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# 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
from typing import Optional, Tuple
import torch
from omegaconf import DictConfig, OmegaConf
from pytorch3d.implicitron.models.implicit_function.utils import (
interpolate_line,
interpolate_plane,
interpolate_volume,
)
from pytorch3d.implicitron.models.implicit_function.voxel_grid import (
CPFactorizedVoxelGrid,
FullResolutionVoxelGrid,
VMFactorizedVoxelGrid,
VoxelGridModule,
)
from pytorch3d.implicitron.tools.config import expand_args_fields, get_default_args
from tests.common_testing import TestCaseMixin
class TestVoxelGrids(TestCaseMixin, unittest.TestCase):
"""
Tests Voxel grids, tests them by setting all elements to zero (after retrieving
they should also return zero) and by setting all of the elements to one and
getting the result. Also tests the interpolation by 'manually' interpolating
one by one sample and comparing with the batched implementation.
"""
def get_random_normalized_points(
self, n_grids, n_points=None, dimension=3
) -> torch.Tensor:
middle_shape = torch.randint(1, 4, tuple(torch.randint(1, 5, size=(1,))))
# create random query points
return (
torch.rand(
n_grids, *(middle_shape if n_points is None else [n_points]), dimension
)
* 2
- 1
)
def _test_query_with_constant_init_cp(
self,
n_grids: int,
n_features: int,
n_components: int,
resolution: Tuple[int],
value: float = 1,
) -> None:
# set everything to 'value' and do query for elementsthe result should
# be of shape (n_grids, n_points, n_features) and be filled with n_components
# * value
grid = CPFactorizedVoxelGrid(
resolution_changes={0: resolution},
n_components=n_components,
n_features=n_features,
)
shapes = grid.get_shapes(epoch=0)
params = grid.values_type(
**{k: torch.ones(n_grids, *shapes[k]) * value for k in shapes}
)
points = self.get_random_normalized_points(n_grids)
assert torch.allclose(
grid.evaluate_local(points, params),
torch.ones(n_grids, *points.shape[1:-1], n_features) * n_components * value,
rtol=0.0001,
)
def _test_query_with_constant_init_vm(
self,
n_grids: int,
n_features: int,
resolution: Tuple[int],
n_components: Optional[int] = None,
distribution: Optional[Tuple[int]] = None,
value: float = 1,
n_points: int = 1,
) -> None:
# set everything to 'value' and do query for elements
grid = VMFactorizedVoxelGrid(
n_features=n_features,
resolution_changes={0: resolution},
n_components=n_components,
distribution_of_components=distribution,
)
shapes = grid.get_shapes(epoch=0)
params = grid.values_type(
**{k: torch.ones(n_grids, *shapes[k]) * value for k in shapes}
)
expected_element = (
n_components * value if distribution is None else sum(distribution) * value
)
points = self.get_random_normalized_points(n_grids)
assert torch.allclose(
grid.evaluate_local(points, params),
torch.ones(n_grids, *points.shape[1:-1], n_features) * expected_element,
)
def _test_query_with_constant_init_full(
self,
n_grids: int,
n_features: int,
resolution: Tuple[int],
value: int = 1,
n_points: int = 1,
) -> None:
# set everything to 'value' and do query for elements
grid = FullResolutionVoxelGrid(
n_features=n_features, resolution_changes={0: resolution}
)
shapes = grid.get_shapes(epoch=0)
params = grid.values_type(
**{k: torch.ones(n_grids, *shapes[k]) * value for k in shapes}
)
expected_element = value
points = self.get_random_normalized_points(n_grids)
assert torch.allclose(
grid.evaluate_local(points, params),
torch.ones(n_grids, *points.shape[1:-1], n_features) * expected_element,
)
def test_query_with_constant_init(self):
with self.subTest("Full"):
self._test_query_with_constant_init_full(
n_grids=5, n_features=6, resolution=(3, 4, 5)
)
with self.subTest("Full with 1 in dimensions"):
self._test_query_with_constant_init_full(
n_grids=5, n_features=1, resolution=(33, 41, 1)
)
with self.subTest("CP"):
self._test_query_with_constant_init_cp(
n_grids=5,
n_features=6,
n_components=7,
resolution=(3, 4, 5),
)
with self.subTest("CP with 1 in dimensions"):
self._test_query_with_constant_init_cp(
n_grids=2,
n_features=1,
n_components=3,
resolution=(3, 1, 1),
)
with self.subTest("VM with symetric distribution"):
self._test_query_with_constant_init_vm(
n_grids=6,
n_features=9,
resolution=(2, 12, 2),
n_components=12,
)
with self.subTest("VM with distribution"):
self._test_query_with_constant_init_vm(
n_grids=5,
n_features=1,
resolution=(5, 9, 7),
distribution=(33, 41, 1),
)
def test_query_with_zero_init(self):
with self.subTest("Query testing with zero init CPFactorizedVoxelGrid"):
self._test_query_with_constant_init_cp(
n_grids=5,
n_features=6,
n_components=7,
resolution=(3, 2, 5),
value=0,
)
with self.subTest("Query testing with zero init VMFactorizedVoxelGrid"):
self._test_query_with_constant_init_vm(
n_grids=2,
n_features=9,
resolution=(2, 11, 3),
n_components=3,
value=0,
)
with self.subTest("Query testing with zero init FullResolutionVoxelGrid"):
self._test_query_with_constant_init_full(
n_grids=4, n_features=2, resolution=(3, 3, 5), value=0
)
def setUp(self):
torch.manual_seed(42)
expand_args_fields(FullResolutionVoxelGrid)
expand_args_fields(CPFactorizedVoxelGrid)
expand_args_fields(VMFactorizedVoxelGrid)
expand_args_fields(VoxelGridModule)
def _interpolate_1D(
self, points: torch.Tensor, vectors: torch.Tensor
) -> torch.Tensor:
"""
interpolate vector from points, which are (batch, 1) and individual point is in [-1, 1]
"""
result = []
_, _, width = vectors.shape
# transform from [-1, 1] to [0, width-1]
points = (points + 1) / 2 * (width - 1)
for vector, row in zip(vectors, points):
newrow = []
for x in row:
xf, xc = int(torch.floor(x)), int(torch.ceil(x))
itemf, itemc = vector[:, xf], vector[:, xc]
tmp = itemf * (xc - x) + itemc * (x - xf)
newrow.append(tmp[None, None, :])
result.append(torch.cat(newrow, dim=1))
return torch.cat(result)
def _interpolate_2D(
self, points: torch.Tensor, matrices: torch.Tensor
) -> torch.Tensor:
"""
interpolate matrix from points, which are (batch, 2) and individual point is in [-1, 1]
"""
result = []
n_grids, _, width, height = matrices.shape
points = (points + 1) / 2 * (torch.tensor([[[width, height]]]) - 1)
for matrix, row in zip(matrices, points):
newrow = []
for x, y in row:
xf, xc = int(torch.floor(x)), int(torch.ceil(x))
yf, yc = int(torch.floor(y)), int(torch.ceil(y))
itemff, itemfc = matrix[:, xf, yf], matrix[:, xf, yc]
itemcf, itemcc = matrix[:, xc, yf], matrix[:, xc, yc]
itemf = itemff * (xc - x) + itemcf * (x - xf)
itemc = itemfc * (xc - x) + itemcc * (x - xf)
tmp = itemf * (yc - y) + itemc * (y - yf)
newrow.append(tmp[None, None, :])
result.append(torch.cat(newrow, dim=1))
return torch.cat(result)
def _interpolate_3D(
self, points: torch.Tensor, tensors: torch.Tensor
) -> torch.Tensor:
"""
interpolate tensors from points, which are (batch, 3) and individual point is in [-1, 1]
"""
result = []
_, _, width, height, depth = tensors.shape
batch_normalized_points = (
(points + 1) / 2 * (torch.tensor([[[width, height, depth]]]) - 1)
)
batch_points = points
for tensor, points, normalized_points in zip(
tensors, batch_points, batch_normalized_points
):
newrow = []
for (x, y, z), (_, _, nz) in zip(points, normalized_points):
zf, zc = int(torch.floor(nz)), int(torch.ceil(nz))
itemf = self._interpolate_2D(
points=torch.tensor([[[x, y]]]), matrices=tensor[None, :, :, :, zf]
)
itemc = self._interpolate_2D(
points=torch.tensor([[[x, y]]]), matrices=tensor[None, :, :, :, zc]
)
tmp = self._interpolate_1D(
points=torch.tensor([[[z]]]),
vectors=torch.cat((itemf, itemc), dim=1).permute(0, 2, 1),
)
newrow.append(tmp)
result.append(torch.cat(newrow, dim=1))
return torch.cat(result)
def test_interpolation(self):
with self.subTest("1D interpolation"):
points = self.get_random_normalized_points(
n_grids=4, n_points=5, dimension=1
)
vector = torch.randn(size=(4, 3, 2))
assert torch.allclose(
self._interpolate_1D(points, vector),
interpolate_line(
points,
vector,
align_corners=True,
padding_mode="zeros",
mode="bilinear",
),
rtol=0.0001,
atol=0.0001,
)
with self.subTest("2D interpolation"):
points = self.get_random_normalized_points(
n_grids=4, n_points=5, dimension=2
)
matrix = torch.randn(size=(4, 2, 3, 5))
assert torch.allclose(
self._interpolate_2D(points, matrix),
interpolate_plane(
points,
matrix,
align_corners=True,
padding_mode="zeros",
mode="bilinear",
),
rtol=0.0001,
atol=0.0001,
)
with self.subTest("3D interpolation"):
points = self.get_random_normalized_points(
n_grids=4, n_points=5, dimension=3
)
tensor = torch.randn(size=(4, 5, 2, 7, 2))
assert torch.allclose(
self._interpolate_3D(points, tensor),
interpolate_volume(
points,
tensor,
align_corners=True,
padding_mode="zeros",
mode="bilinear",
),
rtol=0.0001,
atol=0.0001,
)
def test_floating_point_query(self):
"""
test querying the voxel grids on some float positions
"""
with self.subTest("FullResolution"):
grid = FullResolutionVoxelGrid(
n_features=1, resolution_changes={0: (1, 1, 1)}
)
params = grid.values_type(**grid.get_shapes(epoch=0))
params.voxel_grid = torch.tensor(
[
[
[[[1, 3], [5, 7]], [[9, 11], [13, 15]]],
[[[2, 4], [6, 8]], [[10, 12], [14, 16]]],
],
[
[[[17, 18], [19, 20]], [[21, 22], [23, 24]]],
[[[25, 26], [27, 28]], [[29, 30], [31, 32]]],
],
],
dtype=torch.float,
)
points = (
torch.tensor(
[
[
[1, 0, 1],
[0.5, 1, 1],
[1 / 3, 1 / 3, 2 / 3],
],
[
[0, 1, 1],
[0, 0.5, 1],
[1 / 4, 1 / 4, 3 / 4],
],
]
)
/ torch.tensor([[1.0, 1, 1]])
* 2
- 1
)
expected_result = torch.tensor(
[
[[11, 12], [11, 12], [6.333333, 7.3333333]],
[[20, 28], [19, 27], [19.25, 27.25]],
]
)
assert torch.allclose(
grid.evaluate_local(points, params),
expected_result,
rtol=0.0001,
atol=0.0001,
), grid.evaluate_local(points, params)
with self.subTest("CP"):
grid = CPFactorizedVoxelGrid(
n_features=1, resolution_changes={0: (1, 1, 1)}, n_components=3
)
params = grid.values_type(**grid.get_shapes(epoch=0))
params.vector_components_x = torch.tensor(
[
[[1, 2], [10.5, 20.5]],
[[10, 20], [2, 4]],
]
)
params.vector_components_y = torch.tensor(
[
[[3, 4, 5], [30.5, 40.5, 50.5]],
[[30, 40, 50], [1, 3, 5]],
]
)
params.vector_components_z = torch.tensor(
[
[[6, 7, 8, 9], [60.5, 70.5, 80.5, 90.5]],
[[60, 70, 80, 90], [6, 7, 8, 9]],
]
)
params.basis_matrix = torch.tensor(
[
[[2.0], [2.0]],
[[1.0], [2.0]],
]
)
points = (
torch.tensor(
[
[
[0, 2, 2],
[1, 2, 0.25],
[0.5, 0.5, 1],
[1 / 3, 2 / 3, 2 + 1 / 3],
],
[
[1, 0, 1],
[0.5, 2, 2],
[1, 0.5, 0.5],
[1 / 4, 3 / 4, 2 + 1 / 4],
],
]
)
/ torch.tensor([[[1.0, 2, 3]]])
* 2
- 1
)
expected_result_matrix = torch.tensor(
[
[[85450.25], [130566.5], [77658.75], [86285.422]],
[[42056], [60240], [45604], [38775]],
]
)
expected_result_sum = torch.tensor(
[
[[42725.125], [65283.25], [38829.375], [43142.711]],
[[42028], [60120], [45552], [38723.4375]],
]
)
with self.subTest("CP with basis_matrix reduction"):
assert torch.allclose(
grid.evaluate_local(points, params),
expected_result_matrix,
rtol=0.0001,
atol=0.0001,
)
del params.basis_matrix
with self.subTest("CP with sum reduction"):
assert torch.allclose(
grid.evaluate_local(points, params),
expected_result_sum,
rtol=0.0001,
atol=0.0001,
)
with self.subTest("VM"):
grid = VMFactorizedVoxelGrid(
n_features=1, resolution_changes={0: (1, 1, 1)}, n_components=3
)
params = VMFactorizedVoxelGrid.values_type(**grid.get_shapes(epoch=0))
params.matrix_components_xy = torch.tensor(
[
[[[1, 2], [3, 4]], [[19, 20], [21, 22.0]]],
[[[35, 36], [37, 38]], [[39, 40], [41, 42]]],
]
)
params.matrix_components_xz = torch.tensor(
[
[[[7, 8], [9, 10]], [[25, 26], [27, 28.0]]],
[[[43, 44], [45, 46]], [[47, 48], [49, 50]]],
]
)
params.matrix_components_yz = torch.tensor(
[
[[[13, 14], [15, 16]], [[31, 32], [33, 34.0]]],
[[[51, 52], [53, 54]], [[55, 56], [57, 58.0]]],
]
)
params.vector_components_z = torch.tensor(
[
[[5, 6], [23, 24.0]],
[[59, 60], [61, 62]],
]
)
params.vector_components_y = torch.tensor(
[
[[11, 12], [29, 30.0]],
[[63, 64], [65, 66]],
]
)
params.vector_components_x = torch.tensor(
[
[[17, 18], [35, 36.0]],
[[67, 68], [69, 70.0]],
]
)
params.basis_matrix = torch.tensor(
[
[2, 2, 2, 2, 2, 2.0],
[1, 2, 1, 2, 1, 2.0],
]
)[:, :, None]
points = (
torch.tensor(
[
[
[1, 0, 1],
[0.5, 1, 1],
[1 / 3, 1 / 3, 2 / 3],
],
[
[0, 1, 0],
[0, 0, 0],
[0, 1, 0],
],
]
)
/ torch.tensor([[[1.0, 1, 1]]])
* 2
- 1
)
expected_result_matrix = torch.tensor(
[
[[5696], [5854], [5484.888]],
[[27377], [26649], [27377]],
]
)
expected_result_sum = torch.tensor(
[
[[2848], [2927], [2742.444]],
[[17902], [17420], [17902]],
]
)
with self.subTest("VM with basis_matrix reduction"):
assert torch.allclose(
grid.evaluate_local(points, params),
expected_result_matrix,
rtol=0.0001,
atol=0.0001,
)
del params.basis_matrix
with self.subTest("VM with sum reduction"):
assert torch.allclose(
grid.evaluate_local(points, params),
expected_result_sum,
rtol=0.0001,
atol=0.0001,
), grid.evaluate_local(points, params)
def test_forward_with_small_init_std(self):
"""
Test does the grid return small values if it is initialized with small
mean and small standard deviation.
"""
def test(cls, **kwargs):
with self.subTest(cls.__name__):
n_grids = 3
grid = cls(**kwargs)
shapes = grid.get_shapes(epoch=0)
params = cls.values_type(
**{
k: torch.normal(mean=torch.zeros(n_grids, *shape), std=0.0001)
for k, shape in shapes.items()
}
)
points = self.get_random_normalized_points(n_grids=n_grids, n_points=3)
max_expected_result = torch.zeros((len(points), 10)) + 1e-2
assert torch.all(
grid.evaluate_local(points, params) < max_expected_result
)
test(
FullResolutionVoxelGrid,
resolution_changes={0: (4, 6, 9)},
n_features=10,
)
test(
CPFactorizedVoxelGrid,
resolution_changes={0: (4, 6, 9)},
n_features=10,
n_components=3,
)
test(
VMFactorizedVoxelGrid,
resolution_changes={0: (4, 6, 9)},
n_features=10,
n_components=3,
)
def test_voxel_grid_module_location(self, n_times=10):
"""
This checks the module uses locator correctly etc..
If we know that voxel grids work for (x, y, z) in local coordinates
to test if the VoxelGridModule does not have permuted dimensions we
create local coordinates, pass them through verified voxelgrids and
compare the result with the result that we get when we convert
coordinates to world and pass them through the VoxelGridModule
"""
for _ in range(n_times):
extents = tuple(torch.randint(1, 50, size=(3,)).tolist())
grid = VoxelGridModule(extents=extents)
local_point = torch.rand(1, 3) * 2 - 1
world_point = local_point * torch.tensor(extents) / 2
grid_values = grid.voxel_grid.values_type(**grid.params)
assert torch.allclose(
grid(world_point)[0, 0],
grid.voxel_grid.evaluate_local(local_point[None], grid_values)[0, 0, 0],
rtol=0.0001,
atol=0.0001,
)
def test_resolution_change(self, n_times=10):
for _ in range(n_times):
n_grids, n_features, n_components = torch.randint(1, 3, (3,)).tolist()
resolution = torch.randint(3, 10, (3,)).tolist()
resolution2 = torch.randint(3, 10, (3,)).tolist()
resolution_changes = {0: resolution, 1: resolution2}
n_components *= 3
for cls, kwargs in (
(
FullResolutionVoxelGrid,
{
"n_features": n_features,
"resolution_changes": resolution_changes,
},
),
(
CPFactorizedVoxelGrid,
{
"n_features": n_features,
"resolution_changes": resolution_changes,
"n_components": n_components,
},
),
(
VMFactorizedVoxelGrid,
{
"n_features": n_features,
"resolution_changes": resolution_changes,
"n_components": n_components,
},
),
):
with self.subTest(cls.__name__):
grid = cls(**kwargs)
self.assertEqual(grid.get_resolution(epoch=0), resolution)
shapes = grid.get_shapes(epoch=0)
params = {
name: torch.randn((n_grids, *shape))
for name, shape in shapes.items()
}
grid_values = grid.values_type(**params)
grid_values_changed_resolution, change = grid.change_resolution(
epoch=1,
grid_values=grid_values,
mode="linear",
)
assert change
self.assertEqual(grid.get_resolution(epoch=1), resolution2)
shapes_changed_resolution = grid.get_shapes(epoch=1)
for name, expected_shape in shapes_changed_resolution.items():
shape = getattr(grid_values_changed_resolution, name).shape
self.assertEqual(expected_shape, shape[1:])
with self.subTest("VoxelGridModule"):
n_changes = 10
grid = VoxelGridModule()
resolution_changes = {i: (i + 2, i + 2, i + 2) for i in range(n_changes)}
grid.voxel_grid = FullResolutionVoxelGrid(
resolution_changes=resolution_changes
)
epochs, apply_func = grid.subscribe_to_epochs()
self.assertEqual(list(range(n_changes)), list(epochs))
for epoch in epochs:
change = apply_func(epoch)
assert change
self.assertEqual(
resolution_changes[epoch],
grid.voxel_grid.get_resolution(epoch=epoch),
)
def _get_min_max_tuple(
self, n=4, denominator_base=2, max_exponent=6, add_edge_cases=True
):
if add_edge_cases:
n -= 2
def get_pair():
def get_one():
sign = -1 if torch.rand((1,)) < 0.5 else 1
exponent = int(torch.randint(1, max_exponent, (1,)))
denominator = denominator_base**exponent
numerator = int(torch.randint(1, denominator, (1,)))
return sign * numerator / denominator * 1.0
while True:
a, b = get_one(), get_one()
if a < b:
return a, b
for _ in range(n):
a, b, c = get_pair(), get_pair(), get_pair()
yield torch.tensor((a[0], b[0], c[0])), torch.tensor((a[1], b[1], c[1]))
if add_edge_cases:
yield torch.tensor((-1.0, -1.0, -1.0)), torch.tensor((1.0, 1.0, 1.0))
yield torch.tensor([0.0, 0.0, 0.0]), torch.tensor([1.0, 1.0, 1.0])
def test_cropping_voxel_grids(self, n_times=1):
"""
If the grid is 1d and we crop at A and B
---------A---------B---
and choose point p between them
---------A-----p---B---
it can be represented as
p = A + (B-A) * p_c
where p_c is local coordinate of p in cropped grid. So we now just see
if grid evaluated at p and cropped grid evaluated at p_c agree.
"""
for points_min, points_max in self._get_min_max_tuple(n=10):
n_grids, n_features, n_components = torch.randint(1, 3, (3,)).tolist()
n_grids = 1
n_components *= 3
resolution_changes = {0: (128 + 1, 128 + 1, 128 + 1)}
for cls, kwargs in (
(
FullResolutionVoxelGrid,
{
"n_features": n_features,
"resolution_changes": resolution_changes,
},
),
(
CPFactorizedVoxelGrid,
{
"n_features": n_features,
"resolution_changes": resolution_changes,
"n_components": n_components,
},
),
(
VMFactorizedVoxelGrid,
{
"n_features": n_features,
"resolution_changes": resolution_changes,
"n_components": n_components,
},
),
):
with self.subTest(
cls.__name__ + f" points {points_min} and {points_max}"
):
grid = cls(**kwargs)
shapes = grid.get_shapes(epoch=0)
params = {
name: torch.normal(
mean=torch.zeros((n_grids, *shape)),
std=1,
)
for name, shape in shapes.items()
}
grid_values = grid.values_type(**params)
grid_values_cropped = grid.crop_local(
points_min, points_max, grid_values
)
points_local_cropped = torch.rand((1, n_times, 3))
points_local = (
points_min[None, None]
+ (points_max - points_min)[None, None] * points_local_cropped
)
points_local_cropped = (points_local_cropped - 0.5) * 2
pred = grid.evaluate_local(points_local, grid_values)
pred_cropped = grid.evaluate_local(
points_local_cropped, grid_values_cropped
)
assert torch.allclose(pred, pred_cropped, rtol=1e-4, atol=1e-4), (
pred,
pred_cropped,
points_local,
points_local_cropped,
)
def test_cropping_voxel_grid_module(self, n_times=1):
for points_min, points_max in self._get_min_max_tuple(n=5, max_exponent=5):
extents = torch.ones((3,)) * 2
translation = torch.ones((3,)) * 0.2
points_min += translation
points_max += translation
default_cfg = get_default_args(VoxelGridModule)
custom_cfg = DictConfig(
{
"extents": tuple(float(e) for e in extents),
"translation": tuple(float(t) for t in translation),
"voxel_grid_FullResolutionVoxelGrid_args": {
"resolution_changes": {0: (128 + 1, 128 + 1, 128 + 1)}
},
}
)
cfg = OmegaConf.merge(default_cfg, custom_cfg)
grid = VoxelGridModule(**cfg)
points = (torch.rand(3) * (points_max - points_min) + points_min)[None]
result = grid(points)
grid.crop_self(points_min, points_max)
result_cropped = grid(points)
assert torch.allclose(result, result_cropped, rtol=0.001, atol=0.001), (
result,
result_cropped,
)
def test_loading_state_dict(self):
"""
Test loading state dict after rescaling.
Create a voxel grid, rescale it and get the state_dict.
Create a new voxel grid with the same args as the first one and load
the state_dict and check if everything is ok.
"""
n_changes = 10
resolution_changes = {i: (i + 2, i + 2, i + 2) for i in range(n_changes)}
cfg = DictConfig(
{
"voxel_grid_class_type": "VMFactorizedVoxelGrid",
"voxel_grid_VMFactorizedVoxelGrid_args": {
"resolution_changes": resolution_changes,
"n_components": 48,
},
}
)
grid = VoxelGridModule(**cfg)
epochs, apply_func = grid.subscribe_to_epochs()
for epoch in epochs:
apply_func(epoch)
loaded_grid = VoxelGridModule(**cfg)
loaded_grid.load_state_dict(grid.state_dict())
for name_loaded, param_loaded in loaded_grid.named_parameters():
for name, param in grid.named_parameters():
if name_loaded == name:
torch.allclose(param_loaded, param)
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