I’ll also start a new thread, just in case I am clogging up this thread.
import pandas as pd
import numpy as np

df=pd.read_csv('train.csv', sep=',', usecols = ['input','output'])
shape = df.shape

for current in range (shape[0]):	
	input_pc = np.loadtxt(df.iloc[current,0], delimiter=' ')
	print(input_pc.shape)
	output_pc = np.loadtxt(df.iloc[current,1], delimiter=' ')
	print(output_pc.shape)

So I wrote some code to load in the csv file for mappings, then load each corresponding input and output point cloud. For the input point cloud, it has the shape of (900,3) and the output point cloud has the shape of (8100,3). Each “point” has its x coordinate in the first layer, the y coordinate in the second layer, and the z coordinates in the third layer. Now that I have out input and the corresponding point clouds loaded as numpy arrays, could you please help me with modifying this function:
class MyDataset(Dataset):
    def __init__(self, image_paths):
        self.image_paths = image_paths

    def __getitem__(self, index):
        image, image_transformed = load_image(self.image_paths[index])
        # transformations, e.g. Random Crop etc. 
        # Make sure to perform the same transformations on image and target
        # Here is a small example: https://discuss.pytorch.org/t/torchvision-transfors-how-to-perform-identical-transform-on-both-image-and-target/10606/7?u=ptrblck

        x, y = TF.to_tensor(image), TF.to_tensor(image_transformed)
        return x, y

    def __len__(self):
        return len(self.image_paths)

to work with the dataloader function?
train_loader = DataLoader(MyDataset("./train.csv"), batch_size=16, shuffle=False, num_workers=0, worker_init_fn=None)


I’m currently not sure about what I should pass in as the “Dataset”, in the MyDataSet function. While I’m sure I’ll need to pass in the mappings in the form of the csv at some point, but I’m to quite sure about how to load the mappings into the Dataloader, or the custom function.