27 lines
653 B
Python
27 lines
653 B
Python
import torch
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import torchvision.models as models
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from PIL import Image
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import torchvision.transforms as transforms
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model = models.resnet50(pretrained=True)
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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model = model.to(device)
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image_path = '1706678996799-2.jpeg'
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image = Image.open(image_path).convert('RGB')
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transform = transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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])
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input_data = transform(image).unsqueeze(0).to(device)
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model.eval()
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with torch.no_grad():
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output = model(input_data)
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print(output) |