File size: 4,249 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 |
# 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, MinkowskiConvolution, \
MinkowskiConvolutionTranspose
import MinkowskiEngine as ME
from tests.common import data_loader
def get_random_coords(dimension=2, tensor_stride=2):
torch.manual_seed(0)
# Create random coordinates with tensor stride == 2
coords = torch.rand(10, dimension + 1)
coords[:, :dimension] *= 5 # random coords
coords[:, -1] *= 2 # random batch index
coords = coords.floor().int()
coords = ME.utils.sparse_quantize(coords)
coords[:, :dimension] *= tensor_stride # make the tensor stride 2
return coords, tensor_stride
class TestConvolution(unittest.TestCase):
def test(self):
print(f"{self.__class__.__name__}: test")
in_channels, out_channels, D = 2, 3, 2
coords, feats, labels = data_loader(in_channels, batch_size=2)
# Create random coordinates with tensor stride == 2
out_coords, tensor_stride = get_random_coords()
feats = feats.double()
feats.requires_grad_()
input = SparseTensor(feats, coords=coords)
cm = input.coords_man
print(cm._get_coords_key(1))
conv = MinkowskiConvolution(
in_channels,
out_channels,
kernel_size=3,
stride=1,
bias=False,
dimension=D).double()
print('Initial input: ', input)
print('Specified output coords: ', out_coords)
output = conv(input, out_coords)
# To specify the tensor stride
out_coords_key = cm.create_coords_key(out_coords, tensor_stride=2)
output = conv(input, out_coords_key)
print('Conv output: ', output)
output.F.sum().backward()
print(input.F.grad)
def test_tr(self):
print(f"{self.__class__.__name__}: test_tr")
in_channels, out_channels, D = 2, 3, 2
coords, feats, labels = data_loader(in_channels, batch_size=2)
# tensor stride must be at least 2 for convolution transpose with stride 2
coords[:, :2] *= 2
out_coords = torch.rand(10, 3)
out_coords[:, :2] *= 10 # random coords
out_coords[:, 2] *= 2 # random batch index
out_coords = out_coords.floor().int()
feats = feats.double()
feats.requires_grad_()
input = SparseTensor(feats, coords=coords, tensor_stride=2)
cm = input.coords_man
print(cm._get_coords_key(2))
conv_tr = MinkowskiConvolutionTranspose(
in_channels,
out_channels,
kernel_size=3,
stride=2,
bias=False,
dimension=D).double()
print('Initial input: ', input)
print('Specified output coords: ', out_coords)
output = conv_tr(input, out_coords)
print('Conv output: ', output)
output.F.sum().backward()
print(input.F.grad)
if __name__ == '__main__':
unittest.main()
|