⚡️ Speed up function select_top_confidence_detection by 188%

[misrasaurabh1]

📄 188% (1.88x) speedup for select_top_confidence_detection in inference/core/workflows/core_steps/common/query_language/operations/detections/base.py

⏱️ Runtime : 1.02 millisecond → 355 microseconds (best of 491 runs)

📝 Explanation and details

Here is an optimized version of the program.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 20 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests Details

from copy import deepcopy

import numpy as np
# imports
import pytest  # used for our unit tests
from inference.core.workflows.core_steps.common.query_language.operations.detections.base import \
    select_top_confidence_detection


# Mocking the sv.Detections class for testing purposes
class Detections:
    def __init__(self, confidence):
        self.confidence = np.array(confidence)
    
    def __len__(self):
        return len(self.confidence)
    
    def __getitem__(self, index):
        return Detections([self.confidence[index]])
from inference.core.workflows.core_steps.common.query_language.operations.detections.base import \
    select_top_confidence_detection

# unit tests

def test_single_detection():
    # Single detection
    detections = Detections([0.5])
    codeflash_output = select_top_confidence_detection(detections)

def test_multiple_unique_confidence_scores():
    # Multiple detections with unique confidence scores
    detections = Detections([0.1, 0.5, 0.9])
    codeflash_output = select_top_confidence_detection(detections)

def test_empty_detections():
    # Empty detections
    detections = Detections([])
    codeflash_output = select_top_confidence_detection(detections)

def test_all_zero_confidence_scores():
    # All zero confidence scores
    detections = Detections([0.0, 0.0, 0.0])
    codeflash_output = select_top_confidence_detection(detections)

def test_multiple_maximum_confidence_scores():
    # Multiple maximum confidence scores
    detections = Detections([0.9, 0.9, 0.8])
    codeflash_output = select_top_confidence_detection(detections)

def test_large_number_of_detections():
    # Large number of detections
    large_confidences = np.random.rand(1000)
    detections = Detections(large_confidences)
    codeflash_output = select_top_confidence_detection(detections)

def test_high_precision_confidence_scores():
    # High precision confidence scores
    detections = Detections([0.123456789, 0.987654321, 0.555555555])
    codeflash_output = select_top_confidence_detection(detections)

def test_negative_confidence_scores():
    # Negative confidence scores
    detections = Detections([-0.1, -0.5, -0.9])
    codeflash_output = select_top_confidence_detection(detections)


def test_minimum_and_maximum_float_values():
    # Minimum and maximum float values
    detections = Detections([np.finfo(float).min, np.finfo(float).max])
    codeflash_output = select_top_confidence_detection(detections)

def test_immutable_input():
    # Ensure the original detections object is not modified
    detections = Detections([0.5, 0.7, 0.9])
    original_confidences = deepcopy(detections.confidence)
    codeflash_output = select_top_confidence_detection(detections)

def test_non_numeric_confidence_scores():
    # Non-numeric confidence scores should raise an error
    detections = Detections([0.5, 'high', None])
    with pytest.raises(TypeError):
        select_top_confidence_detection(detections)

# Run the tests
if __name__ == "__main__":
    pytest.main()
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from copy import deepcopy

import numpy as np
# imports
import pytest  # used for our unit tests
from inference.core.workflows.core_steps.common.query_language.operations.detections.base import \
    select_top_confidence_detection


# Mocking the sv.Detections class for testing purposes
class MockDetections:
    def __init__(self, confidence):
        self.confidence = np.array(confidence)
    
    def __len__(self):
        return len(self.confidence)
    
    def __getitem__(self, index):
        return MockDetections([self.confidence[index]])
from inference.core.workflows.core_steps.common.query_language.operations.detections.base import \
    select_top_confidence_detection


# unit tests
def test_empty_detections():
    # Test with empty detections
    detections = MockDetections([])
    codeflash_output = select_top_confidence_detection(detections)

def test_single_detection():
    # Test with a single detection
    detections = MockDetections([0.5])
    codeflash_output = select_top_confidence_detection(detections)

def test_multiple_unique_confidences():
    # Test with multiple detections with unique confidence scores
    detections = MockDetections([0.1, 0.5, 0.9])
    codeflash_output = select_top_confidence_detection(detections)

def test_multiple_duplicate_highest_confidences():
    # Test with multiple detections with duplicate highest confidence scores
    detections = MockDetections([0.5, 0.9, 0.9])
    codeflash_output = select_top_confidence_detection(detections)

def test_all_equal_confidences():
    # Test with multiple detections with all equal confidence scores
    detections = MockDetections([0.5, 0.5, 0.5])
    codeflash_output = select_top_confidence_detection(detections)

def test_non_numeric_confidences():
    # Test with non-numeric confidence scores (if applicable)
    detections = MockDetections(['high', 'medium', 'low'])
    with pytest.raises(TypeError):
        select_top_confidence_detection(detections)


def test_large_number_of_detections():
    # Test with a large number of detections
    detections = MockDetections(np.random.rand(10000))
    codeflash_output = select_top_confidence_detection(detections)

def test_performance_large_scale():
    # Performance test with large scale dataset
    detections = MockDetections(np.random.rand(1000000))
    codeflash_output = select_top_confidence_detection(detections)

# Run the tests
if __name__ == "__main__":
    pytest.main()
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

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