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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 torch
import torch.nn as nn
import MinkowskiEngine as ME
import MinkowskiEngine.MinkowskiFunctional as MF
from tests.python.common import data_loader
class StackUNet(ME.MinkowskiNetwork):
def __init__(self, in_nchannel, out_nchannel, D):
ME.MinkowskiNetwork.__init__(self, D)
channels = [in_nchannel, 16, 32]
self.net = nn.Sequential(
ME.MinkowskiStackSum(
ME.MinkowskiConvolution(
channels[0],
channels[1],
kernel_size=3,
stride=1,
dimension=D,
),
nn.Sequential(
ME.MinkowskiConvolution(
channels[0],
channels[1],
kernel_size=3,
stride=2,
dimension=D,
),
ME.MinkowskiStackSum(
nn.Identity(),
nn.Sequential(
ME.MinkowskiConvolution(
channels[1],
channels[2],
kernel_size=3,
stride=2,
dimension=D,
),
ME.MinkowskiConvolutionTranspose(
channels[2],
channels[1],
kernel_size=3,
stride=1,
dimension=D,
),
ME.MinkowskiPoolingTranspose(
kernel_size=2, stride=2, dimension=D
),
),
),
ME.MinkowskiPoolingTranspose(kernel_size=2, stride=2, dimension=D),
),
),
ME.MinkowskiToFeature(),
nn.Linear(channels[1], out_nchannel, bias=True),
)
def forward(self, x):
return self.net(x)
if __name__ == "__main__":
# loss and network
net = StackUNet(3, 5, D=2)
print(net)
# a data loader must return a tuple of coords, features, and labels.
coords, feat, label = data_loader()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = net.to(device)
input = ME.SparseTensor(feat, coords, device=device)
# Forward
output = net(input)
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