| from transformers import PretrainedConfig |
| from typing import Optional |
| import math |
|
|
|
|
| class LightGTSConfig(PretrainedConfig): |
|
|
| model_type = "LightGTS" |
|
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|
|
| def __init__(self, context_points:int = 512, c_in:int = 1, target_dim:int = 96, patch_len:int = 32, stride:int = 32, mask_mode:str = 'patch',mask_nums:int = 3, |
| e_layers:int=3, d_layers:int=3, d_model=256, n_heads=16, shared_embedding=True, d_ff:int=512, |
| norm:str='BatchNorm', attn_dropout:float=0.4, dropout:float=0., act:str="gelu", |
| res_attention:bool=True, pre_norm:bool=False, store_attn:bool=False, |
| pe:str='sincos', learn_pe:bool=False, head_dropout = 0, |
| head_type = "prediction", individual = False, |
| y_range:Optional[tuple]=None, verbose:bool=False, **kwargs): |
|
|
| self.context_points = context_points |
| self.c_in = c_in |
| self.target_dim = target_dim |
| self.patch_len = patch_len |
| self.stride = stride |
| self.num_patch = (max(self.context_points, self.patch_len)-self.patch_len) // self.stride + 1 |
| self.mask_mode = mask_mode |
| self.mask_nums = mask_nums |
| self.e_layers = e_layers |
| self.d_layers = d_layers |
| self.d_model = d_model |
| self.n_heads = n_heads |
| self.shared_embedding = shared_embedding |
| self.d_ff = d_ff |
| self.dropout = dropout |
| self.attn_dropout = attn_dropout |
| self.head_dropout = head_dropout |
| self.act = act |
| self.head_type = head_type |
| self.initializer_range = 0.02 |
| super().__init__(**kwargs) |
|
|
