from simplenet import SimpleNet
from PIL import Image
import torchvision.transforms as transforms
import torch
import os

s = SimpleNet(device='cuda')

image_path = '~/SimpleNetv2/datasets/bottle/test/broken_large/000.png'

image_path = os.path.expanduser(image_path)

image = Image.open(image_path).convert("RGB")

transform = transforms.Compose([
transforms.Resize((256, 256)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

image = transform(image)

image = image.unsqueeze(0)
image = image.to(s.device)

Make predictions

predictions = s.predict(image)

return self._modules[key]
KeyError: 'feature_aggregator'

Excellent work. Thank you very much for sharing.

File "test.py", line 4, in
s.predict()
TypeError: predict() missing 1 required positional argument: 'data'