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Let harmonic embedding layer include input (NeRF)

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Summary: When harmonic embedding is used, we always cat its input onto its output before proceeding. Avoid an intermediate tensor by making the module do that for itself.

Reviewed By: davnov134

Differential Revision: D28185791

fbshipit-source-id: 98d92c94a918dd42e16cdadcaac71dabbc7de5c3
This commit is contained in:
Jeremy Reizenstein 2021-06-02 05:42:15 -07:00 committed by Facebook GitHub Bot
parent ab73f8c3fd
commit f63e49d245
2 changed files with 22 additions and 21 deletions
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30
projects/nerf/nerf/harmonic_embedding.py
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@ -8,14 +8,16 @@ class HarmonicEmbedding(torch.nn.Module):
n_harmonic_functions: int = 6,
omega0: float = 1.0,
logspace: bool = True,
include_input: bool = True,
):
"""
Given an input tensor `x` of shape [minibatch, ... , dim],
the harmonic embedding layer converts each feature
in `x` into a series of harmonic features `embedding`
as follows:
in `x` into a series of harmonic features `embedding`,
where for each i in range(dim) the following are present
in embedding[...]:
```
embedding[..., i*dim:(i+1)*dim] = [
[
sin(x[..., i]),
sin(f_1*x[..., i]),
sin(f_2*x[..., i]),
@ -25,17 +27,20 @@ class HarmonicEmbedding(torch.nn.Module):
cos(f_1*x[..., i]),
cos(f_2*x[..., i]),
...
cos(f_N * x[..., i])
cos(f_N * x[..., i]),
x[..., i] # only present if include_input is True.
]
```
where N corresponds to `n_harmonic_functions`, and f_i is a scalar
denoting the i-th frequency of the harmonic embedding.
The shape of the output is [minibatch, ... , dim * (2 * N + 1)] if
include_input is True, otherwise [minibatch, ... , dim * (2 * N)].
If `logspace==True`, the frequencies `[f_1, ..., f_N]` are
either powers of 2:
`f_1, ..., f_N = 2**torch.arange(n_harmonic_functions)`
powers of 2:
`f_1 = 1, ..., f_N = 2**torch.arange(n_harmonic_functions)`
If `logspace==False`, frequencies are linearly spaced between
If `logspace==False`, frequencies are linearly spaced between
`1.0` and `2**(n_harmonic_functions-1)`:
`f_1, ..., f_N = torch.linspace(
1.0, 2**(n_harmonic_functions-1), n_harmonic_functions
@ -60,14 +65,19 @@ class HarmonicEmbedding(torch.nn.Module):
)
self.register_buffer("_frequencies", omega0 * frequencies)
self.include_input = include_input
def forward(self, x: torch.Tensor):
"""
Args:
x: tensor of shape [..., dim]
Returns:
embedding: a harmonic embedding of `x`
of shape [..., n_harmonic_functions * dim * 2]
embedding: a harmonic embedding of `x` of shape
[..., dim * (n_harmonic_functions * 2 + T)] where
T is 1 if include_input is True and 0 otherwise.
"""
embed = (x[..., None] * self._frequencies).view(*x.shape[:-1], -1)
return torch.cat((embed.sin(), embed.cos()), dim=-1)
if self.include_input:
return torch.cat((embed.sin(), embed.cos(), x), dim=-1)
else:
return torch.cat((embed.sin(), embed.cos()), dim=-1)

13
projects/nerf/nerf/implicit_function.py
View File

@ -121,13 +121,7 @@ class NeuralRadianceField(torch.nn.Module):
rays_directions_normed = torch.nn.functional.normalize(rays_directions, dim=-1)
# Obtain the harmonic embedding of the normalized ray directions.
rays_embedding = torch.cat(
(
self.harmonic_embedding_dir(rays_directions_normed),
rays_directions_normed,
),
dim=-1,
)
rays_embedding = self.harmonic_embedding_dir(rays_directions_normed)
return self.color_layer((self.intermediate_linear(features), rays_embedding))
@ -168,10 +162,7 @@ class NeuralRadianceField(torch.nn.Module):
# rays_points_world.shape = [minibatch x ... x 3]
# For each 3D world coordinate, we obtain its harmonic embedding.
embeds_xyz = torch.cat(
(self.harmonic_embedding_xyz(rays_points_world), rays_points_world),
dim=-1,
)
embeds_xyz = self.harmonic_embedding_xyz(rays_points_world)
# embeds_xyz.shape = [minibatch x ... x self.n_harmonic_functions*6 + 3]
# self.mlp maps each harmonic embedding to a latent feature space.