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# ----------------------------------------------------------------------------
# - Open3D: www.open3d.org -
# ----------------------------------------------------------------------------
# Copyright (c) 2018-2024 www.open3d.org
# SPDX-License-Identifier: MIT
# ----------------------------------------------------------------------------
"""Tests the reference python implementation of the continuous conv"""
import open3d as o3d
import numpy as np
import pytest
import mltest
from check_gradients import check_gradients
from cconv_python import *
# skip all tests if the ml ops were not built
pytestmark = mltest.default_marks
# yapf: disable
@pytest.mark.parametrize("filter_size, out_channels, in_channels, with_inp_importance, with_normalization",[
([3,5,1], 2, 7, True, False),
([3,3,3], 1, 1, False, False),
([5,5,5], 5, 3, False, True),
])
# yapf: enable
@pytest.mark.parametrize('dtype', [np.float32, np.float64])
def test_compare_to_conv3d(dtype, filter_size, out_channels, in_channels,
with_inp_importance, with_normalization):
"""Compares to the 3D convolution in tensorflow"""
tf = pytest.importorskip('tensorflow')
ml3d = pytest.importorskip('open3d.ml.tf')
np.random.seed(0)
conv_attrs = {
'align_corners': False,
'coordinate_mapping': IDENTITY,
'normalize': with_normalization,
'interpolation': NEAREST_NEIGHBOR,
}
filters = np.random.random(size=(*filter_size, in_channels,
out_channels)).astype(dtype)
max_grid_extent = 10
inp_positions = np.unique(np.random.randint(0, max_grid_extent,
(256, 3)).astype(dtype),
axis=0)
inp_positions_int = inp_positions.astype(np.int32)
if (with_inp_importance):
inp_importance = np.random.rand(
inp_positions.shape[0]).astype(dtype) - 0.5
else:
inp_importance = np.empty((0,))
out_positions = np.unique(np.random.randint(
np.max(filter_size) // 2, max_grid_extent - np.max(filter_size) // 2,
(5, 3)).astype(dtype),
axis=0)
out_positions_int = out_positions.astype(np.int32)
voxel_size = np.array([1, 1, 1], dtype=dtype)
voxel_offset = np.array([0, 0, 0], dtype=dtype)
extent = voxel_size[np.newaxis, :] * np.array(filter_size[::-1])
offset = np.array([0.0, 0.0, 0.0], dtype=dtype)
inp_features = np.random.uniform(size=inp_positions.shape[0:1] +
(in_channels,)).astype(dtype)
fixed_radius_search = ml3d.layers.FixedRadiusSearch(metric='Linf')
neighbors_index, neighbors_row_splits, _ = fixed_radius_search(
inp_positions / extent,
out_positions / extent,
voxel_size[0] / 2 + 0.01,
)
neighbors_index = neighbors_index.numpy()
neighbors_row_splits = neighbors_row_splits.numpy()
neighbors_importance = np.empty((0,))
y = cconv(filters, out_positions, extent, offset, inp_positions,
inp_features, inp_importance, neighbors_index,
neighbors_importance, neighbors_row_splits, **conv_attrs)
# Compare the output to a standard 3d conv
# store features in a volume to use standard 3d convs
inp_volume = np.zeros(
(1, max_grid_extent, max_grid_extent, max_grid_extent, in_channels))
if with_inp_importance:
inp_features *= inp_importance[:, np.newaxis]
inp_volume[0, inp_positions_int[:, 2], inp_positions_int[:, 1],
inp_positions_int[:, 0], :] = inp_features
y_conv3d = tf.nn.conv3d(
inp_volume,
filters,
strides=[1] * 5,
padding='SAME',
).numpy()
# extract result at output positions
y_conv3d = np.ascontiguousarray(y_conv3d[0, out_positions_int[:, 2],
out_positions_int[:, 1],
out_positions_int[:, 0], :])
if with_normalization:
for i, v in enumerate(y_conv3d):
num_neighbors = neighbors_row_splits[i +
1] - neighbors_row_splits[i]
v /= dtype(num_neighbors)
np.testing.assert_allclose(y, y_conv3d, rtol=1e-5, atol=1e-8)
# yapf: disable
@pytest.mark.parametrize("filter_size, out_channels, in_channels, with_inp_importance, with_neighbors_importance, with_individual_extent, with_normalization, align_corners, coordinate_mapping, interpolation",[
([3,5,1], 2, 7, True, False, False, False, True, IDENTITY, NEAREST_NEIGHBOR),
([3,3,3], 1, 1, False, False, True, False, False, BALL_TO_CUBE_RADIAL, LINEAR),
([5,5,5], 5, 3, False, True, False, True, False, BALL_TO_CUBE_VOLUME_PRESERVING, LINEAR_BORDER),
([5,1,3], 3, 4, False, True, False, False, False, IDENTITY, LINEAR),
])
# yapf: enable
def test_cconv_gradient(filter_size, out_channels, in_channels,
with_inp_importance, with_neighbors_importance,
with_individual_extent, with_normalization,
align_corners, coordinate_mapping, interpolation):
tf = pytest.importorskip('tensorflow')
ml3d = pytest.importorskip('open3d.ml.tf')
dtype = np.float64
np.random.seed(0)
conv_attrs = {
'align_corners': align_corners,
'coordinate_mapping': coordinate_mapping,
'normalize': with_normalization,
'interpolation': interpolation,
}
filters = np.random.random(size=(*filter_size, in_channels,
out_channels)).astype(dtype)
inp_positions = np.random.rand(128, 3).astype(dtype)
if (with_inp_importance):
inp_importance = np.random.rand(inp_positions.shape[0]).astype(dtype)
else:
inp_importance = np.empty((0,))
out_positions = np.random.rand(5, 3).astype(dtype)
if with_individual_extent:
extent = 0.4 + 0.01 * (np.random.rand(out_positions.shape[0], 1) - 0.5)
else:
extent = np.array([[0.4]], dtype=dtype)
offset = np.array([0.0, 0.0, 0.0], dtype=dtype)
inp_features = np.random.uniform(size=inp_positions.shape[0:1] +
(in_channels,)).astype(dtype)
fixed_radius_search = ml3d.layers.FixedRadiusSearch(metric='Linf')
neighbors_index, neighbors_row_splits, _ = fixed_radius_search(
inp_positions, out_positions, extent[0, 0] / 2)
neighbors_index = neighbors_index.numpy()
neighbors_row_splits = neighbors_row_splits.numpy()
if (with_neighbors_importance):
neighbors_importance = np.random.rand(
neighbors_index.shape[0]).astype(dtype) - 0.5
else:
neighbors_importance = np.empty((0,))
inverted_neighbors_index, inverted_neighbors_row_splits, inverted_neighbors_importance = ml3d.ops.invert_neighbors_list(
inp_positions.shape[0], neighbors_index, neighbors_row_splits,
neighbors_importance)
inverted_neighbors_index = inverted_neighbors_index.numpy()
inverted_neighbors_row_splits = inverted_neighbors_row_splits.numpy()
inverted_neighbors_importance = inverted_neighbors_importance.numpy()
# print(neighbors_row_splits, inverted_neighbors_row_splits)
# print(neighbors_index, inverted_neighbors_index)
# define functions for the gradient checker
def conv_infeats(inp_features):
return cconv(filters, out_positions, extent, offset, inp_positions,
inp_features, inp_importance, neighbors_index,
neighbors_importance, neighbors_row_splits, **conv_attrs)
def conv_filter(filter):
return cconv(filter, out_positions, extent, offset, inp_positions,
inp_features, inp_importance, neighbors_index,
neighbors_importance, neighbors_row_splits, **conv_attrs)
def conv_filter_backprop(out_features_gradient, filter):
return cconv_backprop_filter(filter, out_positions, extent, offset,
inp_positions, inp_features,
inp_importance, neighbors_index,
neighbors_importance, neighbors_row_splits,
out_features_gradient, **conv_attrs)
def conv_transpose_as_infeat_backprop(out_features_gradient, inp_features):
return cconv_transpose(filters.transpose([0, 1, 2, 4, 3]),
inp_positions, inp_importance, extent, offset,
out_positions, out_features_gradient,
neighbors_index, neighbors_importance,
neighbors_row_splits, inverted_neighbors_index,
inverted_neighbors_importance,
inverted_neighbors_row_splits, **conv_attrs)
def conv_transpose_filter(filter):
return cconv_transpose(filter.transpose([0, 1, 2, 4, 3]), inp_positions,
inp_importance, extent, offset, out_positions,
y_arr, neighbors_index, neighbors_importance,
neighbors_row_splits, inverted_neighbors_index,
inverted_neighbors_importance,
inverted_neighbors_row_splits, **conv_attrs)
def conv_transpose_infeats(inp_features):
return cconv_transpose(filters.transpose([0, 1, 2, 4, 3]),
inp_positions, inp_importance, extent, offset,
out_positions, inp_features, neighbors_index,
neighbors_importance, neighbors_row_splits,
inverted_neighbors_index,
inverted_neighbors_importance,
inverted_neighbors_row_splits, **conv_attrs)
def conv_transpose_filter_backprop(out_features_gradient, filter):
ans = cconv_transpose_backprop_filter(
filter.transpose([0, 1, 2, 4, 3]), inp_positions, inp_importance,
extent, offset, out_positions, y_arr, neighbors_index,
neighbors_importance, neighbors_row_splits,
inverted_neighbors_index, inverted_neighbors_importance,
inverted_neighbors_row_splits, out_features_gradient, **conv_attrs)
return ans.transpose([0, 1, 2, 4, 3])
def conv_transpose_infeat_backprop(out_features_gradient, inp_features):
ans = cconv(filters, out_positions, extent, offset, inp_positions,
out_features_gradient, inp_importance, neighbors_index,
neighbors_importance, neighbors_row_splits, **conv_attrs)
return ans
y_arr = conv_infeats(inp_features)
dbg = {}
filter_gradient_OK = check_gradients(filters,
conv_filter,
conv_filter_backprop,
debug_outputs=dbg)
assert filter_gradient_OK
feature_gradient_OK = check_gradients(inp_features,
conv_infeats,
conv_transpose_as_infeat_backprop,
debug_outputs=dbg)
assert feature_gradient_OK
transpose_filter_gradient_OK = check_gradients(
filters,
conv_transpose_filter,
conv_transpose_filter_backprop,
debug_outputs=dbg)
assert transpose_filter_gradient_OK
transpose_feature_gradient_OK = check_gradients(
y_arr,
conv_transpose_infeats,
conv_transpose_infeat_backprop,
debug_outputs=dbg)
assert transpose_feature_gradient_OK
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