--- a
+++ b/Retrieval/ATME_retrieval.py
@@ -0,0 +1,514 @@
+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 BrainAligning_retrieval.eegdatasets_leaveone import EEGDataset
+from einops.layers.torch import Rearrange, Reduce
+from lavis.models.clip_models.loss import ClipLoss
+from sklearn.metrics import confusion_matrix
+from torch.utils.data import DataLoader, Dataset
+import random
+from utils import wandb_logger
+import csv
+
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model, max_len=5000):
+        super(PositionalEncoding, self).__init__()
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
+        
+        div_term = torch.exp(torch.arange(0, d_model + 1, 2).float() * (-math.log(10000.0) / d_model))
+        
+        pe[:, 0::2] = torch.sin(position * div_term[:d_model // 2 + 1])
+        pe[:, 1::2] = torch.cos(position * div_term[:d_model // 2])
+
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        pe = self.pe[:x.size(0), :].unsqueeze(1).repeat(1, x.size(1), 1)
+        x = x + pe
+        return x
+
+class EEGAttention(nn.Module):
+    def __init__(self, channel, d_model, nhead):
+        super(EEGAttention, self).__init__()
+        self.pos_encoder = PositionalEncoding(d_model)
+        self.encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead)
+        self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=1)
+        self.channel = channel
+        self.d_model = d_model
+
+    def forward(self, src):
+        src = src.permute(2, 0, 1)  # Change shape to [time_length, batch_size, channel]
+        src = self.pos_encoder(src)
+        output = self.transformer_encoder(src)
+        return output.permute(1, 2, 0)  # Change shape back to [batch_size, channel, time_length]
+
+class PatchEmbedding(nn.Module):
+    def __init__(self, emb_size=40):
+        super().__init__()
+        # revised from shallownet
+        self.shape = (63, 250)
+        self.tsconv = EEGNetv4(
+            in_chans=self.shape[0],
+            n_classes=1440,   
+            input_window_samples=self.shape[1],
+            final_conv_length='auto',
+            pool_mode='mean',
+            F1=8,
+            D=20,
+            F2=160,
+            kernel_length=4,
+            third_kernel_size=(4, 2),
+            drop_prob=0.25
+        )
+
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = x.unsqueeze(3)     
+        # print("x", x.shape)   
+        x = self.tsconv(x)
+        
+        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 ATM_E(nn.Module):    
+    def __init__(self, num_channels=63, sequence_length=250, num_subjects=1, num_features=64, num_latents=1024, num_blocks=1):
+        super(ATM_E, self).__init__()
+        self.attention_model = EEGAttention(num_channels, num_channels, nhead=1)   
+        self.subject_wise_linear = nn.ModuleList([nn.Linear(sequence_length, 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):
+        x = self.attention_model(x)
+        # print(f'After attention shape: {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)
+        # print(f'After enc_eeg shape: {eeg_embedding.shape}')
+        
+        out = self.proj_eeg(eeg_embedding)
+        return out  
+
+
+def train_model(model, dataloader, optimizer, device, text_features_all, img_features_all):
+    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()
+        eeg_features = model(eeg_data).float()
+        features_list.append(eeg_features)
+        logit_scale = model.logit_scale
+        
+        img_loss = model.loss_func(eeg_features, img_features, logit_scale)
+        text_loss = 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()
+        
+        # logits = logit_scale * eeg_features @ text_features_all.T # (n_batch, n_cls)
+        
+        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()
+
+    average_loss = total_loss / (batch_idx+1)
+    accuracy = correct / total
+    return average_loss, accuracy
+
+def evaluate_model(model, dataloader, device, text_features_all, img_features_all, k):
+    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
+    
+    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()
+            eeg_features = model(eeg_data).float()
+            logit_scale = model.logit_scale 
+            # print(eeg_features.type, text_features.type, img_features.type)
+            img_loss = model.loss_func(eeg_features, img_features, logit_scale)
+            text_loss = 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):
+                
+                possible_classes = list(all_labels - {label.item()})
+                selected_classes = random.sample(possible_classes, k-1) + [label.item()]
+                # selected_text_features = text_features_all[selected_classes]
+                selected_img_features = img_features_all[selected_classes]
+                if k==200:
+                    
+                    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)
+                    
+                    # 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
+                    
+                    
+                    
+                    
+                    # print("logits_single", logits_single)
+                    _, top5_indices = torch.topk(logits_single, 5, largest =True)
+                                                           
+                    
+                    if label.item() in [selected_classes[i] for i in top5_indices.tolist()]:     
+                        # print("top5_indices", top5_indices)
+                        # print("Yes")               
+                        top5_correct_count+=1     
+                    # print("*"*50)                               
+                    total += 1
+                    
+                elif k==2 or k==4 or k==10:
+                    
+                    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)
+                    
+                    # 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.")
+                    
+    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, 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(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 = []
+    for epoch in range(config['epochs']):
+        
+        train_loss, train_accuracy = train_model(model, train_dataloader, optimizer, device, text_features_train_all, img_features_train_all)
+        
+        if epoch%5 == 0:                        
+            if config['insubject']==True:                
+                torch.save(model.state_dict(), f"./models/{sub}_{epoch}.pth")
+            else:
+                torch.save(model.state_dict(), f"./models/across_{epoch}.pth")
+        train_losses.append(train_loss)
+        train_accuracies.append(train_accuracy)
+
+        
+        test_loss, test_accuracy, top5_acc = evaluate_model(model, test_dataloader, device, text_features_test_all, img_features_test_all,k=200)
+        _, v2_acc, _ = evaluate_model(model, test_dataloader, device, text_features_test_all, img_features_test_all, k = 2)
+        _, v4_acc, _ = evaluate_model(model, test_dataloader, device, text_features_test_all, img_features_test_all, k = 4)
+        _, v10_acc, _ = evaluate_model(model, test_dataloader, device, text_features_test_all, img_features_test_all, k = 10)
+        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
+        }
+        results.append(epoch_results)
+        
+        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}")
+  
+    
+    # model.load_state_dict(best_model_weights)
+
+    
+    # torch.save(model.state_dict(), '{train_pos_img_text}.pth')
+
+    
+    fig, axs = plt.subplots(3, 2, figsize=(10, 15))
+
+    
+    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")
+
+    
+    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")
+
+    
+    
+    axs[1, 0].plot(v2_accs, label='2-class Accuracy')
+    axs[1, 0].legend()
+    axs[1, 0].set_title("2-Class Accuracy Curve")
+
+    
+    axs[1, 1].plot(v4_accs, label='4-class Accuracy')
+    axs[1, 1].legend()
+    axs[1, 1].set_title("4-Class Accuracy Curve")
+
+    
+    axs[2, 0].plot(v10_accs, label='10-class Accuracy')
+    axs[2, 0].legend()
+    axs[2, 0].set_title("10-Class Accuracy Curve")
+
+    
+    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()
+
+    
+    plt.suptitle('pos_img_text', fontsize=16, y=1.05)
+    plt.savefig('pos_img_text')
+    logger.finish()
+    return results
+
+def main():
+    parser = argparse.ArgumentParser(description='Train EEG-Image/Text Model')
+
+    parser.add_argument('--data_path', type=str, default="/home/ldy/Workspace/THINGS/Preprocessed_data_250Hz", help='Path to the preprocessed data')
+    parser.add_argument('--project', type=str, default="train_pos_img_text_rep", help='Project name')
+    parser.add_argument('--entity', type=str, default="sustech_rethinkingbci", help='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 training epochs')
+    parser.add_argument('--batch_size', type=int, default=1024, help='Batch size')
+    parser.add_argument('--logger', action='store_true', help='Enable logging')
+    parser.add_argument('--insubject', action='store_true', help='Train within subject')
+    parser.add_argument('--encoder_type', type=str, default='ATM_E', help='EEG encoder model type')
+    parser.add_argument('--device', type=str, default='cuda:0', help='Device to use for training (e.g., "cuda:0" or "cpu")')
+
+    args = parser.parse_args()
+
+    device = torch.device(args.device if torch.cuda.is_available() else 'cpu')
+    data_path = args.data_path
+    subjects = ['sub-01', 'sub-02', 'sub-03', 'sub-04', 'sub-05', 'sub-06', 'sub-07', 'sub-08', 'sub-09', 'sub-10']
+
+    for sub in subjects:
+        # Re-initialize the model for each subject
+        model = globals()[args.encoder_type]((63, 250))
+        model.to(device)
+        optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr)
+
+        print(f'Processing {sub}: number of parameters:', sum(p.numel() for p in model.parameters()))
+
+        train_dataset = EEGDataset(
+            data_path,
+            subjects=[sub] if args.insubject else [],
+            exclude_subject=sub if not args.insubject else None,
+            train=True
+        )
+        test_dataset = EEGDataset(
+            data_path,
+            subjects=[sub] if args.insubject else [],
+            exclude_subject=sub if not args.insubject else None,
+            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
+
+        config = vars(args)
+
+        results = main_train_loop(
+            sub,
+            model,
+            train_loader,
+            test_loader,
+            optimizer,
+            device,
+            text_features_train_all,
+            text_features_test_all,
+            img_features_train_all,
+            img_features_test_all,
+            config,
+            logger=args.logger
+        )
+
+        # Save results to a CSV file
+        current_time = datetime.datetime.now().strftime("%m-%d_%H-%M")
+        results_dir = f"./outputs/{args.encoder_type}/{sub}/{current_time}"
+        os.makedirs(results_dir, exist_ok=True)
+        results_file = f"{results_dir}/{args.encoder_type}_{'cross_exclude_' if not args.insubject else ''}{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()
+    
+