Advanced Use: Custom Dataset and Rendering Loss

In this advanced tutorial, we will cover how to integrate the PyPBR library into a dataset class and use it for training a deep learning model with a rendering loss. Specifically, we’ll look at how to:

  1. Create a Custom Dataset: Use PyPBR to create a materials dataset class compatible with PyTorch.

  2. Rendering Loss Using BRDF: Utilize the Cook-Torrance BRDF evaluation to compute a rendering loss.

By the end of this tutorial, you will understand how to leverage PyPBR’s BRDF evaluation to create a more photorealistic loss for training deep learning models.

Creating a Custom Dataset with PyPBR

The PyPBR library can be easily integrated into a PyTorch dataset to facilitate the loading and handling of PBR materials.

Below is a custom PyTorch dataset class that loads PBR materials from folders.

import os
import torch
from torch.utils.data import Dataset
from pypbr.material import load_material_from_folder

class PBRMaterialDataset(Dataset):
    def __init__(self, root_dir, color_space='srgb', workflow='metallic', resolution=(256, 256)):
        """
        Initialize the dataset by specifying the root directory containing material folders.

        Args:
            root_dir (str): The root directory containing the material folders.
            color_space (str): Color space for the albedo map, either 'srgb' or 'linear'. Defaults to 'srgb'.
            workflow (str): Preferred workflow, either 'metallic' or 'specular'. Defaults to 'metallic'.
            resolution (tuple): Desired resolution for the texture maps. Defaults to (256, 256).
        """
        self.root_dir = root_dir
        self.material_dirs = [os.path.join(root_dir, d) for d in os.listdir(root_dir) if os.path.isdir(os.path.join(root_dir, d))]
        self.color_space = color_space
        self.workflow = workflow
        self.resolution = resolution

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

    def __getitem__(self, idx):
        folder_path = self.material_dirs[idx]

        # Load the material from the folder
        is_srgb = self.color_space == 'srgb'
        material = load_material_from_folder(folder_path, preferred_workflow=self.workflow, is_srgb=is_srgb)

        # Resize the material to the desired resolution
        material.resize(self.resolution)

        return material

The PBRMaterialDataset allows for easy loading of materials from a directory. You can now use this dataset with standard PyTorch data loaders to provide batches of PBR materials during training.

Rendering Loss Using BRDF

To use PyPBR for a rendering loss, we can evaluate the BRDF of the predicted material properties and compare it with a reference rendering. This type of loss encourages the network to learn physically plausible material properties.

In this tutorial, we will use the CookTorranceBRDF model to evaluate both a predicted material and a ground truth material, and compute a rendering loss based on the difference between the two.

import torch
import torch.nn as nn
from pypbr.models import CookTorranceBRDF

class RenderingLoss(nn.Module):
    def __init__(self, light_type='point', view_dir=torch.tensor([0.0, 0.0, 1.0]), light_dir=torch.tensor([0.1, 0.1, 1.0]), light_intensity=torch.tensor([1.0, 1.0, 1.0])):
        """
        Initialize the rendering loss module.

        Args:
            light_type (str): The type of light ('point' or 'directional'). Defaults to 'point'.
            view_dir (torch.Tensor): The view direction vector. Defaults to viewing straight on.
            light_dir (torch.Tensor): The light direction vector. Defaults to light from slightly top right.
            light_intensity (torch.Tensor): The light intensity. Defaults to white light.
        """
        super(RenderingLoss, self).__init__()
        self.brdf = CookTorranceBRDF(light_type=light_type)
        self.view_dir = view_dir
        self.light_dir = light_dir
        self.light_intensity = light_intensity

    def forward(self, predicted_material, ground_truth_material):
        """
        Forward pass to compute the rendering loss.

        Args:
            predicted_material: Predicted material properties.
            ground_truth_material: Ground truth material properties.

        Returns:
            torch.Tensor: The computed rendering loss.
        """
        # Compute rendered color for predicted and ground truth materials
        rendered_pred = self.brdf(predicted_material, self.view_dir, self.light_dir, self.light_intensity)
        rendered_gt = self.brdf(ground_truth_material, self.view_dir, self.light_dir, self.light_intensity)

        # Compute L2 loss between the rendered outputs
        loss = nn.MSELoss()(rendered_pred, rendered_gt)
        return loss

Usage Example

Below is an example of how to use the PBRMaterialDataset and RenderingLoss in a typical training loop.

from torch.utils.data import DataLoader

# Initialize dataset and dataloader
root_dir = "path/to/materials_root"
dataset = PBRMaterialDataset(root_dir, color_space='srgb', workflow='metallic', resolution=(256, 256))
dataloader = DataLoader(dataset, batch_size=4, shuffle=True)

# Initialize the rendering loss
rendering_loss = RenderingLoss(light_type='point')

# Example training loop
for batch in dataloader:
    predicted_material = batch  # Here, replace this with your model's predicted material output
    ground_truth_material = batch  # Assuming the batch itself is the ground truth material for this example

    # Compute rendering loss
    loss = rendering_loss(predicted_material, ground_truth_material)

    # Print the loss
    print("Rendering Loss:", loss.item())

    # Perform backpropagation and optimization steps here
    ...

Summary

In this tutorial, we covered:

  1. Dataset Integration: How to load materials into a PyTorch dataset using PyPBR.

  2. Rendering Loss: How to create a custom rendering loss using BRDF evaluation.

By leveraging PyPBR, you can incorporate physically-based rendering into your deep learning workflows, helping models learn realistic material properties and improving the visual quality of results.