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View Code? Open in Web Editor NEW[NeurIPS 2023] T2T: From Distribution Learning in Training to Gradient Search in Testing for Combinatorial Optimization
License: MIT License
[NeurIPS 2023] T2T: From Distribution Learning in Training to Gradient Search in Testing for Combinatorial Optimization
License: MIT License
Dear authors, thanks for your excellent research. When I tried to reproduce your experiments on TSPLIB, I found that the optimal solutions provided in the data/tsp/tsplib200-1000.txt may be wrong since they are just 1,2,3,...,n,1 and their cost are quite high.
Hi, I am trying to evaluate on tsp100 data, and have this error saying
File "/T2TCO/diffusion/pl_tsp_model.py", line 247, in test_step
g_xt = self.guided_categorical_denoise_step(points, g_xt, t1, device, edge_index, target_t=t2)
File "/T2TCO/diffusion/pl_tsp_model.py", line 91, in guided_categorical_denoise_step
assert xt.grad is not None
The function has this assert statement to assert xt has gradient, but the input g_xt is converted to long type in line 238, and long type tensor cannot have gradient. Can someone help me with this? Thanks
(g_xt > 0).long()
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