Data(face=[3, 43517], pos=[43812, 3])

how could transfrom this data

What kind of transformation would you like to use?

It seems you are dealing with some kind of features or coordinates?

i am dealing with mesh data, i have done first step by use .numpy.

Now, i need to reshape data into following form.

this is my data, it is really similar to the data in pretrained model. however, it has no shape and look like this:

how could i reshape into (a,b,c)shape

i just check the datatype, i want to reshape this turple into ndarray, the single array in my data is the array in pretrained model

Since `d`

is a `tuple`

, you could index it and get the shapes of all internal arrays via:

```
for tmp in d:
print(tmp.shape)
```

Could you post these shapes, so that we can see how to reshape these arrays?

the shape of pretrained model:

Sorry, my data turns out to be list.

it works with ur code as well, this my shape, each data have different numbers of clound points.

`data`

seems to be a numpy array, not a list, since `type(data)`

returns `numpy.ndarray`

and also `data.shape`

seems to work, while it was raising an error before, so apparently something in the code has changed.

Anyway, `tmp`

seems to have varying shapes, so you won’t be able to reshape it to e.g. a tensor with the shape `[2048, 2048, 3]`

, but would need to interpolate, slice, etc. the tensors somehow.

Could you explain your use case a bit, i.e. why would you need to reshape all tensors to a particular shape and how this reshaping should be done, if the number of elements doesn’t match?

PS: you can post code snippets by wrapping them into three backticks ```, which makes debugging easier.

1 Like

hey, i do not need shape to shape exactly like `[2048, 2048, 3]`

, However, i need to shape it to 3 dimension `[a, b, 3]`

so that i can implement a graph-classifcation modelhttps://github.com/aboulch/ConvPoint/blob/master/examples/modelnet/modelnet_classif.py.

Forward pass of this model have (x, inputs_pts) which has form: input_pts.shape = [BatchSize, Dim, NPoints] and x.shape = [BatchSize, C, NPoints]

i was trying to load data exactly like pretrained model, so i can run this classfication without further concern. However, as shape varing for each single object. i do not know how to do it.

But here comes now a another solution: i have now data.pos with shape[Points.Dim] and x[C, Npoints], all i need to do is add to 3d dimension. Still i can not concatnate,of np.stock, cuz different dimension.

, i truely wish u could help me further, after transfrom my data, it become a list like this, if i do data[0].pos, it will have shape(2048,3). how do i turn all data to array then np.stack it