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miner.py

@@ -80,7 +80,6 @@ class Miner:
     def __init__(self, path_hf_repo: Path) -> None:
         try:
             device = "cuda" if torch.cuda.is_available() else "cpu"
-            print(device)
 
             providers = [
                 'CUDAExecutionProvider',
@@ -338,8 +337,6 @@ class Miner:
         if len(batch_images) == 0:
             return bboxes
             
-        print(f"Processing batch of {len(batch_images)} images")
-        
         # Get original image dimensions for scaling
         height, width = batch_images[0].shape[:2]
         scale = 640.0
@@ -349,14 +346,12 @@ class Miner:
         # Memory optimization: Process smaller batches if needed
         max_batch_size = 32  # Reduce batch size further to prevent memory issues
         if len(batch_images) > max_batch_size:
-            print(f"Large batch detected ({len(batch_images)} images), splitting into smaller batches of {max_batch_size}")
             # Process in smaller chunks
             all_bboxes = {}
             for chunk_start in range(0, len(batch_images), max_batch_size):
                 chunk_end = min(chunk_start + max_batch_size, len(batch_images))
                 chunk_images = batch_images[chunk_start:chunk_end]
                 chunk_offset = offset + chunk_start
-                print(f"Processing chunk {chunk_start//max_batch_size + 1}: images {chunk_start}-{chunk_end-1}")
                 chunk_bboxes = self._detect_objects_batch(chunk_images, chunk_offset)
                 all_bboxes.update(chunk_bboxes)
             return all_bboxes
@@ -367,7 +362,6 @@ class Miner:
         
         # Handle batch size mismatch - pad if needed
         model_batch_size = self.bbox_model.get_inputs()[0].shape[0]
-        print(f"Model input shape: {self.bbox_model.get_inputs()[0].shape}, batch_size: {model_batch_size}")
         
         if model_batch_size is not None:
             try:
@@ -378,8 +372,6 @@ class Miner:
                     model_batch_size = int(model_batch_size)
             except (ValueError, TypeError):
                 model_batch_size = None
-        
-        print(f"Processed model_batch_size: {model_batch_size}, actual_batch_size: {actual_batch_size}")
                 
         if model_batch_size and actual_batch_size < model_batch_size:
             padding_size = model_batch_size - actual_batch_size
@@ -394,7 +386,6 @@ class Miner:
             start_time = time.time()
             outputs = self.bbox_model.run(None, {input_name: imgs})[0]
             inference_time = time.time() - start_time
-            print(f"Inference time: {inference_time:.3f}s for {actual_batch_size} images")
             
             # Remove padded results if we added padding
             if model_batch_size and isinstance(model_batch_size, int) and actual_batch_size < model_batch_size:
@@ -516,7 +507,6 @@ class Miner:
                     else:
                         frame_keypoints = frame_keypoints[:n_keypoints]
                     keypoints[offset + frame_number_in_batch] = frame_keypoints
-            print("✅ Keypoints predicted")
             break
             # except RuntimeError as e:
             #     print(self.pitch_batch_size)