Add EEGViT model

config.json

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+{
+  "architectures": [
+    "EEGViTAutoModel"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoModel": "eegvit_model.EEGViTAutoModel"
+  },
+  "encoder_stride": 16,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 16,
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.1"
+}

eegvit_model.py

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+import torch
+import transformers
+from transformers import ViTModel
+import torch
+from torch import nn
+import transformers
+from transformers import PreTrainedModel
+
+class EEGViTAutoModel(PreTrainedModel):
+    config_class = transformers.ViTConfig
+    
+    def __init__(self, config=None):
+        if config is None:
+            config = transformers.ViTConfig()
+        super().__init__(config)
+        self.model = EEGViT_pretrained()
+
+class EEGViT_pretrained(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(
+            in_channels=1, 
+            out_channels=256,
+            kernel_size=(1, 36),
+            stride=(1, 36),
+            padding=(0,2),
+            bias=False
+        )
+        self.batchnorm1 = nn.BatchNorm2d(256, False)
+        model_name = "google/vit-base-patch16-224"
+        config = transformers.ViTConfig.from_pretrained(model_name)
+        config.update({'num_channels': 256})
+        config.update({'image_size': (129,14)})
+        config.update({'patch_size': (8,1)})
+
+        model = transformers.ViTForImageClassification.from_pretrained(model_name, config=config, ignore_mismatched_sizes=True)
+        model.vit.embeddings.patch_embeddings.projection = torch.nn.Conv2d(256, 768, kernel_size=(8, 1), stride=(8, 1), padding=(0,0), groups=256)
+        model.classifier=torch.nn.Sequential(torch.nn.Linear(768,1000,bias=True),
+                                     torch.nn.Dropout(p=0.1),
+                                     torch.nn.Linear(1000,2,bias=True))
+        self.ViT = model
+        
+    def forward(self,x):
+        x=self.conv1(x)
+        x=self.batchnorm1(x)
+        x=self.ViT.forward(x).logits
+        
+        return x

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7147906aa1f15600d12f0799c6cf0117ffc407c017d74817977eaff9c04ad91e
+size 344096872