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config.json

@@ -50,5 +50,5 @@
   "qkv_bias": true,
   "representation_size": null,
   "torch_dtype": "float32",
-  "transformers_version": "4.40.2"
+  "transformers_version": "4.39.3"
 }

configuration_uniformer.py

@@ -0,0 +1,51 @@
+from transformers import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class UniFormerWithProjectionHeadConfig(PretrainedConfig): 
+    
+    model_type = 'uniformer'
+      
+    def __init__(
+            self, 
+            projection_size=None, 
+            embed_dim=[64, 128, 320, 512],
+            image_size=384,
+            in_chans=3,
+            depth=[5, 8, 20, 7],
+            patch_size=[4, 2, 2, 2],
+            head_dim=64, 
+            mlp_ratio=4, 
+            qkv_bias=True, 
+            num_classes=1000, 
+            qk_scale=None, 
+            representation_size=None,
+            drop_rate=0.0, 
+            drop_path_rate=0.3,
+            attn_drop_rate=0.0, 
+            conv_stem=False,
+            layer_norm_eps=1e-6,
+            **kwargs,
+        ):
+        super().__init__(
+            layer_norm_eps=layer_norm_eps, 
+            image_size=image_size, 
+            qkv_bias=qkv_bias, 
+            **kwargs,
+        )
+        self.projection_size = projection_size
+        self.embed_dim = embed_dim
+        self.in_chans = in_chans
+        self.depth = depth
+        self.patch_size = patch_size
+        self.head_dim = head_dim
+        self.mlp_ratio = mlp_ratio 
+        self.num_classes = num_classes
+        self.qk_scale = qk_scale
+        self.representation_size = representation_size
+        self.drop_rate = drop_rate 
+        self.drop_path_rate = drop_path_rate
+        self.attn_drop_rate = attn_drop_rate
+        self.conv_stem = conv_stem

modelling_uniformer.py

@@ -1,7 +1,7 @@
 from collections import OrderedDict
 from functools import partial
-from typing import Optional, Tuple, Union
 from math import isqrt
+from typing import Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -11,6 +11,8 @@ from transformers.modeling_outputs import ModelOutput
 from transformers.modeling_utils import PreTrainedModel
 from transformers.utils import logging
 
+from .configuration_uniformer import UniFormerWithProjectionHeadConfig
+
 logger = logging.get_logger(__name__)
 
 
@@ -293,8 +295,7 @@ class UniFormerPreTrainedModel(PreTrainedModel):
     models.
     """
 
-    config_class = ViTConfig
-    base_model_prefix = "vit"
+    config_class = UniFormerWithProjectionHeadConfig
     main_input_name = "pixel_values"
 
     def _init_weights(self, m):