kelym / fast Goto Github PK

Hello, thank u for sharing your code.
I want to duplicate your model performances reported in your paper.
(Table 1- 9nd row: SMNA + FAST (beam) , 6nd row : SMNA + FAST (Greedy))

Would you please tell me how to duplicate the performance?

And I followed your readme(6th command).
It made two files(cache_val_unseen40False.json, search_val_unseen40False.json).
And I ran "Training Reranker.ipynb" file ,but i encountered some problem.
"FileNotFoundError: [Errno 2] No such file or directory: 'cache_train40False.json'"
There is a line about this in .ipynb
[train_data, val_seen, val_unseen] = load_data(['cache_train40False.json','cache_val_seen40False.json','cache_val_unseen40False.json'])
I think this code needs additional files(train40False.json file, val_seen40False.json)
How to get these file?
Where i should modify? ==> I fix this problem by modify the run_search.py.
Now, I got cache_train40False.json, cache_val_seen40False.json, cache_val_unseen40False.json, search_train40False.json, search_val_seen40False.json, search_val_unseen40False.json

But, the another problem still occurs.
I got issue on (line #40) Training Reranker.ipynb(below)
_, world_states, actions, sum_logits, mean_logits, sum_logp, mean_logp, pm, speaker, scorer = candidate

ValueError: not enough values to unpack (expected 10, got 4)
I printed the candidate(value) but, It only includes "traj"

How do i fix this problem?
Please answer to me.
Thank you!

Hi. I cant download the http://people.eecs.berkeley.edu/~ronghang/projects/speaker_follower/data_augmentation/R2R_literal_speaker_data_augmentation_paths.json from download_precomputed_augmentation.sh. Can you repair the link?

Thank you

Hi,

Can you explain where the backtracking code is? I read that in follwer.py, the _rollout_with_search function uses a greedy decoding method. Is that correct?

Thanks.

When I execute wget https://storage.googleapis.com/bringmeaspoon/R2Rdata/R2R_train.json -P tasks/R2R/data/, an error will be returned as Anonymous caller does not have storage.objects.get access to bringmeaspoon/R2Rdata/R2R_train.json

Hi, this is Hualin. Thanks for sharing your code. I am trying to reproduce your results using the steps in README.md. I encountered several errors and hope you could help me with it.

1. I notice that the Matterport3D repo has changed their c++ api. Hence, I couldn't directly copy your code to tasks/R2R and run it. Could you give me the exact version number of their repo based on which you conduct your experiment?

2. After I found the above mentioned repo API problem I switched to the the Regretful Agent repo (Another work from the same author of SMNA). It seemed they used the old api and I got the training procedure running. Then another bug arose. After I trained the agent for one iteration using first command in step 5, some errors happened in the evaluation step. The error logs are as follows:

Training with sample2step feedback
100%|███████████████████████████████████████████████████████████████████████████████| 100/100 [02:33<00:00, 1.19s/it]
/opt/conda/lib/python3.6/site-packages/numpy/lib/function_base.py:392: RuntimeWarning: Mean of empty slice.
avg = a.mean(axis)
/opt/conda/lib/python3.6/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars
ret = ret.dtype.type(ret / rcount)
evaluating on val_seen
Traceback (most recent call last):
File "tasks/R2R/train.py", line 378, in
utils.run(make_arg_parser(), train_val)
File "/workspace/selfmonitoring-agent/tasks/R2R/utils.py", line 315, in run
entry_function(args)
File "tasks/R2R/train.py", line 321, in train_val
args.n_pretrain_iters, val_envs=val_envs)
File "tasks/R2R/train.py", line 125, in train
score_summary, _ = evaluator.score_results(agent.results)
File "/workspace/selfmonitoring-agent/tasks/R2R/eval.py", line 144, in score_results
score_summary['model_score'] = np.average(model_scores)
File "/opt/conda/lib/python3.6/site-packages/numpy/lib/function_base.py", line 392, in average
avg = a.mean(axis)
File "/opt/conda/lib/python3.6/site-packages/numpy/core/_methods.py", line 85, in _mean
ret = ret.dtype.type(ret / rcount)
AttributeError: 'torch.dtype' object has no attribute 'type'

May I know if you have any insights on what could possibly cause this? Thank you and looking forward to your reply.

Thank you for sharing nice work.
I have trouble in training.
While my model trained with below command, my computer suddenly shut down.

python tasks/R2R/train.py --use_pretraining
                          --pretrain_splits train literal_speaker_data_augmentation_paths 
                          --feedback_method sample2step
                          --experiment_name smna

I checked the process and saved model file, it was already finished training with augmented data and was getting process with real data.
I want to resuming training with real data at that final shut down timestep(epoch).
What could i do to resume the training?

hello.
I'm sorry to raise another issue.
I have problem with final command.
python tasks/R2R/run_search.py --job search --load_follower tasks/R2R/experiments/smna/snapshots/[name of the latest model] --max_episode_len 55 --K 20 --logit --experiment_name tmp --load_reranker tasks/R2R/experiments/[name of the reranker model]
I ran Training reranker.ipynb and got new candidates_ranker_xxxx models.
but, after that i ran your command, got error like below.

I think these caused by this line in your .ipynb file.

I don't know why you did the input_dim size to 7. Could you please explain about this?
And how do i fix the problem?

I'll be waiting for the reply.
Thank you.