Initial commit

models/Old/pnasnet.py

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+'''PNASNet in PyTorch.
+
+Paper: Progressive Neural Architecture Search
+'''
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class SepConv(nn.Module):
+    '''Separable Convolution.'''
+    def __init__(self, in_planes, out_planes, kernel_size, stride):
+        super(SepConv, self).__init__()
+        self.conv1 = nn.Conv2d(in_planes, out_planes,
+                               kernel_size, stride,
+                               padding=(kernel_size-1)//2,
+                               bias=False, groups=in_planes)
+        self.bn1 = nn.BatchNorm2d(out_planes)
+
+    def forward(self, x):
+        return self.bn1(self.conv1(x))
+
+
+class CellA(nn.Module):
+    def __init__(self, in_planes, out_planes, stride=1):
+        super(CellA, self).__init__()
+        self.stride = stride
+        self.sep_conv1 = SepConv(in_planes, out_planes, kernel_size=7, stride=stride)
+        if stride==2:
+            self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False)
+            self.bn1 = nn.BatchNorm2d(out_planes)
+
+    def forward(self, x):
+        y1 = self.sep_conv1(x)
+        y2 = F.max_pool2d(x, kernel_size=3, stride=self.stride, padding=1)
+        if self.stride==2:
+            y2 = self.bn1(self.conv1(y2))
+        return F.relu(y1+y2)
+
+class CellB(nn.Module):
+    def __init__(self, in_planes, out_planes, stride=1):
+        super(CellB, self).__init__()
+        self.stride = stride
+        # Left branch
+        self.sep_conv1 = SepConv(in_planes, out_planes, kernel_size=7, stride=stride)
+        self.sep_conv2 = SepConv(in_planes, out_planes, kernel_size=3, stride=stride)
+        # Right branch
+        self.sep_conv3 = SepConv(in_planes, out_planes, kernel_size=5, stride=stride)
+        if stride==2:
+            self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False)
+            self.bn1 = nn.BatchNorm2d(out_planes)
+        # Reduce channels
+        self.conv2 = nn.Conv2d(2*out_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False)
+        self.bn2 = nn.BatchNorm2d(out_planes)
+
+    def forward(self, x):
+        # Left branch
+        y1 = self.sep_conv1(x)
+        y2 = self.sep_conv2(x)
+        # Right branch
+        y3 = F.max_pool2d(x, kernel_size=3, stride=self.stride, padding=1)
+        if self.stride==2:
+            y3 = self.bn1(self.conv1(y3))
+        y4 = self.sep_conv3(x)
+        # Concat & reduce channels
+        b1 = F.relu(y1+y2)
+        b2 = F.relu(y3+y4)
+        y = torch.cat([b1,b2], 1)
+        return F.relu(self.bn2(self.conv2(y)))
+
+class PNASNet(nn.Module):
+    def __init__(self, cell_type, num_cells, num_planes):
+        super(PNASNet, self).__init__()
+        self.in_planes = num_planes
+        self.cell_type = cell_type
+
+        self.conv1 = nn.Conv2d(3, num_planes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(num_planes)
+
+        self.layer1 = self._make_layer(num_planes, num_cells=6)
+        self.layer2 = self._downsample(num_planes*2)
+        self.layer3 = self._make_layer(num_planes*2, num_cells=6)
+        self.layer4 = self._downsample(num_planes*4)
+        self.layer5 = self._make_layer(num_planes*4, num_cells=6)
+
+        self.linear = nn.Linear(num_planes*4, 10)
+
+    def _make_layer(self, planes, num_cells):
+        layers = []
+        for _ in range(num_cells):
+            layers.append(self.cell_type(self.in_planes, planes, stride=1))
+            self.in_planes = planes
+        return nn.Sequential(*layers)
+
+    def _downsample(self, planes):
+        layer = self.cell_type(self.in_planes, planes, stride=2)
+        self.in_planes = planes
+        return layer
+
+    def forward(self, x):
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+        out = self.layer4(out)
+        out = self.layer5(out)
+        out = F.avg_pool2d(out, 8)
+        out = self.linear(out.view(out.size(0), -1))
+        return out
+
+
+def PNASNetA():
+    return PNASNet(CellA, num_cells=6, num_planes=44)
+
+def PNASNetB():
+    return PNASNet(CellB, num_cells=6, num_planes=32)
+
+
+def test():
+    net = PNASNetB()
+    x = torch.randn(1,3,32,32)
+    y = net(x)
+    print(y)
+
+# test()