move LinearWithRepeat to pytorch3d

Summary: Move this simple layer from the NeRF project into pytorch3d.

Reviewed By: shapovalov

Differential Revision: D34126972

fbshipit-source-id: a9c6d6c3c1b662c1b844ea5d1b982007d4df83e6

projects/nerf/nerf/implicit_function.py

@@ -7,10 +7,9 @@
 from typing import Tuple
 
 import torch
+from pytorch3d.common.linear_with_repeat import LinearWithRepeat
 from pytorch3d.renderer import HarmonicEmbedding, RayBundle, ray_bundle_to_ray_points
 
-from .linear_with_repeat import LinearWithRepeat
-
 
 def _xavier_init(linear):
     """

projects/nerf/nerf/linear_with_repeat.py → pytorch3d/common/linear_with_repeat.py

@@ -4,13 +4,15 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+import math
 from typing import Tuple
 
 import torch
 import torch.nn.functional as F
+from torch.nn import Parameter, init
 
 
-class LinearWithRepeat(torch.nn.Linear):
+class LinearWithRepeat(torch.nn.Module):
     """
     if x has shape (..., k, n1)
     and y has shape (..., n2)
@@ -50,6 +52,40 @@ class LinearWithRepeat(torch.nn.Linear):
     and sent that through the Linear.
     """
 
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        bias: bool = True,
+        device=None,
+        dtype=None,
+    ) -> None:
+        """
+        Copied from torch.nn.Linear.
+        """
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.weight = Parameter(
+            torch.empty((out_features, in_features), **factory_kwargs)
+        )
+        if bias:
+            self.bias = Parameter(torch.empty(out_features, **factory_kwargs))
+        else:
+            self.register_parameter("bias", None)
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        """
+        Copied from torch.nn.Linear.
+        """
+        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+        if self.bias is not None:
+            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
+            bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0
+            init.uniform_(self.bias, -bound, bound)
+
     def forward(self, input: Tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor:
         n1 = input[0].shape[-1]
         output1 = F.linear(input[0], self.weight[:, :n1], self.bias)

pytorch3d/renderer/__init__.py

@@ -73,8 +73,8 @@ from .points import (
 from .utils import (
     TensorProperties,
     convert_to_tensors_and_broadcast,
-    ndc_to_grid_sample_coords,
     ndc_grid_sample,
+    ndc_to_grid_sample_coords,
 )
 
 

pytorch3d/renderer/utils.py

@@ -8,7 +8,7 @@
 import copy
 import inspect
 import warnings
-from typing import Any, Optional, Union, Tuple
+from typing import Any, Optional, Tuple, Union
 
 import numpy as np
 import torch

tests/test_common_linear_with_repeat.py

+# 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 common_testing import TestCaseMixin
+from pytorch3d.common.linear_with_repeat import LinearWithRepeat
+
+
+class TestLinearWithRepeat(TestCaseMixin, unittest.TestCase):
+    def setUp(self) -> None:
+        super().setUp()
+        torch.manual_seed(42)
+
+    def test_simple(self):
+        x = torch.rand(4, 6, 7, 3)
+        y = torch.rand(4, 6, 4)
+
+        linear = torch.nn.Linear(7, 8)
+        torch.nn.init.xavier_uniform_(linear.weight.data)
+        linear.bias.data.uniform_()
+        equivalent = torch.cat([x, y.unsqueeze(-2).expand(4, 6, 7, 4)], dim=-1)
+        expected = linear.forward(equivalent)
+
+        linear_with_repeat = LinearWithRepeat(7, 8)
+        linear_with_repeat.load_state_dict(linear.state_dict())
+        actual = linear_with_repeat.forward((x, y))
+        self.assertClose(actual, expected, rtol=1e-4)

tests/test_rendering_utils.py

@@ -12,16 +12,16 @@ import torch
 from common_testing import TestCaseMixin
 from pytorch3d.ops import eyes
 from pytorch3d.renderer import (
-    PerspectiveCameras,
     AlphaCompositor,
-    PointsRenderer,
+    PerspectiveCameras,
     PointsRasterizationSettings,
     PointsRasterizer,
+    PointsRenderer,
 )
 from pytorch3d.renderer.utils import (
     TensorProperties,
-    ndc_to_grid_sample_coords,
     ndc_grid_sample,
+    ndc_to_grid_sample_coords,
 )
 from pytorch3d.structures import Pointclouds