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# ----------------------------------------------------------------------------
# - Open3D: www.open3d.org -
# ----------------------------------------------------------------------------
# Copyright (c) 2018-2024 www.open3d.org
# SPDX-License-Identifier: MIT
# ----------------------------------------------------------------------------
import open3d as o3d
import numpy as np
import pytest
import mltest
import torch
# skip all tests if the tf ops were not built and disable warnings caused by
# tensorflow
pytestmark = mltest.default_marks
# the supported dtypes for the values
dtypes = pytest.mark.parametrize('dtype',
[np.int32, np.int64, np.float32, np.float64])
# this class is only available for torch
@dtypes
@mltest.parametrize.ml_torch_only
def test_creation(dtype, ml):
values = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=dtype)
row_splits = np.array([0, 2, 4, 4, 5, 12, 13], dtype=np.int64)
# From numpy arrays
r_tensor = ml.classes.RaggedTensor.from_row_splits(values, row_splits)
for i, tensor in enumerate(r_tensor):
np.testing.assert_equal(mltest.to_numpy(tensor),
values[row_splits[i]:row_splits[i + 1]])
# From List
r_tensor = ml.classes.RaggedTensor.from_row_splits(list(values),
list(row_splits))
for i, tensor in enumerate(r_tensor):
np.testing.assert_equal(mltest.to_numpy(tensor),
values[row_splits[i]:row_splits[i + 1]])
# Incompatible tensors.
# Non zero first element.
row_splits = np.array([1, 2, 4, 4, 5, 12, 13], dtype=np.int64)
with np.testing.assert_raises(RuntimeError):
ml.classes.RaggedTensor.from_row_splits(values, row_splits)
# Rank > 1.
row_splits = np.array([[0, 2, 4, 4, 5, 12, 13]], dtype=np.int64)
with np.testing.assert_raises(RuntimeError):
ml.classes.RaggedTensor.from_row_splits(values, row_splits)
# Not increasing monotonically.
row_splits = np.array([[0, 2, 4, 6, 5, 12, 13]], dtype=np.int64)
with np.testing.assert_raises(RuntimeError):
ml.classes.RaggedTensor.from_row_splits(values, row_splits)
# Wrong dtype.
row_splits = np.array([0, 2, 4, 4, 5, 12, 13], dtype=np.float32)
with np.testing.assert_raises(RuntimeError):
ml.classes.RaggedTensor.from_row_splits(values, row_splits)
# test with more dimensions
@dtypes
@mltest.parametrize.ml_torch_only
def test_creation_more_dims(dtype, ml):
values = np.array([[0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6],
[7, 7], [8, 8], [9, 9], [10, 10], [11, 11], [12, 12]],
dtype=dtype)
row_splits = np.array([0, 2, 4, 4, 5, 12, 13], dtype=np.int64)
# From numpy arrays
r_tensor = ml.classes.RaggedTensor.from_row_splits(values, row_splits)
for i, tensor in enumerate(r_tensor):
np.testing.assert_equal(mltest.to_numpy(tensor),
values[row_splits[i]:row_splits[i + 1]])
# From List
r_tensor = ml.classes.RaggedTensor.from_row_splits(list(values),
list(row_splits))
for i, tensor in enumerate(r_tensor):
np.testing.assert_equal(mltest.to_numpy(tensor),
values[row_splits[i]:row_splits[i + 1]])
@mltest.parametrize.ml_torch_only
def test_backprop(ml):
# Create 3 different RaggedTensors and torch.tensor
t_1 = torch.randn(10, 3, requires_grad=True)
row_splits = torch.tensor([0, 4, 6, 6, 8, 10])
r_1 = ml.classes.RaggedTensor.from_row_splits(t_1.detach().numpy(),
row_splits)
r_1.requires_grad = True
t_2 = torch.randn(10, 3, requires_grad=True)
r_2 = ml.classes.RaggedTensor.from_row_splits(t_2.detach().numpy(),
row_splits)
r_2.requires_grad = True
t_3 = torch.randn(10, 3, requires_grad=True)
r_3 = ml.classes.RaggedTensor.from_row_splits(t_3.detach().numpy(),
row_splits)
r_3.requires_grad = True
r_ans = (r_1 + r_2) * r_3
t_ans = (t_1 + t_2) * t_3
np.testing.assert_equal(mltest.to_numpy(t_ans),
mltest.to_numpy(r_ans.values))
# Compute gradients
t_ans.sum().backward()
r_ans.values.sum().backward()
np.testing.assert_equal(mltest.to_numpy(t_1.grad),
mltest.to_numpy(r_1.values.grad))
@dtypes
@mltest.parametrize.ml_torch_only
def test_binary_ew_ops(dtype, ml):
# Binary Ops.
t_1 = torch.from_numpy(
np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
dtype=dtype)).to(ml.device)
t_2 = torch.from_numpy(
np.array([2, 3, 6, 3, 11, 3, 43, 12, 8, 15, 12, 87, 45],
dtype=dtype)).to(ml.device)
row_splits = torch.from_numpy(
np.array([0, 2, 4, 4, 5, 12, 13], dtype=np.int64)).to(ml.device)
a = ml.classes.RaggedTensor.from_row_splits(t_1, row_splits)
b = ml.classes.RaggedTensor.from_row_splits(t_2, row_splits)
np.testing.assert_equal(
(a + b).values.cpu().numpy(),
np.array([2, 4, 8, 6, 15, 8, 49, 19, 16, 24, 22, 98, 57]))
np.testing.assert_equal(
(a - b).values.cpu().numpy(),
np.array([-2, -2, -4, 0, -7, 2, -37, -5, 0, -6, -2, -76, -33]))
np.testing.assert_equal(
(a * b).values.cpu().numpy(),
np.array([0, 3, 12, 9, 44, 15, 258, 84, 64, 135, 120, 957, 540]))
np.testing.assert_equal((a / b).values.cpu().numpy(),
(t_1 / t_2).cpu().numpy())
np.testing.assert_equal((a // b).values.cpu().numpy(),
np.array([0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0]))
# Assignment Ops.
a = ml.classes.RaggedTensor.from_row_splits(t_1, row_splits)
a += b
np.testing.assert_equal(
a.values.cpu().numpy(),
np.array([2, 4, 8, 6, 15, 8, 49, 19, 16, 24, 22, 98, 57]))
a = ml.classes.RaggedTensor.from_row_splits(t_1, row_splits)
a -= b
np.testing.assert_equal(
a.values.cpu().numpy(),
np.array([-2, -2, -4, 0, -7, 2, -37, -5, 0, -6, -2, -76, -33]))
a = ml.classes.RaggedTensor.from_row_splits(t_1, row_splits)
a *= b
np.testing.assert_equal(
a.values.cpu().numpy(),
np.array([0, 3, 12, 9, 44, 15, 258, 84, 64, 135, 120, 957, 540]))
a = ml.classes.RaggedTensor.from_row_splits(t_1, row_splits)
a //= b
np.testing.assert_equal(a.values.cpu().numpy(),
np.array([0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0]))
# Failure cases with incompatible shape.
# Different row_splits.
row_splits = [0, 4, 5, 13]
a = ml.classes.RaggedTensor.from_row_splits(t_1, row_splits)
row_splits = [0, 4, 6, 13]
b = ml.classes.RaggedTensor.from_row_splits(t_2, row_splits)
with np.testing.assert_raises(ValueError):
a + b
with np.testing.assert_raises(ValueError):
a += b
# Different length
row_splits = [0, 4, 5, 13]
a = ml.classes.RaggedTensor.from_row_splits(t_1, row_splits)
row_splits = [0, 4, 13]
b = ml.classes.RaggedTensor.from_row_splits(t_2, row_splits)
with np.testing.assert_raises(ValueError):
a + b
with np.testing.assert_raises(ValueError):
a += b
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