release code

Dassl.ProGrad.pytorch/dassl/modeling/backbone/wide_resnet.py

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+"""
+Modified from https://github.com/xternalz/WideResNet-pytorch
+"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .build import BACKBONE_REGISTRY
+from .backbone import Backbone
+
+
+class BasicBlock(nn.Module):
+
+    def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
+        super().__init__()
+        self.bn1 = nn.BatchNorm2d(in_planes)
+        self.relu1 = nn.LeakyReLU(0.01, inplace=True)
+        self.conv1 = nn.Conv2d(
+            in_planes,
+            out_planes,
+            kernel_size=3,
+            stride=stride,
+            padding=1,
+            bias=False
+        )
+        self.bn2 = nn.BatchNorm2d(out_planes)
+        self.relu2 = nn.LeakyReLU(0.01, inplace=True)
+        self.conv2 = nn.Conv2d(
+            out_planes,
+            out_planes,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            bias=False
+        )
+        self.droprate = dropRate
+        self.equalInOut = in_planes == out_planes
+        self.convShortcut = (
+            (not self.equalInOut) and nn.Conv2d(
+                in_planes,
+                out_planes,
+                kernel_size=1,
+                stride=stride,
+                padding=0,
+                bias=False,
+            ) or None
+        )
+
+    def forward(self, x):
+        if not self.equalInOut:
+            x = self.relu1(self.bn1(x))
+        else:
+            out = self.relu1(self.bn1(x))
+        out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
+        if self.droprate > 0:
+            out = F.dropout(out, p=self.droprate, training=self.training)
+        out = self.conv2(out)
+        return torch.add(x if self.equalInOut else self.convShortcut(x), out)
+
+
+class NetworkBlock(nn.Module):
+
+    def __init__(
+        self, nb_layers, in_planes, out_planes, block, stride, dropRate=0.0
+    ):
+        super().__init__()
+        self.layer = self._make_layer(
+            block, in_planes, out_planes, nb_layers, stride, dropRate
+        )
+
+    def _make_layer(
+        self, block, in_planes, out_planes, nb_layers, stride, dropRate
+    ):
+        layers = []
+        for i in range(int(nb_layers)):
+            layers.append(
+                block(
+                    i == 0 and in_planes or out_planes,
+                    out_planes,
+                    i == 0 and stride or 1,
+                    dropRate,
+                )
+            )
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layer(x)
+
+
+class WideResNet(Backbone):
+
+    def __init__(self, depth, widen_factor, dropRate=0.0):
+        super().__init__()
+        nChannels = [
+            16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor
+        ]
+        assert (depth-4) % 6 == 0
+        n = (depth-4) / 6
+        block = BasicBlock
+        # 1st conv before any network block
+        self.conv1 = nn.Conv2d(
+            3, nChannels[0], kernel_size=3, stride=1, padding=1, bias=False
+        )
+        # 1st block
+        self.block1 = NetworkBlock(
+            n, nChannels[0], nChannels[1], block, 1, dropRate
+        )
+        # 2nd block
+        self.block2 = NetworkBlock(
+            n, nChannels[1], nChannels[2], block, 2, dropRate
+        )
+        # 3rd block
+        self.block3 = NetworkBlock(
+            n, nChannels[2], nChannels[3], block, 2, dropRate
+        )
+        # global average pooling and classifier
+        self.bn1 = nn.BatchNorm2d(nChannels[3])
+        self.relu = nn.LeakyReLU(0.01, inplace=True)
+
+        self._out_features = nChannels[3]
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode="fan_out", nonlinearity="relu"
+                )
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.Linear):
+                m.bias.data.zero_()
+
+    def forward(self, x):
+        out = self.conv1(x)
+        out = self.block1(out)
+        out = self.block2(out)
+        out = self.block3(out)
+        out = self.relu(self.bn1(out))
+        out = F.adaptive_avg_pool2d(out, 1)
+        return out.view(out.size(0), -1)
+
+
+@BACKBONE_REGISTRY.register()
+def wide_resnet_28_2(**kwargs):
+    return WideResNet(28, 2)
+
+
+@BACKBONE_REGISTRY.register()
+def wide_resnet_16_4(**kwargs):
+    return WideResNet(16, 4)