--- a
+++ b/Retrieval/ATMS_retrieval.py
@@ -0,0 +1,585 @@
+import os
+
+import torch
+import torch.optim as optim
+from torch.nn import CrossEntropyLoss
+from torch.nn import functional as F
+from torch.optim import Adam
+from torch.utils.data import DataLoader
+
+os.environ["WANDB_API_KEY"] = "KEY"
+os.environ["WANDB_MODE"] = 'offline'
+from itertools import combinations
+
+import clip
+import matplotlib.pyplot as plt
+import numpy as np
+import torch.nn as nn
+import torchvision.transforms as transforms
+import tqdm
+from eegdatasets_leaveone import EEGDataset
+
+from einops.layers.torch import Rearrange, Reduce
+
+from sklearn.metrics import confusion_matrix
+from torch.utils.data import DataLoader, Dataset
+import random
+from util import wandb_logger
+from braindecode.models import EEGNetv4, ATCNet, EEGConformer, EEGITNet, ShallowFBCSPNet
+import csv
+from torch import Tensor
+import itertools
+import math
+import re
+from subject_layers.Transformer_EncDec import Encoder, EncoderLayer
+from subject_layers.SelfAttention_Family import FullAttention, AttentionLayer
+from subject_layers.Embed import DataEmbedding
+import numpy as np
+from loss import ClipLoss
+import argparse
+from torch import nn
+from torch.optim import AdamW
+
+class Config:
+    def __init__(self):
+        self.task_name = 'classification'  # Example task name
+        self.seq_len = 250                 # Sequence length
+        self.pred_len = 250                # Prediction length
+        self.output_attention = False      # Whether to output attention weights
+        self.d_model = 250                 # Model dimension
+        self.embed = 'timeF'               # Time encoding method
+        self.freq = 'h'                    # Time frequency
+        self.dropout = 0.25                # Dropout rate
+        self.factor = 1                    # Attention scaling factor
+        self.n_heads = 4                   # Number of attention heads
+        self.e_layers = 1                  # Number of encoder layers
+        self.d_ff = 256                    # Feedforward network dimension
+        self.activation = 'gelu'           # Activation function
+        self.enc_in = 63                   # Encoder input dimension (example value)
+        
+class iTransformer(nn.Module):
+    def __init__(self, configs, joint_train=False,  num_subjects=10):
+        super(iTransformer, self).__init__()
+        self.task_name = configs.task_name
+        self.seq_len = configs.seq_len
+        self.pred_len = configs.pred_len
+        self.output_attention = configs.output_attention
+        # Embedding
+        self.enc_embedding = DataEmbedding(configs.seq_len, configs.d_model, configs.embed, configs.freq, configs.dropout, joint_train=False, num_subjects=num_subjects)
+        # Encoder
+        self.encoder = Encoder(
+            [
+                EncoderLayer(
+                    AttentionLayer(
+                        FullAttention(False, configs.factor, attention_dropout=configs.dropout, output_attention=configs.output_attention),
+                        configs.d_model, configs.n_heads
+                    ),
+                    configs.d_model,
+                    configs.d_ff,
+                    dropout=configs.dropout,
+                    activation=configs.activation
+                ) for l in range(configs.e_layers)
+            ],
+            norm_layer=torch.nn.LayerNorm(configs.d_model)
+        )
+
+    def forward(self, x_enc, x_mark_enc, subject_ids=None):
+        # Embedding
+        enc_out = self.enc_embedding(x_enc, x_mark_enc, subject_ids)
+        enc_out, attns = self.encoder(enc_out, attn_mask=None)
+        enc_out = enc_out[:, :63, :]      
+        # print("enc_out", enc_out.shape)
+        return enc_out
+
+
+
+class PatchEmbedding(nn.Module):
+    def __init__(self, emb_size=40):
+        super().__init__()
+        # Revised from ShallowNet
+        self.tsconv = nn.Sequential(
+            nn.Conv2d(1, 40, (1, 25), stride=(1, 1)),
+            nn.AvgPool2d((1, 51), (1, 5)),
+            nn.BatchNorm2d(40),
+            nn.ELU(),
+            nn.Conv2d(40, 40, (63, 1), stride=(1, 1)),
+            nn.BatchNorm2d(40),
+            nn.ELU(),
+            nn.Dropout(0.5),
+        )
+
+        self.projection = nn.Sequential(
+            nn.Conv2d(40, emb_size, (1, 1), stride=(1, 1)),  
+            Rearrange('b e (h) (w) -> b (h w) e'),
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        # b, _, _, _ = x.shape
+        x = x.unsqueeze(1)     
+        # print("x", x.shape)   
+        x = self.tsconv(x)
+        # print("tsconv", x.shape)   
+        x = self.projection(x)
+        # print("projection", x.shape)  
+        return x
+
+
+class ResidualAdd(nn.Module):
+    def __init__(self, fn):
+        super().__init__()
+        self.fn = fn
+
+    def forward(self, x, **kwargs):
+        res = x
+        x = self.fn(x, **kwargs)
+        x += res
+        return x
+
+
+class FlattenHead(nn.Sequential):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        x = x.contiguous().view(x.size(0), -1)
+        return x
+
+
+class Enc_eeg(nn.Sequential):
+    def __init__(self, emb_size=40, **kwargs):
+        super().__init__(
+            PatchEmbedding(emb_size),
+            FlattenHead()
+        )
+
+        
+class Proj_eeg(nn.Sequential):
+    def __init__(self, embedding_dim=1440, proj_dim=1024, drop_proj=0.5):
+        super().__init__(
+            nn.Linear(embedding_dim, proj_dim),
+            ResidualAdd(nn.Sequential(
+                nn.GELU(),
+                nn.Linear(proj_dim, proj_dim),
+                nn.Dropout(drop_proj),
+            )),
+            nn.LayerNorm(proj_dim),
+        )
+
+
+
+class ATMS(nn.Module):    
+    def __init__(self, num_channels=63, sequence_length=250, num_subjects=2, num_features=64, num_latents=1024, num_blocks=1):
+        super(ATMS, self).__init__()
+        default_config = Config()
+        self.encoder = iTransformer(default_config)   
+        self.subject_wise_linear = nn.ModuleList([nn.Linear(default_config.d_model, sequence_length) for _ in range(num_subjects)])
+        self.enc_eeg = Enc_eeg()
+        self.proj_eeg = Proj_eeg()        
+        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
+        self.loss_func = ClipLoss()       
+         
+    def forward(self, x, subject_ids):
+        x = self.encoder(x, None, subject_ids)
+        # print(f'After attention shape: {x.shape}')
+        # print("x", x.shape)
+        # x = self.subject_wise_linear[0](x)
+        # print(f'After subject-specific linear transformation shape: {x.shape}')
+        eeg_embedding = self.enc_eeg(x)
+        
+        out = self.proj_eeg(eeg_embedding)
+        return out  
+    
+def extract_id_from_string(s):
+    match = re.search(r'\d+$', s)
+    if match:
+        return int(match.group())
+    return None
+
+def train_model(sub, eeg_model, dataloader, optimizer, device, text_features_all, img_features_all, config):
+    eeg_model.train()
+    text_features_all = text_features_all.to(device).float() # (n_cls, d)
+    img_features_all = (img_features_all[::10]).to(device).float()
+    total_loss = 0
+    correct = 0
+    total = 0
+    alpha=0.99
+    features_list = []  # List to store features
+    save_features= True
+    for batch_idx, (eeg_data, labels, text, text_features, img, img_features) in enumerate(dataloader):
+        eeg_data = eeg_data.to(device)
+        text_features = text_features.to(device).float()
+        img_features = img_features.to(device).float()
+        labels = labels.to(device)
+        
+        optimizer.zero_grad()
+        
+        batch_size = eeg_data.size(0)  
+        subject_id = extract_id_from_string(sub)
+        # eeg_data = eeg_data.permute(0, 2, 1)
+        subject_ids = torch.full((batch_size,), subject_id, dtype=torch.long).to(device)
+        # if not config.insubject:
+        #     subject_ids = torch.full((batch_size,), -1, dtype=torch.long).to(device)     
+        eeg_features = eeg_model(eeg_data, subject_ids).float()
+
+        
+        features_list.append(eeg_features)
+        logit_scale = eeg_model.logit_scale
+        
+        img_loss = eeg_model.loss_func(eeg_features, img_features, logit_scale)
+        text_loss = eeg_model.loss_func(eeg_features, text_features, logit_scale)
+        # loss = img_loss + text_loss
+        # print("text_loss", text_loss)
+        # print("img_loss", img_loss)
+        loss = alpha * img_loss + (1 - alpha) * text_loss
+        loss.backward()
+
+        optimizer.step()
+        total_loss += loss.item()
+        
+        # Compute the corresponding logits
+        logits_img = logit_scale * eeg_features @ img_features_all.T
+        # logits_text = logit_scale * eeg_features @ text_features_all.T
+        # logits_single = (logits_text + logits_img) / 2.0        
+        # logits_text = logit_scale * eeg_features @ text_features_all.T
+        logits_single = logits_img
+        predicted = torch.argmax(logits_single, dim=1) # (n_batch, ) in {0, 1, ..., n_cls-1}
+
+        batch_size = predicted.shape[0]
+        total += batch_size
+        correct += (predicted == labels).sum().item()
+        del eeg_data, eeg_features, img_features
+    average_loss = total_loss / (batch_idx+1)
+    accuracy = correct / total
+    return average_loss, accuracy, torch.cat(features_list, dim=0)
+
+
+
+def evaluate_model(sub, eeg_model, dataloader, device, text_features_all, img_features_all, k, config):
+    eeg_model.eval()
+
+    
+    text_features_all = text_features_all.to(device).float()
+    img_features_all = img_features_all.to(device).float()
+    total_loss = 0
+    correct = 0
+    total = 0
+    alpha = 0.99
+    top5_correct = 0
+    top5_correct_count = 0
+    # Get all unique classes
+    all_labels = set(range(text_features_all.size(0)))
+    top5_acc = 0
+    with torch.no_grad():
+        for batch_idx, (eeg_data, labels, text, text_features, img, img_features) in enumerate(dataloader):
+            eeg_data = eeg_data.to(device)
+            text_features = text_features.to(device).float()
+            labels = labels.to(device)
+            img_features = img_features.to(device).float()
+            
+            batch_size = eeg_data.size(0) 
+            subject_id = extract_id_from_string(sub)
+            # eeg_data = eeg_data.permute(0, 2, 1)
+            subject_ids = torch.full((batch_size,), subject_id, dtype=torch.long).to(device)
+            # if not config.insubject:
+            #     subject_ids = torch.full((batch_size,), -1, dtype=torch.long).to(device)          
+            eeg_features = eeg_model(eeg_data, subject_ids)
+
+        
+            logit_scale = eeg_model.logit_scale 
+            # print(eeg_features.type, text_features.type, img_features.type)
+            img_loss = eeg_model.loss_func(eeg_features, img_features, logit_scale)
+            text_loss = eeg_model.loss_func(eeg_features, text_features, logit_scale)
+            loss = img_loss*alpha + text_loss*(1-alpha)
+            
+            total_loss += loss.item()
+            
+            for idx, label in enumerate(labels):
+                # First, select k-1 classes excluding the correct class
+                possible_classes = list(all_labels - {label.item()})
+                selected_classes = random.sample(possible_classes, k-1) + [label.item()]
+                selected_img_features = img_features_all[selected_classes]
+                selected_text_features = text_features_all[selected_classes]
+                
+                if k==200:
+                    # Compute the corresponding logits
+                    logits_img = logit_scale * eeg_features[idx] @ selected_img_features.T
+                    logits_single = logits_img
+                    # print("logits_single", logits_single.shape)
+                    # Get the predicted class
+                    # predicted_label = selected_classes[torch.argmax(logits_single).item()]
+                    predicted_label = selected_classes[torch.argmax(logits_single).item()] # (n_batch, ) in {0, 1, ..., n_cls-1}
+                    if predicted_label == label.item():
+                        # print("predicted_label", predicted_label)
+                        correct += 1
+                    
+                    # logits_single is the model's output, shape (n_batch, n_classes)
+                    # label is the true label, shape (n_batch,)
+                    # Get the indices of the top-5 predictions
+                    # print("logits_single", logits_single)
+                    _, top5_indices = torch.topk(logits_single, 5, largest =True)
+                                                   
+                    # Check if the true label is in the top-5 predictions
+                    if label.item() in [selected_classes[i] for i in top5_indices.tolist()]:                
+                        top5_correct_count+=1                                
+                    total += 1
+                elif k == 50 or k == 100:
+                    # For k=50 or 100, select k classes for evaluation
+                    selected_classes = random.sample(possible_classes, k-1) + [label.item()]
+
+                    logits_img = logit_scale * eeg_features[idx] @ selected_img_features.T
+                    logits_single = logits_img
+                    
+                    predicted_label = selected_classes[torch.argmax(logits_single).item()]
+                    if predicted_label == label.item():
+                        correct += 1
+                    _, top5_indices = torch.topk(logits_single, 5, largest =True)
+                                                   
+                    # Check if the true label is in the top-5 predictions
+                    if label.item() in [selected_classes[i] for i in top5_indices.tolist()]:                
+                        top5_correct_count+=1                                
+                    total += 1
+                elif k==2 or k==4 or k==10:
+                    selected_classes = random.sample(possible_classes, k-1) + [label.item()]
+                    # Compute the corresponding logits
+                    logits_img = logit_scale * eeg_features[idx] @ selected_img_features.T
+                    # logits_text = logit_scale * eeg_features[idx] @ selected_text_features.T
+                    # logits_single = (logits_text + logits_img) / 2.0
+                    logits_single = logits_img
+                    # print("logits_single", logits_single.shape)
+                    # Get the predicted class
+                    # predicted_label = selected_classes[torch.argmax(logits_single).item()]
+                    predicted_label = selected_classes[torch.argmax(logits_single).item()] # (n_batch, ) in {0, 1, ..., n_cls-1}
+                    if predicted_label == label.item():
+                        correct += 1
+                    total += 1
+                else:
+                    print("Error.")
+            del eeg_data, eeg_features, img_features
+    average_loss = total_loss / (batch_idx+1)
+    accuracy = correct / total
+    top5_acc = top5_correct_count / total
+    return average_loss, accuracy, top5_acc
+
+def main_train_loop(sub, current_time, eeg_model, train_dataloader, test_dataloader, optimizer, device, text_features_train_all, text_features_test_all, img_features_train_all, img_features_test_all, config, logger=None):
+    logger = wandb_logger(config) if logger else None
+    logger.watch(eeg_model,logger) 
+    train_losses, train_accuracies = [], []
+    test_losses, test_accuracies = [], []
+    v2_accs = []
+    v4_accs = []
+    v10_accs = []
+
+    best_accuracy = 0.0
+    best_model_weights = None
+    best_epoch_info = {}
+    results = []  # List to store results for each epoch
+    
+    for epoch in range(config.epochs):
+        # Train the model
+        train_loss, train_accuracy, features_tensor = train_model(sub, eeg_model, train_dataloader, optimizer, device, text_features_train_all, img_features_train_all, config=config)
+        if (epoch +1) % 5 == 0:                    
+            # Get the current time and format it as a string (e.g., '2024-01-17_15-30-00')                  
+            if config.insubject==True:       
+                os.makedirs(f"./models/contrast/{config.encoder_type}/{sub}/{current_time}", exist_ok=True)             
+                file_path = f"./models/contrast/{config.encoder_type}/{sub}/{current_time}/{epoch+1}.pth"
+                torch.save(eeg_model.state_dict(), file_path)            
+            else:                
+                os.makedirs(f"./models/contrast/across/{config.encoder_type}/{current_time}", exist_ok=True)             
+                file_path = f"./models/contrast/across/{config.encoder_type}/{current_time}/{epoch+1}.pth"
+                torch.save(eeg_model.state_dict(), file_path)
+            print(f"model saved in {file_path}!")
+        train_losses.append(train_loss)
+        train_accuracies.append(train_accuracy)
+
+
+        # Evaluate the model
+        test_loss, test_accuracy, top5_acc = evaluate_model(sub, eeg_model, test_dataloader, device, text_features_test_all, img_features_test_all,k=200, config=config)
+        _, v2_acc, _ = evaluate_model(sub, eeg_model, test_dataloader, device, text_features_test_all, img_features_test_all, k = 2, config=config)
+        _, v4_acc, _ = evaluate_model(sub, eeg_model, test_dataloader, device, text_features_test_all, img_features_test_all, k = 4, config=config)
+        _, v10_acc, _ = evaluate_model(sub, eeg_model, test_dataloader, device, text_features_test_all, img_features_test_all, k = 10, config=config)
+        _, v50_acc, v50_top5_acc = evaluate_model(sub, eeg_model, test_dataloader, device, text_features_test_all, img_features_test_all,  k=50, config=config)
+        _, v100_acc, v100_top5_acc = evaluate_model(sub, eeg_model, test_dataloader, device, text_features_test_all, img_features_test_all,  k=100, config=config)
+        test_losses.append(test_loss)
+        test_accuracies.append(test_accuracy)
+        v2_accs.append(v2_acc)
+        v4_accs.append(v4_acc)
+        v10_accs.append(v10_acc)
+        
+        # Append results for this epoch
+        epoch_results = {
+        "epoch": epoch + 1,
+        # "train_loss": train_loss,
+        # "train_accuracy": train_accuracy,
+        "test_loss": test_loss,
+        "test_accuracy": test_accuracy,
+        "v2_acc": v2_acc,
+        "v4_acc": v4_acc,
+        "v10_acc": v10_acc,
+        "top5_acc":top5_acc,
+        "v50_acc": v50_acc,
+        "v100_acc": v100_acc,
+        "v50_top5_acc":v50_top5_acc,
+        "v100_top5_acc": v100_top5_acc
+        }
+
+        results.append(epoch_results)
+        # If the test accuracy of the current epoch is the best, save the model and related information
+        if test_accuracy > best_accuracy:
+            best_accuracy = test_accuracy
+            # best_model_weights = model.state_dict().copy()
+            
+            best_epoch_info = {
+                "epoch": epoch + 1,
+                "train_loss": train_loss,
+                "train_accuracy": train_accuracy,
+                "test_loss": test_loss,
+                "test_accuracy": test_accuracy,
+                "v2_acc":v2_acc,
+                "v4_acc":v4_acc,
+                "v10_acc":v10_acc
+            }
+        logger.log({
+            "Train Loss": train_loss,
+            "Train Accuracy": train_accuracy,
+            "Test Loss": test_loss,
+            "Test Accuracy": test_accuracy,
+            "v2 Accuracy": v2_acc,
+            "v4 Accuracy": v4_acc,
+            "v10 Accuracy": v10_acc,
+            "Epoch": epoch
+        })
+
+        print(f"Epoch {epoch + 1}/{config.epochs} - Train Loss: {train_loss:.4f}, Train Accuracy: {train_accuracy:.4f}, Test Loss: {test_loss:.4f}, Test Accuracy: {test_accuracy:.4f}, Top5 Accuracy: {top5_acc:.4f}")
+        print(f"Epoch {epoch + 1}/{config.epochs} - v2 Accuracy:{v2_acc} - v4 Accuracy:{v4_acc} - v10 Accuracy:{v10_acc} - v50 Accuracy:{v50_acc} - v100 Accuracy:{v100_acc}")
+  
+    # # Load the best model weights
+    # model.load_state_dict(best_model_weights)
+
+    # # # Save the best model
+    # torch.save(model.state_dict(), '{train_pos_img_text}.pth')
+
+    # Create 5 subplots
+    fig, axs = plt.subplots(3, 2, figsize=(10, 15))
+
+    # Loss curve
+    axs[0, 0].plot(train_losses, label='Train Loss')
+    axs[0, 0].plot(test_losses, label='Test Loss')
+    axs[0, 0].legend()
+    axs[0, 0].set_title("Loss Curve")
+
+    # Overall accuracy curve
+    axs[0, 1].plot(train_accuracies, label='Train Accuracy')
+    axs[0, 1].plot(test_accuracies, label='Test Accuracy')
+    axs[0, 1].legend()
+    axs[0, 1].set_title("Accuracy Curve")
+
+    # The following are the three new plots you added, assuming you've already calculated the corresponding accuracies
+    # 2-class accuracy plot
+    axs[1, 0].plot(v2_accs, label='2-class Accuracy')
+    axs[1, 0].legend()
+    axs[1, 0].set_title("2-Class Accuracy Curve")
+
+    # 4-class accuracy plot
+    axs[1, 1].plot(v4_accs, label='4-class Accuracy')
+    axs[1, 1].legend()
+    axs[1, 1].set_title("4-Class Accuracy Curve")
+
+    # 10-class accuracy plot
+    axs[2, 0].plot(v10_accs, label='10-class Accuracy')
+    axs[2, 0].legend()
+    axs[2, 0].set_title("10-Class Accuracy Curve")
+
+    # Construct the string information for annotation
+    info_text = (f"Best Model Info (from Epoch {best_epoch_info['epoch']}):\n"
+                f"Train Loss: {best_epoch_info['train_loss']:.4f}\n"
+                f"Train Accuracy: {best_epoch_info['train_accuracy']:.4f}\n"
+                f"Test Loss: {best_epoch_info['test_loss']:.4f}\n"
+                f"Test Accuracy: {best_epoch_info['test_accuracy']:.4f}\n"
+                f"v2_acc:{best_epoch_info['v2_acc']:.4f}\n"
+                f"v4_acc:{best_epoch_info['v4_acc']:.4f}\n"
+                f"v10_acc:{best_epoch_info['v10_acc']:.4f}")
+
+    axs[2, 1].axis('off')  
+    axs[2, 1].text(0.5, 0.5, info_text, fontsize=10, ha='center', va='center', transform=axs[2, 1].transAxes)
+
+    plt.tight_layout()
+
+    # Add main title
+    plt.suptitle('pos_img_text', fontsize=16, y=1.05)
+    plt.savefig('pos_img_text')
+    logger.finish()
+    return results
+
+import datetime
+
+def main():
+    # Use argparse to parse the command-line arguments
+    parser = argparse.ArgumentParser(description='EEG Transformer Training Script')
+    parser.add_argument('--data_path', type=str, default="/root/autodl-tmp/THINGS/Preprocessed_data_250Hz", help='Path to the EEG dataset')
+    parser.add_argument('--output_dir', type=str, default='./outputs/contrast', help='Directory to save output results')    
+    parser.add_argument('--project', type=str, default="train_pos_img_text_rep", help='WandB project name')
+    parser.add_argument('--entity', type=str, default="sustech_rethinkingbci", help='WandB entity name')
+    parser.add_argument('--name', type=str, default="lr=3e-4_img_pos_pro_eeg", help='Experiment name')
+    parser.add_argument('--lr', type=float, default=3e-4, help='Learning rate')
+    parser.add_argument('--epochs', type=int, default=40, help='Number of epochs')
+    parser.add_argument('--batch_size', type=int, default=64, help='Batch size')
+    parser.add_argument('--logger', type=bool, default=True, help='Enable WandB logging')
+    parser.add_argument('--gpu', type=str, default='cuda:0', help='GPU device to use')
+    parser.add_argument('--device', type=str, choices=['cpu', 'gpu'], default='gpu', help='Device to run on (cpu or gpu)')    
+    parser.add_argument('--insubject', type=bool, default=True, help='In-subject mode or cross-subject mode')
+    parser.add_argument('--encoder_type', type=str, default='ATMS', help='Encoder type')
+    parser.add_argument('--subjects', nargs='+', default=['sub-01', 'sub-02', 'sub-03', 'sub-04', 'sub-05', 'sub-06', 'sub-07', 'sub-08', 'sub-09', 'sub-10'], help='List of subject IDs (default: sub-01 to sub-10)')    
+    args = parser.parse_args()
+
+    # Set device based on the argument
+    if args.device == 'gpu' and torch.cuda.is_available():
+        device = torch.device(args.gpu)
+    else:
+        device = torch.device('cpu')
+
+    subjects = args.subjects        
+    current_time = datetime.datetime.now().strftime("%m-%d_%H-%M")
+
+    for sub in subjects:
+        eeg_model = globals()[args.encoder_type]()
+        eeg_model.to(device)
+
+        optimizer = AdamW(itertools.chain(eeg_model.parameters()), lr=args.lr)
+
+        if args.insubject:
+            train_dataset = EEGDataset(args.data_path, subjects=[sub], train=True)
+            test_dataset = EEGDataset(args.data_path, subjects=[sub], train=False)
+        else:
+            train_dataset = EEGDataset(args.data_path, exclude_subject=sub, subjects=subjects, train=True)
+            test_dataset = EEGDataset(args.data_path, exclude_subject=sub, subjects=subjects, train=False)
+
+        train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=0, drop_last=True)
+        test_loader = DataLoader(test_dataset, batch_size=1, shuffle=True, num_workers=0, drop_last=True)
+
+        text_features_train_all = train_dataset.text_features
+        text_features_test_all = test_dataset.text_features
+        img_features_train_all = train_dataset.img_features
+        img_features_test_all = test_dataset.img_features
+
+        results = main_train_loop(sub, current_time, eeg_model, train_loader, test_loader, optimizer, device, 
+                                  text_features_train_all, text_features_test_all, img_features_train_all, img_features_test_all, config=args, logger=args.logger)
+
+
+        # Save results to a CSV file
+        results_dir = os.path.join(args.output_dir, args.encoder_type, sub, current_time)
+        os.makedirs(results_dir, exist_ok=True)
+
+        if args.insubject:
+            results_file = f"{results_dir}/{args.encoder_type}_{sub}.csv"
+        else:
+            results_file = f"{results_dir}/{args.encoder_type}_cross_exclude_{sub}.csv"
+
+        with open(results_file, 'w', newline='') as file:
+            writer = csv.DictWriter(file, fieldnames=results[0].keys())
+            writer.writeheader()
+            writer.writerows(results)
+            print(f'Results saved to {results_file}')
+
+                
+if __name__ == '__main__':
+    main()