2025-04-19 08:05:49 +09:00

22 lines
865 B
Python

from PIL import Image
from tinygrad.helpers import getenv
import torch, torchvision, pathlib
import torchvision.transforms as transforms
import extra.torch_backend.backend
device = "tiny"
torch.set_default_device(device)
if __name__ == "__main__":
img = Image.open(pathlib.Path(__file__).parent.parent.parent / "test/models/efficientnet/Chicken.jpg").convert('RGB')
transform = transforms.Compose([
transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
img = transform(img).unsqueeze(0).to(device)
model = torchvision.models.resnet18(weights=torchvision.models.ResNet18_Weights.DEFAULT)
if getenv("EVAL", 1): model.eval()
out = model(img).detach().cpu().numpy()
print("output:", out.shape, out.argmax())
assert out.argmax() == 7 # cock