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055ab3a2e3
Summary:
Converts the directory specified to use the Ruff formatter in pyfmt
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allow-large-files
Reviewed By: bottler
Differential Revision: D66472063
fbshipit-source-id: 35841cb397e4f8e066e2159550d2f56b403b1bef
96 lines
3.4 KiB
Python
96 lines
3.4 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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"""Test the sorting of the closest spheres."""
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import logging
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import os
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import sys
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import unittest
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from os import path
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import imageio
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import numpy as np
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import torch
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from ..common_testing import TestCaseMixin
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# Making sure you can run this, even if pulsar hasn't been installed yet.
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sys.path.insert(0, path.join(path.dirname(__file__), "..", ".."))
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devices = [torch.device("cuda"), torch.device("cpu")]
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IN_REF_FP = path.join(path.dirname(__file__), "reference", "nr0000-in.pth")
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OUT_REF_FP = path.join(path.dirname(__file__), "reference", "nr0000-out.pth")
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class TestDepth(TestCaseMixin, unittest.TestCase):
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"""Test different numbers of channels."""
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def test_basic(self):
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from pytorch3d.renderer.points.pulsar import Renderer
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for device in devices:
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gamma = 1e-5
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max_depth = 15.0
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min_depth = 5.0
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renderer = Renderer(
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256,
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256,
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10000,
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orthogonal_projection=True,
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right_handed_system=False,
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n_channels=1,
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).to(device)
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data = torch.load(IN_REF_FP, map_location="cpu")
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# For creating the reference files.
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# Use in case of updates.
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# data["pos"] = torch.rand_like(data["pos"])
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# data["pos"][:, 0] = data["pos"][:, 0] * 2. - 1.
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# data["pos"][:, 1] = data["pos"][:, 1] * 2. - 1.
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# data["pos"][:, 2] = data["pos"][:, 2] + 9.5
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result, result_info = renderer.forward(
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data["pos"].to(device),
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data["col"].to(device),
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data["rad"].to(device),
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data["cam_params"].to(device),
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gamma,
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min_depth=min_depth,
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max_depth=max_depth,
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return_forward_info=True,
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bg_col=torch.zeros(1, device=device, dtype=torch.float32),
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percent_allowed_difference=0.01,
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)
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depth_map = Renderer.depth_map_from_result_info_nograd(result_info)
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depth_vis = (depth_map - depth_map[depth_map > 0].min()) * 200 / (
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depth_map.max() - depth_map[depth_map > 0.0].min()
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) + 50
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if not os.environ.get("FB_TEST", False):
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imageio.imwrite(
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path.join(
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path.dirname(__file__),
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"test_out",
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"test_depth_test_basic_depth.png",
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),
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depth_vis.cpu().numpy().astype(np.uint8),
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)
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# For creating the reference files.
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# Use in case of updates.
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# torch.save(
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# data, path.join(path.dirname(__file__), "reference", "nr0000-in.pth")
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# )
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# torch.save(
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# {"sphere_ids": sphere_ids, "depth_map": depth_map},
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# path.join(path.dirname(__file__), "reference", "nr0000-out.pth"),
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# )
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# sys.exit(0)
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reference = torch.load(OUT_REF_FP, map_location="cpu")
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self.assertClose(reference["depth_map"].to(device), depth_map)
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if __name__ == "__main__":
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logging.basicConfig(level=logging.INFO)
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unittest.main()
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