File size: 5,766 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 |
# 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
from MinkowskiEngine import (
SparseTensor,
MinkowskiGlobalSumPooling,
MinkowskiBroadcastFunction,
MinkowskiBroadcastAddition,
MinkowskiBroadcastMultiplication,
MinkowskiBroadcast,
MinkowskiBroadcastConcatenation,
BroadcastMode,
)
from utils.gradcheck import gradcheck
from tests.python.common import data_loader
class TestBroadcast(unittest.TestCase):
def test_broadcast_gpu(self):
in_channels, D = 2, 2
coords, feats, labels = data_loader(in_channels)
coords, feats_glob, labels = data_loader(in_channels)
feats = feats.double()
feats_glob = feats_glob.double()
feats.requires_grad_()
feats_glob.requires_grad_()
input = SparseTensor(feats, coords)
pool = MinkowskiGlobalSumPooling()
input_glob = pool(input).detach()
input_glob.F.requires_grad_()
broadcast_add = MinkowskiBroadcastAddition()
broadcast_mul = MinkowskiBroadcastMultiplication()
broadcast_cat = MinkowskiBroadcastConcatenation()
cpu_add = broadcast_add(input, input_glob)
cpu_mul = broadcast_mul(input, input_glob)
cpu_cat = broadcast_cat(input, input_glob)
# Check backward
fn = MinkowskiBroadcastFunction()
device = torch.device("cuda")
input = SparseTensor(feats, coords, device=device)
input_glob = pool(input).detach()
gpu_add = broadcast_add(input, input_glob)
gpu_mul = broadcast_mul(input, input_glob)
gpu_cat = broadcast_cat(input, input_glob)
self.assertTrue(torch.prod(gpu_add.F.cpu() - cpu_add.F < 1e-5).item() == 1)
self.assertTrue(torch.prod(gpu_mul.F.cpu() - cpu_mul.F < 1e-5).item() == 1)
self.assertTrue(torch.prod(gpu_cat.F.cpu() - cpu_cat.F < 1e-5).item() == 1)
self.assertTrue(
gradcheck(
fn,
(
input.F,
input_glob.F,
broadcast_add.operation_type,
input.coordinate_map_key,
input_glob.coordinate_map_key,
input.coordinate_manager,
),
)
)
self.assertTrue(
gradcheck(
fn,
(
input.F,
input_glob.F,
broadcast_mul.operation_type,
input.coordinate_map_key,
input_glob.coordinate_map_key,
input.coordinate_manager,
),
)
)
def test_broadcast(self):
in_channels, D = 2, 2
coords, feats, labels = data_loader(in_channels)
coords, feats_glob, labels = data_loader(in_channels)
feats = feats.double()
feats_glob = feats_glob.double()
feats.requires_grad_()
feats_glob.requires_grad_()
input = SparseTensor(feats, coords)
pool = MinkowskiGlobalSumPooling()
input_glob = pool(input).detach()
input_glob.requires_grad_()
broadcast = MinkowskiBroadcast()
broadcast_cat = MinkowskiBroadcastConcatenation()
broadcast_add = MinkowskiBroadcastAddition()
broadcast_mul = MinkowskiBroadcastMultiplication()
output = broadcast(input, input_glob)
print(output)
output = broadcast_cat(input, input_glob)
print(output)
output = broadcast_add(input, input_glob)
print(output)
output = broadcast_mul(input, input_glob)
print(output)
# Check backward
fn = MinkowskiBroadcastFunction()
self.assertTrue(
gradcheck(
fn,
(
input.F,
input_glob.F,
broadcast_add.operation_type,
input.coordinate_map_key,
input_glob.coordinate_map_key,
input.coordinate_manager,
),
)
)
self.assertTrue(
gradcheck(
fn,
(
input.F,
input_glob.F,
broadcast_mul.operation_type,
input.coordinate_map_key,
input_glob.coordinate_map_key,
input.coordinate_manager,
),
)
)
if __name__ == "__main__":
unittest.main()
|