Skip to content

GitLab

Switch branch/tag
Find file Normal viewHistoryPermalink
test_srn.py 4.35 KB
Newer Older
Jeremy Reizenstein's avatar
implicitron v0 (#1133)  
Jeremy Reizenstein committed
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17 
# 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
Jeremy Reizenstein's avatar
move targets  
Jeremy Reizenstein committed
18 
from tests.common_testing import TestCaseMixin
Jeremy Reizenstein's avatar
implicitron v0 (#1133)  
Jeremy Reizenstein committed
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108 

_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()