data = ImageList.from_folder().random_split_by_pct().label_from_folder().transform().databunch()
After we perform the above code to gather Images and Labels from respective sources and save it in the ‘data’ object, everything ( training data, test data and validation data) is systematically maintained in that one variable. My question is after we perform the above code, is there a way to extract, say, training images and its labels separately from the ‘data’ object ?
The reason why I am asking this is, generally, we find training images in various formats and labels in various formats. The fastai data block api is an easy one stop solution for gathering the information from various sources. So the plan is to utilize the datablock api to systematically assemble (train data - labels),(val data - labels),(test data - labels) and then later play around with pytorch by ourselves to experiment with the algorithms which would require to access the (train data - labels),(val data - labels),(test data - labels) separately.
After extracting images and targets from the data (i.e. by using data.train_dl.x and data.train_dl.y) I am getting an error when i send this data through my CNN. The error states:
TypeError: conv2d(): argument ‘input’ (position 1) must be Tensor, not Image
So i suppose I have to convert the image to a Tensor ? I thought the get_transforms() handled conversion into tensor but I checked the type of the images and its a ‘fastai.vision.data.ImageList’ and not a tensor. How do I convert imagelist into a tensor ?
I have attached Screenshots.
