From 9ef1ee84552a82dd42dffc5c4dd5521e2cc6fa09 Mon Sep 17 00:00:00 2001 From: Nikhila Ravi Date: Mon, 20 Apr 2020 14:51:19 -0700 Subject: [PATCH] coarse rasterization bug fix Summary: Fix a bug which resulted in a rendering artifacts if the image size was not a multiple of 16. Fix: Revert coarse rasterization to original implementation and only update fine rasterization to reverse the ordering of Y and X axis. This is much simpler than the previous approach! Additional changes: - updated mesh rendering end-end tests to check outputs from both naive and coarse to fine rasterization. - added pointcloud rendering end-end tests Reviewed By: gkioxari Differential Revision: D21102725 fbshipit-source-id: 2e7e1b013dd6dd12b3a00b79eb8167deddb2e89a --- docs/notes/assets/world_camera_image.png | Bin 63883 -> 65284 bytes docs/tutorials/render_colored_points.ipynb | 57 +++--- .../csrc/rasterize_meshes/rasterize_meshes.cu | 29 ++- .../rasterize_meshes/rasterize_meshes_cpu.cpp | 20 +- .../csrc/rasterize_points/rasterize_points.cu | 44 ++--- .../rasterize_points/rasterize_points_cpu.cpp | 20 +- pytorch3d/renderer/mesh/rasterize_meshes.py | 2 +- pytorch3d/renderer/points/rasterizer.py | 28 ++- tests/common_testing.py | 10 + tests/data/test_bridge_pointcloud.png | Bin 0 -> 76219 bytes tests/data/test_simple_pointcloud_sphere.png | Bin 0 -> 2122 bytes tests/test_rasterize_meshes.py | 8 +- tests/test_rasterize_points.py | 37 ++-- ...dering_meshes.py => test_render_meshes.py} | 126 +++++++------ tests/test_render_points.py | 173 ++++++++++++++++++ 15 files changed, 381 insertions(+), 173 deletions(-) create mode 100644 tests/data/test_bridge_pointcloud.png create mode 100644 tests/data/test_simple_pointcloud_sphere.png rename tests/{test_rendering_meshes.py => test_render_meshes.py} (76%) create mode 100644 tests/test_render_points.py diff --git a/docs/notes/assets/world_camera_image.png b/docs/notes/assets/world_camera_image.png index ea0d0a4edde628047ca9f99598a0ad6eaefd2b59..5e61582fd28b2a5821e702cd700e28509c59f477 100644 GIT binary patch literal 65284 zcmeFZWmjBH*DZ_%8h3YhcMI%N}*ea3kI!1-`` zjAmQys@sZbJeLRdKc&HD^T7XSA2j*zZR-}J-+&uv2T3hwVDrG*0P>(w z%phkVAc7!LqC%=3pl8`ohG;{p!;E9echWFjoI-+eb|R{SFm7jE0bTCzt3x$qWtx3~ z5%;LHG+;1b5p&Q;+BoH@x)a6TJ%8HhCW`fl0%X$mTPNi=Egs!+JU87ppvx;NSjt6+ z^N{{=zov=g$4h)2!KG_R-6g=_vmlYsTh9}yeWkGZ~rOq zpS#7l7FhpxU5%u90EtGCDxCjT53CRw;U4ba#{O16dj_ED|8<5`3HeX^|F#$`!Vmtx 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rasterizer=PointsRasterizer(\n", - " cameras=cameras, \n", - " raster_settings=raster_settings\n", - " ),\n", - " compositor=NormWeightedCompositor(\n", - " device=device, \n", - " composite_params=None\n", - " )\n", + " rasterizer=PointsRasterizer(cameras=cameras, raster_settings=raster_settings),\n", + " compositor=NormWeightedCompositor(composite_params=None)\n", ")\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -274,18 +266,21 @@ "(-0.5, 511.5, 511.5, -0.5)" ] }, - "execution_count": 8, - "metadata": {}, + "execution_count": 6, + "metadata": { + "bento_obj_id": "139688210384464" + }, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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" ] }, "metadata": { + "bento_obj_id": "139688209815888", "needs_background": "light" }, "output_type": "display_data" @@ -310,9 +305,9 @@ "bento/extensions/theme/main.css": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "pytorch3d (local)", "language": "python", - "name": "python3" + "name": "pytorch3d_local" }, "language_info": { "codemirror_mode": { diff --git a/pytorch3d/csrc/rasterize_meshes/rasterize_meshes.cu b/pytorch3d/csrc/rasterize_meshes/rasterize_meshes.cu index 0b9adaf5..47f0664e 100644 --- a/pytorch3d/csrc/rasterize_meshes/rasterize_meshes.cu +++ b/pytorch3d/csrc/rasterize_meshes/rasterize_meshes.cu @@ -556,18 +556,16 @@ __global__ void RasterizeMeshesCoarseCudaKernel( // PixToNdc gives the location of the center of each pixel, so we // need to add/subtract a half pixel to get the true extent of the bin. // Reverse ordering of Y axis so that +Y is upwards in the image. - const int yidx = num_bins - by; - const float bin_y_max = PixToNdc(yidx * bin_size - 1, H) + half_pix; - const float bin_y_min = PixToNdc((yidx - 1) * bin_size, H) - half_pix; - + const float bin_y_min = PixToNdc(by * bin_size, H) - half_pix; + const float bin_y_max = PixToNdc((by + 1) * bin_size - 1, H) + half_pix; const bool y_overlap = (ymin <= bin_y_max) && (bin_y_min < ymax); for (int bx = 0; bx < num_bins; ++bx) { // X coordinate of the left and right of the bin. // Reverse ordering of x axis so that +X is left. - const int xidx = num_bins - bx; - const float bin_x_max = PixToNdc(xidx * bin_size - 1, W) + half_pix; - const float bin_x_min = PixToNdc((xidx - 1) * bin_size, W) - half_pix; + const float bin_x_max = + PixToNdc((bx + 1) * bin_size - 1, W) + half_pix; + const float bin_x_min = PixToNdc(bx * bin_size, W) - half_pix; const bool x_overlap = (xmin <= bin_x_max) && (bin_x_min < xmax); if (y_overlap && x_overlap) { @@ -629,6 +627,7 @@ torch::Tensor RasterizeMeshesCoarseCuda( const int N = num_faces_per_mesh.size(0); const int num_bins = 1 + (image_size - 1) / bin_size; // Divide round up. const int M = max_faces_per_bin; + if (num_bins >= 22) { std::stringstream ss; ss << "Got " << num_bins << "; that's too many!"; @@ -702,13 +701,8 @@ __global__ void RasterizeMeshesFineCudaKernel( if (yi >= H || xi >= W) continue; - // Reverse ordering of the X and Y axis so that - // in the image +Y is pointing up and +X is pointing left. - const int yidx = H - 1 - yi; - const int xidx = W - 1 - xi; - - const float xf = PixToNdc(xidx, W); - const float yf = PixToNdc(yidx, H); + const float xf = PixToNdc(xi, W); + const float yf = PixToNdc(yi, H); const float2 pxy = make_float2(xf, yf); // This part looks like the naive rasterization kernel, except we use @@ -743,7 +737,12 @@ __global__ void RasterizeMeshesFineCudaKernel( // output for the current pixel. // TODO: make sorting an option as only top k is needed, not sorted values. BubbleSort(q, q_size); - const int pix_idx = n * H * W * K + yi * H * K + xi * K; + + // Reverse ordering of the X and Y axis so that + // in the image +Y is pointing up and +X is pointing left. + const int yidx = H - 1 - yi; + const int xidx = W - 1 - xi; + const int pix_idx = n * H * W * K + yidx * H * K + xidx * K; for (int k = 0; k < q_size; k++) { face_idxs[pix_idx + k] = q[k].idx; zbuf[pix_idx + k] = q[k].z; diff --git a/pytorch3d/csrc/rasterize_meshes/rasterize_meshes_cpu.cpp b/pytorch3d/csrc/rasterize_meshes/rasterize_meshes_cpu.cpp index 90ccfec4..dd810d35 100644 --- a/pytorch3d/csrc/rasterize_meshes/rasterize_meshes_cpu.cpp +++ b/pytorch3d/csrc/rasterize_meshes/rasterize_meshes_cpu.cpp @@ -430,13 +430,13 @@ torch::Tensor RasterizeMeshesCoarseCpu( const int face_stop_idx = (face_start_idx + num_faces_per_mesh[n].item().to()); - float bin_y_max = 1.0f; - float bin_y_min = bin_y_max - bin_width; + float bin_y_min = -1.0f; + float bin_y_max = bin_y_min + bin_width; // Iterate through the horizontal bins from top to bottom. for (int by = 0; by < BH; ++by) { - float bin_x_max = 1.0f; - float bin_x_min = bin_x_max - bin_width; + float bin_x_min = -1.0f; + float bin_x_max = bin_x_min + bin_width; // Iterate through bins on this horizontal line, left to right. for (int bx = 0; bx < BW; ++bx) { @@ -473,13 +473,13 @@ torch::Tensor RasterizeMeshesCoarseCpu( } } - // Shift the bin to the left for the next loop iteration. - bin_x_max = bin_x_min; - bin_x_min = bin_x_min - bin_width; + // Shift the bin to the right for the next loop iteration + bin_x_min = bin_x_max; + bin_x_max = bin_x_min + bin_width; } - // Shift the bin down for the next loop iteration. - bin_y_max = bin_y_min; - bin_y_min = bin_y_min - bin_width; + // Shift the bin down for the next loop iteration + bin_y_min = bin_y_max; + bin_y_max = bin_y_min + bin_width; } } return bin_faces; diff --git a/pytorch3d/csrc/rasterize_points/rasterize_points.cu b/pytorch3d/csrc/rasterize_points/rasterize_points.cu index 542d0c86..4323a53b 100644 --- a/pytorch3d/csrc/rasterize_points/rasterize_points.cu +++ b/pytorch3d/csrc/rasterize_points/rasterize_points.cu @@ -95,7 +95,8 @@ __global__ void RasterizePointsNaiveCudaKernel( const int n = i / (S * S); // Batch index const int pix_idx = i % (S * S); - // Reverse ordering of X and Y axes. + // Reverse ordering of the X and Y axis as the camera coordinates + // assume that +Y is pointing up and +X is pointing left. const int yi = S - 1 - pix_idx / S; const int xi = S - 1 - pix_idx % S; @@ -260,23 +261,20 @@ __global__ void RasterizePointsCoarseCudaKernel( // Get y extent for the bin. PixToNdc gives us the location of // the center of each pixel, so we need to add/subtract a half // pixel to get the true extent of the bin. - // Reverse ordering of Y axis so that +Y is upwards in the image. - const int yidx = num_bins - by; - const float bin_y_max = PixToNdc(yidx * bin_size - 1, S) + half_pix; - const float bin_y_min = PixToNdc((yidx - 1) * bin_size, S) - half_pix; + const float by0 = PixToNdc(by * bin_size, S) - half_pix; + const float by1 = PixToNdc((by + 1) * bin_size - 1, S) + half_pix; + const bool y_overlap = (py0 <= by1) && (by0 <= py1); - const bool y_overlap = (py0 <= bin_y_max) && (bin_y_min <= py1); if (!y_overlap) { continue; } for (int bx = 0; bx < num_bins; ++bx) { // Get x extent for the bin; again we need to adjust the // output of PixToNdc by half a pixel. - // Reverse ordering of x axis so that +X is left. - const int xidx = num_bins - bx; - const float bin_x_max = PixToNdc(xidx * bin_size - 1, S) + half_pix; - const float bin_x_min = PixToNdc((xidx - 1) * bin_size, S) - half_pix; - const bool x_overlap = (px0 <= bin_x_max) && (bin_x_min <= px1); + const float bx0 = PixToNdc(bx * bin_size, S) - half_pix; + const float bx1 = PixToNdc((bx + 1) * bin_size - 1, S) + half_pix; + const bool x_overlap = (px0 <= bx1) && (bx0 <= px1); + if (x_overlap) { binmask.set(by, bx, p); } @@ -330,6 +328,7 @@ torch::Tensor RasterizePointsCoarseCuda( const int N = num_points_per_cloud.size(0); const int num_bins = 1 + (image_size - 1) / bin_size; // divide round up const int M = max_points_per_bin; + if (points.ndimension() != 2 || points.size(1) != 3) { AT_ERROR("points must have dimensions (num_points, 3)"); } @@ -346,6 +345,7 @@ torch::Tensor RasterizePointsCoarseCuda( const size_t shared_size = num_bins * num_bins * chunk_size / 8; const size_t blocks = 64; const size_t threads = 512; + RasterizePointsCoarseCudaKernel<<>>( points.contiguous().data_ptr(), cloud_to_packed_first_idx.contiguous().data_ptr(), @@ -372,7 +372,7 @@ __global__ void RasterizePointsFineCudaKernel( const float radius, const int bin_size, const int N, - const int B, + const int B, // num_bins const int M, const int S, const int K, @@ -397,19 +397,15 @@ __global__ void RasterizePointsFineCudaKernel( i %= B * bin_size * bin_size; const int bx = i / (bin_size * bin_size); i %= bin_size * bin_size; + const int yi = i / bin_size + by * bin_size; const int xi = i % bin_size + bx * bin_size; if (yi >= S || xi >= S) continue; - // Reverse ordering of the X and Y axis so that - // in the image +Y is pointing up and +X is pointing left. - const int yidx = S - 1 - yi; - const int xidx = S - 1 - xi; - - const float xf = PixToNdc(xidx, S); - const float yf = PixToNdc(yidx, S); + const float xf = PixToNdc(xi, S); + const float yf = PixToNdc(yi, S); // This part looks like the naive rasterization kernel, except we use // bin_points to only look at a subset of points already known to fall @@ -431,7 +427,13 @@ __global__ void RasterizePointsFineCudaKernel( // Now we've looked at all the points for this bin, so we can write // output for the current pixel. BubbleSort(q, q_size); - const int pix_idx = n * S * S * K + yi * S * K + xi * K; + + // Reverse ordering of the X and Y axis as the camera coordinates + // assume that +Y is pointing up and +X is pointing left. + const int yidx = S - 1 - yi; + const int xidx = S - 1 - xi; + + const int pix_idx = n * S * S * K + yidx * S * K + xidx * K; for (int k = 0; k < q_size; ++k) { point_idxs[pix_idx + k] = q[k].idx; zbuf[pix_idx + k] = q[k].z; @@ -448,7 +450,7 @@ std::tuple RasterizePointsFineCuda( const int bin_size, const int points_per_pixel) { const int N = bin_points.size(0); - const int B = bin_points.size(1); + const int B = bin_points.size(1); // num_bins const int M = bin_points.size(3); const int S = image_size; const int K = points_per_pixel; diff --git a/pytorch3d/csrc/rasterize_points/rasterize_points_cpu.cpp b/pytorch3d/csrc/rasterize_points/rasterize_points_cpu.cpp index dc30540c..21aa68fa 100644 --- a/pytorch3d/csrc/rasterize_points/rasterize_points_cpu.cpp +++ b/pytorch3d/csrc/rasterize_points/rasterize_points_cpu.cpp @@ -125,13 +125,13 @@ torch::Tensor RasterizePointsCoarseCpu( const int point_stop_idx = (point_start_idx + num_points_per_cloud[n].item().to()); - float bin_y_max = 1.0f; - float bin_y_min = bin_y_max - bin_width; + float bin_y_min = -1.0f; + float bin_y_max = bin_y_min + bin_width; // Iterate through the horizontal bins from top to bottom. for (int by = 0; by < B; by++) { - float bin_x_max = 1.0f; - float bin_x_min = bin_x_max - bin_width; + float bin_x_min = -1.0f; + float bin_x_max = bin_x_min + bin_width; // Iterate through bins on this horizontal line, left to right. for (int bx = 0; bx < B; bx++) { @@ -166,13 +166,13 @@ torch::Tensor RasterizePointsCoarseCpu( // Record the number of points found in this bin points_per_bin_a[n][by][bx] = points_hit; - // Shift the bin to the left for the next loop iteration. - bin_x_max = bin_x_min; - bin_x_min = bin_x_min - bin_width; + // Shift the bin to the right for the next loop iteration + bin_x_min = bin_x_max; + bin_x_max = bin_x_min + bin_width; } - // Shift the bin down for the next loop iteration. - bin_y_max = bin_y_min; - bin_y_min = bin_y_min - bin_width; + // Shift the bin down for the next loop iteration + bin_y_min = bin_y_max; + bin_y_max = bin_y_min + bin_width; } } return bin_points; diff --git a/pytorch3d/renderer/mesh/rasterize_meshes.py b/pytorch3d/renderer/mesh/rasterize_meshes.py index 34079a5c..e72a9596 100644 --- a/pytorch3d/renderer/mesh/rasterize_meshes.py +++ b/pytorch3d/renderer/mesh/rasterize_meshes.py @@ -9,7 +9,7 @@ from pytorch3d import _C # TODO make the epsilon user configurable -kEpsilon = 1e-30 +kEpsilon = 1e-8 def rasterize_meshes( diff --git a/pytorch3d/renderer/points/rasterizer.py b/pytorch3d/renderer/points/rasterizer.py index 2eb39c50..a732eccb 100644 --- a/pytorch3d/renderer/points/rasterizer.py +++ b/pytorch3d/renderer/points/rasterizer.py @@ -19,12 +19,28 @@ class PointFragments(NamedTuple): # Class to store the point rasterization params with defaults -class PointsRasterizationSettings(NamedTuple): - image_size: int = 256 - radius: float = 0.01 - points_per_pixel: int = 8 - bin_size: Optional[int] = None - max_points_per_bin: Optional[int] = None +class PointsRasterizationSettings: + __slots__ = [ + "image_size", + "radius", + "points_per_pixel", + "bin_size", + "max_points_per_bin", + ] + + def __init__( + self, + image_size: int = 256, + radius: float = 0.01, + points_per_pixel: int = 8, + bin_size: Optional[int] = None, + max_points_per_bin: Optional[int] = None, + ): + self.image_size = image_size + self.radius = radius + self.points_per_pixel = points_per_pixel + self.bin_size = bin_size + self.max_points_per_bin = max_points_per_bin class PointsRasterizer(nn.Module): diff --git a/tests/common_testing.py b/tests/common_testing.py index 141e28af..ac3855d0 100644 --- a/tests/common_testing.py +++ b/tests/common_testing.py @@ -1,10 +1,20 @@ # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. import unittest +from pathlib import Path from typing import Callable, Optional, Union import numpy as np import torch +from PIL import Image + + +def load_rgb_image(filename: str, data_dir: Union[str, Path]): + filepath = data_dir / filename + with Image.open(filepath) as raw_image: + image = torch.from_numpy(np.array(raw_image) / 255.0) + image = image.to(dtype=torch.float32) + return image[..., :3] TensorOrArray = Union[torch.Tensor, np.ndarray] diff --git a/tests/data/test_bridge_pointcloud.png b/tests/data/test_bridge_pointcloud.png new file mode 100644 index 0000000000000000000000000000000000000000..be76dea6c8162a188b1d77981e07dee24f5a303b GIT binary patch literal 76219 zcmeEt`#aPB|NoOzE6Ro(a$Y1QjFLGYCSDyVUP4-ASiMFLbCyFn&6$!zCQ*3hm~&23 zaz0EYb3TO)!*U#(*>|tcpYgrkyDom1>w3DL&&U1oxE-Idcg#&sojiLI0Dx0}nIS9z zfNT4|9}qX^8$^xS6u<=le<2M24#2On{F`0v?p`>s89u44GxtF+rzqGv=?k9#kK8Go 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diff --git a/tests/test_rasterize_meshes.py b/tests/test_rasterize_meshes.py index 64090058..69844d93 100644 --- a/tests/test_rasterize_meshes.py +++ b/tests/test_rasterize_meshes.py @@ -896,10 +896,10 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase): torch.ones((1, 2, 2, max_faces_per_bin), dtype=torch.int32, device=device) * -1 ) - bin_faces_expected[0, 0, 0, 0] = torch.tensor([1]) - bin_faces_expected[0, 1, 0, 0:2] = torch.tensor([1, 2]) - bin_faces_expected[0, 0, 1, 0:2] = torch.tensor([0, 1]) bin_faces_expected[0, 1, 1, 0] = torch.tensor([1]) + bin_faces_expected[0, 0, 1, 0:2] = torch.tensor([1, 2]) + bin_faces_expected[0, 1, 0, 0:2] = torch.tensor([0, 1]) + bin_faces_expected[0, 0, 0, 0] = torch.tensor([1]) # +Y up, +X left, +Z in bin_faces = _C._rasterize_meshes_coarse( @@ -911,7 +911,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase): bin_size, max_faces_per_bin, ) - # Flip x and y axis of output before comparing to expected + bin_faces_same = (bin_faces.squeeze() == bin_faces_expected).all() self.assertTrue(bin_faces_same.item() == 1) diff --git a/tests/test_rasterize_points.py b/tests/test_rasterize_points.py index f10bca40..0230d164 100644 --- a/tests/test_rasterize_points.py +++ b/tests/test_rasterize_points.py @@ -434,23 +434,21 @@ class TestRasterizePoints(TestCaseMixin, unittest.TestCase): def _test_coarse_rasterize(self, device): # - # Note that +Y is up and +X is left in the diagram below. # - # (4) |2 - # | - # | - # | - # |1 - # | - # (1) | - # | (2) - # ____________(0)__(5)___________________ - # 2 1 | -1 -2 - # | - # (3) | - # | - # |-1 - # | + # |2 (4) + # | + # | + # | + # |1 + # | + # | (1) + # (2)| + # _________(5)___(0)_______________ + # -1 | 1 2 + # | + # | (3) + # | + # |-1 # # Locations of the points are shown by o. The screen bounding box # is between [-1, 1] in both the x and y directions. @@ -486,9 +484,9 @@ class TestRasterizePoints(TestCaseMixin, unittest.TestCase): # fit in one chunk. This will the the case for this small example, but # to properly exercise coordianted writes among multiple chunks we need # to use a bigger test case. - bin_points_expected[0, 1, 0, :2] = torch.tensor([0, 3]) - bin_points_expected[0, 0, 1, 0] = torch.tensor([2]) - bin_points_expected[0, 0, 0, :2] = torch.tensor([0, 1]) + bin_points_expected[0, 0, 1, :2] = torch.tensor([0, 3]) + bin_points_expected[0, 1, 0, 0] = torch.tensor([2]) + bin_points_expected[0, 1, 1, :2] = torch.tensor([0, 1]) pointclouds = Pointclouds(points=[points]) args = ( @@ -502,4 +500,5 @@ class TestRasterizePoints(TestCaseMixin, unittest.TestCase): ) bin_points = _C._rasterize_points_coarse(*args) bin_points_same = (bin_points == bin_points_expected).all() + self.assertTrue(bin_points_same.item() == 1) diff --git a/tests/test_rendering_meshes.py b/tests/test_render_meshes.py similarity index 76% rename from tests/test_rendering_meshes.py rename to tests/test_render_meshes.py index 2cde664d..000ad679 100644 --- a/tests/test_rendering_meshes.py +++ b/tests/test_render_meshes.py @@ -9,6 +9,7 @@ from pathlib import Path import numpy as np import torch +from common_testing import TestCaseMixin, load_rgb_image from PIL import Image from pytorch3d.io import load_objs_as_meshes from pytorch3d.renderer.cameras import OpenGLPerspectiveCameras, look_at_view_transform @@ -35,15 +36,7 @@ DEBUG = False DATA_DIR = Path(__file__).resolve().parent / "data" -def load_rgb_image(filename, data_dir=DATA_DIR): - filepath = data_dir / filename - with Image.open(filepath) as raw_image: - image = torch.from_numpy(np.array(raw_image) / 255.0) - image = image.to(dtype=torch.float32) - return image[..., :3] - - -class TestRenderingMeshes(unittest.TestCase): +class TestRenderMeshes(TestCaseMixin, unittest.TestCase): def test_simple_sphere(self, elevated_camera=False): """ Test output of phong and gouraud shading matches a reference image using @@ -81,7 +74,7 @@ class TestRenderingMeshes(unittest.TestCase): lights.location = torch.tensor([0.0, 0.0, +2.0], device=device)[None] raster_settings = RasterizationSettings( - image_size=512, blur_radius=0.0, faces_per_pixel=1, bin_size=0 + image_size=512, blur_radius=0.0, faces_per_pixel=1 ) rasterizer = MeshRasterizer(cameras=cameras, raster_settings=raster_settings) @@ -96,14 +89,14 @@ class TestRenderingMeshes(unittest.TestCase): renderer = MeshRenderer(rasterizer=rasterizer, shader=shader) images = renderer(sphere_mesh) filename = "simple_sphere_light_%s%s.png" % (name, postfix) - image_ref = load_rgb_image("test_%s" % filename) + image_ref = load_rgb_image("test_%s" % filename, DATA_DIR) rgb = images[0, ..., :3].squeeze().cpu() if DEBUG: - filename = "DEBUG_" % filename + filename = "DEBUG_%s" % filename Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save( DATA_DIR / filename ) - self.assertTrue(torch.allclose(rgb, image_ref, atol=0.05)) + self.assertClose(rgb, image_ref, atol=0.05) ######################################################## # Move the light to the +z axis in world space so it is @@ -124,8 +117,10 @@ class TestRenderingMeshes(unittest.TestCase): ) # Load reference image - image_ref_phong_dark = load_rgb_image("test_simple_sphere_dark%s.png" % postfix) - self.assertTrue(torch.allclose(rgb, image_ref_phong_dark, atol=0.05)) + image_ref_phong_dark = load_rgb_image( + "test_simple_sphere_dark%s.png" % postfix, DATA_DIR + ) + self.assertClose(rgb, image_ref_phong_dark, atol=0.05) def test_simple_sphere_elevated_camera(self): """ @@ -160,7 +155,7 @@ class TestRenderingMeshes(unittest.TestCase): R, T = look_at_view_transform(dist, elev, azim) cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T) raster_settings = RasterizationSettings( - image_size=512, blur_radius=0.0, faces_per_pixel=1, bin_size=0 + image_size=512, blur_radius=0.0, faces_per_pixel=1 ) # Init shader settings @@ -179,10 +174,12 @@ class TestRenderingMeshes(unittest.TestCase): shader = shader_init(lights=lights, cameras=cameras, materials=materials) renderer = MeshRenderer(rasterizer=rasterizer, shader=shader) images = renderer(sphere_meshes) - image_ref = load_rgb_image("test_simple_sphere_light_%s.png" % name) + image_ref = load_rgb_image( + "test_simple_sphere_light_%s.png" % name, DATA_DIR + ) for i in range(batch_size): rgb = images[i, ..., :3].squeeze().cpu() - self.assertTrue(torch.allclose(rgb, image_ref, atol=0.05)) + self.assertClose(rgb, image_ref, atol=0.05) def test_silhouette_with_grad(self): """ @@ -200,7 +197,6 @@ class TestRenderingMeshes(unittest.TestCase): image_size=512, blur_radius=np.log(1.0 / 1e-4 - 1.0) * blend_params.sigma, faces_per_pixel=80, - bin_size=0, ) # Init rasterizer settings @@ -222,7 +218,7 @@ class TestRenderingMeshes(unittest.TestCase): with Image.open(image_ref_filename) as raw_image_ref: image_ref = torch.from_numpy(np.array(raw_image_ref)) image_ref = image_ref.to(dtype=torch.float32) / 255.0 - self.assertTrue(torch.allclose(alpha, image_ref, atol=0.055)) + self.assertClose(alpha, image_ref, atol=0.055) # Check grad exist verts.requires_grad = True @@ -237,8 +233,8 @@ class TestRenderingMeshes(unittest.TestCase): The pupils in the eyes of the cow should always be looking to the left. """ device = torch.device("cuda:0") - DATA_DIR = Path(__file__).resolve().parent.parent / "docs/tutorials/data" - obj_filename = DATA_DIR / "cow_mesh/cow.obj" + obj_dir = Path(__file__).resolve().parent.parent / "docs/tutorials/data" + obj_filename = obj_dir / "cow_mesh/cow.obj" # Load mesh + texture mesh = load_objs_as_meshes([obj_filename], device=device) @@ -247,7 +243,7 @@ class TestRenderingMeshes(unittest.TestCase): R, T = look_at_view_transform(2.7, 0, 0) cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T) raster_settings = RasterizationSettings( - image_size=512, blur_radius=0.0, faces_per_pixel=1, bin_size=0 + image_size=512, blur_radius=0.0, faces_per_pixel=1 ) # Init shader settings @@ -265,22 +261,26 @@ class TestRenderingMeshes(unittest.TestCase): lights=lights, cameras=cameras, materials=materials ), ) - images = renderer(mesh) - rgb = images[0, ..., :3].squeeze().cpu() # Load reference image - image_ref = load_rgb_image("test_texture_map_back.png") + image_ref = load_rgb_image("test_texture_map_back.png", DATA_DIR) - if DEBUG: - Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save( - DATA_DIR / "DEBUG_texture_map_back.png" - ) + for bin_size in [0, None]: + # Check both naive and coarse to fine produce the same output. + renderer.rasterizer.raster_settings.bin_size = bin_size + images = renderer(mesh) + rgb = images[0, ..., :3].squeeze().cpu() - # NOTE some pixels can be flaky and will not lead to - # `cond1` being true. Add `cond2` and check `cond1 or cond2` - cond1 = torch.allclose(rgb, image_ref, atol=0.05) - cond2 = ((rgb - image_ref).abs() > 0.05).sum() < 5 - self.assertTrue(cond1 or cond2) + if DEBUG: + Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save( + DATA_DIR / "DEBUG_texture_map_back.png" + ) + + # NOTE some pixels can be flaky and will not lead to + # `cond1` being true. Add `cond2` and check `cond1 or cond2` + cond1 = torch.allclose(rgb, image_ref, atol=0.05) + cond2 = ((rgb - image_ref).abs() > 0.05).sum() < 5 + self.assertTrue(cond1 or cond2) # Check grad exists [verts] = mesh.verts_list() @@ -299,16 +299,27 @@ class TestRenderingMeshes(unittest.TestCase): # Move light to the front of the cow in world space lights.location = torch.tensor([0.0, 0.0, -2.0], device=device)[None] - images = renderer(mesh, cameras=cameras, lights=lights) - rgb = images[0, ..., :3].squeeze().cpu() # Load reference image - image_ref = load_rgb_image("test_texture_map_front.png") + image_ref = load_rgb_image("test_texture_map_front.png", DATA_DIR) - if DEBUG: - Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save( - DATA_DIR / "DEBUG_texture_map_front.png" - ) + for bin_size in [0, None]: + # Check both naive and coarse to fine produce the same output. + renderer.rasterizer.raster_settings.bin_size = bin_size + + images = renderer(mesh, cameras=cameras, lights=lights) + rgb = images[0, ..., :3].squeeze().cpu() + + if DEBUG: + Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save( + DATA_DIR / "DEBUG_texture_map_front.png" + ) + + # NOTE some pixels can be flaky and will not lead to + # `cond1` being true. Add `cond2` and check `cond1 or cond2` + cond1 = torch.allclose(rgb, image_ref, atol=0.05) + cond2 = ((rgb - image_ref).abs() > 0.05).sum() < 5 + self.assertTrue(cond1 or cond2) ################################# # Add blurring to rasterization @@ -320,23 +331,26 @@ class TestRenderingMeshes(unittest.TestCase): image_size=512, blur_radius=np.log(1.0 / 1e-4 - 1.0) * blend_params.sigma, faces_per_pixel=100, - bin_size=0, ) - images = renderer( - mesh.clone(), - cameras=cameras, - raster_settings=raster_settings, - blend_params=blend_params, - ) - rgb = images[0, ..., :3].squeeze().cpu() - # Load reference image - image_ref = load_rgb_image("test_blurry_textured_rendering.png") + image_ref = load_rgb_image("test_blurry_textured_rendering.png", DATA_DIR) - if DEBUG: - Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save( - DATA_DIR / "DEBUG_blurry_textured_rendering.png" + for bin_size in [0, None]: + # Check both naive and coarse to fine produce the same output. + renderer.rasterizer.raster_settings.bin_size = bin_size + + images = renderer( + mesh.clone(), + cameras=cameras, + raster_settings=raster_settings, + blend_params=blend_params, ) + rgb = images[0, ..., :3].squeeze().cpu() - self.assertTrue(torch.allclose(rgb, image_ref, atol=0.05)) + if DEBUG: + Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save( + DATA_DIR / "DEBUG_blurry_textured_rendering.png" + ) + + self.assertClose(rgb, image_ref, atol=0.05) diff --git a/tests/test_render_points.py b/tests/test_render_points.py new file mode 100644 index 00000000..0220fa73 --- /dev/null +++ b/tests/test_render_points.py @@ -0,0 +1,173 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. + + +""" +Sanity checks for output images from the pointcloud renderer. +""" +import unittest +import warnings +from os import path +from pathlib import Path + +import numpy as np +import torch +from common_testing import TestCaseMixin, load_rgb_image +from PIL import Image +from pytorch3d.renderer.cameras import ( + OpenGLOrthographicCameras, + OpenGLPerspectiveCameras, + look_at_view_transform, +) +from pytorch3d.renderer.points import ( + AlphaCompositor, + NormWeightedCompositor, + PointsRasterizationSettings, + PointsRasterizer, + PointsRenderer, +) +from pytorch3d.structures.pointclouds import Pointclouds +from pytorch3d.utils.ico_sphere import ico_sphere + + +# If DEBUG=True, save out images generated in the tests for debugging. +# All saved images have prefix DEBUG_ +DEBUG = False +DATA_DIR = Path(__file__).resolve().parent / "data" + + +class TestRenderPoints(TestCaseMixin, unittest.TestCase): + def test_simple_sphere(self): + device = torch.device("cuda:0") + sphere_mesh = ico_sphere(1, device) + verts_padded = sphere_mesh.verts_padded() + # Shift vertices to check coordinate frames are correct. + verts_padded[..., 1] += 0.2 + verts_padded[..., 0] += 0.2 + pointclouds = Pointclouds( + points=verts_padded, features=torch.ones_like(verts_padded) + ) + R, T = look_at_view_transform(2.7, 0.0, 0.0) + cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T) + raster_settings = PointsRasterizationSettings( + image_size=256, radius=5e-2, points_per_pixel=1 + ) + rasterizer = PointsRasterizer(cameras=cameras, raster_settings=raster_settings) + compositor = NormWeightedCompositor() + renderer = PointsRenderer(rasterizer=rasterizer, compositor=compositor) + + # Load reference image + filename = "simple_pointcloud_sphere.png" + image_ref = load_rgb_image("test_%s" % filename, DATA_DIR) + + for bin_size in [0, None]: + # Check both naive and coarse to fine produce the same output. + renderer.rasterizer.raster_settings.bin_size = bin_size + images = renderer(pointclouds) + rgb = images[0, ..., :3].squeeze().cpu() + if DEBUG: + filename = "DEBUG_%s" % filename + Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save( + DATA_DIR / filename + ) + self.assertClose(rgb, image_ref) + + def test_pointcloud_with_features(self): + device = torch.device("cuda:0") + file_dir = Path(__file__).resolve().parent.parent / "docs/tutorials/data" + pointcloud_filename = file_dir / "PittsburghBridge/pointcloud.npz" + + # Note, this file is too large to check in to the repo. + # Download the file to run the test locally. + if not path.exists(pointcloud_filename): + url = "https://dl.fbaipublicfiles.com/pytorch3d/data/PittsburghBridge/pointcloud.npz" + msg = ( + "pointcloud.npz not found, download from %s, save it at the path %s, and rerun" + % (url, pointcloud_filename) + ) + warnings.warn(msg) + return True + + # Load point cloud + pointcloud = np.load(pointcloud_filename) + verts = torch.Tensor(pointcloud["verts"]).to(device) + rgb_feats = torch.Tensor(pointcloud["rgb"]).to(device) + + verts.requires_grad = True + rgb_feats.requires_grad = True + point_cloud = Pointclouds(points=[verts], features=[rgb_feats]) + + R, T = look_at_view_transform(20, 10, 0) + cameras = OpenGLOrthographicCameras(device=device, R=R, T=T, znear=0.01) + + raster_settings = PointsRasterizationSettings( + # Set image_size so it is not a multiple of 16 (min bin_size) + # in order to confirm that there are no errors in coarse rasterization. + image_size=500, + radius=0.003, + points_per_pixel=10, + ) + + renderer = PointsRenderer( + rasterizer=PointsRasterizer( + cameras=cameras, raster_settings=raster_settings + ), + compositor=AlphaCompositor(), + ) + + images = renderer(point_cloud) + + # Load reference image + filename = "bridge_pointcloud.png" + image_ref = load_rgb_image("test_%s" % filename, DATA_DIR) + + for bin_size in [0, None]: + # Check both naive and coarse to fine produce the same output. + renderer.rasterizer.raster_settings.bin_size = bin_size + images = renderer(point_cloud) + rgb = images[0, ..., :3].squeeze().cpu() + if DEBUG: + filename = "DEBUG_%s" % filename + Image.fromarray((rgb.detach().numpy() * 255).astype(np.uint8)).save( + DATA_DIR / filename + ) + self.assertClose(rgb, image_ref, atol=0.015) + + # Check grad exists. + grad_images = torch.randn_like(images) + images.backward(grad_images) + self.assertIsNotNone(verts.grad) + self.assertIsNotNone(rgb_feats.grad) + + def test_simple_sphere_batched(self): + device = torch.device("cuda:0") + sphere_mesh = ico_sphere(1, device) + verts_padded = sphere_mesh.verts_padded() + verts_padded[..., 1] += 0.2 + verts_padded[..., 0] += 0.2 + pointclouds = Pointclouds( + points=verts_padded, features=torch.ones_like(verts_padded) + ) + batch_size = 20 + pointclouds = pointclouds.extend(batch_size) + R, T = look_at_view_transform(2.7, 0.0, 0.0) + cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T) + raster_settings = PointsRasterizationSettings( + image_size=256, radius=5e-2, points_per_pixel=1 + ) + rasterizer = PointsRasterizer(cameras=cameras, raster_settings=raster_settings) + compositor = NormWeightedCompositor() + renderer = PointsRenderer(rasterizer=rasterizer, compositor=compositor) + + # Load reference image + filename = "simple_pointcloud_sphere.png" + image_ref = load_rgb_image("test_%s" % filename, DATA_DIR) + + images = renderer(pointclouds) + for i in range(batch_size): + rgb = images[i, ..., :3].squeeze().cpu() + if i == 0 and DEBUG: + filename = "DEBUG_%s" % filename + Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save( + DATA_DIR / filename + ) + self.assertClose(rgb, image_ref)