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