import torch import torch.nn as nn import torch.nn.functional as F from torch.utils import model_zoo __all__ = ['GoogLeNet', 'googlenet'] model_urls = { # GoogLeNet ported from TensorFlow 'googlenet': 'https://download.pytorch.org/models/googlenet-1378be20.pth', } def googlenet(pretrained=False, **kwargs): r"""GoogLeNet (Inception v1) model architecture from `"Going Deeper with Convolutions" `_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ if pretrained: if 'transform_input' not in kwargs: kwargs['transform_input'] = True kwargs['init_weights'] = False model = GoogLeNet(**kwargs) model.load_state_dict(model_zoo.load_url(model_urls['googlenet'])) return model return GoogLeNet(**kwargs) class GoogLeNet(nn.Module): def __init__(self, num_classes=1000, aux_logits=True, transform_input=False, init_weights=True): super(GoogLeNet, self).__init__() self.aux_logits = aux_logits self.transform_input = transform_input self.conv1 = BasicConv2d(3, 64, kernel_size=7, stride=2, padding=3) self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True) self.conv2 = BasicConv2d(64, 64, kernel_size=1) self.conv3 = BasicConv2d(64, 192, kernel_size=3, padding=1) self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True) self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32) self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64) self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True) self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64) self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64) self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64) self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64) self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128) self.maxpool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128) self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128) if aux_logits: self.aux1 = InceptionAux(512, num_classes) self.aux2 = InceptionAux(528, num_classes) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.dropout = nn.Dropout(0.4) self.fc = nn.Linear(1024, num_classes) if init_weights: self._initialize_weights() def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.xavier_uniform_(m.weight) if m.bias is not None: nn.init.constant_(m.bias, 0.2) elif isinstance(m, nn.Linear): nn.init.xavier_uniform_(m.weight) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) def forward(self, x): if self.transform_input: x_ch0 = torch.unsqueeze(x[:, 0], 1) * (0.229 / 0.5) + (0.485 - 0.5) / 0.5 x_ch1 = torch.unsqueeze(x[:, 1], 1) * (0.224 / 0.5) + (0.456 - 0.5) / 0.5 x_ch2 = torch.unsqueeze(x[:, 2], 1) * (0.225 / 0.5) + (0.406 - 0.5) / 0.5 x = torch.cat((x_ch0, x_ch1, x_ch2), 1) x = self.conv1(x) x = self.maxpool1(x) x = self.conv2(x) x = self.conv3(x) x = self.maxpool2(x) x = self.inception3a(x) x = self.inception3b(x) x = self.maxpool3(x) x = self.inception4a(x) if self.training and self.aux_logits: aux1 = self.aux1(x) x = self.inception4b(x) x = self.inception4c(x) x = self.inception4d(x) if self.training and self.aux_logits: aux2 = self.aux2(x) x = self.inception4e(x) x = self.maxpool4(x) x = self.inception5a(x) x = self.inception5b(x) x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.dropout(x) x = self.fc(x) if self.training and self.aux_logits: return aux1, aux2, x return x class Inception(nn.Module): def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj): super(Inception, self).__init__() self.branch1 = BasicConv2d(in_channels, ch1x1, kernel_size=1) self.branch2 = nn.Sequential( BasicConv2d(in_channels, ch3x3red, kernel_size=1), BasicConv2d(ch3x3red, ch3x3, kernel_size=3, padding=1) ) self.branch3 = nn.Sequential( BasicConv2d(in_channels, ch5x5red, kernel_size=1), BasicConv2d(ch5x5red, ch5x5, kernel_size=3, padding=1) ) self.branch4 = nn.Sequential( nn.MaxPool2d(kernel_size=3, stride=1, padding=1, ceil_mode=True), BasicConv2d(in_channels, pool_proj, kernel_size=1) ) def forward(self, x): branch1 = self.branch1(x) branch2 = self.branch2(x) branch3 = self.branch3(x) branch4 = self.branch4(x) outputs = [branch1, branch2, branch3, branch4] return torch.cat(outputs, 1) class InceptionAux(nn.Module): def __init__(self, in_channels, num_classes): super(InceptionAux, self).__init__() self.conv = BasicConv2d(in_channels, 128, kernel_size=1) self.fc1 = nn.Linear(2048, 1024) self.fc2 = nn.Linear(1024, num_classes) def forward(self, x): x = F.adaptive_avg_pool2d(x, (4, 4)) x = self.conv(x) x = x.view(x.size(0), -1) x = F.relu(self.fc1(x), inplace=True) x = F.dropout(x, 0.7, training=self.training) x = self.fc2(x) return x class 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)