Add GoogLeNet (Inception v1)

docs/source/models.rst

@@ -10,6 +10,7 @@ architectures:
 -  `SqueezeNet`_
 -  `DenseNet`_
 -  `Inception`_ v3
+-  `GoogLeNet`_
 
 You can construct a model with random weights by calling its constructor:
 
@@ -22,6 +23,7 @@ You can construct a model with random weights by calling its constructor:
     squeezenet = models.squeezenet1_0()
     densenet = models.densenet161()
     inception = models.inception_v3()
+    googlenet = models.googlenet()
 
 We provide pre-trained models, using the PyTorch :mod:`torch.utils.model_zoo`.
 These can be constructed by passing ``pretrained=True``:
@@ -35,6 +37,7 @@ These can be constructed by passing ``pretrained=True``:
     vgg16 = models.vgg16(pretrained=True)
     densenet = models.densenet161(pretrained=True)
     inception = models.inception_v3(pretrained=True)
+    googlenet = models.googlenet(pretrained=True)
 
 Instancing a pre-trained model will download its weights to a cache directory.
 This directory can be set using the `TORCH_MODEL_ZOO` environment variable. See
@@ -84,6 +87,7 @@ Densenet-169                      24.00           7.00
 Densenet-201                      22.80           6.43
 Densenet-161                      22.35           6.20
 Inception v3                      22.55           6.44
+GoogleNet                         31.67           11.45
 ================================  =============   =============
 
 
@@ -93,6 +97,7 @@ Inception v3                      22.55           6.44
 .. _SqueezeNet: https://arxiv.org/abs/1602.07360
 .. _DenseNet: https://arxiv.org/abs/1608.06993
 .. _Inception: https://arxiv.org/abs/1512.00567
+.. _GoogLeNet: https://arxiv.org/abs/1409.4842
 
 .. currentmodule:: torchvision.models
 
@@ -142,3 +147,8 @@ Inception v3
 
 .. autofunction:: inception_v3
 
+GoogLeNet
+------------
+
+.. autofunction:: googlenet
+

torchvision/models/__init__.py

@@ -4,3 +4,4 @@
 from .squeezenet import *
 from .inception import *
 from .densenet import *
+from .googlenet import *

torchvision/models/googlenet.py

@@ -0,0 +1,183 @@
+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" <http://arxiv.org/abs/1409.4842>`_.
+    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(3, 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)