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Christoph Lassner's avatar
pulsar integration.  
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
"""Test the sorting of the closest spheres."""
import logging
import os
import sys
import unittest
from os import path

import imageio
import numpy as np
import torch


# fmt: off
# Make the mixin available.
sys.path.insert(0, path.join(path.dirname(__file__), ".."))
from common_testing import TestCaseMixin  # isort:skip  # noqa: E402
# fmt: on

# Making sure you can run this, even if pulsar hasn't been installed yet.
sys.path.insert(0, path.join(path.dirname(__file__), "..", ".."))

devices = [torch.device("cuda"), torch.device("cpu")]
IN_REF_FP = path.join(path.dirname(__file__), "reference", "nr0000-in.pth")
OUT_REF_FP = path.join(path.dirname(__file__), "reference", "nr0000-out.pth")


class TestDepth(TestCaseMixin, unittest.TestCase):
    """Test different numbers of channels."""

    def test_basic(self):
        from pytorch3d.renderer.points.pulsar import Renderer

        for device in devices:
            gamma = 1e-5
            max_depth = 15.0
            min_depth = 5.0
            renderer = Renderer(
                256,
                256,
                10000,
                orthogonal_projection=True,
                right_handed_system=False,
                n_channels=1,
            ).to(device)
            data = torch.load(IN_REF_FP, map_location="cpu")
            # data["pos"] = torch.rand_like(data["pos"])
            # data["pos"][:, 0] = data["pos"][:, 0] * 2. - 1.
            # data["pos"][:, 1] = data["pos"][:, 1] * 2. - 1.
            # data["pos"][:, 2] = data["pos"][:, 2] + 9.5
            result, result_info = renderer.forward(
                data["pos"].to(device),
                data["col"].to(device),
                data["rad"].to(device),
                data["cam_params"].to(device),
                gamma,
                min_depth=min_depth,
                max_depth=max_depth,
                return_forward_info=True,
                bg_col=torch.zeros(1, device=device, dtype=torch.float32),
                percent_allowed_difference=0.01,
            )
            sphere_ids = Renderer.sphere_ids_from_result_info_nograd(result_info)
            depth_map = Renderer.depth_map_from_result_info_nograd(result_info)
            depth_vis = (depth_map - depth_map[depth_map > 0].min()) * 200 / (
                depth_map.max() - depth_map[depth_map > 0.0].min()
            ) + 50
            if not os.environ.get("FB_TEST", False):
                imageio.imwrite(
                    path.join(
                        path.dirname(__file__),
                        "test_out",
                        "test_depth_test_basic_depth.png",
                    ),
                    depth_vis.cpu().numpy().astype(np.uint8),
                )
            # torch.save(
            #     data, path.join(path.dirname(__file__), "reference", "nr0000-in.pth")
            # )
            # torch.save(
            #     {"sphere_ids": sphere_ids, "depth_map": depth_map},
            #     path.join(path.dirname(__file__), "reference", "nr0000-out.pth"),
            # )
            # sys.exit(0)
            reference = torch.load(OUT_REF_FP, map_location="cpu")
            self.assertTrue(
                torch.sum(
                    reference["sphere_ids"][..., 0].to(device) == sphere_ids[..., 0]
                )
                > 65530
            )
            self.assertClose(reference["depth_map"].to(device), depth_map)


if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    unittest.main()