File size: 4,712 Bytes
a6dd040 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 |
# Copyright (c) Chris Choy (chrischoy@ai.stanford.edu).
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
# of the Software, and to permit persons to whom the Software is furnished to do
# so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
# Please cite "4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural
# Networks", CVPR'19 (https://arxiv.org/abs/1904.08755) if you use any part
# of the code.
import torch
import unittest
import numpy as np
from MinkowskiEngine.utils import sparse_quantize
import MinkowskiEngineBackend._C as MEB
class TestQuantization(unittest.TestCase):
def test(self):
N = 16575
ignore_label = 255
coords = np.random.rand(N, 3) * 100
feats = np.random.rand(N, 4)
labels = np.floor(np.random.rand(N) * 3)
labels = labels.astype(np.int32)
# Make duplicates
coords[:3] = 0
labels[:3] = 2
quantized_coords, quantized_feats, quantized_labels = sparse_quantize(
coords.astype(np.int32), feats, labels, ignore_label
)
print(quantized_labels)
def test_device(self):
N = 16575
coords = np.random.rand(N, 3) * 100
# Make duplicates
coords[:3] = 0
unique_map = sparse_quantize(
coords.astype(np.int32), return_maps_only=True, device="cpu"
)
print(len(unique_map))
unique_map = sparse_quantize(
coords.astype(np.int32), return_maps_only=True, device="cuda"
)
print(len(unique_map))
def test_mapping(self):
N = 16575
coords = (np.random.rand(N, 3) * 100).astype(np.int32)
mapping, inverse_mapping = MEB.quantize_np(coords)
print("N unique:", len(mapping), "N:", N)
self.assertTrue((coords == coords[mapping][inverse_mapping]).all())
self.assertTrue((coords == coords[mapping[inverse_mapping]]).all())
coords = torch.from_numpy(coords)
mapping, inverse_mapping = MEB.quantize_th(coords)
print("N unique:", len(mapping), "N:", N)
self.assertTrue((coords == coords[mapping[inverse_mapping]]).all())
unique_coords, index, reverse_index = sparse_quantize(
coords, return_index=True, return_inverse=True
)
self.assertTrue((coords == coords[index[reverse_index]]).all())
def test_label_np(self):
N = 16575
coords = (np.random.rand(N, 3) * 100).astype(np.int32)
labels = np.floor(np.random.rand(N) * 3).astype(np.int32)
# Make duplicates
coords[:3] = 0
labels[:3] = 2
mapping, inverse_mapping, colabel = MEB.quantize_label_np(coords, labels, -1)
self.assertTrue(np.sum(np.abs(coords[mapping][inverse_mapping] - coords)) == 0)
self.assertTrue(np.sum(colabel < 0) > 3)
def test_collision(self):
coords = np.array([[0, 0], [0, 0], [0, 0], [0, 1]], dtype=np.int32)
labels = np.array([0, 1, 2, 3], dtype=np.int32)
unique_coords, colabels = sparse_quantize(
coords, labels=labels, ignore_label=255
)
print(unique_coords)
print(colabels)
self.assertTrue(len(unique_coords) == 2)
self.assertTrue(np.array([0, 0]) in unique_coords)
self.assertTrue(np.array([0, 1]) in unique_coords)
self.assertTrue(len(colabels) == 2)
self.assertTrue(255 in colabels)
def test_quantization_size(self):
coords = torch.randn((1000, 3), dtype=torch.float)
feats = torch.randn((1000, 10), dtype=torch.float)
res = sparse_quantize(coords, feats, quantization_size=0.1)
print(res[0].shape, res[1].shape)
res = sparse_quantize(coords.numpy(), feats.numpy(), quantization_size=0.1)
print(res[0].shape, res[1].shape)
if __name__ == "__main__":
unittest.main()
|