Create resnet.py

resnet.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from pytorch_lightning import LightningModule
+
+class BasicBlock(LightningModule):
+    expansion = 1
+
+    def __init__(self, in_planes, planes, stride=1):
+        super().__init__()
+        self.conv1 = torch.nn.Conv2d(in_planes, planes, (3,3), stride=stride, padding=1, bias=False)
+        self.bn1 = torch.nn.BatchNorm2d(planes)
+        self.conv2 = torch.nn.Conv2d(planes, planes, (3,3), stride=1, padding=1, bias=False)
+        self.bn2 = torch.nn.BatchNorm2d(planes)
+
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_planes != self.expansion*planes:
+            self.shortcut = nn.Sequential(
+                torch.nn.Conv2d(in_planes, self.expansion*planes, (1,1), stride=stride, bias=False),
+                torch.nn.BatchNorm2d(self.expansion*planes)
+            )
+
+    def forward(self, x):
+        out = torch.relu(self.bn1(self.conv1(x)))
+        out = self.bn2(self.conv2(out))
+        out += self.shortcut(x)
+        out = torch.relu(out)
+        return out
+
+class LitResnet(LightningModule):
+    def __init__(self, block, num_blocks, num_classes=10, lr=0.05):
+        super().__init__()
+
+        self.save_hyperparameters()
+        self.in_planes = 64
+
+        self.conv1 = torch.nn.Conv2d(3, 64, (3,3), stride=1, padding=1, bias=False)
+        self.bn1 = torch.nn.BatchNorm2d(64)
+        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
+        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
+        self.linear = torch.nn.Linear(512*block.expansion, num_classes)
+
+    def _make_layer(self, block, planes, num_blocks, stride):
+        strides = [stride] + [1]*(num_blocks-1)
+        layers = []
+        for stride in strides:
+            layers.append(block(self.in_planes, planes, stride))
+            self.in_planes = planes * block.expansion
+        return torch.nn.Sequential(*layers)
+
+    def forward(self, x):
+        out = torch.relu(self.bn1(self.conv1(x)))
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+        out = self.layer4(out)
+        out = F.avg_pool2d(out, 4)
+        out = out.view(out.size(0), -1)
+        # out = self.linear(out)
+        return F.log_softmax(out, dim=1)
+
+
+# def ResNet18():
+#     return ResNet(BasicBlock, [2, 2, 2, 2])
+
+
+# def ResNet34():
+#     return ResNet(BasicBlock, [3, 4, 6, 3])