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https://github.com/facebookresearch/pytorch3d.git
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90f1d5c258
Reviewed By: zertosh Differential Revision: D24880203 fbshipit-source-id: 2034cdfc2712209e86d3d05c119c58f979b05c52
150 lines
4.5 KiB
Python
150 lines
4.5 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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"""Test number of channels."""
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import logging
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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 torch
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# fmt: off
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# Make the mixin available.
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sys.path.insert(0, path.join(path.dirname(__file__), ".."))
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from common_testing import TestCaseMixin # isort:skip # noqa: E402
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# fmt: on
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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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class TestChannels(TestCaseMixin, unittest.TestCase):
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"""Test different numbers of channels."""
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def test_basic(self):
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"""Basic forward test."""
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import torch
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from pytorch3d.renderer.points.pulsar import Renderer
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n_points = 10
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width = 1_000
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height = 1_000
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renderer_1 = Renderer(width, height, n_points, n_channels=1)
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renderer_3 = Renderer(width, height, n_points, n_channels=3)
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renderer_8 = Renderer(width, height, n_points, n_channels=8)
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# Generate sample data.
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torch.manual_seed(1)
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vert_pos = torch.rand(n_points, 3, dtype=torch.float32) * 10.0
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vert_pos[:, 2] += 25.0
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vert_pos[:, :2] -= 5.0
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vert_col = torch.rand(n_points, 8, dtype=torch.float32)
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vert_rad = torch.rand(n_points, dtype=torch.float32)
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cam_params = torch.tensor(
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[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.0, 2.0], dtype=torch.float32
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)
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for device in devices:
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vert_pos = vert_pos.to(device)
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vert_col = vert_col.to(device)
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vert_rad = vert_rad.to(device)
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cam_params = cam_params.to(device)
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renderer_1 = renderer_1.to(device)
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renderer_3 = renderer_3.to(device)
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renderer_8 = renderer_8.to(device)
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result_1 = (
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renderer_1.forward(
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vert_pos,
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vert_col[:, :1],
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vert_rad,
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cam_params,
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1.0e-1,
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45.0,
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percent_allowed_difference=0.01,
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)
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.cpu()
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.detach()
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.numpy()
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)
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hits_1 = (
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renderer_1.forward(
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vert_pos,
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vert_col[:, :1],
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vert_rad,
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cam_params,
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1.0e-1,
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45.0,
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percent_allowed_difference=0.01,
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mode=1,
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)
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.cpu()
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.detach()
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.numpy()
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)
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result_3 = (
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renderer_3.forward(
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vert_pos,
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vert_col[:, :3],
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vert_rad,
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cam_params,
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1.0e-1,
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45.0,
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percent_allowed_difference=0.01,
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)
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.cpu()
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.detach()
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.numpy()
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)
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hits_3 = (
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renderer_3.forward(
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vert_pos,
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vert_col[:, :3],
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vert_rad,
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cam_params,
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1.0e-1,
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45.0,
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percent_allowed_difference=0.01,
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mode=1,
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)
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.cpu()
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.detach()
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.numpy()
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)
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result_8 = (
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renderer_8.forward(
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vert_pos,
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vert_col,
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vert_rad,
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cam_params,
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1.0e-1,
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45.0,
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percent_allowed_difference=0.01,
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)
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.cpu()
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.detach()
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.numpy()
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)
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hits_8 = (
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renderer_8.forward(
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vert_pos,
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vert_col,
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vert_rad,
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cam_params,
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1.0e-1,
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45.0,
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percent_allowed_difference=0.01,
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mode=1,
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)
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.cpu()
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.detach()
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.numpy()
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)
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self.assertClose(result_1, result_3[:, :, :1])
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self.assertClose(result_3, result_8[:, :, :3])
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self.assertClose(hits_1, hits_3)
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self.assertClose(hits_8, hits_3)
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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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