mirror of
https://github.com/facebookresearch/pytorch3d.git
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ad8907d373
Summary: new implicitronRayBundle with added cameraIDs and camera counts. Added to enable a single raybundle inside Implicitron and easier extension in the future. Since RayBundle is named tuple and RayBundleHeterogeneous is dataclass and RayBundleHeterogeneous cannot inherit RayBundle. So if there was no ImplicitronRayBundle every function that uses RayBundle now would have to use Union[RayBundle, RaybundleHeterogeneous] which is confusing and unecessary complicated. Reviewed By: bottler, kjchalup Differential Revision: D39262999 fbshipit-source-id: ece160e32f6c88c3977e408e966789bf8307af59
122 lines
4.4 KiB
Python
122 lines
4.4 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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import unittest
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import torch
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from pytorch3d.implicitron.models.generic_model import GenericModel
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from pytorch3d.implicitron.models.implicit_function.scene_representation_networks import (
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SRNHyperNetImplicitFunction,
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SRNImplicitFunction,
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SRNPixelGenerator,
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)
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from pytorch3d.implicitron.models.renderer.ray_sampler import ImplicitronRayBundle
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from pytorch3d.implicitron.tools.config import get_default_args
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from pytorch3d.renderer import PerspectiveCameras
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from tests.common_testing import TestCaseMixin
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_BATCH_SIZE: int = 3
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_N_RAYS: int = 100
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_N_POINTS_ON_RAY: int = 10
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class TestSRN(TestCaseMixin, unittest.TestCase):
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def setUp(self) -> None:
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torch.manual_seed(42)
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get_default_args(SRNHyperNetImplicitFunction)
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get_default_args(SRNImplicitFunction)
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def test_pixel_generator(self):
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SRNPixelGenerator()
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def _get_bundle(self, *, device) -> ImplicitronRayBundle:
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origins = torch.rand(_BATCH_SIZE, _N_RAYS, 3, device=device)
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directions = torch.rand(_BATCH_SIZE, _N_RAYS, 3, device=device)
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lengths = torch.rand(_BATCH_SIZE, _N_RAYS, _N_POINTS_ON_RAY, device=device)
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bundle = ImplicitronRayBundle(
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lengths=lengths,
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origins=origins,
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directions=directions,
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xys=None,
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camera_ids=None,
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camera_counts=None,
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)
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return bundle
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def test_srn_implicit_function(self):
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implicit_function = SRNImplicitFunction()
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device = torch.device("cpu")
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bundle = self._get_bundle(device=device)
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rays_densities, rays_colors = implicit_function(ray_bundle=bundle)
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out_features = implicit_function.raymarch_function.out_features
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self.assertEqual(
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rays_densities.shape,
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(_BATCH_SIZE, _N_RAYS, _N_POINTS_ON_RAY, out_features),
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)
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self.assertIsNone(rays_colors)
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def test_srn_hypernet_implicit_function(self):
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# TODO investigate: If latent_dim_hypernet=0, why does this crash and dump core?
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latent_dim_hypernet = 39
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device = torch.device("cuda:0")
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implicit_function = SRNHyperNetImplicitFunction(
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latent_dim_hypernet=latent_dim_hypernet
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)
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implicit_function.to(device)
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global_code = torch.rand(_BATCH_SIZE, latent_dim_hypernet, device=device)
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bundle = self._get_bundle(device=device)
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rays_densities, rays_colors = implicit_function(
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ray_bundle=bundle, global_code=global_code
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)
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out_features = implicit_function.hypernet.out_features
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self.assertEqual(
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rays_densities.shape,
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(_BATCH_SIZE, _N_RAYS, _N_POINTS_ON_RAY, out_features),
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)
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self.assertIsNone(rays_colors)
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@torch.no_grad()
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def test_lstm(self):
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args = get_default_args(GenericModel)
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args.render_image_height = 80
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args.render_image_width = 80
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args.implicit_function_class_type = "SRNImplicitFunction"
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args.renderer_class_type = "LSTMRenderer"
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args.raysampler_class_type = "NearFarRaySampler"
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args.raysampler_NearFarRaySampler_args.n_pts_per_ray_training = 1
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args.raysampler_NearFarRaySampler_args.n_pts_per_ray_evaluation = 1
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args.renderer_LSTMRenderer_args.bg_color = [0.4, 0.4, 0.2]
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gm = GenericModel(**args)
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camera = PerspectiveCameras()
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image = gm.forward(
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camera=camera,
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image_rgb=None,
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fg_probability=None,
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sequence_name="",
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mask_crop=None,
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depth_map=None,
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)["images_render"]
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self.assertEqual(image.shape, (1, 3, 80, 80))
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self.assertGreater(image.max(), 0.8)
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# Force everything to be background
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pixel_generator = gm._implicit_functions[0]._fn.pixel_generator
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pixel_generator._density_layer.weight.zero_()
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pixel_generator._density_layer.bias.fill_(-1.0e6)
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image = gm.forward(
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camera=camera,
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image_rgb=None,
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fg_probability=None,
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sequence_name="",
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mask_crop=None,
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depth_map=None,
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)["images_render"]
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self.assertConstant(image[:, :2], 0.4)
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self.assertConstant(image[:, 2], 0.2)
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