update

ytracking/tracking/trackers/reid/model/fmobilenet.py

@@ -0,0 +1,124 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+
+class Flatten(nn.Module):
+    def forward(self, x):
+        return x.view(x.shape[0], -1)
+
+class ConvBn(nn.Module):
+
+    def __init__(self, in_c, out_c, kernel=(1, 1), stride=1, padding=0, groups=1):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Conv2d(in_c, out_c, kernel, stride, padding, groups=groups, bias=False),
+            nn.BatchNorm2d(out_c)
+        )
+        
+    def forward(self, x):
+        return self.net(x)
+
+
+class ConvBnPrelu(nn.Module):
+
+    def __init__(self, in_c, out_c, kernel=(1, 1), stride=1, padding=0, groups=1):
+        super().__init__()
+        self.net = nn.Sequential(
+            ConvBn(in_c, out_c, kernel, stride, padding, groups),
+            nn.PReLU(out_c)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class DepthWise(nn.Module):
+
+    def __init__(self, in_c, out_c, kernel=(3, 3), stride=2, padding=1, groups=1):
+        super().__init__()
+        self.net = nn.Sequential(
+            ConvBnPrelu(in_c, groups, kernel=(1, 1), stride=1, padding=0),
+            ConvBnPrelu(groups, groups, kernel=kernel, stride=stride, padding=padding, groups=groups),
+            ConvBn(groups, out_c, kernel=(1, 1), stride=1, padding=0),
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class DepthWiseRes(nn.Module):
+    """DepthWise with Residual"""
+
+    def __init__(self, in_c, out_c, kernel=(3, 3), stride=2, padding=1, groups=1):
+        super().__init__()
+        self.net = DepthWise(in_c, out_c, kernel, stride, padding, groups)
+
+    def forward(self, x):
+        return self.net(x) + x
+
+
+class MultiDepthWiseRes(nn.Module):
+
+    def __init__(self, num_block, channels, kernel=(3, 3), stride=1, padding=1, groups=1):
+        super().__init__()
+
+        self.net = nn.Sequential(*[
+            DepthWiseRes(channels, channels, kernel, stride, padding, groups)
+            for _ in range(num_block)
+        ])
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class FaceMobileNet(nn.Module):
+
+    def __init__(self, embedding_size):
+        super().__init__()
+        self.conv1 = ConvBnPrelu(1, 64, kernel=(3, 3), stride=2, padding=1)
+        self.conv2 = ConvBn(64, 64, kernel=(3, 3), stride=1, padding=1, groups=64)
+        self.conv3 = DepthWise(64, 64, kernel=(3, 3), stride=2, padding=1, groups=128)
+        self.conv4 = MultiDepthWiseRes(num_block=4, channels=64, kernel=3, stride=1, padding=1, groups=128)
+        self.conv5 = DepthWise(64, 128, kernel=(3, 3), stride=2, padding=1, groups=256)
+        self.conv6 = MultiDepthWiseRes(num_block=6, channels=128, kernel=(3, 3), stride=1, padding=1, groups=256)
+        self.conv7 = DepthWise(128, 128, kernel=(3, 3), stride=2, padding=1, groups=512)
+        self.conv8 = MultiDepthWiseRes(num_block=2, channels=128, kernel=(3, 3), stride=1, padding=1, groups=256)
+        self.conv9 = ConvBnPrelu(128, 512, kernel=(1, 1))
+        self.conv10 = ConvBn(512, 512, groups=512, kernel=(7, 7))
+        self.flatten = Flatten()
+        self.linear = nn.Linear(2048, embedding_size, bias=False)
+        self.bn = nn.BatchNorm1d(embedding_size)
+        
+    def forward(self, x):
+        #print('x',x.shape)
+        out = self.conv1(x)
+        out = self.conv2(out)
+        out = self.conv3(out)
+        out = self.conv4(out)
+        out = self.conv5(out)
+        out = self.conv6(out)
+        out = self.conv7(out)
+        out = self.conv8(out)
+        out = self.conv9(out)
+        out = self.conv10(out)
+        out = self.flatten(out)
+        out = self.linear(out)
+        out = self.bn(out)
+        return out
+
+if __name__ == "__main__":
+    from PIL import Image
+    import numpy as np
+
+    x = Image.open("../samples/009.jpg").convert('L')
+    x = x.resize((128, 128))
+    x = np.asarray(x, dtype=np.float32)
+    x = x[None, None, ...]
+    x = torch.from_numpy(x)
+    net = FaceMobileNet(512)
+    net.eval()
+    with torch.no_grad():
+        out = net(x)
+    print(out.shape)