Update configuration_eat.py

configuration_eat.py

@@ -1,58 +1,67 @@
-# configuration_eat.py
-
-from transformers import PretrainedConfig
-
-class EATConfig(PretrainedConfig):
-    model_type = "eat"
-
-    def __init__(
-        self,
-        embed_dim=768,
-        depth=12,
-        num_heads=12,
-        patch_size=16,
-        stride=16,
-        in_chans=1,
-        num_classes=527,
-        model_variant="pretrain",  # or "finetune"
-
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        drop_rate=0.0,
-        attn_drop_rate=0.0,
-        activation_dropout=0.0,
-        post_mlp_drop=0.0,
-        start_drop_path_rate=0.0,
-        end_drop_path_rate=0.0,
-
-        layer_norm_first=False,
-        norm_eps=1e-6,
-        norm_affine=True,
-        fixed_positions=True,
-
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-
-        self.embed_dim = embed_dim
-        self.depth = depth
-        self.num_heads = num_heads
-        self.patch_size = patch_size
-        self.stride = stride
-        self.in_chans = in_chans
-        self.num_classes = num_classes
-        self.model_variant = model_variant
-
-        self.mlp_ratio = mlp_ratio
-        self.qkv_bias = qkv_bias
-        self.drop_rate = drop_rate
-        self.attn_drop_rate = attn_drop_rate
-        self.activation_dropout = activation_dropout
-        self.post_mlp_drop = post_mlp_drop
-        self.start_drop_path_rate = start_drop_path_rate
-        self.end_drop_path_rate = end_drop_path_rate
-
-        self.layer_norm_first = layer_norm_first
-        self.norm_eps = norm_eps
-        self.norm_affine = norm_affine
-        self.fixed_positions = fixed_positions
+from transformers import PretrainedConfig  
+  
+class EATConfig(PretrainedConfig):  
+    model_type = "eat"  
+  
+    def __init__(  
+        self,  
+        # --- 1. Core Architecture (Dimensions) ---  
+        embed_dim=768,  
+        depth=12,  
+        num_heads=12,  
+        mlp_ratio=4.0,  
+        num_classes=527,  
+        model_variant="pretrain",  # or "finetune"  
+  
+        # --- 2. Input & Patching ---  
+        in_chans=1,  
+        patch_size=16,  
+        stride=16,  
+        fixed_positions=True,  
+  
+        # --- 3. Normalization & Bias Structure ---  
+        qkv_bias=True,  
+        layer_norm_first=False,  
+        norm_affine=True,  
+        norm_eps=1e-6,  
+  
+        # --- 4. Fine-Tuning Knobs (Regularization) ---  
+        drop_rate=0.0,  
+        attn_drop_rate=0.0,  
+        activation_dropout=0.0,  
+        post_mlp_drop=0.0,  
+        start_drop_path_rate=0.0,  
+        end_drop_path_rate=0.0,  
+          
+        # --- 5. Hugging Face Extras ---  
+        **kwargs,  
+    ):  
+        super().__init__(**kwargs)  
+  
+        # --- 1. Core Architecture ---  
+        self.embed_dim = embed_dim  
+        self.depth = depth  
+        self.num_heads = num_heads  
+        self.mlp_ratio = mlp_ratio  
+        self.num_classes = num_classes  
+        self.model_variant = model_variant  
+  
+        # --- 2. Input & Patching ---  
+        self.in_chans = in_chans  
+        self.patch_size = patch_size  
+        self.stride = stride  
+        self.fixed_positions = fixed_positions  
+  
+        # --- 3. Normalization & Bias ---  
+        self.qkv_bias = qkv_bias  
+        self.layer_norm_first = layer_norm_first  
+        self.norm_affine = norm_affine  
+        self.norm_eps = norm_eps  
+  
+        # --- 4. Regularization ---  
+        self.drop_rate = drop_rate  
+        self.attn_drop_rate = attn_drop_rate  
+        self.activation_dropout = activation_dropout  
+        self.post_mlp_drop = post_mlp_drop  
+        self.start_drop_path_rate = start_drop_path_rate  
+        self.end_drop_path_rate = end_drop_path_rate