论文在此: Going deeper with convolutions

论文下载: https://arxiv.org/pdf/1409.4842.pdf

网络结构图:

 

 

import torch
import torch.nn as nn
import torch.nn.functional as Fclass Inception3(nn.Module):def __init__(self, num_classes=1000, aux_logits=True, transform_input=False):super(Inception3, self).__init__()self.aux_logits = aux_logitsself.transform_input = transform_inputself.Conv2d_1a_3x3 = BasicConv2d(3, 32, kernel_size=3, stride=2)self.Conv2d_2a_3x3 = BasicConv2d(32, 32, kernel_size=3)self.Conv2d_2b_3x3 = BasicConv2d(32, 64, kernel_size=3, padding=1)self.Conv2d_3b_1x1 = BasicConv2d(64, 80, kernel_size=1)self.Conv2d_4a_3x3 = BasicConv2d(80, 192, kernel_size=3)self.Mixed_5b = InceptionA(192, pool_features=32)self.Mixed_5c = InceptionA(256, pool_features=64)self.Mixed_5d = InceptionA(288, pool_features=64)self.Mixed_6a = InceptionB(288)self.Mixed_6b = InceptionC(768, channels_7x7=128)self.Mixed_6c = InceptionC(768, channels_7x7=160)self.Mixed_6d = InceptionC(768, channels_7x7=160)self.Mixed_6e = InceptionC(768, channels_7x7=192)if aux_logits:self.AuxLogits = InceptionAux(768, num_classes)self.Mixed_7a = InceptionD(768)self.Mixed_7b = InceptionE(1280)self.Mixed_7c = InceptionE(2048)self.fc = nn.Linear(2048, num_classes)for m in self.modules():if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):import scipy.stats as statsstddev = m.stddev if hasattr(m, 'stddev') else 0.1X = stats.truncnorm(-2, 2, scale=stddev)values = torch.Tensor(X.rvs(m.weight.data.numel()))values = values.view(m.weight.data.size())m.weight.data.copy_(values)elif isinstance(m, nn.BatchNorm2d):m.weight.data.fill_(1)m.bias.data.zero_()def forward(self, x):if self.transform_input:x = x.clone()x[:, 0] = x[:, 0] * (0.229 / 0.5) + (0.485 - 0.5) / 0.5x[:, 1] = x[:, 1] * (0.224 / 0.5) + (0.456 - 0.5) / 0.5x[:, 2] = x[:, 2] * (0.225 / 0.5) + (0.406 - 0.5) / 0.5# 299 x 299 x 3x = self.Conv2d_1a_3x3(x)# 149 x 149 x 32x = self.Conv2d_2a_3x3(x)# 147 x 147 x 32x = self.Conv2d_2b_3x3(x)# 147 x 147 x 64x = F.max_pool2d(x, kernel_size=3, stride=2)# 73 x 73 x 64x = self.Conv2d_3b_1x1(x)# 73 x 73 x 80x = self.Conv2d_4a_3x3(x)# 71 x 71 x 192x = F.max_pool2d(x, kernel_size=3, stride=2)# 35 x 35 x 192x = self.Mixed_5b(x)# 35 x 35 x 256x = self.Mixed_5c(x)# 35 x 35 x 288x = self.Mixed_5d(x)# 35 x 35 x 288x = self.Mixed_6a(x)# 17 x 17 x 768x = self.Mixed_6b(x)# 17 x 17 x 768x = self.Mixed_6c(x)# 17 x 17 x 768x = self.Mixed_6d(x)# 17 x 17 x 768x = self.Mixed_6e(x)# 17 x 17 x 768if self.training and self.aux_logits:aux = self.AuxLogits(x)# 17 x 17 x 768x = self.Mixed_7a(x)# 8 x 8 x 1280x = self.Mixed_7b(x)# 8 x 8 x 2048x = self.Mixed_7c(x)# 8 x 8 x 2048x = F.avg_pool2d(x, kernel_size=8)# 1 x 1 x 2048x = F.dropout(x, training=self.training)# 1 x 1 x 2048x = x.view(x.size(0), -1)# 2048x = self.fc(x)# 1000 (num_classes)if self.training and self.aux_logits:return x, auxreturn xclass InceptionA(nn.Module):def __init__(self, in_channels, pool_features):super(InceptionA, self).__init__()self.branch1x1 = BasicConv2d(in_channels, 64, kernel_size=1)self.branch5x5_1 = BasicConv2d(in_channels, 48, kernel_size=1)self.branch5x5_2 = BasicConv2d(48, 64, kernel_size=5, padding=2)self.branch3x3dbl_1 = BasicConv2d(in_channels, 64, kernel_size=1)self.branch3x3dbl_2 = BasicConv2d(64, 96, kernel_size=3, padding=1)self.branch3x3dbl_3 = BasicConv2d(96, 96, kernel_size=3, padding=1)self.branch_pool = BasicConv2d(in_channels, pool_features, kernel_size=1)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 = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)branch_pool = self.branch_pool(branch_pool)outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]return torch.cat(outputs, 1)class InceptionB(nn.Module):def __init__(self, in_channels):super(InceptionB, self).__init__()self.branch3x3 = BasicConv2d(in_channels, 384, kernel_size=3, stride=2)self.branch3x3dbl_1 = BasicConv2d(in_channels, 64, kernel_size=1)self.branch3x3dbl_2 = BasicConv2d(64, 96, kernel_size=3, padding=1)self.branch3x3dbl_3 = BasicConv2d(96, 96, 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 = F.max_pool2d(x, kernel_size=3, stride=2)outputs = [branch3x3, branch3x3dbl, branch_pool]return torch.cat(outputs, 1)class InceptionC(nn.Module):def __init__(self, in_channels, channels_7x7):super(InceptionC, self).__init__()self.branch1x1 = BasicConv2d(in_channels, 192, kernel_size=1)c7 = channels_7x7self.branch7x7_1 = BasicConv2d(in_channels, c7, kernel_size=1)self.branch7x7_2 = BasicConv2d(c7, c7, kernel_size=(1, 7), padding=(0, 3))self.branch7x7_3 = BasicConv2d(c7, 192, kernel_size=(7, 1), padding=(3, 0))self.branch7x7dbl_1 = BasicConv2d(in_channels, c7, kernel_size=1)self.branch7x7dbl_2 = BasicConv2d(c7, c7, kernel_size=(7, 1), padding=(3, 0))self.branch7x7dbl_3 = BasicConv2d(c7, c7, kernel_size=(1, 7), padding=(0, 3))self.branch7x7dbl_4 = BasicConv2d(c7, c7, kernel_size=(7, 1), padding=(3, 0))self.branch7x7dbl_5 = BasicConv2d(c7, 192, kernel_size=(1, 7), padding=(0, 3))self.branch_pool = BasicConv2d(in_channels, 192, kernel_size=1)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 = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)branch_pool = self.branch_pool(branch_pool)outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]return torch.cat(outputs, 1)class InceptionD(nn.Module):def __init__(self, in_channels):super(InceptionD, self).__init__()self.branch3x3_1 = BasicConv2d(in_channels, 192, kernel_size=1)self.branch3x3_2 = BasicConv2d(192, 320, kernel_size=3, stride=2)self.branch7x7x3_1 = BasicConv2d(in_channels, 192, kernel_size=1)self.branch7x7x3_2 = BasicConv2d(192, 192, kernel_size=(1, 7), padding=(0, 3))self.branch7x7x3_3 = BasicConv2d(192, 192, kernel_size=(7, 1), padding=(3, 0))self.branch7x7x3_4 = BasicConv2d(192, 192, 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 = F.max_pool2d(x, kernel_size=3, stride=2)outputs = [branch3x3, branch7x7x3, branch_pool]return torch.cat(outputs, 1)class InceptionE(nn.Module):def __init__(self, in_channels):super(InceptionE, self).__init__()self.branch1x1 = BasicConv2d(in_channels, 320, kernel_size=1)self.branch3x3_1 = BasicConv2d(in_channels, 384, kernel_size=1)self.branch3x3_2a = BasicConv2d(384, 384, kernel_size=(1, 3), padding=(0, 1))self.branch3x3_2b = BasicConv2d(384, 384, kernel_size=(3, 1), padding=(1, 0))self.branch3x3dbl_1 = BasicConv2d(in_channels, 448, kernel_size=1)self.branch3x3dbl_2 = BasicConv2d(448, 384, kernel_size=3, padding=1)self.branch3x3dbl_3a = BasicConv2d(384, 384, kernel_size=(1, 3), padding=(0, 1))self.branch3x3dbl_3b = BasicConv2d(384, 384, kernel_size=(3, 1), padding=(1, 0))self.branch_pool = BasicConv2d(in_channels, 192, kernel_size=1)def forward(self, x):branch1x1 = self.branch1x1(x)branch3x3 = self.branch3x3_1(x)branch3x3 = [self.branch3x3_2a(branch3x3),self.branch3x3_2b(branch3x3),]branch3x3 = torch.cat(branch3x3, 1)branch3x3dbl = self.branch3x3dbl_1(x)branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)branch3x3dbl = [self.branch3x3dbl_3a(branch3x3dbl),self.branch3x3dbl_3b(branch3x3dbl),]branch3x3dbl = torch.cat(branch3x3dbl, 1)branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)branch_pool = self.branch_pool(branch_pool)outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]return torch.cat(outputs, 1)class InceptionAux(nn.Module):def __init__(self, in_channels, num_classes):super(InceptionAux, self).__init__()self.conv0 = BasicConv2d(in_channels, 128, kernel_size=1)self.conv1 = BasicConv2d(128, 768, kernel_size=5)self.conv1.stddev = 0.01self.fc = nn.Linear(768, num_classes)self.fc.stddev = 0.001def forward(self, x):# 17 x 17 x 768x = F.avg_pool2d(x, kernel_size=5, stride=3)# 5 x 5 x 768x = self.conv0(x)# 5 x 5 x 128x = self.conv1(x)# 1 x 1 x 768x = x.view(x.size(0), -1)# 768x = self.fc(x)# 1000return xclass BasicConv2d(nn.Module):def __init__(self, in_channels, out_channels, **kwargs):super(BasicConv2d, self).__init__()self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)self.bn = nn.BatchNorm2d(out_channels, eps=0.001)def forward(self, x):x = self.conv(x)x = self.bn(x)return F.relu(x, inplace=True)if __name__ == '__main__':# 'Inception3'# Examplenet = Inception3()print(net)