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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
import torch.nn as nn
import MinkowskiEngine as ME
from MinkowskiEngine import (
SparseTensor,
MinkowskiConvolution,
MinkowskiConvolutionTranspose,
MinkowskiBatchNorm,
MinkowskiReLU,
)
from MinkowskiOps import (
MinkowskiToSparseTensor,
to_sparse,
dense_coordinates,
MinkowskiToDenseTensor,
)
class TestDense(unittest.TestCase):
def test(self):
print(f"{self.__class__.__name__}: test_dense")
in_channels, out_channels, D = 2, 3, 2
coords1 = torch.IntTensor([[0, 0], [0, 1], [1, 1]])
feats1 = torch.DoubleTensor([[1, 2], [3, 4], [5, 6]])
coords2 = torch.IntTensor([[1, 1], [1, 2], [2, 1]])
feats2 = torch.DoubleTensor([[7, 8], [9, 10], [11, 12]])
coords, feats = ME.utils.sparse_collate([coords1, coords2], [feats1, feats2])
input = SparseTensor(feats, coords)
input.requires_grad_()
dinput, min_coord, tensor_stride = input.dense()
self.assertTrue(dinput[0, 0, 0, 1] == 3)
self.assertTrue(dinput[0, 1, 0, 1] == 4)
self.assertTrue(dinput[0, 0, 1, 1] == 5)
self.assertTrue(dinput[0, 1, 1, 1] == 6)
self.assertTrue(dinput[1, 0, 1, 1] == 7)
self.assertTrue(dinput[1, 1, 1, 1] == 8)
self.assertTrue(dinput[1, 0, 2, 1] == 11)
self.assertTrue(dinput[1, 1, 2, 1] == 12)
# Initialize context
conv = MinkowskiConvolution(
in_channels, out_channels, kernel_size=3, stride=2, bias=True, dimension=D,
)
conv = conv.double()
output = conv(input)
print(input.C, output.C)
# Convert to a dense tensor
dense_output, min_coord, tensor_stride = output.dense()
print(dense_output.shape)
print(dense_output)
print(min_coord)
print(tensor_stride)
dense_output, min_coord, tensor_stride = output.dense(
min_coordinate=torch.IntTensor([-2, -2])
)
print(dense_output)
print(min_coord)
print(tensor_stride)
print(feats.grad)
loss = dense_output.sum()
loss.backward()
print(feats.grad)
def test_empty(self):
x = torch.zeros(4, 1, 34, 34)
to_dense = ME.MinkowskiToDenseTensor(x.shape)
# Convert to sparse data
sparse_data = ME.to_sparse(x)
dense_data = to_dense(sparse_data)
self.assertEqual(dense_data.shape, x.shape)
class TestDenseToSparse(unittest.TestCase):
def test(self):
dense_tensor = torch.rand(3, 4, 5, 6)
sparse_tensor = to_sparse(dense_tensor)
self.assertEqual(len(sparse_tensor), 3 * 5 * 6)
self.assertEqual(sparse_tensor.F.size(1), 4)
def test_format(self):
dense_tensor = torch.rand(3, 4, 5, 6)
sparse_tensor = to_sparse(dense_tensor, format="BXXC")
self.assertEqual(len(sparse_tensor), 3 * 4 * 5)
self.assertEqual(sparse_tensor.F.size(1), 6)
def test_network(self):
dense_tensor = torch.rand(3, 4, 11, 11, 11, 11) # BxCxD1xD2x....xDN
dense_tensor.requires_grad = True
# Since the shape is fixed, cache the coordinates for faster inference
coordinates = dense_coordinates(dense_tensor.shape)
network = nn.Sequential(
# Add layers that can be applied on a regular pytorch tensor
nn.ReLU(),
MinkowskiToSparseTensor(remove_zeros=False, coordinates=coordinates),
MinkowskiConvolution(4, 5, stride=2, kernel_size=3, dimension=4),
MinkowskiBatchNorm(5),
MinkowskiReLU(),
MinkowskiConvolutionTranspose(5, 6, stride=2, kernel_size=3, dimension=4),
MinkowskiToDenseTensor(
dense_tensor.shape
), # must have the same tensor stride.
)
for i in range(5):
print(f"Iteration: {i}")
output = network(dense_tensor)
output.sum().backward()
assert dense_tensor.grad is not None
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