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https://github.com/facebookresearch/pytorch3d.git
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340662e98e
Summary: - Updated the C++/CUDA mesh rasterization kernels to handle the clipped faces. In particular this required careful handling of the distance calculation for faces which are cut into a quadrilateral by the image plane and then split into two sub triangles i.e. both sub triangles can't be part of the top K faces. - Updated `rasterize_meshes.py` to use the utils functions to clip the meshes and convert the fragments back to in terms of the unclipped mesh - Added end to end tests Reviewed By: jcjohnson Differential Revision: D26169685 fbshipit-source-id: d64cd0d656109b965f44a35c301b7c81f451cfa0
122 lines
3.3 KiB
Python
122 lines
3.3 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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from itertools import product
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import torch
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from fvcore.common.benchmark import benchmark
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from test_rasterize_meshes import TestRasterizeMeshes
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# ico levels:
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# 0: (12 verts, 20 faces)
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# 1: (42 verts, 80 faces)
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# 3: (642 verts, 1280 faces)
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# 4: (2562 verts, 5120 faces)
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# 5: (10242 verts, 20480 faces)
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# 6: (40962 verts, 81920 faces)
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def bm_rasterize_meshes() -> None:
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kwargs_list = [
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{
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"num_meshes": 1,
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"ico_level": 0,
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"image_size": 10, # very slow with large image size
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"blur_radius": 0.0,
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"faces_per_pixel": 3,
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}
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]
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benchmark(
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TestRasterizeMeshes.rasterize_meshes_python_with_init,
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"RASTERIZE_MESHES",
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kwargs_list,
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warmup_iters=1,
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)
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kwargs_list = []
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num_meshes = [1]
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ico_level = [1]
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image_size = [64, 128]
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blur = [1e-6]
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faces_per_pixel = [3, 50]
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test_cases = product(num_meshes, ico_level, image_size, blur, faces_per_pixel)
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for case in test_cases:
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n, ic, im, b, f = case
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kwargs_list.append(
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{
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"num_meshes": n,
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"ico_level": ic,
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"image_size": im,
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"blur_radius": b,
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"faces_per_pixel": f,
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}
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)
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benchmark(
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TestRasterizeMeshes.rasterize_meshes_cpu_with_init,
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"RASTERIZE_MESHES",
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kwargs_list,
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warmup_iters=1,
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)
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if torch.cuda.is_available():
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kwargs_list = []
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num_meshes = [8, 16]
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ico_level = [4, 5, 6]
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# Square and non square cases
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image_size = [64, 128, 512, (512, 256), (256, 512)]
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blur = [1e-6]
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faces_per_pixel = [40]
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test_cases = product(num_meshes, ico_level, image_size, blur, faces_per_pixel)
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for case in test_cases:
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n, ic, im, b, f = case
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kwargs_list.append(
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{
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"num_meshes": n,
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"ico_level": ic,
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"image_size": im,
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"blur_radius": b,
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"faces_per_pixel": f,
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}
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)
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benchmark(
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TestRasterizeMeshes.rasterize_meshes_cuda_with_init,
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"RASTERIZE_MESHES_CUDA",
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kwargs_list,
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warmup_iters=1,
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)
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# Test a subset of the cases with the
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# image plane intersecting the mesh.
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kwargs_list = []
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num_meshes = [8, 16]
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# Square and non square cases
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image_size = [64, 128, 512, (512, 256), (256, 512)]
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dist = [3, 0.8, 0.5]
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test_cases = product(num_meshes, dist, image_size)
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for case in test_cases:
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n, d, im = case
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kwargs_list.append(
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{
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"num_meshes": n,
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"ico_level": 4,
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"image_size": im,
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"blur_radius": 1e-6,
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"faces_per_pixel": 40,
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"dist": d,
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}
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)
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benchmark(
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TestRasterizeMeshes.bm_rasterize_meshes_with_clipping,
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"RASTERIZE_MESHES_CUDA_CLIPPING",
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kwargs_list,
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warmup_iters=1,
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)
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if __name__ == "__main__":
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bm_rasterize_meshes()
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