File size: 1,731 Bytes
3bd162c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |
from typing import Dict, Any
from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
from PIL import Image
import torch
class EndpointHandler():
def __init__(self, path=""):
# Ładowanie modelu i procesora
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.model = AutoModelForZeroShotObjectDetection.from_pretrained(path).to(self.device)
self.processor = AutoProcessor.from_pretrained(path)
def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
# Sprawdź, czy dane wejściowe zawierają wymagane pola
if "image" not in data or "text" not in data:
return {"error": "Payload must contain 'image' (base64 or URL) and 'text' (queries)."}
# Załaduj obraz
image = Image.open(data["image"]) if isinstance(data["image"], str) else data["image"]
# Pobierz teksty zapytań
text_queries = data["text"]
if isinstance(text_queries, list):
text_queries = ". ".join([t.lower().strip() + "." for t in text_queries])
# Przygotuj dane wejściowe
inputs = self.processor(images=image, text=text_queries, return_tensors="pt").to(self.device)
# Przeprowadź inferencję
with torch.no_grad():
outputs = self.model(**inputs)
# Post-process detekcji
results = self.processor.post_process_grounded_object_detection(
outputs,
inputs.input_ids,
box_threshold=0.4,
text_threshold=0.3,
target_sizes=[image.size[::-1]]
)
# Przygotuj wynik
return {
"detections": results
}
|