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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 unittest
import MinkowskiEngineBackend._C as _C
from MinkowskiEngine import (
SparseTensor,
MinkowskiConvolution,
MinkowskiConvolutionTranspose,
MinkowskiPruning,
MinkowskiPruningFunction,
)
from utils.gradcheck import gradcheck
from tests.python.common import data_loader
class TestPruning(unittest.TestCase):
def test(self):
in_channels = 2
coords, feats, labels = data_loader(in_channels, batch_size=1)
feats = feats.double()
feats.requires_grad_()
input = SparseTensor(feats, coords)
use_feat = torch.rand(feats.size(0)) < 0.5
pruning = MinkowskiPruning()
output = pruning(input, use_feat)
print(input)
print(use_feat)
print(output)
# Check backward
fn = MinkowskiPruningFunction()
self.assertTrue(
gradcheck(
fn,
(
input.F,
use_feat,
input.coordinate_map_key,
output.coordinate_map_key,
input.coordinate_manager,
),
)
)
def test_device(self):
in_channels = 2
coords, feats, labels = data_loader(in_channels, batch_size=1)
feats = feats.double()
feats.requires_grad_()
input = SparseTensor(feats, coords, device="cuda")
use_feat = torch.rand(feats.size(0)) < 0.5
pruning = MinkowskiPruning()
output = pruning(input, use_feat.cuda())
print(input)
print(use_feat)
print(output)
def test_empty(self):
in_channels = 2
coords, feats, labels = data_loader(in_channels, batch_size=1)
feats = feats.double()
feats.requires_grad_()
input = SparseTensor(feats, coords)
use_feat = torch.BoolTensor(len(input))
use_feat.zero_()
pruning = MinkowskiPruning()
output = pruning(input, use_feat)
print(input)
print(use_feat)
print(output)
# Check backward
fn = MinkowskiPruningFunction()
self.assertTrue(
gradcheck(
fn,
(
input.F,
use_feat,
input.coordinate_map_key,
output.coordinate_map_key,
input.coordinate_manager,
),
)
)
def test_pruning(self):
in_channels, D = 2, 2
coords, feats, labels = data_loader(in_channels, batch_size=1)
feats = feats.double()
feats.requires_grad_()
input = SparseTensor(feats, coords)
use_feat = torch.rand(feats.size(0)) < 0.5
pruning = MinkowskiPruning()
output = pruning(input, use_feat)
print(input)
print(use_feat)
print(output)
# Check backward
fn = MinkowskiPruningFunction()
self.assertTrue(
gradcheck(
fn,
(
input.F,
use_feat,
input.coordinate_map_key,
output.coordinate_map_key,
input.coordinate_manager,
),
)
)
def test_device(self):
in_channels, D = 2, 2
device = torch.device("cuda")
coords, feats, labels = data_loader(in_channels, batch_size=1)
feats = feats.double()
feats.requires_grad_()
use_feat = (torch.rand(feats.size(0)) < 0.5).to(device)
pruning = MinkowskiPruning()
input = SparseTensor(feats, coords, device=device)
output = pruning(input, use_feat)
print(input)
print(output)
fn = MinkowskiPruningFunction()
self.assertTrue(
gradcheck(
fn,
(
input.F,
use_feat,
input.coordinate_map_key,
output.coordinate_map_key,
input.coordinate_manager,
),
)
)
def test_with_convtr(self):
channels, D = [2, 3, 4], 2
coords, feats, labels = data_loader(channels[0], batch_size=1)
feats = feats.double()
feats.requires_grad_()
# Create a sparse tensor with large tensor strides for upsampling
start_tensor_stride = 4
input = SparseTensor(
feats, coords * start_tensor_stride, tensor_stride=start_tensor_stride,
)
conv_tr1 = MinkowskiConvolutionTranspose(
channels[0],
channels[1],
kernel_size=3,
stride=2,
generate_new_coords=True,
dimension=D,
).double()
conv1 = MinkowskiConvolution(
channels[1], channels[1], kernel_size=3, dimension=D
).double()
conv_tr2 = MinkowskiConvolutionTranspose(
channels[1],
channels[2],
kernel_size=3,
stride=2,
generate_new_coords=True,
dimension=D,
).double()
conv2 = MinkowskiConvolution(
channels[2], channels[2], kernel_size=3, dimension=D
).double()
pruning = MinkowskiPruning()
out1 = conv_tr1(input)
self.assertTrue(torch.prod(torch.abs(out1.F) > 0).item() == 1)
out1 = conv1(out1)
use_feat = torch.rand(len(out1)) < 0.5
out1 = pruning(out1, use_feat)
out2 = conv_tr2(out1)
self.assertTrue(torch.prod(torch.abs(out2.F) > 0).item() == 1)
use_feat = torch.rand(len(out2)) < 0.5
out2 = pruning(out2, use_feat)
out2 = conv2(out2)
print(out2)
out2.F.sum().backward()
# Check gradient flow
print(input.F.grad)
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