Add Inception v3 model ported from TensorFlow (#101)

torchvision/models/__init__.py

@@ -35,6 +35,7 @@ ResNet-34                26.70           8.58
 ResNet-50                23.85           7.13
 ResNet-101               22.63           6.44
 ResNet-152               21.69           5.94
+Inception v3             22.55           6.44
 AlexNet                  43.45           20.91
 VGG-11                   32.14           12.12
 VGG-13                   31.04           11.40
@@ -55,3 +56,4 @@ from .alexnet import *
 from .resnet import *
 from .vgg import *
 from .squeezenet import *
+from .inception import *

torchvision/models/inception.py

+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.model_zoo as model_zoo
+
+
+__all__ = ['Inception3', 'inception_v3']
+
+
+model_urls = {
+    # Inception v3 ported from TensorFlow
+    'inception_v3_google': 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth',
+}
+
+
+def inception_v3(pretrained=False, **kwargs):
+    r"""Inception v3 model architecture from
+    `"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    if pretrained:
+        if 'transform_input' not in kwargs:
+            kwargs['transform_input'] = True
+        model = Inception3(**kwargs)
+        model.load_state_dict(model_zoo.load_url(model_urls['inception_v3_google']))
+        return model
+
+    return Inception3(**kwargs)
+
+
+class Inception3(nn.Module):
+    def __init__(self, num_classes=1000, aux_logits=True, transform_input=False):
+        super(Inception3, self).__init__()
+        self.aux_logits = aux_logits
+        self.transform_input = transform_input
+        self.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 stats
+                stddev = m.stddev if hasattr(m, 'stddev') else 0.1
+                X = stats.truncnorm(-2, 2, scale=stddev)
+                values = torch.Tensor(X.rvs(m.weight.data.numel()))
+                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) / 0.5
+            x[1] = x[1] * (0.224 / 0.5) + (0.456 - 0.5) / 0.5
+            x[2] = x[2] * (0.225 / 0.5) + (0.406 - 0.5) / 0.5
+        # 299 x 299 x 3
+        x = self.Conv2d_1a_3x3(x)
+        # 149 x 149 x 32
+        x = self.Conv2d_2a_3x3(x)
+        # 147 x 147 x 32
+        x = self.Conv2d_2b_3x3(x)
+        # 147 x 147 x 64
+        x = F.max_pool2d(x, kernel_size=3, stride=2)
+        # 73 x 73 x 64
+        x = self.Conv2d_3b_1x1(x)
+        # 73 x 73 x 80
+        x = self.Conv2d_4a_3x3(x)
+        # 71 x 71 x 192
+        x = F.max_pool2d(x, kernel_size=3, stride=2)
+        # 35 x 35 x 192
+        x = self.Mixed_5b(x)
+        # 35 x 35 x 256
+        x = self.Mixed_5c(x)
+        # 35 x 35 x 288
+        x = self.Mixed_5d(x)
+        # 35 x 35 x 288
+        x = self.Mixed_6a(x)
+        # 17 x 17 x 768
+        x = self.Mixed_6b(x)
+        # 17 x 17 x 768
+        x = self.Mixed_6c(x)
+        # 17 x 17 x 768
+        x = self.Mixed_6d(x)
+        # 17 x 17 x 768
+        x = self.Mixed_6e(x)
+        # 17 x 17 x 768
+        if self.training and self.aux_logits:
+            aux = self.AuxLogits(x)
+        # 17 x 17 x 768
+        x = self.Mixed_7a(x)
+        # 8 x 8 x 1280
+        x = self.Mixed_7b(x)
+        # 8 x 8 x 2048
+        x = self.Mixed_7c(x)
+        # 8 x 8 x 2048
+        x = F.avg_pool2d(x, kernel_size=8)
+        # 1 x 1 x 2048
+        x = F.dropout(x, training=self.training)
+        # 1 x 1 x 2048
+        x = x.view(x.size(0), -1)
+        # 2048
+        x = self.fc(x)
+        # 1000 (num_classes)
+        if self.training and self.aux_logits:
+            return x, aux
+        return x
+
+
+class 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_7x7
+        self.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 = 0.01
+        self.fc = nn.Linear(768, num_classes)
+        self.fc.stddev = 0.001
+
+    def forward(self, x):
+        # 17 x 17 x 768
+        x = F.avg_pool2d(x, kernel_size=5, stride=3)
+        # 5 x 5 x 768
+        x = self.conv0(x)
+        # 5 x 5 x 128
+        x = self.conv1(x)
+        # 1 x 1 x 768
+        x = x.view(x.size(0), -1)
+        # 768
+        x = self.fc(x)
+        # 1000
+        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)