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from transformers import PretrainedConfig
class EEGViTConfig(PretrainedConfig):
model_type = "eegvit"
def __init__(
self,
conv1_out_channels=256,
conv1_kernel_size=(1, 36),
conv1_stride=(1, 36),
conv1_padding=(0, 2),
num_channels=256,
image_size=(129, 14),
patch_size=(8, 1),
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
initializer_range=0.02,
layer_norm_eps=1e-12,
classifier_dropout=0.1,
num_labels=2,
**kwargs
):
super().__init__(**kwargs)
# Conv1 settings
self.conv1_out_channels = conv1_out_channels
self.conv1_kernel_size = conv1_kernel_size
self.conv1_stride = conv1_stride
self.conv1_padding = conv1_padding
# ViT specific settings
self.num_channels = num_channels
self.image_size = image_size
self.patch_size = patch_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
# Classifier settings
self.classifier_dropout = classifier_dropout
self.num_labels = num_labels |