From f49705a556b5428bc6577be41d7e7428f249eb21 Mon Sep 17 00:00:00 2001
From: unanmed <1319491857@qq.com>
Date: Thu, 20 Mar 2025 23:14:47 +0800
Subject: [PATCH] =?UTF-8?q?fix:=20GINKA=20=E9=AA=8C=E8=AF=81=E8=84=9A?=
 =?UTF-8?q?=E6=9C=AC?=
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---
 ginka/validate.py | 30 ++++++++++++++++++++----------
 1 file changed, 20 insertions(+), 10 deletions(-)

diff --git a/ginka/validate.py b/ginka/validate.py
index 428b048..60318d8 100644
--- a/ginka/validate.py
+++ b/ginka/validate.py
@@ -1,4 +1,5 @@
 import os
+import json
 import cv2
 import numpy as np
 import torch
@@ -11,6 +12,7 @@ from .model.loss import GinkaLoss
 from .model.model import GinkaModel
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+os.makedirs('result/ginka_img', exist_ok=True)
 
 def blend_alpha(bg, fg, alpha):
     """ 使用 alpha 通道混合前景图块和背景图 """
@@ -35,8 +37,8 @@ def matrix_to_image_cv(map_matrix, tile_set, tile_size=32):
             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 '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:
@@ -64,13 +66,16 @@ def matrix_to_image_cv(map_matrix, tile_set, tile_size=32):
 def validate():
     print(f"Using {'cuda' if torch.cuda.is_available() else 'cpu'} to validate model.")
     model = GinkaModel()
+    state = torch.load("result/ginka_checkpoint/15.pth", map_location=device)["model_state"]
+    model.load_state_dict(state)
+    model.to(device)
     
     minamo = MinamoModel(32)
     minamo.load_state_dict(torch.load("result/minamo.pth", map_location=device)["model_state"])
     minamo.to(device)
 
     # 准备数据集
-    val_dataset = GinkaDataset("ginka-eval.json")
+    val_dataset = GinkaDataset("ginka-eval.json", device, minamo)
     val_loader = DataLoader(
         val_dataset,
         batch_size=32,
@@ -80,9 +85,10 @@ def validate():
     criterion = GinkaLoss(minamo)
     
     tile_dict = dict()
+    val_output = dict()
     
     for file in os.listdir('tiles'):
-        name = os.path.basename(file)
+        name = os.path.splitext(file)[0]
         tile_dict[name] = cv2.imread(f"tiles/{file}", cv2.IMREAD_UNCHANGED)
     
     minamo.eval()
@@ -90,28 +96,32 @@ def validate():
     val_loss = 0
     idx = 0
     with torch.no_grad():
-         for batch in val_loader:
+         for batch in tqdm(val_loader):
             # 数据迁移到设备
             target = batch["target"].to(device)
             target_vision_feat = batch["target_vision_feat"].to(device)
             target_topo_feat = batch["target_topo_feat"].to(device)
             feat_vec = torch.cat([target_vision_feat, target_topo_feat], dim=-1).to(device)
             # 前向传播
-            output, _ = model(feat_vec)
+            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)
+            for matrix in map_matrix[:].cpu():
+                image = matrix_to_image_cv(matrix.numpy(), tile_dict)
+                cv2.imwrite(f"result/ginka_img/{idx}.png", image)
+                val_output[f"val_{idx}"] = matrix.tolist()
                 idx += 1
             
             # 计算损失
             loss = criterion(output, target, target_vision_feat, target_topo_feat)
-            total_loss += loss.item()
+            val_loss += loss.item()
             
     avg_val_loss = val_loss / len(val_loader)
     tqdm.write(f"Validation::loss: {avg_val_loss:.6f}")
     
+    with open('result/ginka_val.json', 'w') as f:
+        json.dump(val_output, f)
+    
 if __name__ == "__main__":
     torch.set_num_threads(2)
     validate()