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- Add golf1/2/4/7/8 dataset classes for semantic segmentation - Add kneron-specific configs (meconfig series, kn_stdc1_golf4class) - Organize scripts into tools/check/ and tools/kneron/ - Add kneron_preprocessing module - Update README with quick-start guide - Update .gitignore to exclude data dirs, onnx, nef outputs Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
34 lines
1.2 KiB
Python
34 lines
1.2 KiB
Python
import torch
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def check_pth_num_classes(pth_path):
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checkpoint = torch.load(pth_path, map_location='cpu')
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if 'state_dict' not in checkpoint:
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print("❌ 找不到 state_dict，這可能不是 MMSegmentation 的模型檔")
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return
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state_dict = checkpoint['state_dict']
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# 找出 decode head 最後一層分類器的 weight tensor
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num_classes = None
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for k in state_dict.keys():
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if 'decode_head' in k and 'weight' in k and 'decode_head.classifier' in k:
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weight_tensor = state_dict[k]
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num_classes = weight_tensor.shape[0]
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print(f"✅ 檢查到類別數: {num_classes}")
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break
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if num_classes is None:
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print("⚠️ 無法判斷類別數，可能模型架構非標準格式")
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else:
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if num_classes == 19:
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print("⚠️ 這是 Cityscapes 預設模型 (19 類)")
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elif num_classes == 4:
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print("✅ 這是 GolfDataset 自訂模型 (4 類)")
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else:
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print("❓ 類別數異常，請確認訓練資料與 config 設定是否一致")
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if __name__ == '__main__':
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pth_path = r'C:\Users\rd_de\kneronstdc\work_dirs\meconfig\latest.pth'
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check_pth_num_classes(pth_path)
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