49 lines
1.7 KiB
Python
49 lines
1.7 KiB
Python
import numpy as np
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import torch
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from torch import nn
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from layers.Transformer_EncDec import Encoder, EncoderLayer
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from layers.SelfAttention_Family import FullAttention, AttentionLayer
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from layers.Embed import PatchEmbedding
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from uni2ts.eval_util.plot import plot_single
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from uni2ts.model.moirai import MoiraiForecast, MoiraiModule
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from uni2ts.model.moirai_moe import MoiraiMoEForecast, MoiraiMoEModule
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from uni2ts.model.moirai2 import Moirai2Forecast, Moirai2Module
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class Model(nn.Module):
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def __init__(self, configs):
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"""
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patch_len: int, patch len for patch_embedding
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stride: int, stride for patch_embedding
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"""
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super().__init__()
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self.model = Moirai2Forecast(
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module=Moirai2Module.from_pretrained(
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f"Salesforce/moirai-2.0-R-small",
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),
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prediction_length=configs.pred_len,
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context_length=configs.seq_len,
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target_dim=1,
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feat_dynamic_real_dim=0,
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past_feat_dynamic_real_dim=0,
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).to('cuda')
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self.task_name = configs.task_name
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self.seq_len = configs.seq_len
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self.pred_len = configs.pred_len
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def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
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outputs = []
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for i in range(x_enc.shape[-1]):
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output = self.model.predict(x_enc[...,i].cpu().numpy())
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output = np.mean(output, axis=1)
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outputs.append(torch.Tensor(output).to(x_enc.device))
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dec_out = torch.stack(outputs, dim=-1)
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return dec_out
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def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
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if self.task_name == 'zero_shot_forecast':
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dec_out = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)
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return dec_out
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return None
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