Add ShuffleNetV2

main.py

@@ -51,7 +51,7 @@
 # Model
 print('==> Building model..')
 # net = VGG('VGG19')
-net = ResNet18()
+# net = ResNet18()
 # net = PreActResNet18()
 # net = GoogLeNet()
 # net = DenseNet121()
@@ -61,6 +61,7 @@
 # net = DPN92()
 # net = ShuffleNetG2()
 # net = SENet18()
+net = ShuffleNetV2(0.5)
 net = net.to(device)
 if device == 'cuda':
     net = torch.nn.DataParallel(net)

models/__init__.py

@@ -6,6 +6,7 @@
 from densenet import *
 from googlenet import *
 from shufflenet import *
+from shufflenetv2 import *
 from resnet import *
 from resnext import *
 from preact_resnet import *

models/shufflenetv2.py

@@ -0,0 +1,158 @@
+'''ShuffleNetV2 in PyTorch.
+
+See the paper "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" for more details.
+'''
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class ShuffleBlock(nn.Module):
+    def __init__(self, groups=2):
+        super(ShuffleBlock, self).__init__()
+        self.groups = groups
+
+    def forward(self, x):
+        '''Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]'''
+        N, C, H, W = x.size()
+        g = self.groups
+        return x.view(N, g, C/g, H, W).permute(0, 2, 1, 3, 4).reshape(N, C, H, W)
+
+
+class SplitBlock(nn.Module):
+    def __init__(self, ratio):
+        super(SplitBlock, self).__init__()
+        self.ratio = ratio
+
+    def forward(self, x):
+        c = int(x.size(1) * self.ratio)
+        return x[:, :c, :, :], x[:, c:, :, :]
+
+
+class BasicBlock(nn.Module):
+    def __init__(self, in_channels, split_ratio=0.5):
+        super(BasicBlock, self).__init__()
+        self.split = SplitBlock(split_ratio)
+        in_channels = int(in_channels * split_ratio)
+        self.conv1 = nn.Conv2d(in_channels, in_channels,
+                               kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(in_channels)
+        self.conv2 = nn.Conv2d(in_channels, in_channels,
+                               kernel_size=3, stride=1, padding=1, groups=in_channels, bias=False)
+        self.bn2 = nn.BatchNorm2d(in_channels)
+        self.conv3 = nn.Conv2d(in_channels, in_channels,
+                               kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(in_channels)
+        self.shuffle = ShuffleBlock()
+
+    def forward(self, x):
+        x1, x2 = self.split(x)
+        out = F.relu(self.bn1(self.conv1(x2)))
+        out = self.bn2(self.conv2(out))
+        out = F.relu(self.bn3(self.conv3(out)))
+        out = torch.cat([x1, out], 1)
+        out = self.shuffle(out)
+        return out
+
+
+class DownBlock(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super(DownBlock, self).__init__()
+        mid_channels = out_channels // 2
+        # left
+        self.conv1 = nn.Conv2d(in_channels, in_channels,
+                               kernel_size=3, stride=2, padding=1, groups=in_channels, bias=False)
+        self.bn1 = nn.BatchNorm2d(in_channels)
+        self.conv2 = nn.Conv2d(in_channels, mid_channels,
+                               kernel_size=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(mid_channels)
+        # right
+        self.conv3 = nn.Conv2d(in_channels, mid_channels,
+                               kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(mid_channels)
+        self.conv4 = nn.Conv2d(mid_channels, mid_channels,
+                               kernel_size=3, stride=2, padding=1, groups=mid_channels, bias=False)
+        self.bn4 = nn.BatchNorm2d(mid_channels)
+        self.conv5 = nn.Conv2d(mid_channels, mid_channels,
+                               kernel_size=1, bias=False)
+        self.bn5 = nn.BatchNorm2d(mid_channels)
+
+        self.shuffle = ShuffleBlock()
+
+    def forward(self, x):
+        # left
+        out1 = self.bn1(self.conv1(x))
+        out1 = F.relu(self.bn2(self.conv2(out1)))
+        # right
+        out2 = F.relu(self.bn3(self.conv3(x)))
+        out2 = self.bn4(self.conv4(out2))
+        out2 = F.relu(self.bn5(self.conv5(out2)))
+        # concat
+        out = torch.cat([out1, out2], 1)
+        out = self.shuffle(out)
+        return out
+
+
+class ShuffleNetV2(nn.Module):
+    def __init__(self, net_size):
+        super(ShuffleNetV2, self).__init__()
+        out_channels = configs[net_size]['out_channels']
+        num_blocks = configs[net_size]['num_blocks']
+
+        self.conv1 = nn.Conv2d(3, 24, kernel_size=3,
+                               stride=2, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(24)
+        self.in_channels = 24
+        self.layer1 = self._make_layer(out_channels[0], num_blocks[0])
+        self.layer2 = self._make_layer(out_channels[1], num_blocks[1])
+        self.layer3 = self._make_layer(out_channels[2], num_blocks[2])
+        self.linear = nn.Linear(out_channels[2], 10)
+
+    def _make_layer(self, out_channels, num_blocks):
+        layers = [DownBlock(self.in_channels, out_channels)]
+        for i in range(num_blocks):
+            layers.append(BasicBlock(out_channels))
+            self.in_channels = out_channels
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        out = F.relu(self.bn1(self.conv1(x)))
+        # out = F.max_pool2d(out, 3, stride=2, padding=1)
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+        out = F.avg_pool2d(out, 2)
+        out = out.view(out.size(0), -1)
+        out = self.linear(out)
+        return out
+
+
+configs = {
+    0.5: {
+        'out_channels': (48, 96, 192),
+        'num_blocks': (3, 7, 3)
+    },
+
+    1: {
+        'out_channels': (116, 232, 464),
+        'num_blocks': (3, 7, 3)
+    },
+    1.5: {
+        'out_channels': (176, 352, 704),
+        'num_blocks': (3, 7, 3)
+    },
+    2: {
+        'out_channels': (224, 488, 976),
+        'num_blocks': (3, 7, 3)
+    }
+}
+
+
+def test():
+    net = ShuffleNetV2(net_size=0.5)
+    x = torch.randn(3, 3, 32, 32)
+    y = net(x)
+    print(y.shape)
+
+
+# test()