Delete debug/test.py

debug/test.py

@@ -1,74 +0,0 @@
-import numpy as np
-import json
-import torch
-
-# Load stats file
-stats_file = "../meta/stats.json"
-with open(stats_file, 'r') as f:
-    stats = json.load(f)
-
-print("Keys in stats file:", list(stats.keys()))
-
-# Check each field more thoroughly by simulating the torch conversion
-problem_found = False
-for key in stats:
-    print(f"\nExamining key: {key}")
-    for stat_type in ["mean", "std"]:
-        if stat_type in stats[key]:
-            value = stats[key][stat_type]
-            print(f"  - {stat_type} type: {type(value)}")
-            
-            try:
-                # Try converting to numpy array (this is what the code does)
-                numpy_val = np.array(value)
-                print(f"    NumPy array shape: {numpy_val.shape}, dtype: {numpy_val.dtype}")
-                
-                # Test if this can be converted to torch tensor
-                try:
-                    torch_val = torch.from_numpy(numpy_val).to(dtype=torch.float32)
-                    print(f"    ✓ Successfully converted to torch tensor")
-                except Exception as e:
-                    print(f"    ✗ ERROR converting to torch tensor: {type(e).__name__}: {e}")
-                    print(f"    Value: {numpy_val}")
-                    problem_found = True
-            except Exception as e:
-                print(f"    ✗ ERROR converting to NumPy array: {type(e).__name__}: {e}")
-                print(f"    Value: {value}")
-                problem_found = True
-
-if not problem_found:
-    print("\nNo problems found in the stats file. The error might be happening in another part of the code.")
-    print("Try fixing the stats file with a preprocessing step:")
-
-    # Create a fixed stats file
-    fixed_stats = {}
-    for key in stats:
-        fixed_stats[key] = {}
-        for field, value in stats[key].items():
-            if field in ["mean", "std"]:
-                # Try to safely convert to float array
-                try:
-                    # First convert to numpy array
-                    arr = np.array(value)
-                    # If object type, replace with safe values
-                    if arr.dtype == np.dtype('O'):
-                        arr = np.ones_like(arr, dtype=np.float32)
-                    else:
-                        # Make sure it's float32
-                        arr = arr.astype(np.float32)
-                    # Convert back to list for JSON
-                    fixed_stats[key][field] = arr.tolist()
-                except Exception as e:
-                    print(f"Fixing {key}.{field}: {e}")
-                    # Just use a safe default
-                    fixed_stats[key][field] = [1.0]
-            else:
-                # Keep other fields unchanged
-                fixed_stats[key][field] = value
-    
-    # Save the fixed stats
-    with open("../meta/stats_fixed.json", 'w') as f:
-        json.dump(fixed_stats, f, indent=2)
-    
-    print("Created fixed stats file: ../meta/stats_fixed.json")
-    print("You can replace the original stats.json with this file.")