doceker support

src/paddle_ocr/test.py

@@ -0,0 +1,114 @@
+# test.py - Simple client to test PaddleOCR REST API
+# Usage: python test.py [--url URL] [--dataset PATH]
+
+import argparse
+import requests
+import time
+import sys
+
+
+def wait_for_health(url: str, timeout: int = 120) -> bool:
+    """Wait for API to be ready."""
+    health_url = f"{url}/health"
+    start = time.time()
+
+    print(f"Waiting for API at {health_url}...")
+    while time.time() - start < timeout:
+        try:
+            resp = requests.get(health_url, timeout=5)
+            if resp.status_code == 200:
+                data = resp.json()
+                if data.get("model_loaded"):
+                    print(f"API ready! Model loaded in {time.time() - start:.1f}s")
+                    return True
+                print(f"  Model loading... ({time.time() - start:.0f}s)")
+        except requests.exceptions.ConnectionError:
+            print(f"  Connecting... ({time.time() - start:.0f}s)")
+        except Exception as e:
+            print(f"  Error: {e}")
+        time.sleep(2)
+
+    print("Timeout waiting for API")
+    return False
+
+
+def test_evaluate(url: str, config: dict) -> dict:
+    """Run evaluation with given config."""
+    eval_url = f"{url}/evaluate"
+
+    print(f"\nTesting config: {config}")
+    start = time.time()
+
+    resp = requests.post(eval_url, json=config, timeout=600)
+    resp.raise_for_status()
+
+    result = resp.json()
+    elapsed = time.time() - start
+
+    print(f"Results (took {elapsed:.1f}s):")
+    print(f"  CER: {result['CER']:.4f} ({result['CER']*100:.2f}%)")
+    print(f"  WER: {result['WER']:.4f} ({result['WER']*100:.2f}%)")
+    print(f"  Pages: {result['PAGES']}")
+    print(f"  Time/page: {result['TIME_PER_PAGE']:.2f}s")
+
+    return result
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Test PaddleOCR REST API")
+    parser.add_argument("--url", default="http://localhost:8000", help="API base URL")
+    parser.add_argument("--dataset", default="/app/dataset", help="Dataset path (inside container)")
+    parser.add_argument("--skip-health", action="store_true", help="Skip health check wait")
+    args = parser.parse_args()
+
+    # Wait for API to be ready
+    if not args.skip_health:
+        if not wait_for_health(args.url):
+            sys.exit(1)
+
+    # Test 1: Baseline config (default PaddleOCR)
+    print("\n" + "="*50)
+    print("TEST 1: Baseline Configuration")
+    print("="*50)
+    baseline = test_evaluate(args.url, {
+        "pdf_folder": args.dataset,
+        "use_doc_orientation_classify": False,
+        "use_doc_unwarping": False,
+        "textline_orientation": False,  # Baseline: disabled
+        "text_det_thresh": 0.0,
+        "text_det_box_thresh": 0.0,
+        "text_det_unclip_ratio": 1.5,
+        "text_rec_score_thresh": 0.0,
+        "start_page": 5,
+        "end_page": 10,
+    })
+
+    # Test 2: Optimized config (from Ray Tune results)
+    print("\n" + "="*50)
+    print("TEST 2: Optimized Configuration")
+    print("="*50)
+    optimized = test_evaluate(args.url, {
+        "pdf_folder": args.dataset,
+        "use_doc_orientation_classify": False,
+        "use_doc_unwarping": False,
+        "textline_orientation": True,  # KEY: enabled
+        "text_det_thresh": 0.4690,
+        "text_det_box_thresh": 0.5412,
+        "text_det_unclip_ratio": 0.0,
+        "text_rec_score_thresh": 0.6350,
+        "start_page": 5,
+        "end_page": 10,
+    })
+
+    # Summary
+    print("\n" + "="*50)
+    print("SUMMARY")
+    print("="*50)
+    cer_reduction = (1 - optimized["CER"] / baseline["CER"]) * 100 if baseline["CER"] > 0 else 0
+    print(f"Baseline CER:  {baseline['CER']*100:.2f}%")
+    print(f"Optimized CER: {optimized['CER']*100:.2f}%")
+    print(f"Improvement:   {cer_reduction:.1f}% reduction in errors")
+
+
+if __name__ == "__main__":
+    main()