diff --git a/ginka/validate.py b/ginka/validate.py
index 71eaf59..428b048 100644
--- a/ginka/validate.py
+++ b/ginka/validate.py
@@ -1,3 +1,6 @@
+import os
+import cv2
+import numpy as np
 import torch
 import torch.nn.functional as F
 from torch_geometric.loader import DataLoader
@@ -9,6 +12,55 @@ from .model.model import GinkaModel
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
+def blend_alpha(bg, fg, alpha):
+    """ 使用 alpha 通道混合前景图块和背景图 """
+    for c in range(3):  # 只混合 RGB 三个通道
+        bg[:, :, c] = (1 - alpha) * bg[:, :, c] + alpha * fg[:, :, c]
+    return bg
+
+def matrix_to_image_cv(map_matrix, tile_set, tile_size=32):
+    """
+    使用OpenCV加速的版本（适合大尺寸地图）
+    :param map_matrix: [H, W] 的numpy数组
+    :param tile_set: 字典 {tile_id: cv2图像（BGR格式）}
+    :param tile_size: 图块边长（像素）
+    """
+    H, W = map_matrix.shape  # 获取地图尺寸
+    canvas = np.zeros((H * tile_size, W * tile_size, 3), dtype=np.uint8)  # 画布（黑色背景）
+    
+    # 遍历地图矩阵
+    for row in range(H):
+        for col in range(W):
+            tile_index = str(map_matrix[row, col])  # 获取当前坐标的图块类型
+            x, y = col * tile_size, row * tile_size  # 计算像素位置
+
+            # 先绘制地面（0）
+            if 0 in tile_set:
+                canvas[y:y+tile_size, x:x+tile_size] = tile_set[0][:, :, :3]  # 仅填充 RGB
+
+            if tile_index == '11':
+                if row == 0:
+                    tile_index = '11_2'
+                elif row == W - 1:
+                    tile_index = '11_4'
+                elif col == 0:
+                    tile_index = '11_1'
+                elif col == H - 1:
+                    tile_index = '11_3'
+
+            # 叠加其他透明图块
+            if tile_index in tile_set and tile_index != 0:
+                tile_rgba = tile_set[tile_index]
+                tile_rgb = tile_rgba[:, :, :3]  # 提取 RGB
+                alpha = tile_rgba[:, :, 3] / 255.0  # 归一化 alpha
+
+                # 混合当前图块到背景
+                canvas[y:y+tile_size, x:x+tile_size] = blend_alpha(
+                    canvas[y:y+tile_size, x:x+tile_size], tile_rgb, alpha
+                )
+
+    return canvas
+
 def validate():
     print(f"Using {'cuda' if torch.cuda.is_available() else 'cpu'} to validate model.")
     model = GinkaModel()
@@ -27,9 +79,16 @@ def validate():
     
     criterion = GinkaLoss(minamo)
     
+    tile_dict = dict()
+    
+    for file in os.listdir('tiles'):
+        name = os.path.basename(file)
+        tile_dict[name] = cv2.imread(f"tiles/{file}", cv2.IMREAD_UNCHANGED)
+    
     minamo.eval()
     model.eval()
     val_loss = 0
+    idx = 0
     with torch.no_grad():
          for batch in val_loader:
             # 数据迁移到设备
@@ -41,6 +100,11 @@ def validate():
             output, _ = model(feat_vec)
             map_matrix = torch.argmax(output, dim=1)
             
+            for matrix in map_matrix[:].cpu().numpy():
+                image = matrix_to_image_cv(matrix, tile_dict)
+                cv2.imwrite(f"result/{idx}.png", image)
+                idx += 1
+            
             # 计算损失
             loss = criterion(output, target, target_vision_feat, target_topo_feat)
             total_loss += loss.item()
diff --git a/minamo/train.py b/minamo/train.py
index dbe2775..153ac90 100644
--- a/minamo/train.py
+++ b/minamo/train.py
@@ -122,8 +122,8 @@ def train():
                     graph1 = graph1.to(device)
                     graph2 = graph2.to(device)
                     
-                    vision_feat1, topo_feat1 = model(map1, graph1)
-                    vision_feat2, topo_feat2 = model(map2, graph2)
+                    vision_feat1, topo_feat1 = model(map1_val, graph1)
+                    vision_feat2, topo_feat2 = model(map2_val, graph2)
             
                     vision_pred = F.cosine_similarity(vision_feat1, vision_feat2, -1).unsqueeze(-1)
                     topo_pred = F.cosine_similarity(topo_feat1, topo_feat2, -1).unsqueeze(-1)