mirror of
https://github.com/unanmed/ginka-generator.git
synced 2026-05-14 21:01:12 +08:00
133 lines
4.8 KiB
Python
133 lines
4.8 KiB
Python
import os
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import json
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import cv2
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import numpy as np
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import torch
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import torch.nn.functional as F
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from torch_geometric.loader import DataLoader
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from tqdm import tqdm
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from minamo.model.model import MinamoModel
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from .dataset import GinkaDataset
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from .model.loss import GinkaLoss
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from .model.model import GinkaModel
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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os.makedirs('result/ginka_img', exist_ok=True)
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def blend_alpha(bg, fg, alpha):
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""" 使用 alpha 通道混合前景图块和背景图 """
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for c in range(3): # 只混合 RGB 三个通道
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bg[:, :, c] = (1 - alpha) * bg[:, :, c] + alpha * fg[:, :, c]
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return bg
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def matrix_to_image_cv(map_matrix, tile_set, tile_size=32):
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"""
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使用OpenCV加速的版本（适合大尺寸地图）
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:param map_matrix: [H, W] 的numpy数组
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:param tile_set: 字典 {tile_id: cv2图像（BGR格式）}
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:param tile_size: 图块边长（像素）
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"""
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H, W = map_matrix.shape # 获取地图尺寸
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canvas = np.zeros((H * tile_size, W * tile_size, 3), dtype=np.uint8) # 画布（黑色背景）
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# 遍历地图矩阵
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for row in range(H):
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for col in range(W):
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tile_index = str(map_matrix[row, col]) # 获取当前坐标的图块类型
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x, y = col * tile_size, row * tile_size # 计算像素位置
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# 先绘制地面（0）
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if '0' in tile_set:
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canvas[y:y+tile_size, x:x+tile_size] = tile_set['0'][:, :, :3] # 仅填充 RGB
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if tile_index == '11':
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if row == 0:
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tile_index = '11_1'
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elif row == W - 1:
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tile_index = '11_3'
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elif col == 0:
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tile_index = '11_2'
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elif col == H - 1:
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tile_index = '11_4'
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# 叠加其他透明图块
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if tile_index in tile_set and tile_index != 0:
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tile_rgba = tile_set[tile_index]
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tile_rgb = tile_rgba[:, :, :3] # 提取 RGB
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alpha = tile_rgba[:, :, 3] / 255.0 # 归一化 alpha
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# 混合当前图块到背景
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canvas[y:y+tile_size, x:x+tile_size] = blend_alpha(
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canvas[y:y+tile_size, x:x+tile_size], tile_rgb, alpha
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)
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return canvas
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def validate():
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print(f"Using {'cuda' if torch.cuda.is_available() else 'cpu'} to validate model.")
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model = GinkaModel()
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state = torch.load("result/ginka.pth", map_location=device)["model_state"]
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model.load_state_dict(state)
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model.to(device)
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for name, param in model.named_parameters():
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print(f"Layer: {name}, Params: {param.numel()}")
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total_params = sum(p.numel() for p in model.parameters())
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print(f"Total parameters: {total_params}")
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minamo = MinamoModel(32)
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minamo.load_state_dict(torch.load("result/minamo.pth", map_location=device)["model_state"])
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minamo.to(device)
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# 准备数据集
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val_dataset = GinkaDataset("ginka-eval.json", device, minamo)
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val_loader = DataLoader(
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val_dataset,
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batch_size=32,
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shuffle=True
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)
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criterion = GinkaLoss(minamo)
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tile_dict = dict()
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val_output = dict()
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for file in os.listdir('tiles'):
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name = os.path.splitext(file)[0]
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tile_dict[name] = cv2.imread(f"tiles/{file}", cv2.IMREAD_UNCHANGED)
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minamo.eval()
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model.eval()
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val_loss = 0
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idx = 0
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with torch.no_grad():
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for batch in tqdm(val_loader):
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# 数据迁移到设备
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target = batch["target"].to(device)
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target_vision_feat = batch["target_vision_feat"].to(device)
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target_topo_feat = batch["target_topo_feat"].to(device)
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feat_vec = torch.cat([target_vision_feat, target_topo_feat], dim=-1).to(device)
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# 前向传播
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output, output_softmax = model(feat_vec)
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map_matrix = torch.argmax(output, dim=1)
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for matrix in map_matrix[:].cpu():
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image = matrix_to_image_cv(matrix.numpy(), tile_dict)
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cv2.imwrite(f"result/ginka_img/{idx}.png", image)
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val_output[f"val_{idx}"] = matrix.tolist()
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idx += 1
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# 计算损失
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_, loss = criterion(output_softmax, target, target_vision_feat, target_topo_feat)
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val_loss += loss.item()
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avg_val_loss = val_loss / len(val_loader)
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tqdm.write(f"Validation::loss: {avg_val_loss:.6f}")
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with open('result/ginka_val.json', 'w') as f:
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json.dump(val_output, f)
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if __name__ == "__main__":
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torch.set_num_threads(2)
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validate()
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