Anpassungen der binchanger/checker und bin angepasst

binchanger.py

@@ -3,27 +3,61 @@ import os
 
 # --- Configuration ---
 # Pfad zur Eingabe-BIN-Datei
-input_checkpoint_path = "./pytorch_model.bin"
+input_checkpoint_path = "./pytorch_model_oldLayerNames.bin"
 # Pfad zur Ausgabe-BIN-Datei (wo die geänderte Version gespeichert wird)
-output_checkpoint_path = "./pytorch_model_renamed.bin"
+output_checkpoint_path = "./pytorch_model_renamed_final.bin"
+
+# Define the layers that *must* be named 'segmentation_head'
+# These are the specific layers you identified
+specific_segmentation_head_layers = [
+    'decode_head.conv_seg.bias',
+    'decode_head.conv_seg.weight',
+    'decode_head.convs.0.conv.bias',
+    'decode_head.convs.0.conv.weight',
+    'decode_head.convs.1.conv.bias',
+    'decode_head.convs.1.conv.weight',
+    'decode_head.convs.2.conv.bias',
+    'decode_head.convs.2.conv.weight',
+    'decode_head.convs.3.conv.bias',
+    'decode_head.convs.3.conv.weight',
+    'decode_head.fusion_conv.conv.bias',
+    'decode_head.fusion_conv.conv.weight'
+]
 
 # --- Check, ob die Eingabedatei existiert ---
 if not os.path.exists(input_checkpoint_path):
-    print(f"Fehler: Eingabedatei nicht gefunden unter {input_checkpoint_path}. Bitte den Pfad korrigieren.")
+    print(f"Fehler: Eingabedatei nicht gefunden unter {input_checkpoint_path}. Bitte den Pfad korrigieren. ❌")
 else:
     # --- Checkpoint laden ---
     state_dict = torch.load(input_checkpoint_path, map_location="cpu")
 
     # --- Layer-Namen ändern und neues State Dict erstellen ---
     new_state_dict = {}
+    renamed_count_segmentation = 0
+    renamed_count_segformer = 0
+    skipped_count = 0
+
     for old_key, value in state_dict.items():
-        if old_key.startswith('decode_head.'):
+        if old_key in specific_segmentation_head_layers:
+            # These specific layers get 'segmentation_head.' prefix
+            new_key = old_key.replace('decode_head.', 'segmentation_head.', 1)
+            new_state_dict[new_key] = value
+            renamed_count_segmentation += 1
+        elif old_key.startswith('decode_head.'):
+            # All other layers starting with 'decode_head.' get 'segformer_head.' prefix
             new_key = old_key.replace('decode_head.', 'segformer_head.', 1)
             new_state_dict[new_key] = value
+            renamed_count_segformer += 1
         else:
+            # Keep other layers as they are (e.g., backbone layers)
             new_state_dict[old_key] = value
+            skipped_count += 1
 
     # --- Geändertes State Dict speichern ---
     torch.save(new_state_dict, output_checkpoint_path)
 
-    print(f"Fertig! Die umbenannte Datei wurde gespeichert unter: {output_checkpoint_path} 🎉")
+    print(f"✅ Fertig! Die umbenannte Datei wurde gespeichert unter: {output_checkpoint_path}")
+    print(f"Zusammenfassung der Umbenennungen:")
+    print(f"  - '{renamed_count_segmentation}' Layer von 'decode_head.' zu 'segmentation_head.' umbenannt.")
+    print(f"  - '{renamed_count_segformer}' Layer von 'decode_head.' zu 'segformer_head.' umbenannt.")
+    print(f"  - '{skipped_count}' Layer behielten ihren ursprünglichen Namen (z.B. Backbone).")

binchecker.py

@@ -2,10 +2,10 @@ import torch
 import os # Import the os module for path manipulation
 
 # Define the output file name
-output_file = 'layer_names_renamed_output.txt'
+output_file = 'layer_names_renamed_final_output.txt'
 
 # Pfad zu deiner .bin Datei
-checkpoint_path = "./pytorch_model_renamed.bin"
+checkpoint_path = "./pytorch_model_renamed_final.bin"
 
 # Check if the checkpoint file exists
 if not os.path.exists(checkpoint_path):

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:42ba38f139e97fda197d9d1d3249e43f8ce461202ac6a9926b174d31e67862fc
-size 328234307
+oid sha256:3ba1746d460b19f3e96cbb256a30fe2c947c609d0144f18895baf3beaa40ab7a
+size 328240287