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22 lines
865 B
22 lines
865 B
20 hours ago
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from PIL import Image
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from tinygrad.helpers import getenv
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import torch, torchvision, pathlib
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import torchvision.transforms as transforms
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import extra.torch_backend.backend
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device = "tiny"
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torch.set_default_device(device)
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if __name__ == "__main__":
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img = Image.open(pathlib.Path(__file__).parent.parent.parent / "test/models/efficientnet/Chicken.jpg").convert('RGB')
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transform = transforms.Compose([
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transforms.Resize(256), transforms.CenterCrop(224), 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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img = transform(img).unsqueeze(0).to(device)
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model = torchvision.models.resnet18(weights=torchvision.models.ResNet18_Weights.DEFAULT)
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if getenv("EVAL", 1): model.eval()
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out = model(img).detach().cpu().numpy()
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print("output:", out.shape, out.argmax())
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assert out.argmax() == 7 # cock
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