hyesulim commited on
Commit
b494b53
·
verified ·
1 Parent(s): 4cd925e

test: fixed typo

Browse files
Files changed (1) hide show
  1. app.py +12 -13
app.py CHANGED
@@ -28,7 +28,9 @@ _CACHE = {
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  'sae_data_dict': {},
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  'model_data': {},
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  'segmasks': {},
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- 'top_images': {}
 
 
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  }
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  def load_all_data(image_root: str, pkl_root: str) -> Tuple[Dict, Dict]:
@@ -638,6 +640,8 @@ def precompute_segmasks():
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  except Exception as e:
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  print(f"Error precomputing mask {cache_key}: {e}")
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  with gr.Blocks(
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  theme=gr.themes.Citrus(),
@@ -837,6 +841,13 @@ if __name__ == "__main__":
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  # Configure cache sizes based on available RAM
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  cache_size = int(total_ram_gb * 100) # Rough estimate: 100 entries per GB
 
 
 
 
 
 
 
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  # Memory monitoring function
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  def monitor_memory_usage():
@@ -859,18 +870,6 @@ if __name__ == "__main__":
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  import threading
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  start_memory_monitor()
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-
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- # Add to initialization
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- preload_all_model_data()
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-
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- _CACHE['precomputed_activations'] = {}
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- precompute_activations()
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- precompute_segmasks()
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-
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- data_dict, sae_data_dict = load_all_data(image_root="./data/image", pkl_root=pkl_root)
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- default_image_name = "christmas-imagenet"
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-
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-
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  # Launch the app with memory-optimized settings
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  demo.queue(max_size=min(20, int(total_ram_gb))) # Scale queue with RAM
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  demo.launch(
 
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  'sae_data_dict': {},
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  'model_data': {},
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  'segmasks': {},
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+ 'top_images': {},
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+ 'precomputed_activations' = {}
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+
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  }
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  def load_all_data(image_root: str, pkl_root: str) -> Tuple[Dict, Dict]:
 
640
  except Exception as e:
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  print(f"Error precomputing mask {cache_key}: {e}")
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+ data_dict, sae_data_dict = load_all_data(image_root="./data/image", pkl_root=pkl_root)
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+ default_image_name = "christmas-imagenet"
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  with gr.Blocks(
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  theme=gr.themes.Citrus(),
 
841
 
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  # Configure cache sizes based on available RAM
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  cache_size = int(total_ram_gb * 100) # Rough estimate: 100 entries per GB
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+
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+ # Precompute all data
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+ print("Starting precomputation...")
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+ preload_all_model_data()
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+ precompute_activations()
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+ precompute_segmasks()
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+ print("Precomputation complete!")
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  # Memory monitoring function
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  def monitor_memory_usage():
 
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  import threading
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  start_memory_monitor()
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  # Launch the app with memory-optimized settings
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  demo.queue(max_size=min(20, int(total_ram_gb))) # Scale queue with RAM
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  demo.launch(