Move Harmonic embedding to core pytorch3d

Summary:
Moved `HarmonicEmbedding` function in core PyTorch3D.
In the next diff will update the NeRF project.

Reviewed By: bottler

Differential Revision: D32833808

fbshipit-source-id: 0a12ccd1627c0ce024463c796544c91eb8d4d122

pytorch3d/renderer/__init__.py

@@ -37,6 +37,7 @@ from .implicit import (
     VolumeSampler,
     ray_bundle_to_ray_points,
     ray_bundle_variables_to_ray_points,
+    HarmonicEmbedding,
 )
 from .lighting import AmbientLights, DirectionalLights, PointLights, diffuse, specular
 from .materials import Materials

pytorch3d/renderer/implicit/__init__.py

@@ -4,6 +4,7 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+from .harmonic_embedding import HarmonicEmbedding
 from .raymarching import AbsorptionOnlyRaymarcher, EmissionAbsorptionRaymarcher
 from .raysampling import GridRaysampler, MonteCarloRaysampler, NDCGridRaysampler
 from .renderer import ImplicitRenderer, VolumeRenderer, VolumeSampler
@@ -13,5 +14,4 @@ from .utils import (
     ray_bundle_variables_to_ray_points,
 )
 
-
 __all__ = [k for k in globals().keys() if not k.startswith("_")]

pytorch3d/renderer/implicit/harmonic_embedding.py

@@ -0,0 +1,127 @@
+# 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,
+        omega_0: float = 1.0,
+        logspace: bool = True,
+        append_input: bool = True,
+    ) -> None:
+        """
+        Given an input tensor `x` of shape [minibatch, ... , dim],
+        the harmonic embedding layer converts each feature
+        (i.e. vector along the last dimension) in `x`
+        into a series of harmonic features `embedding`,
+        where for each i in range(dim) the following are present
+        in embedding[...]:
+            ```
+            [
+                sin(f_1*x[..., i]),
+                sin(f_2*x[..., i]),
+                ...
+                sin(f_N * x[..., i]),
+                cos(f_1*x[..., i]),
+                cos(f_2*x[..., i]),
+                ...
+                cos(f_N * x[..., i]),
+                x[..., i],              # only present if append_input is True.
+            ]
+            ```
+        where N corresponds to `n_harmonic_functions-1`, and f_i is a scalar
+        denoting the i-th frequency of the harmonic embedding.
+
+        If `logspace==True`, the frequencies `[f_1, ..., f_N]` are
+        powers of 2:
+            `f_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 `omega_0`
+        before evaluating the harmonic functions.
+
+        Args:
+            n_harmonic_functions: int, number of harmonic
+                features
+            omega_0: float, base frequency
+            logspace: bool, Whether to space the frequencies in
+                logspace or linear space
+            append_input: bool, whether to concat the original
+                input to the harmonic embedding. If true the
+                output is of the form (x, embed.sin(), embed.cos()
+
+        """
+        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", frequencies * omega_0, persistent=False)
+        self.append_input = append_input
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            x: tensor of shape [..., dim]
+        Returns:
+            embedding: a harmonic embedding of `x`
+                of shape [..., (n_harmonic_functions * 2 + int(append_input)) * dim]
+        """
+        embed = (x[..., None] * self._frequencies).view(*x.shape[:-1], -1)
+        embed = torch.cat(
+            (embed.sin(), embed.cos(), x)
+            if self.append_input
+            else (embed.sin(), embed.cos()),
+            dim=-1,
+        )
+        return embed
+
+    @staticmethod
+    def get_output_dim_static(
+        input_dims: int,
+        n_harmonic_functions: int,
+        append_input: bool,
+    ) -> int:
+        """
+        Utility to help predict the shape of the output of `forward`.
+
+        Args:
+            input_dims: length of the last dimension of the input tensor
+            n_harmonic_functions: number of embedding frequencies
+            append_input: whether or not to concat the original
+                input to the harmonic embedding
+        Returns:
+            int: the length of the last dimension of the output tensor
+        """
+        return input_dims * (2 * n_harmonic_functions + int(append_input))
+
+    def get_output_dim(self, input_dims: int = 3) -> int:
+        """
+        Same as above. The default for input_dims is 3 for 3D applications
+        which use harmonic embedding for positional encoding,
+        so the input might be xyz.
+        """
+        return self.get_output_dim_static(
+            input_dims, len(self._frequencies), self.append_input
+        )