Update Harmonic embedding in NeRF

Summary: Removed harmonic embedding function from projects/nerf and changed import to be from core pytorch3d.

Reviewed By: patricklabatut

Differential Revision: D33142358

fbshipit-source-id: 3004247d50392dbd04ea72e9cd4bace0dc03606b

projects/nerf/nerf/harmonic_embedding.py

@@ -1,88 +0,0 @@
-# Copyright (c) Facebook, Inc. and its 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 torch
-
-
-class HarmonicEmbedding(torch.nn.Module):
-    def __init__(
-        self,
-        n_harmonic_functions: int = 6,
-        omega0: float = 1.0,
-        logspace: bool = True,
-        include_input: bool = True,
-    ) -> None:
-        """
-        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`,
-        where for each i in range(dim) the following are present
-        in embedding[...]:
-            ```
-            [
-                sin(x[..., i]),
-                sin(f_1*x[..., i]),
-                sin(f_2*x[..., i]),
-                ...
-                sin(f_N * x[..., i]),
-                cos(x[..., i]),
-                cos(f_1*x[..., i]),
-                cos(f_2*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
-        powers of 2:
-            `f_1 = 1, ..., f_N = 2**torch.arange(n_harmonic_functions)`
-
-        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
-            )`
-
-        Note that `x` is also premultiplied by the base frequency `omega0`
-        before evaluating the harmonic functions.
-        """
-        super().__init__()
-
-        if logspace:
-            frequencies = 2.0 ** torch.arange(
-                n_harmonic_functions,
-                dtype=torch.float32,
-            )
-        else:
-            frequencies = torch.linspace(
-                1.0,
-                2.0 ** (n_harmonic_functions - 1),
-                n_harmonic_functions,
-                dtype=torch.float32,
-            )
-
-        self.register_buffer("_frequencies", omega0 * frequencies, persistent=False)
-        self.include_input = include_input
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """
-        Args:
-            x: tensor of shape [..., dim]
-        Returns:
-            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)
-        if self.include_input:
-            return torch.cat((embed.sin(), embed.cos(), x), dim=-1)
-        else:
-            return torch.cat((embed.sin(), embed.cos()), dim=-1)

projects/nerf/nerf/implicit_function.py

@@ -7,9 +7,8 @@
 from typing import Tuple
 
 import torch
-from pytorch3d.renderer import RayBundle, ray_bundle_to_ray_points
+from pytorch3d.renderer import RayBundle, ray_bundle_to_ray_points, HarmonicEmbedding
 
-from .harmonic_embedding import HarmonicEmbedding
 from .linear_with_repeat import LinearWithRepeat