KoopmanESN/TrajTrans/StateOnly/ESN/DeepKoopmanModel.py

import torch
import torch.nn as nn
from dataclasses import dataclass
import torch.optim as optim
import numpy as np


@dataclass
class DeepKoopManConfig:
    state_dim: int
    latent_dim: int
    hidden_dim: int
    # seq_len: int


class DeepKoopMan(nn.Module):
    def __init__(self, config: DeepKoopManConfig, encoder, decoder):
        super().__init__()
        self.config = config

        state_dim = config.state_dim
        latent_dim = config.latent_dim
        hidden_dim = config.hidden_dim
        # seq_len = config.seq_len

        self.state_dim = state_dim
        self.latent_dim = latent_dim
        # self.seq_len = seq_len

        self.encoder = encoder
        self.decoder = decoder

        self.lambda1 = 0.6

        # for param in self.encoder.parameters():
        #     param.requires_grad = False
        # for param in self.decoder.parameters():
        #     param.requires_grad = False

        # # 编码器 Encoder
        # self.encoder = nn.Sequential(
        #     nn.Linear(state_dim, hidden_dim),
        #     nn.ReLU(),
        #     nn.Linear(hidden_dim, hidden_dim),
        #     nn.ReLU(),
        #     nn.Linear(hidden_dim, latent_dim)
        # )
        #
        # # 解码器 Decoder
        # self.decoder = nn.Sequential(
        #     nn.Linear(latent_dim, hidden_dim),
        #     nn.ReLU(),
        #     nn.Linear(hidden_dim, hidden_dim),
        #     nn.ReLU(),
        #     nn.Linear(hidden_dim, state_dim)
        # )

        # Koopman算子A
        self.A = nn.Parameter(torch.randn(latent_dim, latent_dim))

    def forward(self, state):
        latent = self.encoder(state)
        dlatent_plus_dt = torch.matmul(latent, self.A)
        latent_next_pre = latent + dlatent_plus_dt
        state_next_pre = self.decoder(latent_next_pre)
        state_pre = self.decoder(latent)
        return state_pre, state_next_pre

    def total_loss_func(self, state, state_pre, state_next, state_next_pre):
        mseloss = nn.MSELoss()
        ae_loss = mseloss(state, state_pre)
        pre_loss = mseloss(state_next, state_next_pre)

        total_loss = pre_loss * self.lambda1 + ae_loss
        return total_loss

    def deepkoopman_train(self, batch_size, epochs, lr, data_train, data_val):
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        self.to(device)

        data_train_input = data_train[:, :-1, :]
        data_train_output = data_train[:, 1:, :]
        data_val_input = data_val[:, :-1, :]
        data_val_output = data_val[:, 1:, :]
        data_train_input = data_train_input.to(device)
        data_train_output = data_train_output.to(device)
        data_val_input = data_val_input.to(device)
        data_val_output = data_val_output.to(device)

        train_dataset = torch.utils.data.TensorDataset(data_train_input, data_train_output)
        val_dataset = torch.utils.data.TensorDataset(data_val_input, data_val_output)

        train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
        val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=True)

        op = optim.Adam(self.parameters(), lr=lr)

        TrainLoss = []
        ValLoss = []

        for epoch in range(epochs):
            train_loss = 0
            for X_batch, Y_batch in train_loader:
                X_batch, Y_batch = X_batch.to(device), Y_batch.to(device)

                X_State_batch = X_batch[:, :, :self.state_dim]
                Y_State_batch = Y_batch[:, :, :self.state_dim]

                X_Pre_State_batch, Y_Pre_State_batch = self.forward(X_State_batch)

                loss = self.total_loss_func(X_State_batch, X_Pre_State_batch, Y_State_batch, Y_Pre_State_batch)
                train_loss += loss.item()

                op.zero_grad()
                loss.backward()
                op.step()

            val_loss = 0
            with torch.no_grad():
                for X_batch, Y_batch in val_loader:
                    X_batch, Y_batch = X_batch.to(device), Y_batch.to(device)

                    X_State_batch = X_batch[:, :, :self.state_dim]
                    Y_State_batch = Y_batch[:, :, :self.state_dim]

                    X_Pre_State_batch, Y_Pre_State_batch = self.forward(X_State_batch)

                    loss = self.total_loss_func(X_State_batch, X_Pre_State_batch, Y_State_batch, Y_Pre_State_batch)
                    val_loss += loss.item()

            TrainLoss.append(train_loss / len(train_loader))
            ValLoss.append(val_loss / len(val_loader))

            train_log_loss = np.log10(train_loss / len(train_loader))
            val_log_loss = np.log10(val_loss / len(val_loader))

            print(
                f"LatentDim[{self.latent_dim}],"
                f"LR[{lr}], Epoch [{epoch + 1}/{epochs}],"
                f"Train Loss: {train_log_loss:.2f},"
                f"Validation Loss: {val_log_loss:.2f}")

        return TrainLoss, ValLoss