Changes since Teledyne

higher/smart_aug/nets/wideresnet.py

@@ -0,0 +1,98 @@
+import torch.nn as nn
+import torch.nn.init as init
+import torch.nn.functional as F
+import numpy as np
+
+
+_bn_momentum = 0.1
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True)
+
+
+def conv_init(m):
+    classname = m.__class__.__name__
+    if classname.find('Conv') != -1:
+        init.xavier_uniform_(m.weight, gain=np.sqrt(2))
+        init.constant_(m.bias, 0)
+    elif classname.find('BatchNorm') != -1:
+        init.constant_(m.weight, 1)
+        init.constant_(m.bias, 0)
+
+
+class WideBasic(nn.Module):
+    def __init__(self, in_planes, planes, dropout_rate, stride=1):
+        super(WideBasic, self).__init__()
+        assert dropout_rate==0.0, 'dropout layer not used'
+        self.bn1 = nn.BatchNorm2d(in_planes, momentum=_bn_momentum)
+        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, bias=True)
+        #self.dropout = nn.Dropout(p=dropout_rate)
+        self.bn2 = nn.BatchNorm2d(planes, momentum=_bn_momentum)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=True)
+
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_planes != planes:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=True),
+            )
+
+    def forward(self, x):
+        # out = self.dropout(self.conv1(F.relu(self.bn1(x))))
+        out = self.conv1(F.relu(self.bn1(x)))
+        out = self.conv2(F.relu(self.bn2(out)))
+        out += self.shortcut(x)
+
+        return out
+
+
+class WideResNet(nn.Module):
+    def __init__(self, depth, widen_factor, dropout_rate, num_classes):
+        super(WideResNet, self).__init__()
+        self.depth=depth
+        self.widen_factor=widen_factor
+        self.in_planes = 16
+
+        assert ((depth - 4) % 6 == 0), 'Wide-resnet depth should be 6n+4'
+        n = int((depth - 4) / 6)
+        k = widen_factor
+
+        nStages = [16, 16*k, 32*k, 64*k]
+
+        self.conv1 = conv3x3(3, nStages[0])
+        self.layer1 = self._wide_layer(WideBasic, nStages[1], n, dropout_rate, stride=1)
+        self.layer2 = self._wide_layer(WideBasic, nStages[2], n, dropout_rate, stride=2)
+        self.layer3 = self._wide_layer(WideBasic, nStages[3], n, dropout_rate, stride=2)
+        self.bn1 = nn.BatchNorm2d(nStages[3], momentum=_bn_momentum)
+        self.linear = nn.Linear(nStages[3], num_classes)
+
+        # self.apply(conv_init)
+
+    def _wide_layer(self, block, planes, num_blocks, dropout_rate, stride):
+        strides = [stride] + [1]*(num_blocks-1)
+        layers = []
+
+        for stride in strides:
+            layers.append(block(self.in_planes, planes, dropout_rate, stride))
+            self.in_planes = planes
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        out = self.conv1(x)
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+        out = F.relu(self.bn1(out))
+        # out = F.avg_pool2d(out, 8)
+        out = F.adaptive_avg_pool2d(out, (1, 1))
+        out = out.view(out.size(0), -1)
+        out = self.linear(out)
+
+        return out
+    
+    def __str__(self):
+        """ Get name of model
+
+        """
+        return "Wide_ResNet%d_%d"%(self.depth,self.widen_factor)