Found the solution; just need to use multiple passes of ConcatDataset:
l = []
l.append(datasets.ImageFolder(file_path, trans))
l.append(datasets.ImageFolder(file_path2, trans))
image_datasets = torch.utils.data.ConcatDataset(l)

df = pd.read_csv(image_file_paths), names=["file_path", "label"])
mydata = MyData(df)

image_datasets = torch.utils.data.ConcatDataset([image_datasets, mydata])

img_datasets = dict()
img_datasets['train'], img_datasets['val'] = torch.utils.data.random_split(image_datasets, (round(0.8*len(image_datasets)), round(0.2*len(image_datasets))))

Good to go from there.