Anpassungen für HF

config.json

@@ -1,57 +1,24 @@
 {
-  "architectures": [
-    "MySegformerForSemanticSegmentation"
-  ],
-  "attn_drop_rate": 0.0,
-  "drop_path_rate": 0.0,
-  "drop_rate": 0.0,
-  "embed_dims": [
-    64,
-    128,
-    320,
-    512
-  ],
-  "mlp_ratio": 4,
-  "model_type": "my_segformer",
-  "num_classes": 19,
-  "num_heads": [
-    1,
-    2,
-    4,
-    8
-  ],
-  "num_layers": [
-    3,
-    4,
-    6,
-    3
-  ],
+  "in_channels": 3,
+  "embed_dims": [32, 64, 160, 256],
   "num_stages": 4,
-  "out_indices": [
-    0,
-    1,
-    2,
-    3
-  ],
-  "patch_sizes": [
-    7,
-    3,
-    3,
-    3
-  ],
+  "num_layers": [2, 2, 2, 2],
+  "num_heads": [1, 2, 5, 8],
+  "patch_sizes": [7, 3, 3, 3],
+  "sr_ratios": [8, 4, 2, 1],
+  "out_indices": [0, 1, 2, 3],
+  "mlp_ratio": 4,
   "qkv_bias": true,
-  "sr_ratios": [
-    8,
-    4,
-    2,
-    1
-  ],
-  "strides": [
-    4,
-    2,
-    2,
-    2
-  ],
-  "torch_dtype": "float32",
-  "transformers_version": "4.55.0"
+  "drop_rate": 0.0,
+  "attn_drop_rate": 0.0,
+  "drop_path_rate": 0.1,
+  "decode_head": {
+    "in_channels": [32, 64, 160, 256],
+    "in_index": [0, 1, 2, 3],
+    "channels": 256,
+    "dropout_ratio": 0.1,
+    "out_channels": 19,
+    "align_corners": false,
+    "interpolate_mode": "bilinear"
+  }
 }

model.safetensors

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:818e1b51093355a5915fd844e68edc7af583b2a397272c84e6e12a670896bc23
-size 98934820

modeling_my_segformer.py

@@ -26,11 +26,6 @@ class SegformerHead(nn.Module):
         self.align_corners = align_corners
         self.interpolate_mode = interpolate_mode
 
-        print(f"in_channels: {self.in_channels}, type: {type(self.in_channels)}")
-        print(f"in_index: {self.in_index}, type: {type(self.in_index)}")
-        print(f"len(in_channels): {len(self.in_channels) if hasattr(self.in_channels, '__len__') else 'no len'}")
-        print(f"len(in_index): {len(self.in_index) if hasattr(self.in_index, '__len__') else 'no len'}")
-
         self.act_cfg = dict(type='ReLU')
         self.conv_seg = nn.Conv2d(channels, out_channels, kernel_size=1)
         self.dropout = nn.Dropout2d(dropout_ratio) if dropout_ratio > 0 else None