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Data

As a person image generator VUNet needs two inputs, one that defines the pose of the displayed person, one that defines its appearance. Additionally for training we need to know what this combination of pose and appearance looks like to be able to define a reconstruction loss.

This repo comes together with a small split of the custom Prjoti dataset, which contains images of a person walking, along with keypoint annotations. These keypoint annotations are converted into stickman images upon loading single examples.

Explore the data

Let us first of all have a look at the data by running the following command:

cd <VUNet root>/VUnet/configs/
edexplore -b prjoti.yaml --dataset VUNet.data.prjoti.Prjoti_VUNet_train

Note that you need to have edflow installed to run edexplore. This happens automatically when you install this repo following README.md.

This will start an interactive browser application, where you can inspect all examples of the dataset.

You can see a single example, which contains an appearance image, a stickman representation and a target image, which VUNet should generate.

Define your own data

To use you own dataset, you need to make sure it follows the edflow dataset conventions. Your dataset class should

  1. inherit from the edflow.data.dataset_mixin.DatasetMixin class
  2. implement a get__example(self, idx) method, which returns a dict given the example index idx.
  3. define the __len__ method.

Take a look at the VUNet.data.prjoti.Prjoti_VUNet class, to see these methods implemented.

Also make sure that the returned example of your custom dataset has entries of the same form as VUNet.data.prjoti.Prjoti_VUNet, i.e. you need to define the keys stickman, appearance, target.

MetaDataset: edflow's dataloading class

Should you plan on implementing a completely new data loader, consider taking a look at the edflow.data.believers.meta.MetaDataset class and its documentation. It defines highly efficient data loading routines and follows a few concepts, which allows for easy loading of e.g. images. Take a look at the Prjoti dataset and the folder its data is contained in to learn more about the structure of a MetaDataset. The three core components of a MetaDataset are

  1. a meta.yaml which contains all dataset relevant information like a description, loading instructions and the like,
  2. a directory labels, which contains numpy.memorymaps of informations for all examples in the dataset
  3. possibly the actual load-heavy data, like images at some directory.

For load heavy data like images you will define an array of paths, which point to all images and store is as label array. Inside the meta.yaml you then explain the dataset class, that it is supposed to turn the paths into actual images in the form of numpy arrays.