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loadmodel.py

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+import torch
+
+from torch import nn
+import torchvision
+from torchvision import transforms, datasets
+from torch.utils.data import DataLoader
+from tqdm.auto import tqdm
+from pathlib import Path
+from PIL import Image
+
+test_transformer = transforms.Compose([
+                    transforms.Resize((64,64)),
+                    transforms.ToTensor()
+])
+class Tinyvgg(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.firstlayer = nn.Sequential(
+            nn.Conv2d(in_channels=3, out_channels=10, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+            nn.Conv2d(in_channels=10, out_channels=10, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+
+        )
+        self.secondlayer = nn.Sequential(
+            nn.Conv2d(in_channels=10, out_channels=10, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+            nn.Conv2d(in_channels=10, out_channels=10, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+        )
+        self.classifier = nn.Sequential(
+            nn.Flatten(),
+            nn.Linear(in_features=2560,out_features=2),
+        )
+
+    def forward(self, x):
+        return self.classifier(self.secondlayer(self.firstlayer(x)))
+
+
+device = torch.device("cuda")
+
+model12 = Tinyvgg()
+model12 = model12.to(device)
+
+model12.load_state_dict(torch.load("C:/pytorchprojesi/model12_weights.pth", map_location=device))
+
+model12.eval()
+with torch.inference_mode():
+    image_path = "C:/Users/ceyhu/Downloads/fe.png"
+    image = Image.open(image_path).convert('RGB')  # PIL Image
+    image = test_transformer(image).unsqueeze(0).to(device)
+    output = model12(image)
+    prediction = torch.softmax(output,dim=1)
+    print(prediction)
+