update lesson45

lesson45-Cifar10与ResNet18实战/main.py

@@ -12,13 +12,17 @@ def main():
 
     cifar_train = datasets.CIFAR10('cifar', True, transform=transforms.Compose([
         transforms.Resize((32, 32)),
-        transforms.ToTensor()
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                             std=[0.229, 0.224, 0.225])
     ]), download=True)
     cifar_train = DataLoader(cifar_train, batch_size=batchsz, shuffle=True)
 
     cifar_test = datasets.CIFAR10('cifar', False, transform=transforms.Compose([
         transforms.Resize((32, 32)),
-        transforms.ToTensor()
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                             std=[0.229, 0.224, 0.225])
     ]), download=True)
     cifar_test = DataLoader(cifar_test, batch_size=batchsz, shuffle=True)
 

lesson45-Cifar10与ResNet18实战/resnet.py

@@ -57,20 +57,20 @@ def __init__(self):
         super(ResNet18, self).__init__()
 
         self.conv1 = nn.Sequential(
-            nn.Conv2d(3, 16, kernel_size=3, stride=3, padding=0),
-            nn.BatchNorm2d(16)
+            nn.Conv2d(3, 64, kernel_size=3, stride=3, padding=0),
+            nn.BatchNorm2d(64)
         )
         # followed 4 blocks
         # [b, 64, h, w] => [b, 128, h ,w]
-        self.blk1 = ResBlk(16, 32, stride=2)
+        self.blk1 = ResBlk(64, 128, stride=2)
         # [b, 128, h, w] => [b, 256, h, w]
-        self.blk2 = ResBlk(32, 64, stride=2)
+        self.blk2 = ResBlk(128, 256, stride=2)
         # # [b, 256, h, w] => [b, 512, h, w]
-        self.blk3 = ResBlk(64, 128, stride=2)
+        self.blk3 = ResBlk(256, 512, stride=2)
         # # [b, 512, h, w] => [b, 1024, h, w]
-        self.blk4 = ResBlk(128, 256, stride=2)
+        self.blk4 = ResBlk(512, 512, stride=2)
 
-        self.outlayer = nn.Linear(256*1*1, 10)
+        self.outlayer = nn.Linear(512*1*1, 10)
 
     def forward(self, x):
         """
@@ -86,7 +86,11 @@ def forward(self, x):
         x = self.blk3(x)
         x = self.blk4(x)
 
-        # print(x.shape)
+
+        # print('after conv:', x.shape) #[b, 512, 2, 2]
+        # [b, 512, h, w] => [b, 512, 1, 1]
+        x = F.adaptive_avg_pool2d(x, [1, 1])
+        # print('after pool:', x.shape)
         x = x.view(x.size(0), -1)
         x = self.outlayer(x)
 
@@ -96,17 +100,19 @@ def forward(self, x):
 
 
 def main():
-    blk = ResBlk(64, 128)
+
+    blk = ResBlk(64, 128, stride=4)
     tmp = torch.randn(2, 64, 32, 32)
     out = blk(tmp)
     print('block:', out.shape)
 
-
+    x = torch.randn(2, 3, 32, 32)
     model = ResNet18()
-    tmp = torch.randn(2, 3, 32, 32)
-    out = model(tmp)
+    out = model(x)
     print('resnet:', out.shape)
 
 
+
+
 if __name__ == '__main__':
     main()