mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-02 20:02:49 +08:00
8d10ba52b2
Summary: For 3D segmentation problems it's really useful to be able to train the models from multiple viewpoints using Pytorch3D as the renderer. Currently due to hardcoded assumptions in a few spots the mesh renderer only supports rendering RGB (3 dimensional) data. You can encode the classification information as 3 channel data but if you have more than 3 classes you're out of luck. This relaxes the assumptions to make rendering semantic classes work with `HardFlatShader` and `AmbientLights` with no diffusion/specular. The other shaders/lights don't make any sense for classification since they mutate the texture values in some way. This only requires changes in `Materials` and `AmbientLights`. The bulk of the code is the unit test. Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1248 Test Plan: Added unit test that renders a 5 dimensional texture and compare dimensions 2-5 to a stored picture. Reviewed By: bottler Differential Revision: D37764610 Pulled By: d4l3k fbshipit-source-id: 031895724d9318a6f6bab5b31055bb3f438176a5
66 lines
2.3 KiB
Python
66 lines
2.3 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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import torch
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from ..common.datatypes import Device
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from .utils import TensorProperties
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class Materials(TensorProperties):
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"""
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A class for storing a batch of material properties. Currently only one
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material per batch element is supported.
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"""
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def __init__(
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self,
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ambient_color=((1, 1, 1),),
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diffuse_color=((1, 1, 1),),
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specular_color=((1, 1, 1),),
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shininess=64,
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device: Device = "cpu",
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) -> None:
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"""
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Args:
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ambient_color: ambient reflectivity of the material
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diffuse_color: diffuse reflectivity of the material
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specular_color: specular reflectivity of the material
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shininess: The specular exponent for the material. This defines
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the focus of the specular highlight with a high value
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resulting in a concentrated highlight. Shininess values
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can range from 0-1000.
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device: Device (as str or torch.device) on which the tensors should be located
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ambient_color, diffuse_color and specular_color can be of shape
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(1, C) or (N, C) where C is typically 3 (for RGB). shininess can be of shape (1,)
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or (N,).
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The colors and shininess are broadcast against each other so need to
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have either the same batch dimension or batch dimension = 1.
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"""
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super().__init__(
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device=device,
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diffuse_color=diffuse_color,
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ambient_color=ambient_color,
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specular_color=specular_color,
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shininess=shininess,
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)
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C = self.ambient_color.shape[-1]
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for n in ["ambient_color", "diffuse_color", "specular_color"]:
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t = getattr(self, n)
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if t.shape[-1] != C:
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msg = "Expected %s to have shape (N, %d); got %r"
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raise ValueError(msg % (n, C, t.shape))
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if self.shininess.shape != torch.Size([self._N]):
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msg = "shininess should have shape (N); got %r"
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raise ValueError(msg % repr(self.shininess.shape))
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def clone(self):
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other = Materials(device=self.device)
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return super().clone(other)
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