Update src/facerender/animate.py

src/facerender/animate.py

@@ -68,7 +68,7 @@ class AnimateFromCoeff():
             raise AttributeError("Checkpoint should be specified for video head pose estimator.")
 
         if sadtalker_path['mappingnet_checkpoint'] is not None:
-            self._load_cpk_mapping(sadtalker_path['mappingnet_checkpoint'], mapping=mapping)
+            self.load_cpk_mapping(sadtalker_path['mappingnet_checkpoint'], mapping=mapping)
         else:
             raise AttributeError("Checkpoint should be specified for video head pose estimator.")
 
@@ -129,26 +129,35 @@ class AnimateFromCoeff():
         return checkpoint.get('epoch', 0)
 
     def load_cpk_mapping(self, checkpoint_path, mapping=None, discriminator=None,
-                     optimizer_mapping=None, optimizer_discriminator=None, device='cpu'):
+                         optimizer_mapping=None, optimizer_discriminator=None, device='cpu'):
+
+        # .tar içeriği kontrolü
+        if checkpoint_path.endswith(".tar"):
+            try:
+                with tarfile.open(checkpoint_path, "r") as tar:
+                    members = tar.getnames()
+                    if not any(name.startswith("storages") for name in members):
+                        print("⚠️ 'storages' klasörü .tar dosyasında bulunamadı. Devam ediliyor...")
+                    else:
+                        print("✔️ 'storages' bulundu.")
+            except Exception as e:
+                print(f"Tar kontrol hatası: {e}")
+
+        # Eğer klasörse ve archive/data.pkl varsa
+        if os.path.isdir(checkpoint_path):
+            archive_path = os.path.join(checkpoint_path, "archive", "data.pkl")
+            if os.path.isfile(archive_path):
+                checkpoint_path = archive_path
 
-    # Eğer dosya bir klasörse ve archive/data.pkl varsa, orayı kullan
-    if os.path.isdir(checkpoint_path):
-        archive_path = os.path.join(checkpoint_path, "archive", "data.pkl")
-        if os.path.isfile(archive_path):
-            checkpoint_path = archive_path
-
-    # Checkpoint yükle
-    checkpoint = torch.load(checkpoint_path, map_location=torch.device(device))
-
-    if mapping is not None and 'mapping' in checkpoint:
-        mapping.load_state_dict(checkpoint['mapping'])
-    if discriminator is not None and 'discriminator' in checkpoint:
-        discriminator.load_state_dict(checkpoint['discriminator'])
-    if optimizer_mapping is not None and 'optimizer_mapping' in checkpoint:
-        optimizer_mapping.load_state_dict(checkpoint['optimizer_mapping'])
-    if optimizer_discriminator is not None and 'optimizer_discriminator' in checkpoint:
-        optimizer_discriminator.load_state_dict(checkpoint['optimizer_discriminator'])
-
-    return checkpoint.get('epoch', 0)
+        checkpoint = torch.load(checkpoint_path, map_location=torch.device(device))
 
+        if mapping is not None and 'mapping' in checkpoint:
+            mapping.load_state_dict(checkpoint['mapping'])
+        if discriminator is not None and 'discriminator' in checkpoint:
+            discriminator.load_state_dict(checkpoint['discriminator'])
+        if optimizer_mapping is not None and 'optimizer_mapping' in checkpoint:
+            optimizer_mapping.load_state_dict(checkpoint['optimizer_mapping'])
+        if optimizer_discriminator is not None and 'optimizer_discriminator' in checkpoint:
+            optimizer_discriminator.load_state_dict(checkpoint['optimizer_discriminator'])
 
+        return checkpoint.get('epoch', 0)