1, padding=(3, 0), act="relu", name="inception_c_branch7x7_3_"+name) self.branch7x7dbl_1 = ConvBNLayer(num_channels=num_channels, num_filters=channels_7x7, filter_size=1, act="relu", name="inception_c_branch7x7dbl_1_"+name) self.branch7x7dbl_2 = ConvBNLayer(num_channels=channels_7x7, num_filters=channels_7x7, filter_size=(7, 1), padding = (3, 0), act="relu", name="inception_c_branch7x7dbl_2_"+name) self.branch7x7dbl_3 = ConvBNLayer(num_channels=channels_7x7, num_filters=channels_7x7, filter_size=(1, 7), padding = (0, 3), act="relu", name="inception_c_branch7x7dbl_3_"+name) self.branch7x7dbl_4 = ConvBNLayer(num_channels=channels_7x7, num_filters=channels_7x7, filter_size=(7, 1), padding = (3, 0), act="relu", name="inception_c_branch7x7dbl_4_"+name) self.branch7x7dbl_5 = ConvBNLayer(num_channels=channels_7x7, num_filters=192, filter_size=(1, 7), padding = (0, 3), act="relu", name="inception_c_branch7x7dbl_5_"+name) self.branch_pool = AvgPool2D(kernel_size=3, stride=1, padding=1, exclusive=False) self.branch_pool_conv = ConvBNLayer(num_channels=num_channels, num_filters=192, filter_size=1, act="relu", name="inception_c_branch_pool_"+name) def forward(self, x): branch1x1 = self.branch1x1(x) branch7x7 = self.branch7x7_1(x) branch7x7 = self.branch7x7_2(branch7x7) branch7x7 = self.branch7x7_3(branch7x7) branch7x7dbl = self.branch7x7dbl_1(x) branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl) branch_pool = self.branch_pool(x) branch_pool = self.branch_pool_conv(branch_pool) outputs = paddle.concat([branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1) return outputs class InceptionD(TheseusLayer): def __init__(self, num_channels, name=None): super(InceptionD, self).__init__() self.branch3x3_1 = ConvBNLayer(num_channels=num_channels, num_filters=192, filter_size=1, act="relu", name="inception_d_branch3x3_1_"+name) self.branch3x3_2 = ConvBNLayer(num_channels=192, num_filters=320, filter_size=3, stride=2, act="relu", name="inception_d_branch3x3_2_"+name) self.branch7x7x3_1 = ConvBNLayer(num_channels=num_channels, num_filters=192, filter_size=1, act="relu", name="inception_d_branch7x7x3_1_"+name) self.branch7x7x3_2 = ConvBNLayer(num_channels=192, num_filters=192, filter_size=(1, 7), padding=(0, 3), act="relu", name="inception_d_branch7x7x3_2_"+name) self.branch7x7x3_3 = ConvBNLayer(num_channels=192, num_filters=192, filter_size=(7, 1), padding=(3, 0), act="relu", name="inception_d_branch7x7x3_3_"+name) self.branch7x7x3_4 = ConvBNLayer(num_channels=192, num_filters=192, filter_size=3, stride=2, act="relu", name="inception_d_branch7x7x3_4_"+name) self.branch_pool = MaxPool2D(kernel_size=3, stride=2) def forward(self, x): branch3x3 = self.branch3x3_1(x) branch3x3 = self.branch3x3_2(branch3x3) branch7x7x3 = self.branch7x7x3_1(x) branch7x7x3 = self.branch7x7x3_2(branch7x7x3) branch7x7x3 = self.branch7x7x3_3(branch7x7x3) branch7x7x3 = self.branch7x7x3_4(branch7x7x3) branch_pool = self.branch_pool(x) outputs = paddle.concat([branch3x3, branch7x7x3, branch_pool], axis=1) return outputs class InceptionE(TheseusLayer): def __init__(self, num_channels, name=None): super(InceptionE, self).__init__() self.branch1x1 = ConvBNLayer(num_channels=num_channels, num_filters=320, filter_size=1, act="relu", name="inception_e_branch1x1_"+name) self.branch3x3_1 = ConvBNLayer(num_channels=num_channels, num_filters=384, filter_size=1, act="relu", name="inception_e_branch3x3_1_"+name) self.branch3x3_2a = ConvBNLayer(num_channels=384, num_filters=384, filter_size=(1, 3), padding=(0, 1), act="relu", name="inception_e_branch3x3_2a_"+name) self.branch3x3_2b = ConvBNLayer(num_channels=384, num_filters=384, filter_size=(3, 1), padding=(1, 0), act="relu", name="inception_e_branch3x3_2b_"+name) self.branch3x3dbl_1 = ConvBNLayer(num_channels=num_channels, num_filters=448, filter_size=1, act="relu", name="inception_e_branch3x3dbl_1_"+name) self.branch3x3dbl_2 = ConvBNLayer(num_channels=448, num_filters=384, filter_size=3, padding=1, act="relu", name="inception_e_branch3x3dbl_2_"+name) self.branch3x3dbl_3a = ConvBNLayer(num_channels=384, num_filters=384, filter_size=(1, 3), padding=(0, 1), act="relu", name="inception_e_branch3x3dbl_3a_"+name) self.branch3x3dbl_3b = ConvBNLayer(num_channels=384, num_filters=384, filter_size=(3, 1), padding=(1, 0), act="relu", name="inception_e_branch3x3dbl_3b_"+name) self.branch_pool = AvgPool2D(kernel_size=3, stride=1, padding=1, exclusive=False) self.branch_pool_conv = ConvBNLayer(num_channels=num_channels, num_filters=192, filter_size=1, act="relu", name="inception_e_branch_pool_"+name) def forward(self, x): branch1x1 = self.branch1x1(x) branch3x3 = self.branch3x3_1(x) branch3x3 = [ self.branch3x3_2a(branch3x3), self.branch3x3_2b(branch3x3), ] branch3x3 = paddle.concat(branch3x3, axis=1) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = [ self.branch3x3dbl_3a(branch3x3dbl), self.branch3x3dbl_3b(branch3x3dbl), ] branch3x3dbl = paddle.concat(branch3x3dbl, axis=1) branch_pool = self.branch_pool(x) branch_pool = self.branch_pool_conv(branch_pool) outputs = paddle.concat([branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1) return outputs class Inception_V3(TheseusLayer): def __init__(self, config, class_num=1000, **kwargs): super(Inception_V3, self).__init__() self.inception_a_list = config['inception_a'] self.inception_c_list = config['inception_c'] self.inception_b_list = config['inception_b'] self.inception_d_list = config['inception_d'] self.inception_e_list = config ['inception_e'] self.inception_stem = InceptionStem() self.inception_block_list = [] for i in range(len(self.inception_a_list[0])): inception_a = InceptionA(self.inception_a_list[0][i], self.inception_a_list[1][i], name=str(i+1)) self.inception_block_list.append(inception_a) for i in range(len(self.inception_b_list)): inception_b = InceptionB(self.inception_b_list[i], name=str(i+1)) self.inception_block_list.append(inception_b) for i in range(len(self.inception_c_list[0])): inception_c = InceptionC(self.inception_c_list[0][i], self.inception_c_list[1][i], name=str(i+1)) self.inception_block_list.append(inception_c) for i in range(len(self.inception_d_list)): inception_d = InceptionD(self.inception_d_list[i], name=str(i+1)) self.inception_block_list.append(inception_d) for i in range(len(self.inception_e_list)): inception_e = InceptionE(self.inception_e_list[i], name=str(i+1)) self.inception_block_list.append(inception_e) self.gap = AdaptiveAvgPool2D(1) self.drop = Dropout(p=0.2, mode="downscale_in_infer") stdv = 1.0 / math.sqrt(2048 * 1.0) self.out = Linear( 2048, class_num, weight_attr=ParamAttr( initializer=Uniform(-stdv, stdv), name="fc_weights"), bias_attr=ParamAttr(name="fc_offset")) def forward(self, x): y = self.inception_stem(x) for inception_block in self.inception_block_list: y = inception_block(y) y = self.gap(y) y = paddle.reshape(y, shape=[-1, 2048]) y = self.drop(y) y = self.out(y) return y