| from dataclasses import dataclass
|
| from typing import List, Literal, Optional, Dict
|
| from enum import Enum
|
|
|
| from transformers.configuration_utils import PretrainedConfig
|
|
|
|
|
| @dataclass
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| class SymTimeConfig(PretrainedConfig):
|
| """
|
| Time series encoder configuration for SymTime Model.
|
|
|
| Parameters
|
| -----------
|
| patch_size
|
| The size of the patch to be used for the input data.
|
| num_layers
|
| The number of layers to be used for the encoder.
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| d_model
|
| The dimension of the model.
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| d_ff
|
| The dimension of the feedforward network.
|
| num_heads
|
| The number of heads to be used for the attention mechanism.
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| norm
|
| The normalization to be used for the encoder.
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| attn_dropout
|
| The dropout rate to be used for the attention mechanism.
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| dropout
|
| The dropout rate to be used for the encoder.
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| act
|
| The activation function to be used for the encoder.
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| pre_norm
|
| Whether to use pre-norm for the encoder.
|
| """
|
|
|
| model_type = "time_series_transformer"
|
|
|
| def __init__(
|
| self,
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| patch_size: int = 16,
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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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| initializer_factor: float = 0.05,
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| **kwargs,
|
| ) -> None:
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| self.patch_size = patch_size
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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
|
|
|
| super().__init__(**kwargs)
|
|
|
