MinkowskiEngine/tests/python/summary.py

# Copyright (c) 2020-2021 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
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# so, subject to the following conditions:
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# 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 open3d as o3d
import numpy as np
import os
from urllib.request import urlretrieve

import torch
import torch.nn as nn

import MinkowskiEngine as ME
from MinkowskiEngine import SparseTensor
from MinkowskiEngine.utils import summary, batched_coordinates


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)



class TestSummary(unittest.TestCase):

    def setUp(self):
        file_name, voxel_size = "1.ply", 0.02
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.net = StackUNet(3, 20, D=3).to(self.device)
        if not os.path.isfile(file_name):
            print('Downloading an example pointcloud...')
            urlretrieve("https://bit.ly/3c2iLhg", file_name)

        pcd = o3d.io.read_point_cloud(file_name)
        coords = np.array(pcd.points)
        colors = np.array(pcd.colors)

        self.sinput = SparseTensor(
            features=torch.from_numpy(colors).float(),
            coordinates=batched_coordinates([coords / voxel_size], dtype=torch.float32),
            device=self.device,
        )

    def test(self):
        summary(self.net, self.sinput)