WenjieDu/PyPOTS

"""
The core wrapper assembles the submodules of Informer imputation model
and takes over the forward progress of the algorithm.
"""

# Created by Wenjie Du <wenjay.du@gmail.com>
# License: BSD-3-Clause

import torch.nn as nn

from ...nn.modules.informer import (
    ProbAttention,
    ConvLayer,
    InformerEncoderLayer,
    InformerEncoder,
)
from ...nn.modules.saits import SaitsLoss, SaitsEmbedding
from ...nn.modules.transformer import MultiHeadAttention


class _Informer(nn.Module):
    def __init__(
        self,
        n_steps,
        n_features,
        n_layers,
        d_model,
        n_heads,
        d_ffn,
        factor,
        dropout,
        ORT_weight: float = 1,
        MIT_weight: float = 1,
        distil=False,
        activation="relu",
    ):
        super().__init__()

        self.saits_embedding = SaitsEmbedding(
            n_features * 2,
            d_model,
            with_pos=True,
            n_max_steps=n_steps,
            dropout=dropout,
        )
        self.encoder = InformerEncoder(
            [
                InformerEncoderLayer(
                    MultiHeadAttention(
                        ProbAttention(False, factor, dropout),
                        d_model,
                        n_heads,
                        d_model // n_heads,
                        d_model // n_heads,
                    ),
                    d_model,
                    d_ffn,
                    dropout,
                    activation,
                )
                for _ in range(n_layers)
            ],
            [ConvLayer(d_model) for _ in range(n_layers - 1)] if distil else None,
            norm_layer=nn.LayerNorm(d_model),
        )

        # for the imputation task, the output dim is the same as input dim
        self.output_projection = nn.Linear(d_model, n_features)

        self.saits_loss_func = SaitsLoss(ORT_weight, MIT_weight)

    def forward(self, inputs: dict, training: bool = True) -> dict:
        X, missing_mask = inputs["X"], inputs["missing_mask"]

        # WDU: the original Informer paper isn't proposed for imputation task. Hence the model doesn't take
        # the missing mask into account, which means, in the process, the model doesn't know which part of
        # the input data is missing, and this may hurt the model's imputation performance. Therefore, I apply the
        # SAITS embedding method to project the concatenation of features and masks into a hidden space, as well as
        # the output layers to project back from the hidden space to the original space.
        enc_out = self.saits_embedding(X, missing_mask)

        # Informer encoder processing
        enc_out, attns = self.encoder(enc_out)

        # project back the original data space
        reconstruction = self.output_projection(enc_out)

        imputed_data = missing_mask * X + (1 - missing_mask) * reconstruction
        results = {
            "imputed_data": imputed_data,
        }

        # if in training mode, return results with losses
        if training:
            X_ori, indicating_mask = inputs["X_ori"], inputs["indicating_mask"]
            loss, ORT_loss, MIT_loss = self.saits_loss_func(reconstruction, X_ori, missing_mask, indicating_mask)
            results["ORT_loss"] = ORT_loss
            results["MIT_loss"] = MIT_loss
            # `loss` is always the item for backward propagating to update the model
            results["loss"] = loss

        return results