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# Copyright (c) 2021 NVIDIA CORPORATION.
#
# 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 numpy as np
import torch
import torch.nn as nn
import MinkowskiEngine as ME
from MinkowskiEngine import MinkowskiStackCat, MinkowskiStackSum
from MinkowskiEngine.utils import batched_coordinates
from utils.gradcheck import gradcheck
from tests.python.common import data_loader, load_file
class TestStack(unittest.TestCase):
def test_sum(self):
coords, colors, pcd = load_file("1.ply")
device = "cuda"
D = 3
batch_size = 16
voxel_size = 0.02
channels = [3, 64, 128]
dcoords = torch.from_numpy(np.floor(coords / voxel_size)).int()
bcoords = batched_coordinates([dcoords for i in range(batch_size)])
in_feats = torch.rand(len(bcoords), 3).to(0)
layer = MinkowskiStackSum(
ME.MinkowskiConvolution(
channels[0],
channels[1],
kernel_size=3,
stride=1,
dimension=3,
),
nn.Sequential(
ME.MinkowskiConvolution(
channels[0],
channels[1],
kernel_size=3,
stride=2,
dimension=3,
),
ME.MinkowskiStackSum(
nn.Identity(),
nn.Sequential(
ME.MinkowskiConvolution(
channels[1],
channels[2],
kernel_size=3,
stride=2,
dimension=3,
),
ME.MinkowskiConvolutionTranspose(
channels[2],
channels[1],
kernel_size=3,
stride=1,
dimension=3,
),
ME.MinkowskiPoolingTranspose(
kernel_size=2, stride=2, dimension=D
),
),
),
ME.MinkowskiPoolingTranspose(kernel_size=2, stride=2, dimension=D),
),
).cuda()
for i in range(1000):
torch.cuda.empty_cache()
sinput = ME.SparseTensor(in_feats, coordinates=bcoords, device=device)
layer(sinput)
def test_baseline(self):
coords, colors, pcd = load_file("1.ply")
device = "cuda"
D = 3
batch_size = 16
voxel_size = 0.02
channels = [3, 64, 128]
dcoords = torch.from_numpy(np.floor(coords / voxel_size)).int()
bcoords = batched_coordinates([dcoords for i in range(batch_size)])
in_feats = torch.rand(len(bcoords), 3).to(0)
layer = nn.Sequential(
ME.MinkowskiConvolution(
channels[0],
channels[1],
kernel_size=3,
stride=1,
dimension=3,
),
ME.MinkowskiConvolution(
channels[1],
channels[2],
kernel_size=3,
stride=2,
dimension=3,
),
ME.MinkowskiConvolutionTranspose(
channels[2],
channels[1],
kernel_size=3,
stride=1,
dimension=3,
),
).cuda()
for i in range(1000):
torch.cuda.empty_cache()
sinput = ME.SparseTensor(in_feats, coordinates=bcoords, device=device)
layer(sinput)
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