dmlc--dgl
Xiagkun Hu
b2f7f0ee7c
* rrn model and sudoku * add README * refine the code, add doc strings * add sudoku solver
58 行
1.7 KiB
Python
58 行
1.7 KiB
Python
import os
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import urllib.request
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import torch
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import numpy as np
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from sudoku_data import _basic_sudoku_graph
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def solve_sudoku(puzzle):
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"""
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Solve sudoku puzzle using RRN.
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:param puzzle: an array-like data with shape [9, 9], blank positions are filled with 0
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:return: a [9, 9] shaped numpy array
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"""
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puzzle = np.array(puzzle, dtype=np.long).reshape([-1])
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model_path = 'ckpt'
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if not os.path.exists(model_path):
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os.mkdir(model_path)
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model_filename = os.path.join(model_path, 'rrn-sudoku.pkl')
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if not os.path.exists(model_filename):
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print('Downloading model...')
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url = 'https://s3.us-east-2.amazonaws.com/dgl.ai/models/rrn-sudoku.pkl'
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urllib.request.urlretrieve(url, model_filename)
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model = torch.load(model_filename, map_location='cpu')
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g = _basic_sudoku_graph()
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sudoku_indices = np.arange(0, 81)
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rows = sudoku_indices // 9
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cols = sudoku_indices % 9
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g.ndata['row'] = torch.tensor(rows, dtype=torch.long)
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g.ndata['col'] = torch.tensor(cols, dtype=torch.long)
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g.ndata['q'] = torch.tensor(puzzle, dtype=torch.long)
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g.ndata['a'] = torch.tensor(puzzle, dtype=torch.long)
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pred, _ = model(g, False)
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pred = pred.cpu().data.numpy().reshape([9, 9])
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return pred
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if __name__ == '__main__':
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q = [
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[9, 7, 0, 4, 0, 2, 0, 5, 3],
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[0, 4, 6, 0, 9, 0, 0, 0, 0],
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[0, 0, 8, 6, 0, 1, 4, 0, 7],
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[0, 0, 0, 0, 0, 3, 5, 0, 0],
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[7, 6, 0, 0, 0, 0, 0, 8, 2],
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[0, 0, 2, 8, 0, 0, 0, 0, 0],
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[6, 0, 5, 1, 0, 7, 2, 0, 0],
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[0, 0, 0, 0, 6, 0, 7, 4, 0],
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[4, 3, 0, 2, 0, 9, 0, 6, 1]
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]
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answer = solve_sudoku(q)
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print(answer)
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