Expected 5D Tensor Got 6D Tensor

#1

Hi,

I created a custom dataset in pytorch. Implemented getitem where I’m reading from a csv file. csv file contains paths to folders which contain video frames. So, one folder contains frames for one video. I read 16 frames from any such folder and create a [3,16,112,112] tensor. Problem is that for my train_loader I had to use unsqueeze(0) for adding one extra dimension to make this tensor look like [1,3,16,112,112] to work with C3D feature extractor (implemented in pytorch) while for my validation set, this same custom dataset already starts giving [N,3,16,112,112] which ultimately results in [1,N,3,16,112,112] hence gives an error saying ValueError: Expected 5D tensor as input, got 6D tensor instead. So dataset is behaving in two different ways.

#2

Then don’t unsqueeze and use N as your batch size, no ?

#3

Point is that why my dataset class is behaving in two different ways. When loader wants a batch of size 8, it should automatically create a batch of size 8, ain’t it? Why I need to use unsqueeze in the first place?

#4

the input of your network should be 5D, first dimension is the size of your batch, second the dimension of data and last 3 related to the 3D data. So I don’t see why you unsqueeze in the first place if you already have 5 dimensions.

#5

@singlepoint Hi,
Which C3D implementation are you using? Can you provide the link?