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# 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 unittest
import torch
from MinkowskiEngine import SparseTensor, MinkowskiConvolution, MinkowskiAlgorithm
from tests.python.common import data_loader
class TestKernelMap(unittest.TestCase):
def test_kernelmap_gpu(self):
print(f"{self.__class__.__name__}: test_kernelmap_gpu")
if not torch.cuda.is_available():
return
in_channels, out_channels, D = 2, 3, 2
coords, feats, labels = data_loader(in_channels)
feats = feats.double()
feats.requires_grad_()
input = SparseTensor(
feats,
coordinates=coords,
minkowski_algorithm=MinkowskiAlgorithm.SPEED_OPTIMIZED,
device="cuda",
)
# Initialize context
conv = (
MinkowskiConvolution(
in_channels,
out_channels,
kernel_size=3,
stride=2,
bias=True,
dimension=D,
)
.double()
.cuda()
)
output = conv(input)
iC = input.C.cpu().numpy()
oC = output.C.cpu().numpy()
print(iC)
print(oC)
kernel_maps = output.coordinate_manager.kernel_map(
1,
2,
stride=2,
kernel_size=3,
)
for kernel_index, in_out_map in kernel_maps.items():
for i, o in zip(in_out_map[0], in_out_map[1]):
print(kernel_index, iC[i], "->", oC[o])
self.assertTrue(sum(len(in_map[0]) for k, in_map in kernel_maps.items()) == 16)
def test_kernelmap(self):
print(f"{self.__class__.__name__}: test_kernelmap")
in_channels, out_channels, D = 2, 3, 2
coords, feats, labels = data_loader(in_channels)
feats = feats.double()
feats.requires_grad_()
input = SparseTensor(feats, coordinates=coords)
# Initialize context
conv = MinkowskiConvolution(
in_channels,
out_channels,
kernel_size=3,
stride=2,
bias=True,
dimension=D,
).double()
output = conv(input)
iC = input.C.numpy()
oC = output.C.numpy()
print(iC)
print(oC)
kernel_maps = output.coordinate_manager.kernel_map(
1, 2, stride=2, kernel_size=3
)
for kernel_index, in_out_map in kernel_maps.items():
for i, o in zip(in_out_map[0], in_out_map[1]):
print(kernel_index, iC[i], "->", oC[o])
self.assertTrue(sum(len(in_map[0]) for k, in_map in kernel_maps.items()) == 16)
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