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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
78 lines
2.3 KiB
Python
Executable File
78 lines
2.3 KiB
Python
Executable File
#!/usr/bin/env python3
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# 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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"""
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This example demonstrates the most trivial, direct interface of the pulsar
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sphere renderer. It renders and saves an image with 10 random spheres.
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Output: basic.png.
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"""
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import logging
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import math
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from os import path
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import imageio
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import torch
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from pytorch3d.renderer.points.pulsar import Renderer
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LOGGER = logging.getLogger(__name__)
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def cli():
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"""
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Basic example for the pulsar sphere renderer.
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Writes to `basic.png`.
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"""
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LOGGER.info("Rendering on GPU...")
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torch.manual_seed(1)
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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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device = torch.device("cuda")
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# The PyTorch3D system is right handed; in pulsar you can choose the handedness.
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# For easy reproducibility we use a right handed coordinate system here.
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renderer = Renderer(width, height, n_points, right_handed_system=True).to(device)
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# Generate sample data.
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vert_pos = torch.rand(n_points, 3, dtype=torch.float32, device=device) * 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, 3, dtype=torch.float32, device=device)
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vert_rad = torch.rand(n_points, dtype=torch.float32, device=device)
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cam_params = torch.tensor(
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[
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0.0,
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0.0,
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0.0, # Position 0, 0, 0 (x, y, z).
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0.0,
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math.pi, # Because of the right handed system, the camera must look 'back'.
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0.0, # Rotation 0, 0, 0 (in axis-angle format).
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5.0, # Focal length in world size.
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2.0, # Sensor size in world size.
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],
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dtype=torch.float32,
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device=device,
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)
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# Render.
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image = renderer(
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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, # Renderer blending parameter gamma, in [1., 1e-5].
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45.0, # Maximum depth.
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)
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LOGGER.info("Writing image to `%s`.", path.abspath("basic.png"))
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imageio.imsave("basic.png", (image.cpu().detach() * 255.0).to(torch.uint8).numpy())
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LOGGER.info("Done.")
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if __name__ == "__main__":
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logging.basicConfig(level=logging.INFO)
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cli()
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