| from dataclasses import dataclass
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|
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| from transformers.configuration_utils import PretrainedConfig
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|
|
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| @dataclass
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| class SymTimeConfig(PretrainedConfig):
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| """
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| Time series encoder configuration for SymTime Model.
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|
|
| Parameters
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| -----------
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| num_layers
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| The number of layers to be used for the encoder.
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| d_model
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| The dimension of the model.
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| d_ff
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| The dimension of the feedforward network.
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| num_heads
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| The number of heads to be used for the attention mechanism.
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| norm
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| The normalization to be used for the encoder.
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| attn_dropout
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| The dropout rate to be used for the attention mechanism.
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| dropout
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| The dropout rate to be used for the encoder.
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| act
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| The activation function to be used for the encoder.
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| pre_norm
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| Whether to use pre-norm for the encoder.
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| patch_size
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| The size of the patch to be used for the input data.
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| stride
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| The stride of the patch to be used for the input data.
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| """
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|
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| model_type = "symtime"
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|
|
| def __init__(
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| self,
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| num_layers: int = 6,
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| d_model: int = 512,
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| d_ff: int = 2048,
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| num_heads: int = 8,
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| norm: str = "BatchNorm",
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| dropout: float = 0.1,
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| act: str = "gelu",
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| pre_norm: bool = False,
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| patch_size: int = 16,
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| stride: int = 16,
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| initializer_factor: float = 0.05,
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| **kwargs,
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| ) -> None:
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| self.patch_size = patch_size
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| self.stride = stride
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| self.num_layers = num_layers
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| self.d_model = d_model
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| self.num_heads = num_heads
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| self.d_ff = d_ff
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| self.norm = norm
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| self.dropout = dropout
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| self.act = act
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| self.pre_norm = pre_norm
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| self.initializer_factor = initializer_factor
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|
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| super().__init__(**kwargs)
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|
|
