opt.inputDims is a required argument but is not enforced

[vzhong]

In this line
https://github.com/twitter/torch-dataset/blob/master/lua/Batch.lua#L19

applyInputDims is called with opt.inputDims which by default is not set. However applyInputDims expects this argument and crashes (eg. assert fails) if this is not set.

I think there should be either

1. Documentation specifying what opt.inputDims is
2. Some automatic way of setting a default opt.InputDims.

Otherwise the README example under Multiple Indices fail when sampledBatcher is called:

   local getBatch, numBatches = dataset.sampledBatcher({
      batchSize = 1,
      samplerKind = { 'part-linear', 'part-linear-permutation' },   -- sample the first linearily, the second with a permutation
      processor = processor,
   })

[zakattacktwitter]

Sorry about that. Can you try the examples, they should all run without
problems.

I don’t think you can default the inputDims, but we can definitely throw a
better error when it is not provided.

On Tue, Feb 9, 2016 at 3:27 PM, Victor Zhong [email protected]
wrote:

In this line
https://github.com/twitter/torch-dataset/blob/master/lua/Batch.lua#L19

applyInputDims is called with opt.inputDims which by default is not set.
However applyInputDims expects this argument and crashes (eg. assert
fails) if this is not set.

I think there should be either

1. Documentation specifying what opt.inputDims is
2. Some automatic way of setting a default opt.InputDims.

Otherwise the README example under Multiple Indices fail when
sampledBatcher is called:

local getBatch, numBatches = dataset.sampledBatcher({
batchSize = 1,
samplerKind = { 'part-linear', 'part-linear-permutation' }, -- sample the first linearily, the second with a permutation
processor = processor,
})

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