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import torch
import numpy as np
from PIL import Image
import json

with open('class_mapping.json') as data:
mappings = json.load(data)

class_mapping = {item['model_idx']: item['tag_string'] for item in mappings}

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model = torch.jit.load('model.pt').to(device)

image_path = '/path/to/your/image'
image = Image.open(image_path)
# Transform your image if the config.yaml shows
# you used any image transforms for validation data
image = np.array(image)
# Convert to torch tensor
x = torch.from_numpy(image).to(device)
with torch.no_grad():
# Convert to channels first, add batch dimension, convert to float datatype
x = x.permute(2, 0, 1).unsqueeze(dim=0).float()
y = model(x)
y = torch.sigmoid(y).squeeze()
# All classes with probabilities > 0.5 are considered present in
# the input. You can tweak this 0.5 threshold if you desire.
idxs = torch.where(y > 0.5)[0].cpu().numpy()
present_tags = []
for idx in idxs:
present_tags.append(
class_mapping[idx]
)
print("Tags for input:", present_tags)
/for
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