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ebe2693b11
Summary: Support variable size pointclouds in the renderer API to allow compatibility with Pulsar rasterizer. If radius is provided as a float, it is converted to a tensor of shape (P). Otherwise radius is expected to be an (N, P_padded) dimensional tensor where P_padded is the max number of points in the batch (following the convention from pulsar: https://our.intern.facebook.com/intern/diffusion/FBS/browse/master/fbcode/frl/gemini/pulsar/pulsar/renderer.py?commit=ee0342850210e5df441e14fd97162675c70d147c&lines=50) Reviewed By: jcjohnson, gkioxari Differential Revision: D21429400 fbshipit-source-id: 65de7d9cd2472b27fc29f96160c33687e88098a2
25 lines
684 B
Python
25 lines
684 B
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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import itertools
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from fvcore.common.benchmark import benchmark
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from test_cameras_alignment import TestCamerasAlignment
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def bm_cameras_alignment() -> None:
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case_grid = {
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"batch_size": [10, 100, 1000],
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"mode": ["centers", "extrinsics"],
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"estimate_scale": [False, True],
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}
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test_cases = itertools.product(*case_grid.values())
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kwargs_list = [dict(zip(case_grid.keys(), case)) for case in test_cases]
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benchmark(
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TestCamerasAlignment.corresponding_cameras_alignment,
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"CORRESPONDING_CAMERAS_ALIGNMENT",
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kwargs_list,
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warmup_iters=1,
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)
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