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import torch
from transformers import AutoProcessor, AutoModel
import numpy as np
from PIL import Image, ImageDraw
# Configuration
MODEL_DIR = r"D:\oeil d'elephant"
def test_inference():
print(f"Loading model from {MODEL_DIR}...")
try:
model = AutoModel.from_pretrained(MODEL_DIR, local_files_only=True)
processor = AutoProcessor.from_pretrained(MODEL_DIR, local_files_only=True)
model.eval()
# Apply fix
if hasattr(model, 'logit_scale'):
with torch.no_grad():
model.logit_scale.data.fill_(4.60517)
print("Model loaded.")
except Exception as e:
print(f"Failed to load model: {e}")
return
# Synthetic Pneumonia X-ray
# Two lungs, one with a big white consolidation
image = Image.new('RGB', (448, 448), color=(0, 0, 0))
draw = ImageDraw.Draw(image)
draw.ellipse([100, 100, 200, 350], fill=(100, 100, 100)) # Left lung (clearer)
draw.ellipse([248, 100, 348, 350], fill=(200, 200, 200)) # Right lung (consolidated/white)
# Check "Thoracic" specific labels
labels = [
'Cardiomédiastin élargi', 'Cardiomégalie', 'Opacité pulmonaire',
'Lésion pulmonaire', 'Consolidation', 'Œdème', 'Pneumonie',
'Atelectasis', 'Pneumothorax', 'Effusion pleurale', 'Pleural Autre'
]
# Try simplified versions too
simple_labels = [
'Coeur', 'Gros coeur', 'Opacité',
'Lésion', 'Blanc', 'Eau', 'Infection',
'Ecrasé', 'Air', 'Liquide', 'Autre'
]
print("\nTesting Pathology Prompts:")
with torch.no_grad():
inputs = processor(text=labels, images=image, padding="max_length", return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits_per_image
probs = torch.sigmoid(logits)[0]
print("\nOriginal Labels:")
for i, label in enumerate(labels):
print(f"'{label}': Logit {logits[0][i]:.4f} | Prob {probs[i]:.6f}")
# Test Simple
inputs_simple = processor(text=simple_labels, images=image, padding="max_length", return_tensors="pt")
outputs_simple = model(**inputs_simple)
logits_simple = outputs_simple.logits_per_image
probs_simple = torch.sigmoid(logits_simple)[0]
print("\nSimple Labels:")
for i, label in enumerate(simple_labels):
print(f"'{label}': Logit {logits_simple[0][i]:.4f} | Prob {probs_simple[0][i]:.6f}")
if __name__ == "__main__":
test_inference()
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