GhostNet.py

@@ -0,0 +1,140 @@
+import torch
+import torch.nn as nn
+import torchvision
+
+def DW_Conv3x3BNReLU(in_channels,out_channels,stride,groups=1):
+    return nn.Sequential(
+            nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=stride, padding=1,groups=groups, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU6(inplace=True)
+        )
+
+class SqueezeAndExcite(nn.Module):
+    def __init__(self, in_channels, out_channels, divide=4):
+        super(SqueezeAndExcite, self).__init__()
+        mid_channels = in_channels // divide
+        self.pool = nn.AdaptiveAvgPool2d(1)
+        self.SEblock = nn.Sequential(
+            nn.Linear(in_features=in_channels, out_features=mid_channels),
+            nn.ReLU6(inplace=True),
+            nn.Linear(in_features=mid_channels, out_features=out_channels),
+            nn.ReLU6(inplace=True),
+        )
+
+    def forward(self, x):
+        b, c, h, w = x.size()
+        out = self.pool(x)
+        out = out.view(b, -1)
+        out = self.SEblock(out)
+        out = out.view(b, c, 1, 1)
+        return out * x
+
+
+class GhostModule(nn.Module):
+    def __init__(self, in_channels,out_channels,s=2, kernel_size=1,stride=1, use_relu=True):
+        super(GhostModule, self).__init__()
+        intrinsic_channels = out_channels//s
+        ghost_channels = intrinsic_channels * (s - 1)
+
+        self.primary_conv = nn.Sequential(
+            nn.Conv2d(in_channels=in_channels, out_channels=intrinsic_channels, kernel_size=kernel_size, stride=stride,
+                          padding=kernel_size // 2, bias=False),
+            nn.BatchNorm2d(intrinsic_channels),
+            nn.ReLU(inplace=True) if use_relu else nn.Sequential()
+        )
+
+        self.cheap_op = DW_Conv3x3BNReLU(in_channels=intrinsic_channels, out_channels=ghost_channels, stride=stride,groups=intrinsic_channels)
+
+    def forward(self, x):
+        y = self.primary_conv(x)
+        z = self.cheap_op(y)
+        out = torch.cat([y, z], dim=1)
+        return out
+
+class GhostBottleneck(nn.Module):
+    def __init__(self, in_channels,mid_channels, out_channels , kernel_size, stride, use_se, se_kernel_size=1):
+        super(GhostBottleneck, self).__init__()
+        self.stride = stride
+
+        self.bottleneck = nn.Sequential(
+            GhostModule(in_channels=in_channels,out_channels=mid_channels,kernel_size=1,use_relu=True),
+            DW_Conv3x3BNReLU(in_channels=mid_channels, out_channels=mid_channels, stride=stride,groups=mid_channels) if self.stride>1 else nn.Sequential(),
+            SqueezeAndExcite(mid_channels,mid_channels,se_kernel_size) if use_se else nn.Sequential(),
+            GhostModule(in_channels=mid_channels, out_channels=out_channels, kernel_size=1, use_relu=False)
+        )
+
+        if self.stride>1:
+            self.shortcut = DW_Conv3x3BNReLU(in_channels=in_channels, out_channels=out_channels, stride=stride)
+        else:
+            self.shortcut = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1)
+
+    def forward(self, x):
+        out = self.bottleneck(x)
+        residual = self.shortcut(x)
+        out += residual
+        return out
+
+
+class GhostNet(nn.Module):
+    def __init__(self, num_classes=1000):
+        super(GhostNet, self).__init__()
+
+        self.first_conv = nn.Sequential(
+            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=2, padding=1),
+            nn.BatchNorm2d(16),
+            nn.ReLU6(inplace=True),
+        )
+
+        self.features = nn.Sequential(
+            GhostBottleneck(in_channels=16, mid_channels=16, out_channels=16, kernel_size=3, stride=1, use_se=False),
+            GhostBottleneck(in_channels=16, mid_channels=64, out_channels=24, kernel_size=3, stride=2,  use_se=False),
+            GhostBottleneck(in_channels=24, mid_channels=72, out_channels=24, kernel_size=3, stride=1,  use_se=False),
+            GhostBottleneck(in_channels=24, mid_channels=72, out_channels=40, kernel_size=5, stride=2, use_se=True, se_kernel_size=28),
+            GhostBottleneck(in_channels=40, mid_channels=120, out_channels=40, kernel_size=5, stride=1, use_se=True, se_kernel_size=28),
+            GhostBottleneck(in_channels=40, mid_channels=120, out_channels=40, kernel_size=5, stride=1, use_se=True, se_kernel_size=28),
+            GhostBottleneck(in_channels=40, mid_channels=240, out_channels=80, kernel_size=3, stride=1, use_se=False),
+            GhostBottleneck(in_channels=80, mid_channels=200, out_channels=80, kernel_size=3, stride=1, use_se=False),
+            GhostBottleneck(in_channels=80, mid_channels=184, out_channels=80, kernel_size=3, stride=2, use_se=False),
+            GhostBottleneck(in_channels=80, mid_channels=184, out_channels=80, kernel_size=3, stride=1, use_se=False),
+            GhostBottleneck(in_channels=80, mid_channels=480, out_channels=112, kernel_size=3, stride=1, use_se=True, se_kernel_size=14),
+            GhostBottleneck(in_channels=112, mid_channels=672, out_channels=112, kernel_size=3, stride=1, use_se=True, se_kernel_size=14),
+            GhostBottleneck(in_channels=112, mid_channels=672, out_channels=160, kernel_size=5, stride=2, use_se=True,se_kernel_size=7),
+            GhostBottleneck(in_channels=160, mid_channels=960, out_channels=160, kernel_size=5, stride=1, use_se=True,se_kernel_size=7),
+            GhostBottleneck(in_channels=160, mid_channels=960, out_channels=160, kernel_size=5, stride=1, use_se=True,se_kernel_size=7),
+        )
+
+        self.last_stage  = nn.Sequential(
+            nn.Conv2d(in_channels=160, out_channels=960, kernel_size=1, stride=1),
+            nn.BatchNorm2d(960),
+            nn.ReLU6(inplace=True),
+            nn.AvgPool2d(kernel_size=7, stride=1),
+            nn.Conv2d(in_channels=960, out_channels=1280, kernel_size=1, stride=1),
+            nn.ReLU6(inplace=True),
+        )
+        self.classifier = nn.Linear(in_features=1280,out_features=num_classes)
+
+    def init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear) or isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+    def forward(self, x):
+        x = self.first_conv(x)
+        x = self.features(x)
+        x= self.last_stage(x)
+        x = x.view(x.size(0), -1)
+        out = self.classifier(x)
+        return out
+
+
+if __name__ == '__main__':
+    model = GhostNet()
+    print(model)
+
+    input = torch.randn(1, 3, 224, 224)
+    out = model(input)
+    print(out.shape)

MixNet.py

@@ -0,0 +1,183 @@
+import torch
+import torch.nn as nn
+
+class HardSwish(nn.Module):
+    def __init__(self, inplace=True):
+        super(HardSwish, self).__init__()
+        self.relu6 = nn.ReLU6(inplace)
+
+    def forward(self, x):
+        return x*self.relu6(x+3)/6
+
+def ConvBNActivation(in_channels,out_channels,kernel_size,stride,activate):
+    return nn.Sequential(
+            nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=(kernel_size-1)//2),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU6(inplace=True) if activate == 'relu' else HardSwish(inplace=True)
+        )
+
+def Conv1x1BNActivation(in_channels,out_channels,activate):
+    return nn.Sequential(
+            nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU6(inplace=True) if activate == 'relu' else HardSwish(inplace=True)
+        )
+
+def Conv1x1BN(in_channels,out_channels):
+    return nn.Sequential(
+            nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1),
+            nn.BatchNorm2d(out_channels)
+        )
+
+class MDConv(nn.Module):
+    def __init__(self, nchannels, kernel_sizes, stride):
+        super(MDConv,self).__init__()
+        self.nchannels = nchannels
+        self.groups = len(kernel_sizes)
+
+        self.split_channels = [nchannels // self.groups for _ in range(self.groups)]
+        self.split_channels[0] += nchannels - sum(self.split_channels)
+
+        self.layers = []
+        for i in range(self.groups):
+            self.layers.append(nn.Conv2d(in_channels=self.split_channels[i],out_channels=self.split_channels[i],
+                                         kernel_size=kernel_sizes[i], stride=stride,padding=int(kernel_sizes[i]//2), groups=self.split_channels[i]))
+
+    def forward(self, x):
+        split_x = torch.split(x, self.split_channels, dim=1)
+        outputs = [layer(sp_x) for layer,sp_x in zip(self.layers, split_x)]
+        return torch.cat(outputs, dim=1)
+
+class SqueezeAndExcite(nn.Module):
+    def __init__(self, nchannels, squeeze_channels, se_ratio=1):
+        super(SqueezeAndExcite, self).__init__()
+        squeeze_channels = int(squeeze_channels * se_ratio)
+
+        self.SEblock = nn.Sequential(
+            nn.Conv2d(in_channels=nchannels, out_channels=squeeze_channels, kernel_size=1, stride=1, padding=0),
+            nn.ReLU6(inplace=True),
+            nn.Conv2d(in_channels=squeeze_channels, out_channels=nchannels, kernel_size=1, stride=1, padding=0),
+            nn.Sigmoid(),
+        )
+
+    def forward(self, x):
+        out = torch.mean(x, (2, 3), keepdim=True)
+        out = self.SEblock(out)
+        return out * x
+
+class MDConvBlock(nn.Module):
+    def __init__(self, in_channels,out_channels, kernel_sizes, stride,expand_ratio, activate='relu', se_ratio=1):
+        super(MDConvBlock,self).__init__()
+        self.stride = stride
+        self.se_ratio = se_ratio
+        mid_channels = in_channels * expand_ratio
+
+        self.expand_conv = Conv1x1BNActivation(in_channels, mid_channels, activate)
+        self.md_conv = nn.Sequential(
+            # in_channels,out_channels,groups, kernel_sizes, strides
+            MDConv(mid_channels, kernel_sizes, stride),
+            nn.BatchNorm2d(mid_channels),
+            nn.ReLU6(inplace=True) if activate == 'relu' else HardSwish(inplace=True),
+        )
+        if self.se_ratio > 0:
+            self.squeeze_excite =  SqueezeAndExcite(mid_channels, in_channels)
+
+        self.projection_conv = Conv1x1BN(mid_channels,out_channels)
+
+    def forward(self, x):
+        x = self.expand_conv(x)
+        x = self.md_conv(x)
+        if self.se_ratio > 0:
+            x = self.squeeze_excite(x)
+        out = self.projection_conv(x)
+        return out
+
+class MixNet(nn.Module):
+    mixnet_s = [(16, 16, [3], 1, 1, 'ReLU', 0.0),
+                (16, 24, [3], 2, 6, 'ReLU', 0.0),
+                (24, 24, [3], 1, 3, 'ReLU', 0.0),
+                (24, 40, [3, 5, 7], 2, 6, 'Swish', 0.5),
+                (40, 40, [3, 5], 1, 6, 'Swish', 0.5),
+                (40, 40, [3, 5], 1, 6, 'Swish', 0.5),
+                (40, 40, [3, 5], 1, 6, 'Swish', 0.5),
+                (40, 80, [3, 5, 7], 2, 6, 'Swish', 0.25),
+                (80, 80, [3, 5], 1, 6, 'Swish', 0.25),
+                (80, 80, [3, 5], 1, 6, 'Swish', 0.25),
+                (80, 120, [3, 5, 7], 1, 6, 'Swish', 0.5),
+                (120, 120, [3, 5, 7, 9], 1, 3, 'Swish', 0.5),
+                (120, 120, [3, 5, 7, 9], 1, 3, 'Swish', 0.5),
+                (120, 200, [3, 5, 7, 9, 11], 2, 6, 'Swish', 0.5),
+                (200, 200, [3, 5, 7, 9], 1, 6, 'Swish', 0.5),
+                (200, 200, [3, 5, 7, 9], 1, 6, 'Swish', 0.5)
+                ]
+
+    mixnet_m = [(24, 24, [3], 1, 1, 'ReLU', 0.0),
+                (24, 32, [3, 5, 7], 2, 6, 'ReLU', 0.0),
+                (32, 32, [3], 1, 3, 'ReLU', 0.0),
+                (32, 40, [3, 5, 7, 9], 2, 6, 'Swish', 0.5),
+                (40, 40, [3, 5], 1, 6, 'Swish', 0.5),
+                (40, 40, [3, 5], 1, 6, 'Swish', 0.5),
+                (40, 40, [3, 5], 1, 6, 'Swish', 0.5),
+                (40, 80, [3, 5, 7], 2, 6, 'Swish', 0.25),
+                (80, 80, [3, 5, 7, 9], 1, 6, 'Swish', 0.25),
+                (80, 80, [3, 5, 7, 9], 1, 6, 'Swish', 0.25),
+                (80, 80, [3, 5, 7, 9], 1, 6, 'Swish', 0.25),
+                (80, 120, [3], 1, 6, 'Swish', 0.5),
+                (120, 120, [3, 5, 7, 9], 1, 3, 'Swish', 0.5),
+                (120, 120, [3, 5, 7, 9], 1, 3, 'Swish', 0.5),
+                (120, 120, [3, 5, 7, 9], 1, 3, 'Swish', 0.5),
+                (120, 200, [3, 5, 7, 9], 2, 6, 'Swish', 0.5),
+                (200, 200, [3, 5, 7, 9], 1, 6, 'Swish', 0.5),
+                (200, 200, [3, 5, 7, 9], 1, 6, 'Swish', 0.5),
+                (200, 200, [3, 5, 7, 9], 1, 6, 'Swish', 0.5)]
+
+    def __init__(self, type='mixnet_s'):
+        super(MixNet,self).__init__()
+
+        if type == 'mixnet_s':
+            config = self.mixnet_s
+            stem_channels = 16
+        elif type == 'mixnet_m':
+            config = self.mixnet_m
+            stem_channels = 24
+
+        self.stem = nn.Sequential(
+            nn.Conv2d(in_channels=3,out_channels=stem_channels,kernel_size=3,stride=2,padding=1),
+            nn.BatchNorm2d(stem_channels),
+            HardSwish(inplace=True),
+        )
+
+        layers = []
+        for in_channels, out_channels, kernel_sizes, stride, expand_ratio, activate, se_ratio in config:
+            layers.append(MDConvBlock(
+                in_channels,
+                out_channels,
+                kernel_sizes=kernel_sizes,
+                stride=stride,
+                expand_ratio=expand_ratio,
+                activate=activate,
+                se_ratio=se_ratio
+            ))
+        self.bottleneck = nn.Sequential(*layers)
+
+    def init_params(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.Linear):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+    def forward(self, x):
+        x = self.stem(x)
+        out = self.bottleneck(x)
+        return out
+
+if __name__ == '__main__':
+    model = MixNet(type ='mixnet_m')
+    print(model)
+
+    input = torch.randn(1, 3, 224, 224)
+    out = model(input)
+    print(out.shape)