⚡️ Speed up function inverse_transformation by 38%

[codeflash-ai]

📄 38% (0.38x) speedup for inverse_transformation in kornia/geometry/linalg.py

⏱️ Runtime : 1.81 millisecond → 1.31 millisecond (best of 143 runs)

📝 Explanation and details

Optimization Details:

1. In-place Operations: In the original code, there were unnecessary additions in the form of +=. The modified code directly assigns the values, taking advantage of the in-place operations where possible, which reduces overhead.

2. Efficient Tensor Manipulations: In the optimized version, the slicing and operations are streamlined to avoid intermediate allocations. For example, the transpose and matmul operations are chained together for efficient memory handling.

By using these optimizations, the code becomes faster, especially for large tensors, by reducing the number of intermediate tensor allocations and avoiding unnecessary operations.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 44 Passed
🌀 Generated Regression Tests	✅ 16 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests Details

- geometry/test_linalg.py

🌀 Generated Regression Tests Details

import pytest  # used for our unit tests
import torch
from kornia.core import Tensor, zeros_like
from kornia.geometry.linalg import inverse_transformation


# unit tests
def test_inverse_transformation_identity():
    # Test with identity matrix
    identity = torch.eye(4)
    codeflash_output = inverse_transformation(identity)

def test_inverse_transformation_known():
    # Test with a known transformation
    trans_12 = torch.tensor([[1, 0, 0, 1],
                             [0, 1, 0, 2],
                             [0, 0, 1, 3],
                             [0, 0, 0, 1]], dtype=torch.float32)
    expected_inverse = torch.tensor([[1, 0, 0, -1],
                                     [0, 1, 0, -2],
                                     [0, 0, 1, -3],
                                     [0, 0, 0, 1]], dtype=torch.float32)
    codeflash_output = inverse_transformation(trans_12)

def test_inverse_transformation_batch():
    # Test with a batch of transformations
    batch_size = 5
    trans_12 = torch.eye(4).repeat(batch_size, 1, 1)
    codeflash_output = inverse_transformation(trans_12)

def test_inverse_transformation_invalid_shape():
    # Test with invalid shape
    with pytest.raises(ValueError):
        invalid_shape = torch.rand(3, 3)
        inverse_transformation(invalid_shape)

def test_inverse_transformation_non_tensor():
    # Test with non-tensor input
    with pytest.raises(TypeError):
        non_tensor_input = [[1, 0, 0, 1], [0, 1, 0, 2], [0, 0, 1, 3], [0, 0, 0, 1]]
        inverse_transformation(non_tensor_input)

def test_inverse_transformation_large_batch():
    # Test with a large batch of transformations
    batch_size = 1000  # Keep under 100MB
    trans_12 = torch.eye(4).repeat(batch_size, 1, 1)
    codeflash_output = inverse_transformation(trans_12)

def test_inverse_transformation_floating_point_precision():
    # Test with floating point precision
    trans_12 = torch.tensor([[1e-10, 0, 0, 1e-10],
                             [0, 1e-10, 0, 2e-10],
                             [0, 0, 1e-10, 3e-10],
                             [0, 0, 0, 1]], dtype=torch.float32)
    codeflash_output = inverse_transformation(trans_12)



import pytest  # used for our unit tests
import torch
from kornia.core import Tensor, zeros_like
from kornia.geometry.linalg import inverse_transformation


# unit tests
def test_identity_matrix_inversion():
    # Test inversion of identity matrix
    identity = torch.eye(4)
    codeflash_output = inverse_transformation(identity)

def test_single_rotation_matrix():
    # Test inversion of a 90-degree rotation matrix around Z-axis
    rotation_90_z = torch.tensor([
        [0.0, -1.0, 0.0, 0.0],
        [1.0,  0.0, 0.0, 0.0],
        [0.0,  0.0, 1.0, 0.0],
        [0.0,  0.0, 0.0, 1.0]
    ])
    expected = torch.tensor([
        [0.0, 1.0, 0.0, 0.0],
        [-1.0, 0.0, 0.0, 0.0],
        [0.0, 0.0, 1.0, 0.0],
        [0.0, 0.0, 0.0, 1.0]
    ])
    codeflash_output = inverse_transformation(rotation_90_z)

def test_translation_matrix():
    # Test inversion of a translation matrix along X-axis
    translation_x = torch.tensor([
        [1.0, 0.0, 0.0, 3.0],
        [0.0, 1.0, 0.0, 0.0],
        [0.0, 0.0, 1.0, 0.0],
        [0.0, 0.0, 0.0, 1.0]
    ])
    expected = torch.tensor([
        [1.0, 0.0, 0.0, -3.0],
        [0.0, 1.0, 0.0, 0.0],
        [0.0, 0.0, 1.0, 0.0],
        [0.0, 0.0, 0.0, 1.0]
    ])
    codeflash_output = inverse_transformation(translation_x)

def test_batched_identity_matrices():
    # Test inversion of a batch of identity matrices
    batch_identity = torch.eye(4).repeat(10, 1, 1)  # Batch of 10 identity matrices
    codeflash_output = inverse_transformation(batch_identity)

def test_large_batch_performance():
    # Test performance with a large batch of random matrices
    batch_size = 1000
    large_batch = torch.rand((batch_size, 4, 4))
    large_batch[..., 3, :] = torch.tensor([0.0, 0.0, 0.0, 1.0])  # Ensure valid homogeneous matrices
    codeflash_output = inverse_transformation(large_batch)

def test_non_tensor_input():
    # Test non-tensor input
    with pytest.raises(TypeError):
        inverse_transformation([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])


def test_degenerate_case():
    # Test degenerate case with all zeros
    zeros_matrix = torch.zeros((4, 4))
    codeflash_output = inverse_transformation(zeros_matrix)
    expected = torch.tensor([
        [0.0, 0.0, 0.0, 0.0],
        [0.0, 0.0, 0.0, 0.0],
        [0.0, 0.0, 0.0, 0.0],
        [0.0, 0.0, 0.0, 1.0]
    ])


def test_tensor_with_nan():
    # Test tensor containing NaN values
    nan_matrix = torch.eye(4)
    nan_matrix[0, 0] = float('nan')
    codeflash_output = inverse_transformation(nan_matrix)

def test_tensor_with_inf():
    # Test tensor containing Inf values
    inf_matrix = torch.eye(4)
    inf_matrix[0, 0] = float('inf')
    codeflash_output = inverse_transformation(inf_matrix)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-inverse_transformation-m8obp3q8 and push.

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