bninception2renlian.py

# -*- coding: utf-8 -*-
from __future__ import print_function, division, absolute_import
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import os
import sys

__all__ = ['BNInception', 'bninception']

pretrained_settings = {
    'bninception': {
        'imagenet': {
            # Was ported using python2 (may trigger warning)
            'url': 'http://data.lip6.fr/cadene/pretrainedmodels/bn_inception-52deb4733.pth',
            # 'url': 'http://yjxiong.me/others/bn_inception-9f5701afb96c8044.pth',
            'input_space': 'BGR',
            'input_size': [3, 224, 224], #改成cunet 256
            'input_range': [0, 255],    #像素值范围，之后可以归一化到0~1之间
            'mean': [104, 117, 128],  #均值像素
            'std': [1, 1, 1],    #方差
            'num_classes': 1000  #68点或106点
        }
    }
}

class BNInception(nn.Module):

    def __init__(self, num_classes=68):  #输入256 大小特征图
        super(BNInception, self).__init__()
        inplace = True   #64
        self.conv1_7x7_s2 = nn.Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3))  #128*128 *64
        self.conv1_7x7_s2_bn = nn.BatchNorm2d(64, affine=True)
        self.conv1_relu_7x7 = nn.ReLU (inplace)
        self.pool1_3x3_s2 = nn.MaxPool2d ((3, 3), stride=(2, 2), dilation=(1, 1), ceil_mode=True)  #卷积核大小扩张1变为5*5,ceil_mode=True,f map补0操作，  62*62 *64
        self.conv2_3x3_reduce = nn.Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))    #63*63 *64
        self.conv2_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True)
        self.conv2_relu_3x3_reduce = nn.ReLU (inplace)
        self.conv2_3x3 = nn.Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))   #63*63 *96 通道数增加
        self.conv2_3x3_bn = nn.BatchNorm2d(96, affine=True)
        self.conv2_relu_3x3 = nn.ReLU (inplace)
        self.pool2_3x3_s2 = nn.MaxPool2d ((3, 3), stride=(2, 2), dilation=(1, 1), ceil_mode=True)  #k=5, 30*30 *96
        #开始inception1_a
        self.inception_3a_1x1 = nn.Conv2d(96, 16, kernel_size=(1, 1), stride=(1, 1))  #30*30 *64 降通道数两个分支先1*1卷积
        self.inception_3a_1x1_bn = nn.BatchNorm2d(16, affine=True)
        self.inception_3a_relu_1x1 = nn.ReLU (inplace)
		
        self.inception_3a_3x3_reduce = nn.Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1))  #另一分支降通道数  30*30 *64
        self.inception_3a_3x3_reduce_bn = nn.BatchNorm2d(48, affine=True)
        self.inception_3a_relu_3x3_reduce = nn.ReLU (inplace)
        self.inception_3a_3x3 = nn.Conv2d(48, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))  #再3*3conv , 30*30 *32
        self.inception_3a_3x3_bn = nn.BatchNorm2d(64, affine=True)
        self.inception_3a_relu_3x3 = nn.ReLU (inplace)

        self.inception_3a_double_3x3_reduce = nn.Conv2d(96, 32, kernel_size=(1, 1), stride=(1, 1))  #30*30 *64
        self.inception_3a_double_3x3_reduce_bn = nn.BatchNorm2d(32, affine=True)
        self.inception_3a_relu_double_3x3_reduce = nn.ReLU (inplace)
        self.inception_3a_double_3x3_1 = nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))  #30*30 *32
        self.inception_3a_double_3x3_1_bn = nn.BatchNorm2d(32, affine=True)
        self.inception_3a_relu_double_3x3_1 = nn.ReLU (inplace)
        self.inception_3a_double_3x3_2 = nn.Conv2d(32, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))  #30*30 *96
        self.inception_3a_double_3x3_2_bn = nn.BatchNorm2d(48, affine=True)
        self.inception_3a_relu_double_3x3_2 = nn.ReLU (inplace)
		
        self.inception_3a_pool = nn.AvgPool2d (3, stride=1, padding=1, ceil_mode=True, count_include_pad=True)  #3*3核大小，30*30 *96
        self.inception_3a_pool_proj = nn.Conv2d(96, 16, kernel_size=(1, 1), stride=(1, 1))  #30*30 *32
        self.inception_3a_pool_proj_bn = nn.BatchNorm2d(16, affine=True) 
        self.inception_3a_relu_pool_proj = nn.ReLU (inplace)
		 #开始inception1_b
        self.inception_3b_1x1 = nn.Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1))    #30*30 *64
        self.inception_3b_1x1_bn = nn.BatchNorm2d(32, affine=True)
        self.inception_3b_relu_1x1 = nn.ReLU (inplace)
		
        self.inception_3b_3x3_reduce = nn.Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1))   #30*30 *64
        self.inception_3b_3x3_reduce_bn = nn.BatchNorm2d(32, affine=True)
        self.inception_3b_relu_3x3_reduce = nn.ReLU (inplace)
        self.inception_3b_3x3 = nn.Conv2d(32, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))  #30*30 *96
        self.inception_3b_3x3_bn = nn.BatchNorm2d(48 affine=True)
        self.inception_3b_relu_3x3 = nn.ReLU (inplace)
		
        self.inception_3b_double_3x3_reduce = nn.Conv2d(144, 48, kernel_size=(1, 1), stride=(1, 1))  #30*30 *64
        self.inception_3b_double_3x3_reduce_bn = nn.BatchNorm2d(48, affine=True)
        self.inception_3b_relu_double_3x3_reduce = nn.ReLU (inplace)
        self.inception_3b_double_3x3_1 = nn.Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))   #30*30 *96
        self.inception_3b_double_3x3_1_bn = nn.BatchNorm2d(48, affine=True)
        self.inception_3b_relu_double_3x3_1 = nn.ReLU (inplace)
        self.inception_3b_double_3x3_2 = nn.Conv2d(48, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))      #30*30 *96
        self.inception_3b_double_3x3_2_bn = nn.BatchNorm2d(64, affine=True)
        self.inception_3b_relu_double_3x3_2 = nn.ReLU (inplace)
		
        self.inception_3b_pool = nn.AvgPool2d (3, stride=1, padding=1, ceil_mode=True, count_include_pad=True)   #30*30 *96
        self.inception_3b_pool_proj = nn.Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1))   #30*30 *64
        self.inception_3b_pool_proj_bn = nn.BatchNorm2d(32, affine=True)
        self.inception_3b_relu_pool_proj = nn.ReLU (inplace)
		#开始inception1_c
        self.inception_3c_3x3_reduce = nn.Conv2d(176, 48, kernel_size=(1, 1), stride=(1, 1))   #30*30 *128
        self.inception_3c_3x3_reduce_bn = nn.BatchNorm2d(48, affine=True)
        self.inception_3c_relu_3x3_reduce = nn.ReLU (inplace)
        self.inception_3c_3x3 = nn.Conv2d(48, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))     #16*16 *160
        self.inception_3c_3x3_bn = nn.BatchNorm2d(32, affine=True)
        self.inception_3c_relu_3x3 = nn.ReLU (inplace)
		
        self.inception_3c_double_3x3_reduce = nn.Conv2d(176, 64, kernel_size=(1, 1), stride=(1, 1))     #30*30 *64
        self.inception_3c_double_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True)
        self.inception_3c_relu_double_3x3_reduce = nn.ReLU (inplace)
        self.inception_3c_double_3x3_1 = nn.Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))       #30*30 *96
        self.inception_3c_double_3x3_1_bn = nn.BatchNorm2d(32, affine=True)
        self.inception_3c_relu_double_3x3_1 = nn.ReLU (inplace)
        self.inception_3c_double_3x3_2 = nn.Conv2d(32, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))       #16*16 *96
        self.inception_3c_double_3x3_2_bn = nn.BatchNorm2d(48, affine=True)
        self.inception_3c_relu_double_3x3_2 = nn.ReLU (inplace)
		
        self.inception_3c_pool = nn.MaxPool2d ((3, 3), stride=(2, 2), dilation=(1, 1), ceil_mode=True)        #7*7 *96
		#开始inception1_4a
        self.inception_4a_1x1 = nn.Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
        self.inception_4a_1x1_bn = nn.BatchNorm2d(64, affine=True)
        self.inception_4a_relu_1x1 = nn.ReLU (inplace)
		
        self.inception_4a_3x3_reduce = nn.Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1))
        self.inception_4a_3x3_reduce_bn = nn.BatchNorm2d(48, affine=True)
        self.inception_4a_relu_3x3_reduce = nn.ReLU (inplace)
        self.inception_4a_3x3 = nn.Conv2d(48, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.inception_4a_3x3_bn = nn.BatchNorm2d(80, affine=True)
        self.inception_4a_relu_3x3 = nn.ReLU (inplace)
		
        self.inception_4a_double_3x3_reduce = nn.Conv2d(256, 96, kernel_size=(1, 1), stride=(1, 1))
        self.inception_4a_double_3x3_reduce_bn = nn.BatchNorm2d(96, affine=True)
        self.inception_4a_relu_double_3x3_reduce = nn.ReLU (inplace)
        self.inception_4a_double_3x3_1 = nn.Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.inception_4a_double_3x3_1_bn = nn.BatchNorm2d(32, affine=True)
        self.inception_4a_relu_double_3x3_1 = nn.ReLU (inplace)
        self.inception_4a_double_3x3_2 = nn.Conv2d(32, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.inception_4a_double_3x3_2_bn = nn.BatchNorm2d(48, affine=True)
        self.inception_4a_relu_double_3x3_2 = nn.ReLU (inplace)
		
        self.inception_4a_pool = nn.AvgPool2d (3, stride=1, padding=1, ceil_mode=True, count_include_pad=True)
        self.inception_4a_pool_proj = nn.Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
        self.inception_4a_pool_proj_bn = nn.BatchNorm2d(64, affine=True)
        self.inception_4a_relu_pool_proj = nn.ReLU (inplace)
		#开始inception1_4b
        self.inception_4b_1x1 = nn.Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1))
        self.inception_4b_1x1_bn = nn.BatchNorm2d(48, affine=True)
        self.inception_4b_relu_1x1 = nn.ReLU (inplace)
		
        self.inception_4b_3x3_reduce = nn.Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
        self.inception_4b_3x3_reduce_bn = nn.BatchNorm2d(32, affine=True)
        self.inception_4b_relu_3x3_reduce = nn.ReLU (inplace)
        self.inception_4b_3x3 = nn.Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.inception_4b_3x3_bn = nn.BatchNorm2d(64, affine=True)
        self.inception_4b_relu_3x3 = nn.ReLU (inplace)
		
        self.inception_4b_double_3x3_reduce = nn.Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
        self.inception_4b_double_3x3_reduce_bn = nn.BatchNorm2d(32, affine=True)
        self.inception_4b_relu_double_3x3_reduce = nn.ReLU (inplace)
        self.inception_4b_double_3x3_1 = nn.Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.inception_4b_double_3x3_1_bn = nn.BatchNorm2d(64, affine=True)
        self.inception_4b_relu_double_3x3_1 = nn.ReLU (inplace)
        self.inception_4b_double_3x3_2 = nn.Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.inception_4b_double_3x3_2_bn = nn.BatchNorm2d(96, affine=True)
        self.inception_4b_relu_double_3x3_2 = nn.ReLU (inplace)
		
        self.inception_4b_pool = nn.AvgPool2d (3, stride=1, padding=1, ceil_mode=True, count_include_pad=True)
        self.inception_4b_pool_proj = nn.Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1))
        self.inception_4b_pool_proj_bn = nn.BatchNorm2d(48, affine=True)
        self.inception_4b_relu_pool_proj = nn.ReLU (inplace)
		#开始inception1_4c
        self.inception_4c_1x1 = nn.Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
        self.inception_4c_1x1_bn = nn.BatchNorm2d(32, affine=True)
        self.inception_4c_relu_1x1 = nn.ReLU (inplace)
		
        self.inception_4c_3x3_reduce = nn.Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
        self.inception_4c_3x3_reduce_bn = nn.BatchNorm2d(32, affine=True)
        self.inception_4c_relu_3x3_reduce = nn.ReLU (inplace)
        self.inception_4c_3x3 = nn.Conv2d(32, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.inception_4c_3x3_bn = nn.BatchNorm2d(48, affine=True)
        self.inception_4c_relu_3x3 = nn.ReLU (inplace)
		
        self.inception_4c_double_3x3_reduce = nn.Conv2d(256, 96, kernel_size=(1, 1), stride=(1, 1))
        self.inception_4c_double_3x3_reduce_bn = nn.BatchNorm2d(96, affine=True)
        self.inception_4c_relu_double_3x3_reduce = nn.ReLU (inplace)
        self.inception_4c_double_3x3_1 = nn.Conv2d(96, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.inception_4c_double_3x3_1_bn = nn.BatchNorm2d(64, affine=True)
        self.inception_4c_relu_double_3x3_1 = nn.ReLU (inplace)
        self.inception_4c_double_3x3_2 = nn.Conv2d(64, 112, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.inception_4c_double_3x3_2_bn = nn.BatchNorm2d(112, affine=True)
        self.inception_4c_relu_double_3x3_2 = nn.ReLU (inplace)
	
        self.inception_4c_pool = nn.AvgPool2d (3, stride=1, padding=1, ceil_mode=True, count_include_pad=True)
        self.inception_4c_pool_proj = nn.Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
        self.inception_4c_pool_proj_bn = nn.BatchNorm2d(64, affine=True)
        self.inception_4c_relu_pool_proj = nn.ReLU (inplace)
		#开始inception1_4d
        self.inception_4d_1x1 = nn.Conv2d(272, 32, kernel_size=(1, 1), stride=(1, 1))
        self.inception_4d_1x1_bn = nn.BatchNorm2d(32, affine=True)
        self.inception_4d_relu_1x1 = nn.ReLU (inplace)
		
        self.inception_4d_3x3_reduce = nn.Conv2d(272, 64, kernel_size=(1, 1), stride=(1, 1))
        self.inception_4d_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True)
        self.inception_4d_relu_3x3_reduce = nn.ReLU (inplace)
        self.inception_4d_3x3 = nn.Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.inception_4d_3x3_bn = nn.BatchNorm2d(96, affine=True)
        self.inception_4d_relu_3x3 = nn.ReLU (inplace)
		
        self.inception_4d_double_3x3_reduce = nn.Conv2d(272, 96, kernel_size=(1, 1), stride=(1, 1))
        self.inception_4d_double_3x3_reduce_bn = nn.BatchNorm2d(96, affine=True)
        self.inception_4d_relu_double_3x3_reduce = nn.ReLU (inplace)
        self.inception_4d_double_3x3_1 = nn.Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.inception_4d_double_3x3_1_bn = nn.BatchNorm2d(96, affine=True)
        self.inception_4d_relu_double_3x3_1 = nn.ReLU (inplace)
        self.inception_4d_double_3x3_2 = nn.Conv2d(96, 112 kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.inception_4d_double_3x3_2_bn = nn.BatchNorm2d(112, affine=True)
        self.inception_4d_relu_double_3x3_2 = nn.ReLU (inplace)
		
        self.inception_4d_pool = nn.AvgPool2d (3, stride=1, padding=1, ceil_mode=True, count_include_pad=True)
        self.inception_4d_pool_proj = nn.Conv2d(272, 32, kernel_size=(1, 1), stride=(1, 1))
        self.inception_4d_pool_proj_bn = nn.BatchNorm2d(32, affine=True)
        self.inception_4d_relu_pool_proj = nn.ReLU (inplace)
		#开始inception1_4e
        self.inception_4e_3x3_reduce = nn.Conv2d(272, 16, kernel_size=(1, 1), stride=(1, 1))
        self.inception_4e_3x3_reduce_bn = nn.BatchNorm2d(16, affine=True)
        self.inception_4e_relu_3x3_reduce = nn.ReLU (inplace)
        self.inception_4e_3x3 = nn.Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
        self.inception_4e_3x3_bn = nn.BatchNorm2d(32, affine=True)
        self.inception_4e_relu_3x3 = nn.ReLU (inplace)
		
        self.inception_4e_double_3x3_reduce = nn.Conv2d(272, 48, kernel_size=(1, 1), stride=(1, 1))
        self.inception_4e_double_3x3_reduce_bn = nn.BatchNorm2d(48, affine=True)
        self.inception_4e_relu_double_3x3_reduce = nn.ReLU (inplace)
        self.inception_4e_double_3x3_1 = nn.Conv2d(48, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.inception_4e_double_3x3_1_bn = nn.BatchNorm2d(16, affine=True)
        self.inception_4e_relu_double_3x3_1 = nn.ReLU (inplace)
        self.inception_4e_double_3x3_2 = nn.Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
        self.inception_4e_double_3x3_2_bn = nn.BatchNorm2d(32, affine=True)
        self.inception_4e_relu_double_3x3_2 = nn.ReLU (inplace)
		
        self.inception_4e_pool = nn.MaxPool2d ((3, 3), stride=(2, 2), dilation=(1, 1), ceil_mode=True)
		#开始inception1_5a
        self.inception_5a_1x1 = nn.Conv2d(336, 32, kernel_size=(1, 1), stride=(1, 1))
        self.inception_5a_1x1_bn = nn.BatchNorm2d(32, affine=True)
        self.inception_5a_relu_1x1 = nn.ReLU (inplace)
		
        self.inception_5a_3x3_reduce = nn.Conv2d(336, 32, kernel_size=(1, 1), stride=(1, 1))
        self.inception_5a_3x3_reduce_bn = nn.BatchNorm2d(32, affine=True)
        self.inception_5a_relu_3x3_reduce = nn.ReLU (inplace)
        self.inception_5a_3x3 = nn.Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.inception_5a_3x3_bn = nn.BatchNorm2d(64, affine=True)
        self.inception_5a_relu_3x3 = nn.ReLU (inplace)
		
        self.inception_5a_double_3x3_reduce = nn.Conv2d(336, 64, kernel_size=(1, 1), stride=(1, 1))
        self.inception_5a_double_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True)
        self.inception_5a_relu_double_3x3_reduce = nn.ReLU (inplace)
        self.inception_5a_double_3x3_1 = nn.Conv2d(64, 238, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.inception_5a_double_3x3_1_bn = nn.BatchNorm2d(128, affine=True)
        self.inception_5a_relu_double_3x3_1 = nn.ReLU (inplace)
        self.inception_5a_double_3x3_2 = nn.Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.inception_5a_double_3x3_2_bn = nn.BatchNorm2d(128, affine=True)
        self.inception_5a_relu_double_3x3_2 = nn.ReLU (inplace)
		
        self.inception_5a_pool = nn.AvgPool2d (3, stride=1, padding=1, ceil_mode=True, count_include_pad=True)
        self.inception_5a_pool_proj = nn.Conv2d(336, 32, kernel_size=(1, 1), stride=(1, 1))
        self.inception_5a_pool_proj_bn = nn.BatchNorm2d(32, affine=True)
        self.inception_5a_relu_pool_proj = nn.ReLU (inplace)
		#开始inception1_5b
        self.inception_5b_1x1 = nn.Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
        self.inception_5b_1x1_bn = nn.BatchNorm2d(32, affine=True)
        self.inception_5b_relu_1x1 = nn.ReLU (inplace)
		
        self.inception_5b_3x3_reduce = nn.Conv2d(256, 96, kernel_size=(1, 1), stride=(1, 1))
        self.inception_5b_3x3_reduce_bn = nn.BatchNorm2d(96, affine=True)
        self.inception_5b_relu_3x3_reduce = nn.ReLU (inplace)
        self.inception_5b_3x3 = nn.Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.inception_5b_3x3_bn = nn.BatchNorm2d(128, affine=True)
        self.inception_5b_relu_3x3 = nn.ReLU (inplace)
		
        self.inception_5b_double_3x3_reduce = nn.Conv2d(256, 96, kernel_size=(1, 1), stride=(1, 1))
        self.inception_5b_double_3x3_reduce_bn = nn.BatchNorm2d(96, affine=True)
        self.inception_5b_relu_double_3x3_reduce = nn.ReLU (inplace)
        self.inception_5b_double_3x3_1 = nn.Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.inception_5b_double_3x3_1_bn = nn.BatchNorm2d(96, affine=True)
        self.inception_5b_relu_double_3x3_1 = nn.ReLU (inplace)
        self.inception_5b_double_3x3_2 = nn.Conv2d(96, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        self.inception_5b_double_3x3_2_bn = nn.BatchNorm2d(64, affine=True)
        self.inception_5b_relu_double_3x3_2 = nn.ReLU (inplace)
		
        self.inception_5b_pool = nn.MaxPool2d ((3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), ceil_mode=True)
        self.inception_5b_pool_proj = nn.Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
        self.inception_5b_pool_proj_bn = nn.BatchNorm2d(32, affine=True)
        self.inception_5b_relu_pool_proj = nn.ReLU (inplace)
		
        self.global_pool = nn.AvgPool2d (7, stride=1, padding=0, ceil_mode=True, count_include_pad=True)  #最终的卷积层之后采用Global Average Pooling层，而不是全连接层，助于减少参数量
		
        self.last_linear = nn.Linear (256, num_classes)

    def features(self, input):
        conv1_7x7_s2_out = self.conv1_7x7_s2(input)
        conv1_7x7_s2_bn_out = self.conv1_7x7_s2_bn(conv1_7x7_s2_out)
        conv1_relu_7x7_out = self.conv1_relu_7x7(conv1_7x7_s2_bn_out)
        pool1_3x3_s2_out = self.pool1_3x3_s2(conv1_relu_7x7_out)
        conv2_3x3_reduce_out = self.conv2_3x3_reduce(pool1_3x3_s2_out)
        conv2_3x3_reduce_bn_out = self.conv2_3x3_reduce_bn(conv2_3x3_reduce_out)
        conv2_relu_3x3_reduce_out = self.conv2_relu_3x3_reduce(conv2_3x3_reduce_bn_out)
        conv2_3x3_out = self.conv2_3x3(conv2_relu_3x3_reduce_out)
        conv2_3x3_bn_out = self.conv2_3x3_bn(conv2_3x3_out)
        conv2_relu_3x3_out = self.conv2_relu_3x3(conv2_3x3_bn_out)
        pool2_3x3_s2_out = self.pool2_3x3_s2(conv2_relu_3x3_out)
        inception_3a_1x1_out = self.inception_3a_1x1(pool2_3x3_s2_out)
        inception_3a_1x1_bn_out = self.inception_3a_1x1_bn(inception_3a_1x1_out)
        inception_3a_relu_1x1_out = self.inception_3a_relu_1x1(inception_3a_1x1_bn_out)
        inception_3a_3x3_reduce_out = self.inception_3a_3x3_reduce(pool2_3x3_s2_out)
        inception_3a_3x3_reduce_bn_out = self.inception_3a_3x3_reduce_bn(inception_3a_3x3_reduce_out)
        inception_3a_relu_3x3_reduce_out = self.inception_3a_relu_3x3_reduce(inception_3a_3x3_reduce_bn_out)
        inception_3a_3x3_out = self.inception_3a_3x3(inception_3a_relu_3x3_reduce_out)
        inception_3a_3x3_bn_out = self.inception_3a_3x3_bn(inception_3a_3x3_out)
        inception_3a_relu_3x3_out = self.inception_3a_relu_3x3(inception_3a_3x3_bn_out)
        inception_3a_double_3x3_reduce_out = self.inception_3a_double_3x3_reduce(pool2_3x3_s2_out)
        inception_3a_double_3x3_reduce_bn_out = self.inception_3a_double_3x3_reduce_bn(inception_3a_double_3x3_reduce_out)
        inception_3a_relu_double_3x3_reduce_out = self.inception_3a_relu_double_3x3_reduce(inception_3a_double_3x3_reduce_bn_out)
        inception_3a_double_3x3_1_out = self.inception_3a_double_3x3_1(inception_3a_relu_double_3x3_reduce_out)
        inception_3a_double_3x3_1_bn_out = self.inception_3a_double_3x3_1_bn(inception_3a_double_3x3_1_out)
        inception_3a_relu_double_3x3_1_out = self.inception_3a_relu_double_3x3_1(inception_3a_double_3x3_1_bn_out)
        inception_3a_double_3x3_2_out = self.inception_3a_double_3x3_2(inception_3a_relu_double_3x3_1_out)
        inception_3a_double_3x3_2_bn_out = self.inception_3a_double_3x3_2_bn(inception_3a_double_3x3_2_out)
        inception_3a_relu_double_3x3_2_out = self.inception_3a_relu_double_3x3_2(inception_3a_double_3x3_2_bn_out)
        inception_3a_pool_out = self.inception_3a_pool(pool2_3x3_s2_out)
        inception_3a_pool_proj_out = self.inception_3a_pool_proj(inception_3a_pool_out)
        inception_3a_pool_proj_bn_out = self.inception_3a_pool_proj_bn(inception_3a_pool_proj_out)
        inception_3a_relu_pool_proj_out = self.inception_3a_relu_pool_proj(inception_3a_pool_proj_bn_out)
        inception_3a_output_out = torch.cat([inception_3a_relu_1x1_out,inception_3a_relu_3x3_out,inception_3a_relu_double_3x3_2_out ,inception_3a_relu_pool_proj_out], 1)
        inception_3b_1x1_out = self.inception_3b_1x1(inception_3a_output_out)
        inception_3b_1x1_bn_out = self.inception_3b_1x1_bn(inception_3b_1x1_out)
        inception_3b_relu_1x1_out = self.inception_3b_relu_1x1(inception_3b_1x1_bn_out)
        inception_3b_3x3_reduce_out = self.inception_3b_3x3_reduce(inception_3a_output_out)
        inception_3b_3x3_reduce_bn_out = self.inception_3b_3x3_reduce_bn(inception_3b_3x3_reduce_out)
        inception_3b_relu_3x3_reduce_out = self.inception_3b_relu_3x3_reduce(inception_3b_3x3_reduce_bn_out)
        inception_3b_3x3_out = self.inception_3b_3x3(inception_3b_relu_3x3_reduce_out)
        inception_3b_3x3_bn_out = self.inception_3b_3x3_bn(inception_3b_3x3_out)
        inception_3b_relu_3x3_out = self.inception_3b_relu_3x3(inception_3b_3x3_bn_out)
        inception_3b_double_3x3_reduce_out = self.inception_3b_double_3x3_reduce(inception_3a_output_out)
        inception_3b_double_3x3_reduce_bn_out = self.inception_3b_double_3x3_reduce_bn(inception_3b_double_3x3_reduce_out)
        inception_3b_relu_double_3x3_reduce_out = self.inception_3b_relu_double_3x3_reduce(inception_3b_double_3x3_reduce_bn_out)
        inception_3b_double_3x3_1_out = self.inception_3b_double_3x3_1(inception_3b_relu_double_3x3_reduce_out)
        inception_3b_double_3x3_1_bn_out = self.inception_3b_double_3x3_1_bn(inception_3b_double_3x3_1_out)
        inception_3b_relu_double_3x3_1_out = self.inception_3b_relu_double_3x3_1(inception_3b_double_3x3_1_bn_out)
        inception_3b_double_3x3_2_out = self.inception_3b_double_3x3_2(inception_3b_relu_double_3x3_1_out)
        inception_3b_double_3x3_2_bn_out = self.inception_3b_double_3x3_2_bn(inception_3b_double_3x3_2_out)
        inception_3b_relu_double_3x3_2_out = self.inception_3b_relu_double_3x3_2(inception_3b_double_3x3_2_bn_out)
        inception_3b_pool_out = self.inception_3b_pool(inception_3a_output_out)
        inception_3b_pool_proj_out = self.inception_3b_pool_proj(inception_3b_pool_out)
        inception_3b_pool_proj_bn_out = self.inception_3b_pool_proj_bn(inception_3b_pool_proj_out)
        inception_3b_relu_pool_proj_out = self.inception_3b_relu_pool_proj(inception_3b_pool_proj_bn_out)
        inception_3b_output_out = torch.cat([inception_3b_relu_1x1_out,inception_3b_relu_3x3_out,inception_3b_relu_double_3x3_2_out,inception_3b_relu_pool_proj_out], 1)
        inception_3c_3x3_reduce_out = self.inception_3c_3x3_reduce(inception_3b_output_out)
        inception_3c_3x3_reduce_bn_out = self.inception_3c_3x3_reduce_bn(inception_3c_3x3_reduce_out)
        inception_3c_relu_3x3_reduce_out = self.inception_3c_relu_3x3_reduce(inception_3c_3x3_reduce_bn_out)
        inception_3c_3x3_out = self.inception_3c_3x3(inception_3c_relu_3x3_reduce_out)
        inception_3c_3x3_bn_out = self.inception_3c_3x3_bn(inception_3c_3x3_out)
        inception_3c_relu_3x3_out = self.inception_3c_relu_3x3(inception_3c_3x3_bn_out)
        inception_3c_double_3x3_reduce_out = self.inception_3c_double_3x3_reduce(inception_3b_output_out)
        inception_3c_double_3x3_reduce_bn_out = self.inception_3c_double_3x3_reduce_bn(inception_3c_double_3x3_reduce_out)
        inception_3c_relu_double_3x3_reduce_out = self.inception_3c_relu_double_3x3_reduce(inception_3c_double_3x3_reduce_bn_out)
        inception_3c_double_3x3_1_out = self.inception_3c_double_3x3_1(inception_3c_relu_double_3x3_reduce_out)
        inception_3c_double_3x3_1_bn_out = self.inception_3c_double_3x3_1_bn(inception_3c_double_3x3_1_out)
        inception_3c_relu_double_3x3_1_out = self.inception_3c_relu_double_3x3_1(inception_3c_double_3x3_1_bn_out)
        inception_3c_double_3x3_2_out = self.inception_3c_double_3x3_2(inception_3c_relu_double_3x3_1_out)
        inception_3c_double_3x3_2_bn_out = self.inception_3c_double_3x3_2_bn(inception_3c_double_3x3_2_out)
        inception_3c_relu_double_3x3_2_out = self.inception_3c_relu_double_3x3_2(inception_3c_double_3x3_2_bn_out)
        inception_3c_pool_out = self.inception_3c_pool(inception_3b_output_out)
        inception_3c_output_out = torch.cat([inception_3c_relu_3x3_out,inception_3c_relu_double_3x3_2_out,inception_3c_pool_out], 1)
        inception_4a_1x1_out = self.inception_4a_1x1(inception_3c_output_out)
        inception_4a_1x1_bn_out = self.inception_4a_1x1_bn(inception_4a_1x1_out)
        inception_4a_relu_1x1_out = self.inception_4a_relu_1x1(inception_4a_1x1_bn_out)
        inception_4a_3x3_reduce_out = self.inception_4a_3x3_reduce(inception_3c_output_out)
        inception_4a_3x3_reduce_bn_out = self.inception_4a_3x3_reduce_bn(inception_4a_3x3_reduce_out)
        inception_4a_relu_3x3_reduce_out = self.inception_4a_relu_3x3_reduce(inception_4a_3x3_reduce_bn_out)
        inception_4a_3x3_out = self.inception_4a_3x3(inception_4a_relu_3x3_reduce_out)
        inception_4a_3x3_bn_out = self.inception_4a_3x3_bn(inception_4a_3x3_out)
        inception_4a_relu_3x3_out = self.inception_4a_relu_3x3(inception_4a_3x3_bn_out)
        inception_4a_double_3x3_reduce_out = self.inception_4a_double_3x3_reduce(inception_3c_output_out)
        inception_4a_double_3x3_reduce_bn_out = self.inception_4a_double_3x3_reduce_bn(inception_4a_double_3x3_reduce_out)
        inception_4a_relu_double_3x3_reduce_out = self.inception_4a_relu_double_3x3_reduce(inception_4a_double_3x3_reduce_bn_out)
        inception_4a_double_3x3_1_out = self.inception_4a_double_3x3_1(inception_4a_relu_double_3x3_reduce_out)
        inception_4a_double_3x3_1_bn_out = self.inception_4a_double_3x3_1_bn(inception_4a_double_3x3_1_out)
        inception_4a_relu_double_3x3_1_out = self.inception_4a_relu_double_3x3_1(inception_4a_double_3x3_1_bn_out)
        inception_4a_double_3x3_2_out = self.inception_4a_double_3x3_2(inception_4a_relu_double_3x3_1_out)
        inception_4a_double_3x3_2_bn_out = self.inception_4a_double_3x3_2_bn(inception_4a_double_3x3_2_out)
        inception_4a_relu_double_3x3_2_out = self.inception_4a_relu_double_3x3_2(inception_4a_double_3x3_2_bn_out)
        inception_4a_pool_out = self.inception_4a_pool(inception_3c_output_out)
        inception_4a_pool_proj_out = self.inception_4a_pool_proj(inception_4a_pool_out)
        inception_4a_pool_proj_bn_out = self.inception_4a_pool_proj_bn(inception_4a_pool_proj_out)
        inception_4a_relu_pool_proj_out = self.inception_4a_relu_pool_proj(inception_4a_pool_proj_bn_out)
        inception_4a_output_out = torch.cat([inception_4a_relu_1x1_out,inception_4a_relu_3x3_out,inception_4a_relu_double_3x3_2_out,inception_4a_relu_pool_proj_out], 1)
        inception_4b_1x1_out = self.inception_4b_1x1(inception_4a_output_out)
        inception_4b_1x1_bn_out = self.inception_4b_1x1_bn(inception_4b_1x1_out)
        inception_4b_relu_1x1_out = self.inception_4b_relu_1x1(inception_4b_1x1_bn_out)
        inception_4b_3x3_reduce_out = self.inception_4b_3x3_reduce(inception_4a_output_out)
        inception_4b_3x3_reduce_bn_out = self.inception_4b_3x3_reduce_bn(inception_4b_3x3_reduce_out)
        inception_4b_relu_3x3_reduce_out = self.inception_4b_relu_3x3_reduce(inception_4b_3x3_reduce_bn_out)
        inception_4b_3x3_out = self.inception_4b_3x3(inception_4b_relu_3x3_reduce_out)
        inception_4b_3x3_bn_out = self.inception_4b_3x3_bn(inception_4b_3x3_out)
        inception_4b_relu_3x3_out = self.inception_4b_relu_3x3(inception_4b_3x3_bn_out)
        inception_4b_double_3x3_reduce_out = self.inception_4b_double_3x3_reduce(inception_4a_output_out)
        inception_4b_double_3x3_reduce_bn_out = self.inception_4b_double_3x3_reduce_bn(inception_4b_double_3x3_reduce_out)
        inception_4b_relu_double_3x3_reduce_out = self.inception_4b_relu_double_3x3_reduce(inception_4b_double_3x3_reduce_bn_out)
        inception_4b_double_3x3_1_out = self.inception_4b_double_3x3_1(inception_4b_relu_double_3x3_reduce_out)
        inception_4b_double_3x3_1_bn_out = self.inception_4b_double_3x3_1_bn(inception_4b_double_3x3_1_out)
        inception_4b_relu_double_3x3_1_out = self.inception_4b_relu_double_3x3_1(inception_4b_double_3x3_1_bn_out)
        inception_4b_double_3x3_2_out = self.inception_4b_double_3x3_2(inception_4b_relu_double_3x3_1_out)
        inception_4b_double_3x3_2_bn_out = self.inception_4b_double_3x3_2_bn(inception_4b_double_3x3_2_out)
        inception_4b_relu_double_3x3_2_out = self.inception_4b_relu_double_3x3_2(inception_4b_double_3x3_2_bn_out)
        inception_4b_pool_out = self.inception_4b_pool(inception_4a_output_out)
        inception_4b_pool_proj_out = self.inception_4b_pool_proj(inception_4b_pool_out)
        inception_4b_pool_proj_bn_out = self.inception_4b_pool_proj_bn(inception_4b_pool_proj_out)
        inception_4b_relu_pool_proj_out = self.inception_4b_relu_pool_proj(inception_4b_pool_proj_bn_out)
        inception_4b_output_out = torch.cat([inception_4b_relu_1x1_out,inception_4b_relu_3x3_out,inception_4b_relu_double_3x3_2_out,inception_4b_relu_pool_proj_out], 1)
        inception_4c_1x1_out = self.inception_4c_1x1(inception_4b_output_out)
        inception_4c_1x1_bn_out = self.inception_4c_1x1_bn(inception_4c_1x1_out)
        inception_4c_relu_1x1_out = self.inception_4c_relu_1x1(inception_4c_1x1_bn_out)
        inception_4c_3x3_reduce_out = self.inception_4c_3x3_reduce(inception_4b_output_out)
        inception_4c_3x3_reduce_bn_out = self.inception_4c_3x3_reduce_bn(inception_4c_3x3_reduce_out)
        inception_4c_relu_3x3_reduce_out = self.inception_4c_relu_3x3_reduce(inception_4c_3x3_reduce_bn_out)
        inception_4c_3x3_out = self.inception_4c_3x3(inception_4c_relu_3x3_reduce_out)
        inception_4c_3x3_bn_out = self.inception_4c_3x3_bn(inception_4c_3x3_out)
        inception_4c_relu_3x3_out = self.inception_4c_relu_3x3(inception_4c_3x3_bn_out)
        inception_4c_double_3x3_reduce_out = self.inception_4c_double_3x3_reduce(inception_4b_output_out)
        inception_4c_double_3x3_reduce_bn_out = self.inception_4c_double_3x3_reduce_bn(inception_4c_double_3x3_reduce_out)
        inception_4c_relu_double_3x3_reduce_out = self.inception_4c_relu_double_3x3_reduce(inception_4c_double_3x3_reduce_bn_out)
        inception_4c_double_3x3_1_out = self.inception_4c_double_3x3_1(inception_4c_relu_double_3x3_reduce_out)
        inception_4c_double_3x3_1_bn_out = self.inception_4c_double_3x3_1_bn(inception_4c_double_3x3_1_out)
        inception_4c_relu_double_3x3_1_out = self.inception_4c_relu_double_3x3_1(inception_4c_double_3x3_1_bn_out)
        inception_4c_double_3x3_2_out = self.inception_4c_double_3x3_2(inception_4c_relu_double_3x3_1_out)
        inception_4c_double_3x3_2_bn_out = self.inception_4c_double_3x3_2_bn(inception_4c_double_3x3_2_out)
        inception_4c_relu_double_3x3_2_out = self.inception_4c_relu_double_3x3_2(inception_4c_double_3x3_2_bn_out)
        inception_4c_pool_out = self.inception_4c_pool(inception_4b_output_out)
        inception_4c_pool_proj_out = self.inception_4c_pool_proj(inception_4c_pool_out)
        inception_4c_pool_proj_bn_out = self.inception_4c_pool_proj_bn(inception_4c_pool_proj_out)
        inception_4c_relu_pool_proj_out = self.inception_4c_relu_pool_proj(inception_4c_pool_proj_bn_out)
        inception_4c_output_out = torch.cat([inception_4c_relu_1x1_out,inception_4c_relu_3x3_out,inception_4c_relu_double_3x3_2_out,inception_4c_relu_pool_proj_out], 1)
        inception_4d_1x1_out = self.inception_4d_1x1(inception_4c_output_out)
        inception_4d_1x1_bn_out = self.inception_4d_1x1_bn(inception_4d_1x1_out)
        inception_4d_relu_1x1_out = self.inception_4d_relu_1x1(inception_4d_1x1_bn_out)
        inception_4d_3x3_reduce_out = self.inception_4d_3x3_reduce(inception_4c_output_out)
        inception_4d_3x3_reduce_bn_out = self.inception_4d_3x3_reduce_bn(inception_4d_3x3_reduce_out)
        inception_4d_relu_3x3_reduce_out = self.inception_4d_relu_3x3_reduce(inception_4d_3x3_reduce_bn_out)
        inception_4d_3x3_out = self.inception_4d_3x3(inception_4d_relu_3x3_reduce_out)
        inception_4d_3x3_bn_out = self.inception_4d_3x3_bn(inception_4d_3x3_out)
        inception_4d_relu_3x3_out = self.inception_4d_relu_3x3(inception_4d_3x3_bn_out)
        inception_4d_double_3x3_reduce_out = self.inception_4d_double_3x3_reduce(inception_4c_output_out)
        inception_4d_double_3x3_reduce_bn_out = self.inception_4d_double_3x3_reduce_bn(inception_4d_double_3x3_reduce_out)
        inception_4d_relu_double_3x3_reduce_out = self.inception_4d_relu_double_3x3_reduce(inception_4d_double_3x3_reduce_bn_out)
        inception_4d_double_3x3_1_out = self.inception_4d_double_3x3_1(inception_4d_relu_double_3x3_reduce_out)
        inception_4d_double_3x3_1_bn_out = self.inception_4d_double_3x3_1_bn(inception_4d_double_3x3_1_out)
        inception_4d_relu_double_3x3_1_out = self.inception_4d_relu_double_3x3_1(inception_4d_double_3x3_1_bn_out)
        inception_4d_double_3x3_2_out = self.inception_4d_double_3x3_2(inception_4d_relu_double_3x3_1_out)
        inception_4d_double_3x3_2_bn_out = self.inception_4d_double_3x3_2_bn(inception_4d_double_3x3_2_out)
        inception_4d_relu_double_3x3_2_out = self.inception_4d_relu_double_3x3_2(inception_4d_double_3x3_2_bn_out)
        inception_4d_pool_out = self.inception_4d_pool(inception_4c_output_out)
        inception_4d_pool_proj_out = self.inception_4d_pool_proj(inception_4d_pool_out)
        inception_4d_pool_proj_bn_out = self.inception_4d_pool_proj_bn(inception_4d_pool_proj_out)
        inception_4d_relu_pool_proj_out = self.inception_4d_relu_pool_proj(inception_4d_pool_proj_bn_out)
        inception_4d_output_out = torch.cat([inception_4d_relu_1x1_out,inception_4d_relu_3x3_out,inception_4d_relu_double_3x3_2_out,inception_4d_relu_pool_proj_out], 1)
        inception_4e_3x3_reduce_out = self.inception_4e_3x3_reduce(inception_4d_output_out)
        inception_4e_3x3_reduce_bn_out = self.inception_4e_3x3_reduce_bn(inception_4e_3x3_reduce_out)
        inception_4e_relu_3x3_reduce_out = self.inception_4e_relu_3x3_reduce(inception_4e_3x3_reduce_bn_out)
        inception_4e_3x3_out = self.inception_4e_3x3(inception_4e_relu_3x3_reduce_out)
        inception_4e_3x3_bn_out = self.inception_4e_3x3_bn(inception_4e_3x3_out)
        inception_4e_relu_3x3_out = self.inception_4e_relu_3x3(inception_4e_3x3_bn_out)
        inception_4e_double_3x3_reduce_out = self.inception_4e_double_3x3_reduce(inception_4d_output_out)
        inception_4e_double_3x3_reduce_bn_out = self.inception_4e_double_3x3_reduce_bn(inception_4e_double_3x3_reduce_out)
        inception_4e_relu_double_3x3_reduce_out = self.inception_4e_relu_double_3x3_reduce(inception_4e_double_3x3_reduce_bn_out)
        inception_4e_double_3x3_1_out = self.inception_4e_double_3x3_1(inception_4e_relu_double_3x3_reduce_out)
        inception_4e_double_3x3_1_bn_out = self.inception_4e_double_3x3_1_bn(inception_4e_double_3x3_1_out)
        inception_4e_relu_double_3x3_1_out = self.inception_4e_relu_double_3x3_1(inception_4e_double_3x3_1_bn_out)
        inception_4e_double_3x3_2_out = self.inception_4e_double_3x3_2(inception_4e_relu_double_3x3_1_out)
        inception_4e_double_3x3_2_bn_out = self.inception_4e_double_3x3_2_bn(inception_4e_double_3x3_2_out)
        inception_4e_relu_double_3x3_2_out = self.inception_4e_relu_double_3x3_2(inception_4e_double_3x3_2_bn_out)
        inception_4e_pool_out = self.inception_4e_pool(inception_4d_output_out)
        inception_4e_output_out = torch.cat([inception_4e_relu_3x3_out,inception_4e_relu_double_3x3_2_out,inception_4e_pool_out], 1)
        inception_5a_1x1_out = self.inception_5a_1x1(inception_4e_output_out)
        inception_5a_1x1_bn_out = self.inception_5a_1x1_bn(inception_5a_1x1_out)
        inception_5a_relu_1x1_out = self.inception_5a_relu_1x1(inception_5a_1x1_bn_out)
        inception_5a_3x3_reduce_out = self.inception_5a_3x3_reduce(inception_4e_output_out)
        inception_5a_3x3_reduce_bn_out = self.inception_5a_3x3_reduce_bn(inception_5a_3x3_reduce_out)
        inception_5a_relu_3x3_reduce_out = self.inception_5a_relu_3x3_reduce(inception_5a_3x3_reduce_bn_out)
        inception_5a_3x3_out = self.inception_5a_3x3(inception_5a_relu_3x3_reduce_out)
        inception_5a_3x3_bn_out = self.inception_5a_3x3_bn(inception_5a_3x3_out)
        inception_5a_relu_3x3_out = self.inception_5a_relu_3x3(inception_5a_3x3_bn_out)
        inception_5a_double_3x3_reduce_out = self.inception_5a_double_3x3_reduce(inception_4e_output_out)
        inception_5a_double_3x3_reduce_bn_out = self.inception_5a_double_3x3_reduce_bn(inception_5a_double_3x3_reduce_out)
        inception_5a_relu_double_3x3_reduce_out = self.inception_5a_relu_double_3x3_reduce(inception_5a_double_3x3_reduce_bn_out)
        inception_5a_double_3x3_1_out = self.inception_5a_double_3x3_1(inception_5a_relu_double_3x3_reduce_out)
        inception_5a_double_3x3_1_bn_out = self.inception_5a_double_3x3_1_bn(inception_5a_double_3x3_1_out)
        inception_5a_relu_double_3x3_1_out = self.inception_5a_relu_double_3x3_1(inception_5a_double_3x3_1_bn_out)
        inception_5a_double_3x3_2_out = self.inception_5a_double_3x3_2(inception_5a_relu_double_3x3_1_out)
        inception_5a_double_3x3_2_bn_out = self.inception_5a_double_3x3_2_bn(inception_5a_double_3x3_2_out)
        inception_5a_relu_double_3x3_2_out = self.inception_5a_relu_double_3x3_2(inception_5a_double_3x3_2_bn_out)
        inception_5a_pool_out = self.inception_5a_pool(inception_4e_output_out)
        inception_5a_pool_proj_out = self.inception_5a_pool_proj(inception_5a_pool_out)
        inception_5a_pool_proj_bn_out = self.inception_5a_pool_proj_bn(inception_5a_pool_proj_out)
        inception_5a_relu_pool_proj_out = self.inception_5a_relu_pool_proj(inception_5a_pool_proj_bn_out)
        inception_5a_output_out = torch.cat([inception_5a_relu_1x1_out,inception_5a_relu_3x3_out,inception_5a_relu_double_3x3_2_out,inception_5a_relu_pool_proj_out], 1)
        inception_5b_1x1_out = self.inception_5b_1x1(inception_5a_output_out)
        inception_5b_1x1_bn_out = self.inception_5b_1x1_bn(inception_5b_1x1_out)
        inception_5b_relu_1x1_out = self.inception_5b_relu_1x1(inception_5b_1x1_bn_out)
        inception_5b_3x3_reduce_out = self.inception_5b_3x3_reduce(inception_5a_output_out)
        inception_5b_3x3_reduce_bn_out = self.inception_5b_3x3_reduce_bn(inception_5b_3x3_reduce_out)
        inception_5b_relu_3x3_reduce_out = self.inception_5b_relu_3x3_reduce(inception_5b_3x3_reduce_bn_out)
        inception_5b_3x3_out = self.inception_5b_3x3(inception_5b_relu_3x3_reduce_out)
        inception_5b_3x3_bn_out = self.inception_5b_3x3_bn(inception_5b_3x3_out)
        inception_5b_relu_3x3_out = self.inception_5b_relu_3x3(inception_5b_3x3_bn_out)
        inception_5b_double_3x3_reduce_out = self.inception_5b_double_3x3_reduce(inception_5a_output_out)
        inception_5b_double_3x3_reduce_bn_out = self.inception_5b_double_3x3_reduce_bn(inception_5b_double_3x3_reduce_out)
        inception_5b_relu_double_3x3_reduce_out = self.inception_5b_relu_double_3x3_reduce(inception_5b_double_3x3_reduce_bn_out)
        inception_5b_double_3x3_1_out = self.inception_5b_double_3x3_1(inception_5b_relu_double_3x3_reduce_out)
        inception_5b_double_3x3_1_bn_out = self.inception_5b_double_3x3_1_bn(inception_5b_double_3x3_1_out)
        inception_5b_relu_double_3x3_1_out = self.inception_5b_relu_double_3x3_1(inception_5b_double_3x3_1_bn_out)
        inception_5b_double_3x3_2_out = self.inception_5b_double_3x3_2(inception_5b_relu_double_3x3_1_out)
        inception_5b_double_3x3_2_bn_out = self.inception_5b_double_3x3_2_bn(inception_5b_double_3x3_2_out)
        inception_5b_relu_double_3x3_2_out = self.inception_5b_relu_double_3x3_2(inception_5b_double_3x3_2_bn_out)
        inception_5b_pool_out = self.inception_5b_pool(inception_5a_output_out)
        inception_5b_pool_proj_out = self.inception_5b_pool_proj(inception_5b_pool_out)
        inception_5b_pool_proj_bn_out = self.inception_5b_pool_proj_bn(inception_5b_pool_proj_out)
        inception_5b_relu_pool_proj_out = self.inception_5b_relu_pool_proj(inception_5b_pool_proj_bn_out)
        inception_5b_output_out = torch.cat([inception_5b_relu_1x1_out,inception_5b_relu_3x3_out,inception_5b_relu_double_3x3_2_out,inception_5b_relu_pool_proj_out], 1)
        return inception_5b_output_out

    def logits(self, features):
        x = self.global_pool(features)
        x = x.view(x.size(0), -1)
        x = self.last_linear(x)
        return x

    def forward(self, input):
        x = self.features(input)
        x = self.logits(x)
        return x
 
def bninception(num_classes=68, pretrained='imagenet'):  #分类  num_classes=1000
    r"""BNInception model architecture from <https://arxiv.org/pdf/1502.03167.pdf>`_ paper.
    """
    model = BNInception(num_classes=num_classes)
    if pretrained is not None:
        settings = pretrained_settings['bninception'][pretrained]
        assert num_classes == settings['num_classes'], \
            "num_classes should be {}, but is {}".format(settings['num_classes'], num_classes)
        model.load_state_dict(model_zoo.load_url(settings['url']))
        model.input_space = settings['input_space']  #'BGR'
        model.input_size = settings['input_size']  #256
        model.input_range = settings['input_range']  #[0, 255] 再bn
        model.mean = settings['mean']  #[104, 117, 128],  #均值像素
        model.std = settings['std']  #[1, 1, 1],    #方差
    return model


if __name__ == '__main__':

    model = bninception()