Add EfficientNetB0

main.py

@@ -59,7 +59,8 @@
 # net = DPN92()
 # net = ShuffleNetG2()
 # net = SENet18()
-net = ShuffleNetV2(1)
+# net = ShuffleNetV2(1)
+net = EfficientNetB0()
 net = net.to(device)
 if device == 'cuda':
     net = torch.nn.DataParallel(net)

models/__init__.py

@@ -12,3 +12,4 @@
 from .preact_resnet import *
 from .mobilenet import *
 from .mobilenetv2 import *
+from .efficientnet import *

models/efficientnet.py

@@ -0,0 +1,99 @@
+'''EfficientNet in PyTorch.
+
+Paper: "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks".
+'''
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Block(nn.Module):
+    '''expand + depthwise + pointwise + squeeze-excitation'''
+
+    def __init__(self, in_planes, out_planes, expansion, stride):
+        super(Block, self).__init__()
+        self.stride = stride
+
+        planes = expansion * in_planes
+        self.conv1 = nn.Conv2d(
+            in_planes, planes, kernel_size=1, stride=1, padding=0, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
+                               stride=stride, padding=1, groups=planes, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(
+            planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False)
+        self.bn3 = nn.BatchNorm2d(out_planes)
+
+        self.shortcut = nn.Sequential()
+        if stride == 1 and in_planes != out_planes:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_planes, out_planes, kernel_size=1,
+                          stride=1, padding=0, bias=False),
+                nn.BatchNorm2d(out_planes),
+            )
+
+        # SE layers
+        self.fc1 = nn.Conv2d(out_planes, out_planes//16, kernel_size=1)
+        self.fc2 = nn.Conv2d(out_planes//16, out_planes, kernel_size=1)
+
+    def forward(self, x):
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = F.relu(self.bn2(self.conv2(out)))
+        out = self.bn3(self.conv3(out))
+        shortcut = self.shortcut(x) if self.stride == 1 else out
+        # Squeeze-Excitation
+        w = F.avg_pool2d(out, out.size(2))
+        w = F.relu(self.fc1(w))
+        w = self.fc2(w).sigmoid()
+        out = out * w + shortcut
+        return out
+
+
+class EfficientNet(nn.Module):
+    def __init__(self, cfg, num_classes=10):
+        super(EfficientNet, self).__init__()
+        self.cfg = cfg
+        self.conv1 = nn.Conv2d(3, 32, kernel_size=3,
+                               stride=1, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(32)
+        self.layers = self._make_layers(in_planes=32)
+        self.linear = nn.Linear(cfg[-1][1], num_classes)
+
+    def _make_layers(self, in_planes):
+        layers = []
+        for expansion, out_planes, num_blocks, stride in self.cfg:
+            strides = [stride] + [1]*(num_blocks-1)
+            for stride in strides:
+                layers.append(Block(in_planes, out_planes, expansion, stride))
+                in_planes = out_planes
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = self.layers(out)
+        out = out.view(out.size(0), -1)
+        out = self.linear(out)
+        return out
+
+
+def EfficientNetB0():
+    # (expansion, out_planes, num_blocks, stride)
+    cfg = [(1,  16, 1, 2),
+           (6,  24, 2, 1),
+           (6,  40, 2, 2),
+           (6,  80, 3, 2),
+           (6, 112, 3, 1),
+           (6, 192, 4, 2),
+           (6, 320, 1, 2)]
+    return EfficientNet(cfg)
+
+
+def test():
+    net = EfficientNetB0()
+    x = torch.randn(2, 3, 32, 32)
+    y = net(x)
+    print(y.shape)
+
+
+# test()