e489ad7a21
- **Add SPDX license headers to python source files** - **Check for SPDX headers using pre-commit** commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745 Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:18:24 2025 -0500 Add SPDX license headers to python source files This commit adds SPDX license headers to python source files as recommended to the project by the Linux Foundation. These headers provide a concise way that is both human and machine readable for communicating license information for each source file. It helps avoid any ambiguity about the license of the code and can also be easily used by tools to help manage license compliance. The Linux Foundation runs license scans against the codebase to help ensure we are in compliance with the licenses of the code we use, including dependencies. Having these headers in place helps that tool do its job. More information can be found on the SPDX site: - https://spdx.dev/learn/handling-license-info/ Signed-off-by: Russell Bryant <rbryant@redhat.com> commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:36:32 2025 -0500 Check for SPDX headers using pre-commit Signed-off-by: Russell Bryant <rbryant@redhat.com> --------- Signed-off-by: Russell Bryant <rbryant@redhat.com>
132 lines
4.1 KiB
Python
132 lines
4.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from typing import Optional, Tuple
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import pytest
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import torch
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from PIL.Image import Image
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from transformers import AutoConfig
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# Import the functions to test
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from vllm.model_executor.models.h2ovl import (calculate_num_blocks,
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image_to_pixel_values_wrapper)
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from vllm.multimodal.image import rescale_image_size
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models = [
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"h2oai/h2ovl-mississippi-800m", # Replace with your actual model names
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"h2oai/h2ovl-mississippi-2b",
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]
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def run_preprocessing_test(
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image: Image,
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config,
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max_dynamic_patch: Optional[int] = None,
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) -> Tuple[torch.Tensor, int]:
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"""Test the image preprocessing and calculate expected blocks."""
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if max_dynamic_patch is None:
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max_dynamic_patch = config.max_dynamic_patch
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width, height = image.size
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use_MSAC = config.use_msac
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# Create the mapper function with the provided configuration
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mapper = image_to_pixel_values_wrapper(config, max_dynamic_patch, use_MSAC)
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pixel_values = mapper(image)
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# Calculate the expected number of blocks
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if use_MSAC:
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# First pass
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blocks1, _, _, aspect_ratio = calculate_num_blocks(
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width,
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height,
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config.min_dynamic_patch,
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max_dynamic_patch,
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config.vision_config.image_size,
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use_thumbnail=False, # Thumbnail is handled separately
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prior_aspect_ratio=None,
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)
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# Second pass
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blocks2, _, _, _ = calculate_num_blocks(
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width,
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height,
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config.min_dynamic_patch,
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max_dynamic_patch,
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config.vision_config.image_size,
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use_thumbnail=False,
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prior_aspect_ratio=aspect_ratio,
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)
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# Add thumbnail if use_thumbnail is True and total_blocks > 1
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if config.use_thumbnail:
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blocks1 += 1 if blocks1 > 1 else 0
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blocks2 += 1 if blocks2 > 1 else 0
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# Total blocks is the sum of blocks from both passes minus overlapping
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total_blocks = blocks1 + blocks2 - 1
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expected_blocks = total_blocks
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else:
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blocks, _, _, _ = calculate_num_blocks(
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width,
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height,
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config.min_dynamic_patch,
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max_dynamic_patch,
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config.vision_config.image_size,
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use_thumbnail=False,
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prior_aspect_ratio=None,
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)
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expected_blocks = blocks
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if config.use_thumbnail and expected_blocks > 1:
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expected_blocks += 1
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return pixel_values, expected_blocks
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@pytest.mark.parametrize("model_name", models)
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@pytest.mark.parametrize(
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"size_factors",
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[
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# Single-scale
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[1.0],
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# Single-scale, batched
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[1.0, 1.0, 1.0],
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# Multi-scale
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[0.25, 0.5, 1.0],
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],
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)
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@pytest.mark.parametrize("max_dynamic_patch", [None, 2, 4, 8])
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def test_image_preprocessing(image_assets, model_name, size_factors,
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max_dynamic_patch):
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"""Test image preprocessing pipeline with different configurations."""
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# Load the configuration from the model
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config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
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for asset in image_assets:
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image = asset.pil_image
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for factor in size_factors:
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scaled_image = rescale_image_size(image, factor)
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# Test preprocessing and get expected number of blocks
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pixel_values, expected_blocks = run_preprocessing_test(
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scaled_image, config, max_dynamic_patch)
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# Verify output shapes and properties
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actual_blocks = pixel_values.shape[0]
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assert actual_blocks == expected_blocks, (
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f"Expected {expected_blocks} blocks, got {actual_blocks}")
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# Check image dimensions
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expected_size = (
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3, # Number of channels (C, H, W)
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config.vision_config.image_size,
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config.vision_config.image_size,
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)
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for img in pixel_values:
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assert img.shape == expected_size, (
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f"Expected image size {expected_size}, got {img.shape}")
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