I just tried to follow the instructions in the repo, and tested models trained but got a fairly low accuracy. I'm using pyTorch 0.1.12_1. Is there anything I should pay attention to?

Hello,

I was wondering how you generated the prebuilt datasets that are downloaded when running download_weights_and_datasets.sh, i.e. what were the max_obs and max_obs_size parameters?

Did you follow this file in the original repo? https://github.com/avivt/VIN/blob/master/scripts/make_data_gridworld_nips.m

Thanks, Emilio

Hello!

I have a little doubt.Does the rollout accuracy indicate the success rate? If so, why is it lower than the prediction accuracy? In the Aviv's implementation, the success rate of the 8x8 grid world was as high as 99.6%. Why is the success rate in your experiment relatively low?

Thanks!

when I run 'python train.py --datafile dataset/gridworld_8x8.npz --imsize 8 --lr 0.005 --epochs 30 --k 10 --batch_size 128', it's ok,but again 'python train.py --datafile dataset/gridworld_16x16.npz --imsize 16 --lr 0.002 --epochs 30 --k 20 --batch_size 128' was run, an error occurred as follows: ni@ubuntu:~/pytorch-value-iteration-networks$ python train.py --datafile dataset/gridworld_16x16.npz --imsize 16 --lr 0.002 --epochs 10 --k 20 --batch_size 128 Traceback (most recent call last): File "train.py", line 135, in config.datafile, imsize=config.imsize, train=True, transform=transform) File "/home/ni/pytorch-value-iteration-networks/dataset/dataset.py", line 22, in init self._process(file, self.train) File "/home/ni/pytorch-value-iteration-networks/dataset/dataset.py", line 58, in _process images = images.astype(np.float32) MemoryError

Hello,

I downloaded the data with the .sh downloading script you provided, I also got an nps weights file after training. When I ran the testing command I got the following error: Traceback (most recent call last): File "/home/research/DL/VIN/pytorch-value-iteration-networks/test.py", line 158, in main(config) File "/home/research/DL/VIN/pytorch-value-iteration-networks/test.py", line 85, in main _, predictions = vin(X_in, S1_in, S2_in, config) File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py", line 357, in call result = self.forward(*input, **kwargs) File "/home/research/DL/VIN/pytorch-value-iteration-networks/model.py", line 64, in forward return logits, self.sm(logits) File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py", line 352, in call for hook in self._forward_pre_hooks.values(): File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py", line 398, in getattr type(self).name, name)) AttributeError: 'Softmax' object has no attribute '_forward_pre_hooks'

Thanks for helping!

My main modification is in the forward method of the model where you extract the q_out from the q values, and not repeating q = F.conv2d(...) in two places. I also made minor improvements, such as adding argparse in the dataset creation script and changing .cuda() into .to(device) in test.py.

Hey there. I'm trying to run

python train.py --datafile dataset/gridworld_8x8.npz --imsize 8 --lr 0.005 --epochs 30 --k 10 --batch_size 128

But I get the following error

Number of Train Samples: 103926
Number of Test Samples: 17434
     Epoch | Train Loss | Train Error | Epoch Time
Traceback (most recent call last):
  File "train.py", line 147, in <module>
    train(net, trainloader, config, criterion, optimizer, use_GPU)
  File "train.py", line 40, in train
    outputs, predictions = net(X, S1, S2, config)
  File "/home/j1k1000o/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 224, in __call__
    result = self.forward(*input, **kwargs)
  File "/media/user_home2/j1k1000o/j1k/VINs/pytorch-value-iteration-networks/model.py", line 44, in forward
    q = F.conv2d(torch.cat([r, v], 1), 
  File "/home/j1k1000o/anaconda3/lib/python3.6/site-packages/torch/autograd/variable.py", line 897, in cat
    return Concat.apply(dim, *iterable)
  File "/home/j1k1000o/anaconda3/lib/python3.6/site-packages/torch/autograd/_functions/tensor.py", line 317, in forward
    return torch.cat(inputs, dim)
RuntimeError: inconsistent tensor sizes at /opt/conda/conda-bld/pytorch_1502009910772/work/torch/lib/THC/generic/THCTensorMath.cu:141

I've executed

./download_weights_and_datasets.sh

as well as

python ./dataset/make_training_data.py

And I'm running it on an Ubuntu 16.04, python 3.6 and with all the requirements installed.

Can you help me out?

    slice_s1 = S1.long().expand(config.imsize, 1, config.l_q, q.size(0))
    slice_s1 = slice_s1.permute(3, 2, 1, 0)
    q_out = q.gather(2, slice_s1).squeeze(2)

What does this 3 lines do?

python3 train.py --datafile dataset/gridworld_8x8.npz --imsize 8 --lr 0.005 --epochs 30 --k 10 --batch_size 128 Traceback (most recent call last): File "train.py", line 135, in config.datafile, imsize=config.imsize, train=True, transform=transform) File "/home/user/pytorch/tutorials/valueiterationnetworks/pytorch-value-iteration-networks/dataset/dataset.py", line 22, in init self._process(file, self.train) File "/home/user/pytorch/tutorials/valueiterationnetworks/pytorch-value-iteration-networks/dataset/dataset.py", line 49, in _process S1 = f['arr_1'] File "/home/user/miniconda3/lib/python3.6/site-packages/numpy/lib/npyio.py", line 255, in getitem raise KeyError("%s is not a file in the archive" % key) KeyError: 'arr_1 is not a file in the archive'

I got this error, could you please

Hi

When I tried to run the make_training_data.py script to generate the gridworld.npz file, I got the following error:

FileNotFoundError: [Errno 2] No such file or directory: 'dataset/gridworld_28x28.npz'

And I found that line 101 should be modified as follows:

save_path = "gridworld_{0}x{1}".format(dom_size[0], dom_size[1])