⚡️ Speed up method DPTImageProcessor.pad_image by 87%

[codeflash-ai]

📄 87% (0.87x) speedup for DPTImageProcessor.pad_image in src/transformers/models/dpt/image_processing_dpt.py

⏱️ Runtime : 4.92 milliseconds → 2.64 milliseconds (best of 21 runs)

📝 Explanation and details

The optimized code achieves an 86% speedup through several key optimizations focused on reducing computational overhead in image padding operations:

Key Optimizations Applied

1. Zero-Padding Fast Path in pad() Function

The most significant optimization adds an early exit for zero-padding cases. When padding values are all zeros, the function now:

• Checks if padding will result in no actual changes using is_zero_pad()
• Returns the image unchanged (or with format conversion only) without calling expensive np.pad()
• This optimization is particularly effective for images that are already the correct size

2. Streamlined infer_channel_dimension_format()

• Condensed conditional logic: Combined the num_channels assignment into a single chained conditional expression, eliminating redundant checks
• Cached shape lookups: Store image.shape once and reuse it, reducing attribute access overhead
• Pre-computed dimension checks: Store first_in_channels and last_in_channels results to avoid repeated in operations

3. Optimized _expand_for_data_format()

• Simplified tuple construction: More direct conditional structure with fewer isinstance checks
• Reduced variable reassignments: Direct tuple building instead of multiple reassignments

Why These Optimizations Work

Zero-padding optimization: The test results show dramatic speedups (500-5000% faster) for cases where no padding is needed, which is common when images are already properly sized. The line profiler shows np.pad() consumes 89-95% of execution time, so bypassing it entirely provides massive gains.

Reduced function call overhead: By caching shape access and minimizing repeated computations in infer_channel_dimension_format(), the optimization reduces the cumulative cost of this frequently-called function.

Test Case Performance

The optimizations excel in scenarios where:

• No padding needed: Images already divisible by size_divisor see 500-1800% speedups
• Large images with no padding: Bigger images benefit more from avoiding unnecessary np.pad() calls
• Cases requiring actual padding: Show minimal overhead (1-4% slower) due to the additional zero-check, but this is negligible compared to the gains in no-padding cases

The optimization maintains correctness while providing substantial performance improvements for the common case where padding isn't actually needed.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 55 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

from enum import Enum, auto

import numpy as np

# imports
import pytest

from transformers.models.dpt.image_processing_dpt import DPTImageProcessor


class ChannelDimension(Enum):
    FIRST = auto()
    LAST = auto()
    NONE = auto()


# --- Unit tests for DPTImageProcessor.pad_image ---


@pytest.fixture
def processor():
    return DPTImageProcessor()


# ------------------ BASIC TEST CASES ------------------


def test_pad_image_no_padding_needed_channels_first(processor):
    # Image is already divisible by size_divisor, no padding needed
    img = np.ones((3, 32, 32), dtype=np.uint8)
    codeflash_output = processor.pad_image(img, size_divisor=16)
    out = codeflash_output  # 79.6μs -> 12.0μs (561% faster)


def test_pad_image_no_padding_needed_channels_last(processor):
    # channels_last
    img = np.ones((32, 32, 3), dtype=np.float32)
    codeflash_output = processor.pad_image(img, size_divisor=16)
    out = codeflash_output  # 80.5μs -> 12.1μs (567% faster)


def test_pad_image_simple_padding_channels_first(processor):
    # Image (3, 30, 30), size_divisor=16, should pad to (3, 32, 32)
    img = np.ones((3, 30, 30), dtype=np.float32)
    codeflash_output = processor.pad_image(img, size_divisor=16)
    out = codeflash_output  # 81.1μs -> 82.9μs (2.25% slower)


def test_pad_image_simple_padding_channels_last(processor):
    # (30, 30, 3), pad to (32, 32, 3)
    img = np.ones((30, 30, 3), dtype=np.float32)
    codeflash_output = processor.pad_image(img, size_divisor=16)
    out = codeflash_output  # 81.0μs -> 82.6μs (1.98% slower)


def test_pad_image_odd_padding(processor):
    # (3, 31, 31), size_divisor=16 -> (3, 32, 32)
    img = np.ones((3, 31, 31), dtype=np.float32)
    codeflash_output = processor.pad_image(img, size_divisor=16)
    out = codeflash_output  # 80.5μs -> 83.9μs (4.15% slower)


def test_pad_image_already_divisible_large_divisor(processor):
    img = np.ones((3, 64, 64), dtype=np.float32)
    codeflash_output = processor.pad_image(img, size_divisor=64)
    out = codeflash_output  # 81.1μs -> 13.9μs (484% faster)


def test_pad_image_one_channel(processor):
    # (1, 30, 30), size_divisor=16
    img = np.ones((1, 30, 30), dtype=np.float32)
    codeflash_output = processor.pad_image(img, size_divisor=16)
    out = codeflash_output  # 81.0μs -> 83.6μs (3.12% slower)


def test_pad_image_non_square_image(processor):
    # (3, 30, 45), size_divisor=16 -> (3, 32, 48)
    img = np.ones((3, 30, 45), dtype=np.float32)
    codeflash_output = processor.pad_image(img, size_divisor=16)
    out = codeflash_output  # 81.5μs -> 84.6μs (3.69% slower)


def test_pad_image_zero_size_divisor(processor):
    # Should raise error if size_divisor is zero
    img = np.ones((3, 32, 32), dtype=np.float32)
    with pytest.raises(ZeroDivisionError):
        processor.pad_image(img, size_divisor=0)  # 7.58μs -> 7.14μs (6.22% faster)


def test_pad_image_invalid_input_shape(processor):
    # 1D image should raise
    img = np.ones((32,), dtype=np.float32)
    with pytest.raises(ValueError):
        processor.pad_image(img, size_divisor=8)  # 3.63μs -> 3.53μs (3.03% faster)


def test_pad_image_large_channels_last(processor):
    # (128, 64, 3), size_divisor=32 -> (128, 64, 3) (already divisible)
    img = np.ones((128, 64, 3), dtype=np.float32)
    codeflash_output = processor.pad_image(img, size_divisor=32)
    out = codeflash_output  # 84.3μs -> 13.4μs (527% faster)


# ------------------ LARGE SCALE TEST CASES ------------------


def test_pad_image_large_image_channels_first(processor):
    # (3, 512, 512), size_divisor=128 -> (3, 512, 512)
    img = np.ones((3, 512, 512), dtype=np.float32)
    codeflash_output = processor.pad_image(img, size_divisor=128)
    out = codeflash_output  # 287μs -> 15.0μs (1816% faster)


def test_pad_image_large_image_channels_last(processor):
    # (500, 700, 3), size_divisor=128 -> (512, 768, 3)
    img = np.ones((500, 700, 3), dtype=np.float32)
    codeflash_output = processor.pad_image(img, size_divisor=128)
    out = codeflash_output  # 411μs -> 423μs (2.81% slower)


def test_pad_image_large_non_square(processor):
    # (3, 255, 511), size_divisor=128 -> (3, 256, 512)
    img = np.ones((3, 255, 511), dtype=np.float32)
    codeflash_output = processor.pad_image(img, size_divisor=128)
    out = codeflash_output  # 195μs -> 196μs (0.979% slower)


def test_pad_image_maximum_allowed_size(processor):
    # 3*512*512*4 bytes = 3MB, well under 100MB
    img = np.ones((3, 512, 512), dtype=np.float32)
    codeflash_output = processor.pad_image(img, size_divisor=256)
    out = codeflash_output  # 289μs -> 14.7μs (1862% faster)


def test_pad_image_batch_of_images(processor):
    # Simulate (batch, C, H, W)
    batch = np.ones((8, 3, 30, 30), dtype=np.float32)
    # Apply pad_image to each image in batch
    outs = [processor.pad_image(img, size_divisor=16) for img in batch]
    for out in outs:
        pass

import numpy as np

# imports
import pytest

from transformers.models.dpt.image_processing_dpt import DPTImageProcessor


# Minimal stubs for dependencies
class ChannelDimension:
    FIRST = "channels_first"
    LAST = "channels_last"
    NONE = "none"


# --- Unit tests ---


@pytest.fixture
def processor():
    return DPTImageProcessor()


# -------------------------
# 1. Basic Test Cases
# -------------------------


def test_pad_image_no_padding_needed(processor):
    # Image size already divisible by size_divisor
    img = np.ones((3, 32, 32), dtype=np.float32)  # channels_first
    codeflash_output = processor.pad_image(img, 16)
    out = codeflash_output  # 80.2μs -> 12.8μs (528% faster)


def test_pad_image_simple_padding(processor):
    # Image size not divisible, needs padding
    img = np.ones((3, 30, 31), dtype=np.float32)  # channels_first
    codeflash_output = processor.pad_image(img, 16)
    out = codeflash_output  # 81.7μs -> 82.8μs (1.35% slower)


def test_pad_image_channels_last(processor):
    # channels_last format
    img = np.ones((28, 29, 3), dtype=np.float32)
    codeflash_output = processor.pad_image(
        img, 8, data_format=ChannelDimension.LAST, input_data_format=ChannelDimension.LAST
    )
    out = codeflash_output  # 83.1μs -> 84.9μs (2.12% slower)


def test_pad_image_size_divisor_is_one(processor):
    # Should not pad
    img = np.ones((3, 5, 7), dtype=np.float32)
    codeflash_output = processor.pad_image(img, 1)
    out = codeflash_output  # 80.4μs -> 13.4μs (502% faster)


def test_pad_image_size_divisor_larger_than_image(processor):
    img = np.ones((3, 5, 7), dtype=np.float32)
    codeflash_output = processor.pad_image(img, 10)
    out = codeflash_output  # 82.2μs -> 84.3μs (2.47% slower)


def test_pad_image_channels_first_vs_last(processor):
    # Check that channels_first and channels_last produce correct shapes
    img_cf = np.ones((3, 20, 22), dtype=np.float32)
    img_cl = np.ones((20, 22, 3), dtype=np.float32)
    codeflash_output = processor.pad_image(
        img_cf, 8, data_format=ChannelDimension.FIRST, input_data_format=ChannelDimension.FIRST
    )
    out_cf = codeflash_output  # 81.8μs -> 84.5μs (3.19% slower)
    codeflash_output = processor.pad_image(
        img_cl, 8, data_format=ChannelDimension.LAST, input_data_format=ChannelDimension.LAST
    )
    out_cl = codeflash_output  # 37.3μs -> 39.4μs (5.18% slower)


def test_pad_image_non_square_size_divisor(processor):
    # Non-square image, check padding
    img = np.ones((3, 15, 23), dtype=np.float32)
    codeflash_output = processor.pad_image(img, 8)
    out = codeflash_output  # 81.1μs -> 82.3μs (1.44% slower)


def test_pad_image_invalid_input_raises(processor):
    # Invalid input should raise
    img = np.ones((3, 4, 5, 6), dtype=np.float32)  # 4D image not supported in stub
    with pytest.raises(ValueError):
        processor.pad_image(img, 8)  # 4.09μs -> 3.81μs (7.30% faster)


def test_pad_image_large_image_channels_first(processor):
    # Large image, but <100MB
    img = np.ones((3, 512, 512), dtype=np.float32)
    codeflash_output = processor.pad_image(img, 128)
    out = codeflash_output  # 288μs -> 16.1μs (1691% faster)


def test_pad_image_large_image_channels_last(processor):
    img = np.ones((512, 512, 3), dtype=np.float32)
    codeflash_output = processor.pad_image(
        img, 128, data_format=ChannelDimension.LAST, input_data_format=ChannelDimension.LAST
    )
    out = codeflash_output  # 294μs -> 15.4μs (1818% faster)


def test_pad_image_large_nondivisible(processor):
    img = np.ones((3, 999, 997), dtype=np.float32)
    codeflash_output = processor.pad_image(img, 128)
    out = codeflash_output  # 939μs -> 973μs (3.48% slower)


def test_pad_image_multiple_images(processor):
    # Test batch of images (simulate by looping)
    imgs = [np.ones((3, 32, 30), dtype=np.float32) for _ in range(10)]
    outs = [processor.pad_image(img, 16) for img in imgs]
    for out in outs:
        pass


def test_pad_image_maximum_allowed_size(processor):
    # 3x1024x1024 float32 is ~12MB
    img = np.ones((3, 1024, 1024), dtype=np.float32)
    codeflash_output = processor.pad_image(img, 256)
    out = codeflash_output  # 863μs -> 15.6μs (5428% faster)


# 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-DPTImageProcessor.pad_image-misgpf9k and push.