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# Copyright (c) 2020 NVIDIA CORPORATION.
# Copyright (c) 2018-2020 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 spmm, MinkowskiSPMMFunction, MinkowskiSPMMAverageFunction
from utils.gradcheck import gradcheck
class TestSPMM(unittest.TestCase):
def test_spmm(self):
rows = torch.Tensor([0, 0, 1, 1]).int()
cols = torch.Tensor([0, 1, 2, 3]).int()
vals = torch.ones(4).double()
size = [2, 4]
mat = torch.rand(4, 3).double()
mat.requires_grad_()
out = spmm(rows, cols, vals, size, mat, is_sorted=False)
print(out)
rows = rows.cuda()
cols = cols.cuda()
vals = vals.cuda()
mat = mat.cuda()
out = spmm(rows, cols, vals, size, mat, is_sorted=False)
print(out)
def test_spmm_sorted(self):
rows = torch.Tensor([0, 0, 1, 1]).int()
cols = torch.Tensor([0, 1, 2, 3]).int()
vals = torch.ones(4).double()
size = [2, 4]
mat = torch.rand(4, 3).double()
mat.requires_grad_()
out = spmm(rows, cols, vals, size, mat, is_sorted=True)
print(out)
rows = rows.cuda()
cols = cols.cuda()
vals = vals.cuda()
mat = mat.cuda()
out = spmm(rows, cols, vals, size, mat, is_sorted=True)
print(out)
def test(self):
rows = torch.Tensor([0, 0, 1, 1]).int()
cols = torch.Tensor([0, 1, 2, 3]).int()
vals = torch.ones(4).double()
size = [2, 4]
mat = torch.rand(4, 3).double()
mat.requires_grad_()
spmm_fn = MinkowskiSPMMFunction()
out = spmm_fn.apply(rows, cols, vals, size, mat)
print(out)
loss = out.sum()
loss.backward()
print(mat.grad)
self.assertTrue(gradcheck(spmm_fn, (rows, cols, vals, size, mat)))
rows = rows.cuda()
cols = cols.cuda()
vals = vals.cuda()
mat = mat.cuda()
mat.requires_grad_()
out = spmm_fn.apply(rows, cols, vals, size, mat)
print(out)
loss = out.sum()
loss.backward()
print(mat.grad)
self.assertTrue(gradcheck(spmm_fn, (rows, cols, vals, size, mat)))
def test_average(self):
rows = torch.Tensor([0, 0, 1, 1]).int()
cols = torch.Tensor([0, 1, 2, 3]).int()
size = [2, 4]
mat = torch.rand(4, 3).double()
mat.requires_grad_()
spmm_fn = MinkowskiSPMMAverageFunction()
out = spmm_fn.apply(rows, cols, size, mat)
print(out)
loss = out.sum()
loss.backward()
print(mat.grad)
self.assertTrue(gradcheck(spmm_fn, (rows, cols, size, mat)))
rows = rows.cuda()
cols = cols.cuda()
mat = mat.cuda()
mat.requires_grad_()
out = spmm_fn.apply(rows, cols, size, mat)
print(out)
loss = out.sum()
loss.backward()
print(mat.grad)
self.assertTrue(gradcheck(spmm_fn, (rows, cols, size, mat)))
def test_dtype(self):
rows = torch.Tensor([0, 0, 1, 1]).float()
cols = torch.Tensor([0, 1, 2, 3]).double()
vals = torch.ones(4).double()
size = [2, 4]
mat = torch.rand(4, 3).double()
mat.requires_grad_()
spmm_fn = MinkowskiSPMMFunction()
out = spmm_fn.apply(rows, cols, vals, size, mat)
print(out)
if not torch.cuda.is_available():
return
rows = torch.cuda.IntTensor([0, 0, 1, 1])
cols = torch.cuda.IntTensor([0, 1, 2, 3])
vals = torch.ones(4).double().to(0)
size = [2, 4]
mat = mat.to(0)
mat.requires_grad_()
out = spmm_fn.apply(rows, cols, vals, size, mat)
print(out)
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