Resnext

torchvision/models/__init__.py

@@ -78,6 +78,7 @@
 
 from .alexnet import *
 from .resnet import *
+from .resnext import *
 from .vgg import *
 from .squeezenet import *
 from .inception import *

torchvision/models/resnext.py

@@ -0,0 +1,171 @@
+import torch.nn as nn
+import math
+import torch.utils.model_zoo as model_zoo
+
+
+__all__ = ['ResNeXt', 'resnext50', 'resnext101',
+           'resnext152']
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    "3x3 convolution with padding"
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class ResNeXtBottleneckC(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=32, baseWidth=4):
+        super(ResNeXtBottleneckC, self).__init__()
+
+        width = math.floor(planes / 64 * cardinality * baseWidth)
+
+        self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(width)
+        self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride,
+                               padding=1, bias=False, groups=cardinality)
+        self.bn2 = nn.BatchNorm2d(width)
+        self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * 4)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ResNeXt(nn.Module):
+
+    def __init__(self, block, layers, num_classes=1000, cardinality=32, baseWidth=4, shortcut='C'):
+        self.inplanes = 64
+        self.cardinality = cardinality
+        self.baseWidth = baseWidth
+        self.shortcut = shortcut
+        super(ResNeXt, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.avgpool = nn.AvgPool2d(7, stride=1)
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        reshape = stride != 1 or self.inplanes != planes * block.expansion
+        useConv = (self.shortcut == 'C') or (self.shortcut == 'B' and reshape)
+
+        if useConv:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+        elif reshape:
+            downsample = nn.AvgPool2d(3, stride=stride)
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample, self.cardinality, self.baseWidth))
+        self.inplanes = planes * block.expansion
+
+        if self.shortcut == 'C':
+            shortcut = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=1, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+        else:
+            shortcut = None
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes, 1, shortcut, self.cardinality, self.baseWidth))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+
+        return x
+
+
+def resnext50(cardinality=32, baseWidth=4, shortcut='C', **kwargs):
+    """Constructs a ResNeXt-50 model.
+
+    Args:
+        cardinality (int): Cardinality of the aggregated transform
+        baseWidth (int): Base width of the grouped convolution
+        shortcut ('A'|'B'|'C'): 'B' use 1x1 conv to downsample, 'C' use 1x1 conv on every residual connection
+    """
+    model = ResNeXt(ResNeXtBottleneckC, [3, 4, 6, 3], cardinality=cardinality,
+                    baseWidth=baseWidth, shortcut=shortcut, **kwargs)
+    return model
+
+
+def resnext101(cardinality=32, baseWidth=4, shortcut='C', **kwargs):
+    """Constructs a ResNeXt-101 model.
+
+    Args:
+        cardinality (int): Cardinality of the aggregated transform
+        baseWidth (int): Base width of the grouped convolution
+        shortcut ('A'|'B'|'C'): 'B' use 1x1 conv to downsample, 'C' use 1x1 conv on every residual connection
+    """
+    model = ResNeXt(ResNeXtBottleneckC, [3, 4, 23, 3], cardinality=cardinality,
+                    baseWidth=baseWidth, shortcut=shortcut, **kwargs)
+    return model
+
+
+def resnext152(cardinality=32, baseWidth=4, shortcut='C', **kwargs):
+    """Constructs a ResNeXt-152 model.
+
+    Args:
+        cardinality (int): Cardinality of the aggregated transform
+        baseWidth (int): Base width of the grouped convolution
+        shortcut ('A'|'B'|'C'): 'B' use 1x1 conv to downsample, 'C' use 1x1 conv on every residual connection
+    """
+    model = ResNeXt(ResNeXtBottleneckC, [3, 8, 36, 3], cardinality=cardinality,
+                    baseWidth=baseWidth, shortcut=shortcut, **kwargs)
+    return model