Create benchmark.py

Do benchmark with model over 500 images.

benchmark.py

@@ -0,0 +1,113 @@
+print(f"[*] Setting up...")
+
+import torch
+import requests
+import random
+import numpy as np
+from io import BytesIO
+from PIL import Image
+from torchvision import transforms
+from transformers import ResNetForImageClassification
+from collections import Counter
+
+# --- 1. CONFIGURATION & SETUP ---
+ANGLES = [0, 90, 180, 270]
+NUM_IMAGES = 500
+MODEL_NAME = "LH-Tech-AI/GyroScope"
+IMG_SOURCE_URL = "https://loremflickr.com/400/400/all"
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"[*] Using device: {device}")
+
+# Modell laden
+print(f"[*] Loading model {MODEL_NAME}...")
+model = ResNetForImageClassification.from_pretrained(MODEL_NAME)
+model.eval()
+model.to(device)
+
+# Vorverarbeitung
+preprocess = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+])
+
+results = []
+
+# --- 2. EVALUATIONS-LOOP ---
+print(f"[*] Starting download and evaluation of {NUM_IMAGES} images (In-Memory)...")
+
+for i in range(1, NUM_IMAGES + 1):
+    try:
+        # Load image into RAM
+        response = requests.get(f"{IMG_SOURCE_URL}?random={i}", timeout=10)
+        img = Image.open(BytesIO(response.content)).convert("RGB")
+
+        # Apply random rotation
+        true_angle = random.choice(ANGLES)
+        label_idx = ANGLES.index(true_angle)
+        
+        # Rotate image
+        rotated_img = img.rotate(true_angle, expand=True)
+
+        # Prediction
+        tensor = preprocess(rotated_img).unsqueeze(0).to(device)
+        with torch.no_grad():
+            logits = model(pixel_values=tensor).logits
+            pred_idx = logits.argmax().item()
+
+        is_correct = (pred_idx == label_idx)
+        results.append({
+            "true": true_angle,
+            "pred": ANGLES[pred_idx],
+            "correct": is_correct
+        })
+
+        status = "✓" if is_correct else "✗"
+        percent = (i / NUM_IMAGES) * 100
+        bar_length = 20
+        filled_length = int(bar_length * i // NUM_IMAGES)
+        bar = '#' * filled_length + ' ' * (bar_length - filled_length)
+
+        status = "✓" if is_correct else "✗"
+        print(f"\rProgress: [{bar}] {percent:.1f}% ({i}/{NUM_IMAGES}) | Last result: {status}", end="")
+
+    except Exception as e:
+        print(f"\n[!] Error processing image {i}: {e}")
+
+# --- 3. RESULTS ---
+print("\n\n" + "="*15)
+print("   RESULTS")
+print("="*15)
+
+total_correct = sum(1 for r in results if r['correct'])
+accuracy = (total_correct / len(results)) * 100
+
+print(f"Overall result: {total_correct}/{len(results)} correct")
+print(f"Hit rate:    {accuracy:.2f} %")
+print("-" * 30)
+
+print("Details per rotation class:")
+for angle in ANGLES:
+    class_results = [r for r in results if r['true'] == angle]
+    if class_results:
+        correct_in_class = sum(1 for r in class_results if r['correct'])
+        class_acc = (correct_in_class / len(class_results)) * 100
+        print(f"  {angle:>3}° : {correct_in_class:>2}/{len(class_results):>2} correct ({class_acc:>6.2f}%)")
+
+print("="*30)
+
+# Result of our benchmark:
+# ===============
+#   RESULTS
+# ===============
+# Overall result: 411/500 correct
+# Hit rate:    82.20 %
+# ------------------------------
+# Details per rotation class:
+#     0° : 96/124 correct ( 77.42%)
+#    90° : 103/119 correct ( 86.55%)
+#   180° : 112/129 correct ( 86.82%)
+#   270° : 100/128 correct ( 78.12%)
+# ==============================