30 lines
769 B
Python
30 lines
769 B
Python
import torch
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from model import ChessPredictModelS
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from fen2vec import parse_fen
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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def run(model_path='best_model.pth'):
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model = ChessPredictModelS().half().to(device)
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model.load_state_dict(torch.load(model_path, weights_only=True))
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model.eval()
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while True:
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fen_str = input("FEN(q) >")
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if fen_str.lower() == 'q':
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break
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try:
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input_tensor, _wdl = parse_fen(fen_str, 0)
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input_tensor = input_tensor.unsqueeze(0).to(device) # add batch dim
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with torch.no_grad():
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output = model(input_tensor)
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print(f"$= {transformed_output[0, 0]} {output[0, 0]}")
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except Exception as e:
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print(f"Error: {e}")
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if __name__ == "__main__":
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model_path = 'best_model.pth'
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run(model_path)
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