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 1.
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.