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src/models/wide_resnet.py

+import sys
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+import torch.nn.functional as F
+from torch.autograd import Variable
+
+
+# This function creates a 3x3 convolution with optional stride.
+def conv3x3(in_planes, out_planes, stride=1):
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True)
+
+
+# A helper to initialize model parameters in a Xavier style.
+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)
+
+
+# This is one "wide block" used in WideResNet.
+# Typically, it's a residual block with dropout.
+class WideBasic(nn.Module):
+    def __init__(self, in_planes, planes, dropout_rate, stride=1, *args, **kwargs):
+        # Notice that there's a duplicated super() call below, might need a fix.
+        # "super(wide_basic, self).__init__()" was probably intended
+        super(WideBasic, self).__init__()  # <-- "wide_basic" might be a leftover name
+        super().__init__(*args, **kwargs)
+
+        self.bn1 = nn.BatchNorm2d(in_planes)
+        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)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=True)
+
+        # Shortcut for dimension/stride mismatch.
+        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):
+        # Classic WRN sequence: BN -> ReLU -> Conv -> Dropout -> BN -> ReLU -> Conv
+        out = self.dropout(self.conv1(F.relu(self.bn1(x))))
+        out = self.conv2(F.relu(self.bn2(out)))
+        # Add skip connection from input x
+        out += self.shortcut(x)
+
+        return out
+
+
+# Full WideResNet model that uses multiple WideBasic blocks.
+class WideResNet(nn.Module):
+    def __init__(self, depth, widen_factor, dropout_rate, num_classes, *args, **kwargs):
+        # Similar duplication of super calls here:
+        super(WideResNet, self).__init__()  # Might be leftover from earlier naming
+        super().__init__(*args, **kwargs)
+
+        self.in_planes = 16
+
+        # According to the paper, wide-resnet depth is 6n + 4.
+        assert ((depth - 4) % 6 == 0), 'Wide-resnet depth should be 6n+4'
+        n = (depth - 4) / 6
+        k = widen_factor
+
+        print('| Wide-Resnet %dx%d' % (depth, k))
+        nStages = [16, 16 * k, 32 * k, 64 * k]
+
+        # Initial conv layer
+        self.conv1 = conv3x3(3, nStages[0])
+
+        # First group of blocks
+        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)
+
+        # Final BN + linear classifier
+        self.bn1 = nn.BatchNorm2d(nStages[3], momentum=0.9)
+        self.linear = nn.Linear(nStages[3], num_classes)
+
+    def _wide_layer(self, block, planes, num_blocks, dropout_rate, stride):
+        # Each "layer" is a sequence of wide_basic blocks, controlling strides
+        strides = [stride] + [1] * (int(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):
+        # Standard WRN forward pass:
+        out = self.conv1(x)
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+
+        out = F.relu(self.bn1(out))
+        # Global average pool, typical for wide resnet
+        out = F.avg_pool2d(out, 8)
+        # Flatten
+        out = out.view(out.size(0), -1)
+        # Final linear layer
+        out = self.linear(out)
+
+        return out
+
+    def forward_features(self, x):
+        out = self.conv1(x)
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+
+        out = F.relu(self.bn1(out))
+        # Global average pool, but we don't flatten it yet for feature extraction
+        out = F.avg_pool2d(out, 8)
+        return out