WenjieDu/PyPOTS

"""

"""

# Created by Linglong Qian, Joseph Arul Raj <linglong.qian@kcl.ac.uk, joseph_arul_raj@kcl.ac.uk>
# License: BSD-3-Clause

import torch
import torch.nn as nn
import torch.nn.functional as F

from ...nn.modules.csai import BackboneBCSAI

# class DiceBCELoss(nn.Module):
#     def __init__(self, weight=None, size_average=True):
#         super(DiceBCELoss, self).__init__()
#         self.bcelogits = nn.BCEWithLogitsLoss()

#     def forward(self, y_score, y_out, targets, smooth=1):

#         #comment out if your model contains a sigmoid or equivalent activation layer
#         # inputs = F.sigmoid(inputs)

#         #flatten label and prediction tensors
#         BCE = self.bcelogits(y_out, targets)

#         y_score = y_score.view(-1)
#         targets = targets.view(-1)
#         intersection = (y_score * targets).sum()
#         dice_loss = 1 - (2.*intersection + smooth)/(y_score.sum() + targets.sum() + smooth)

#         Dice_BCE = BCE + dice_loss

#         return BCE, Dice_BCE


class _BCSAI(nn.Module):
    def __init__(
        self,
        n_steps: int,
        n_features: int,
        rnn_hidden_size: int,
        imputation_weight: float,
        consistency_weight: float,
        classification_weight: float,
        n_classes: int,
        step_channels: int,
        dropout: float = 0.5,
        intervals=None,
    ):
        super().__init__()
        self.n_steps = n_steps
        self.n_features = n_features
        self.rnn_hidden_size = rnn_hidden_size
        self.imputation_weight = imputation_weight
        self.consistency_weight = consistency_weight
        self.classification_weight = classification_weight
        self.n_classes = n_classes
        self.step_channels = step_channels
        self.intervals = intervals

        # create models
        self.model = BackboneBCSAI(n_steps, n_features, rnn_hidden_size, step_channels, intervals)
        self.f_classifier = nn.Linear(self.rnn_hidden_size, n_classes)
        self.b_classifier = nn.Linear(self.rnn_hidden_size, n_classes)
        self.imputer = nn.Linear(self.rnn_hidden_size, n_features)
        self.dropout = nn.Dropout(dropout)

    def forward(self, inputs: dict, training: bool = True) -> dict:

        (
            imputed_data,
            f_reconstruction,
            b_reconstruction,
            f_hidden_states,
            b_hidden_states,
            consistency_loss,
            reconstruction_loss,
        ) = self.model(inputs)

        results = {
            "imputed_data": imputed_data,
        }

        f_logits = self.f_classifier(self.dropout(f_hidden_states))
        b_logits = self.b_classifier(self.dropout(b_hidden_states))

        # f_prediction = torch.sigmoid(f_logits)
        # b_prediction = torch.sigmoid(b_logits)

        f_prediction = torch.softmax(f_logits, dim=1)
        b_prediction = torch.softmax(b_logits, dim=1)
        classification_pred = (f_prediction + b_prediction) / 2

        results = {
            "imputed_data": imputed_data,
            "classification_pred": classification_pred,
        }

        # if in training mode, return results with losses
        if training:
            # criterion = DiceBCELoss().to(imputed_data.device)
            results["consistency_loss"] = consistency_loss
            results["reconstruction_loss"] = reconstruction_loss
            # print(inputs["labels"].unsqueeze(1))
            f_classification_loss = F.nll_loss(torch.log(f_prediction), inputs["labels"])
            b_classification_loss = F.nll_loss(torch.log(b_prediction), inputs["labels"])
            # f_classification_loss, _ = criterion(f_prediction, f_logits, inputs["labels"].unsqueeze(1).float())
            # b_classification_loss, _ = criterion(b_prediction, b_logits, inputs["labels"].unsqueeze(1).float())
            classification_loss = f_classification_loss + b_classification_loss

            loss = (
                self.consistency_weight * consistency_loss
                + self.imputation_weight * reconstruction_loss
                + self.classification_weight * classification_loss
            )

            results["loss"] = loss
            results["classification_loss"] = classification_loss
            results["f_reconstruction"] = f_reconstruction
            results["b_reconstruction"] = b_reconstruction

        return results