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convolution.py
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convolution.py
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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 MinkowskiEngine as ME
from tests.common import data_loader
def get_random_coords(dimension=2, tensor_stride=2):
torch.manual_seed(0)
# Create random coordinates with tensor stride == 2
coords = torch.rand(10, dimension + 1)
coords[:, :dimension] *= 5 # random coords
coords[:, -1] *= 2 # random batch index
coords = coords.floor().int()
coords = ME.utils.sparse_quantize(coords)
coords[:, :dimension] *= tensor_stride # make the tensor stride 2
return coords, tensor_stride
def print_sparse_tensor(tensor):
for c, f in zip(tensor.C.numpy(), tensor.F.detach().numpy()):
print(f"Coordinate {c} : Feature {f}")
def conv():
in_channels, out_channels, D = 2, 3, 2
coords, feats, labels = data_loader(in_channels, batch_size=1)
# Convolution
input = ME.SparseTensor(feats=feats, coords=coords)
conv = ME.MinkowskiConvolution(
in_channels,
out_channels,
kernel_size=3,
stride=2,
has_bias=False,
dimension=D)
output = conv(input)
print('Input:')
print_sparse_tensor(input)
print('Output:')
print_sparse_tensor(output)
# Convolution transpose and generate new coordinates
strided_coords, tensor_stride = get_random_coords()
input = ME.SparseTensor(
feats=torch.rand(len(strided_coords), in_channels), #
coords=strided_coords,
tensor_stride=tensor_stride)
conv_tr = ME.MinkowskiConvolutionTranspose(
in_channels,
out_channels,
kernel_size=3,
stride=2,
has_bias=False,
dimension=D)
output = conv_tr(input)
print('\nInput:')
print_sparse_tensor(input)
print('Convolution Transpose Output:')
print_sparse_tensor(output)
def conv_on_coords():
in_channels, out_channels, D = 2, 3, 2
coords, feats, labels = data_loader(in_channels, batch_size=1)
# Create input with tensor stride == 4
strided_coords4, tensor_stride4 = get_random_coords(tensor_stride=4)
strided_coords2, tensor_stride2 = get_random_coords(tensor_stride=2)
input = ME.SparseTensor(
feats=torch.rand(len(strided_coords4), in_channels), #
coords=strided_coords4,
tensor_stride=tensor_stride4)
cm = input.coords_man
# Convolution transpose and generate new coordinates
conv_tr = ME.MinkowskiConvolutionTranspose(
in_channels,
out_channels,
kernel_size=3,
stride=2,
has_bias=False,
dimension=D)
pool_tr = ME.MinkowskiPoolingTranspose(
kernel_size=2,
stride=2,
dimension=D)
# If the there is no coordinates defined for the tensor stride, it will create one
# tensor stride 4 -> conv_tr with stride 2 -> tensor stride 2
output1 = conv_tr(input)
# output1 = pool_tr(input)
# convolution on the specified coords
output2 = conv_tr(input, coords)
# output2 = pool_tr(input, coords)
# convolution on the specified coords with tensor stride == 2
coords_key = cm.create_coords_key(strided_coords2, tensor_stride=2)
output3 = conv_tr(input, coords_key)
# output3 = pool_tr(input, coords_key)
# convolution on the coordinates of a sparse tensor
output4 = conv_tr(input, output1)
# output4 = pool_tr(input, output1)
if __name__ == '__main__':
conv()
conv_on_coords()