xbresson / tsp_transformer Goto Github PK

Hi,
I want to know how long it will take to train this model.
You set the epoch = 10000, and the depth of model is also twice that of Kool et-al, so I think the good results are probably due to this reason.May I ask if you have used the model of Kool et-al to do comparative experiment under the same super parameter setting?

Hello Sir, currently I am doing a project related to this. I want to ask about the minimum requirement for the gpu to do this project. Thanks for your help.

model_baseline.load_state_dict(checkpoint['model_baseline']) throws an error complaining about missing/unexpected keys. After transforming the keys from "module.start_placeholder", "module.input_emb.weight"... etc. to the form "start_placeholder", "input_emb.weight" ... in the OrderedDict, this time I get the following size mismatches:
RuntimeError: Error(s) in loading state_dict for TSP_net:
size mismatch for encoder.norm1_layers.1.weight: copying a param with shape torch.Size([]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for encoder.norm1_layers.2.bias: copying a param with shape torch.Size([]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for encoder.norm1_layers.3.running_mean: copying a param with shape torch.Size([]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for encoder.norm1_layers.4.running_var: copying a param with shape torch.Size([]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for encoder.norm2_layers.0.weight: copying a param with shape torch.Size([]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for encoder.norm2_layers.1.bias: copying a param with shape torch.Size([]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for encoder.norm2_layers.2.running_mean: copying a param with shape torch.Size([]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for encoder.norm2_layers.3.running_var: copying a param with shape torch.Size([]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for encoder.norm2_layers.5.weight: copying a param with shape torch.Size([]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for decoder.decoder_layers.0.Wq_selfatt.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([128, 128]).
size mismatch for decoder.decoder_layers.0.Wq_selfatt.bias: copying a param with shape torch.Size([]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for decoder.decoder_layers.0.Wk_selfatt.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([128, 128]).
size mismatch for decoder.decoder_layers.0.Wv_selfatt.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([128, 128]).
size mismatch for decoder.decoder_layers.0.W0_selfatt.weight: copying a param with shape torch.Size([]) from checkpoint, the shape in current model is torch.Size([128, 128]).
size mismatch for decoder.decoder_layers.0.W0_att.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([128, 128]).
size mismatch for decoder.decoder_layers.0.Wq_att.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([128, 128]).
size mismatch for decoder.decoder_layers.0.Wq_att.bias: copying a param with shape torch.Size([]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for decoder.decoder_layers.0.BN_selfatt.weight: copying a param with shape torch.Size([128, 128]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for decoder.decoder_layers.0.BN_att.weight: copying a param with shape torch.Size([128, 128]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for decoder.decoder_layers.0.BN_MLP.weight: copying a param with shape torch.Size([128, 128]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for WK_att_decoder.weight: copying a param with shape torch.Size([128, 128]) from checkpoint, the shape in current model is torch.Size([256, 128]).
size mismatch for WK_att_decoder.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for WV_att_decoder.weight: copying a param with shape torch.Size([128, 128]) from checkpoint, the shape in current model is torch.Size([256, 128]).
size mismatch for WV_att_decoder.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).

Dear Xavier,

We read your TSP transformer paper (https://arxiv.org/abs/2103.03012) quite interesting and have some questions regarding your paper and GitHub code.

In your Github ('train_tsp_transformer_TSP50.ipythonb') code, the input x with dimension (bsz, nb_nodes, dim_input_nodes) is embedded into x with dimension (bsz, nb_nodes, dim_emb). We are wondering why this embedding process (into dim_emb dimension) is needed for the TSP problem.