mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-01 03:12:49 +08:00
34f648ede0
Summary: Move testing targets from pytorch3d/tests/TARGETS to pytorch3d/TARGETS. Reviewed By: shapovalov Differential Revision: D36186940 fbshipit-source-id: a4c52c4d99351f885e2b0bf870532d530324039b
109 lines
4.4 KiB
Python
109 lines
4.4 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
|
# All rights reserved.
|
|
#
|
|
# This source code is licensed under the BSD-style license found in the
|
|
# LICENSE file in the root directory of this source tree.
|
|
|
|
import unittest
|
|
|
|
import torch
|
|
from pytorch3d.implicitron.models.implicit_function.scene_representation_networks import (
|
|
SRNHyperNetImplicitFunction,
|
|
SRNImplicitFunction,
|
|
SRNPixelGenerator,
|
|
)
|
|
from pytorch3d.implicitron.models.renderer.base import ImplicitFunctionWrapper
|
|
from pytorch3d.implicitron.tools.config import get_default_args
|
|
from pytorch3d.renderer import RayBundle
|
|
from tests.common_testing import TestCaseMixin
|
|
|
|
_BATCH_SIZE: int = 3
|
|
_N_RAYS: int = 100
|
|
_N_POINTS_ON_RAY: int = 10
|
|
|
|
|
|
class TestSRN(TestCaseMixin, unittest.TestCase):
|
|
def setUp(self) -> None:
|
|
torch.manual_seed(42)
|
|
get_default_args(SRNHyperNetImplicitFunction)
|
|
get_default_args(SRNImplicitFunction)
|
|
|
|
def test_pixel_generator(self):
|
|
SRNPixelGenerator()
|
|
|
|
def _get_bundle(self, *, device) -> RayBundle:
|
|
origins = torch.rand(_BATCH_SIZE, _N_RAYS, 3, device=device)
|
|
directions = torch.rand(_BATCH_SIZE, _N_RAYS, 3, device=device)
|
|
lengths = torch.rand(_BATCH_SIZE, _N_RAYS, _N_POINTS_ON_RAY, device=device)
|
|
bundle = RayBundle(
|
|
lengths=lengths, origins=origins, directions=directions, xys=None
|
|
)
|
|
return bundle
|
|
|
|
def test_srn_implicit_function(self):
|
|
implicit_function = SRNImplicitFunction()
|
|
device = torch.device("cpu")
|
|
bundle = self._get_bundle(device=device)
|
|
rays_densities, rays_colors = implicit_function(bundle)
|
|
out_features = implicit_function.raymarch_function.out_features
|
|
self.assertEqual(
|
|
rays_densities.shape,
|
|
(_BATCH_SIZE, _N_RAYS, _N_POINTS_ON_RAY, out_features),
|
|
)
|
|
self.assertIsNone(rays_colors)
|
|
|
|
def test_srn_hypernet_implicit_function(self):
|
|
# TODO investigate: If latent_dim_hypernet=0, why does this crash and dump core?
|
|
latent_dim_hypernet = 39
|
|
hypernet_args = {"latent_dim_hypernet": latent_dim_hypernet}
|
|
device = torch.device("cuda:0")
|
|
implicit_function = SRNHyperNetImplicitFunction(hypernet_args=hypernet_args)
|
|
implicit_function.to(device)
|
|
global_code = torch.rand(_BATCH_SIZE, latent_dim_hypernet, device=device)
|
|
bundle = self._get_bundle(device=device)
|
|
rays_densities, rays_colors = implicit_function(bundle, global_code=global_code)
|
|
out_features = implicit_function.hypernet.out_features
|
|
self.assertEqual(
|
|
rays_densities.shape,
|
|
(_BATCH_SIZE, _N_RAYS, _N_POINTS_ON_RAY, out_features),
|
|
)
|
|
self.assertIsNone(rays_colors)
|
|
|
|
def test_srn_hypernet_implicit_function_optim(self):
|
|
# Test optimization loop, requiring that the cache is properly
|
|
# cleared in new_args_bound
|
|
latent_dim_hypernet = 39
|
|
hyper_args = {"latent_dim_hypernet": latent_dim_hypernet}
|
|
device = torch.device("cuda:0")
|
|
global_code = torch.rand(_BATCH_SIZE, latent_dim_hypernet, device=device)
|
|
bundle = self._get_bundle(device=device)
|
|
|
|
implicit_function = SRNHyperNetImplicitFunction(hypernet_args=hyper_args)
|
|
implicit_function2 = SRNHyperNetImplicitFunction(hypernet_args=hyper_args)
|
|
implicit_function.to(device)
|
|
implicit_function2.to(device)
|
|
|
|
wrapper = ImplicitFunctionWrapper(implicit_function)
|
|
optimizer = torch.optim.Adam(implicit_function.parameters())
|
|
for _step in range(3):
|
|
optimizer.zero_grad()
|
|
wrapper.bind_args(global_code=global_code)
|
|
rays_densities, _rays_colors = wrapper(bundle)
|
|
wrapper.unbind_args()
|
|
loss = rays_densities.sum()
|
|
loss.backward()
|
|
optimizer.step()
|
|
|
|
wrapper2 = ImplicitFunctionWrapper(implicit_function)
|
|
optimizer2 = torch.optim.Adam(implicit_function2.parameters())
|
|
implicit_function2.load_state_dict(implicit_function.state_dict())
|
|
optimizer2.load_state_dict(optimizer.state_dict())
|
|
for _step in range(3):
|
|
optimizer2.zero_grad()
|
|
wrapper2.bind_args(global_code=global_code)
|
|
rays_densities, _rays_colors = wrapper2(bundle)
|
|
wrapper2.unbind_args()
|
|
loss = rays_densities.sum()
|
|
loss.backward()
|
|
optimizer2.step()
|