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python_version >= \"3.8\" and python_version < \"3.9\""}, +] + [[package]] name = "packaging" version = "24.0" @@ -1300,4 +1426,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more [metadata] lock-version = "2.0" python-versions = "^3.8.10" -content-hash = "8bf43216e6e836b291ecbf137d0418b0707c21bda71a59e68a3cacd42f7f0c46" +content-hash = "bcacde8ddd30d7fbc95a7e5d6845047b3217a5a2e4c259665319b30fa60daf77" diff --git a/pyproject.toml b/pyproject.toml index f3b6ac3..672f3a5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "mistral_common" -version = "1.4.0" +version = "1.4.1" description = "" authors = ["bam4d "] readme = "README.md" @@ -35,6 +35,7 @@ typing-extensions = "^4.11.0" tiktoken = "^0.7.0" pillow = "^10.3.0" requests = "^2.0.0" +opencv-python-headless = "^4.10.0.84" [tool.poetry.group.dev.dependencies] types-jsonschema = "4.21.0.20240118" diff --git a/src/mistral_common/__init__.py b/src/mistral_common/__init__.py index 3e8d9f9..bf25615 100644 --- a/src/mistral_common/__init__.py +++ b/src/mistral_common/__init__.py @@ -1 +1 @@ -__version__ = "1.4.0" +__version__ = "1.4.1" diff --git a/src/mistral_common/tokens/tokenizers/multimodal.py b/src/mistral_common/tokens/tokenizers/multimodal.py index aefe3ab..04be0a8 100644 --- a/src/mistral_common/tokens/tokenizers/multimodal.py +++ b/src/mistral_common/tokens/tokenizers/multimodal.py @@ -3,6 +3,7 @@ from io import BytesIO from typing import Tuple, Union +import cv2 import numpy as np from PIL import Image @@ -54,7 +55,7 @@ def _convert_to_rgb(image: Image.Image) -> Image.Image: def normalize( - image: Image.Image, + np_image: np.ndarray, mean: Tuple[float, float, float], std: Tuple[float, float, float], ) -> np.ndarray: @@ -62,14 +63,13 @@ def normalize( Normalize a tensor image with mean and standard deviation. Args: - image (Image.Image): Image to be normalized. + image (np.ndarray): Image to be normalized. mean (tuple[float, float, float]): Mean for each channel. std (tuple[float, float, float]): Standard deviation for each channel. Returns: np.ndarray: Normalized image with shape (C, H, W). """ - np_image = np.array(image, dtype=np.float32) np_image = np_image / 255.0 assert len(np_image.shape) == 3, f"{np_image.shape=}" @@ -81,9 +81,8 @@ def normalize( def transform_image(image: Image.Image, new_size: Tuple[int, int]) -> np.ndarray: - image = _convert_to_rgb(image) - image = image.resize(new_size, Image.Resampling.BICUBIC) - return normalize(image, DATASET_MEAN, DATASET_STD) + np_image = cv2.resize(np.array(_convert_to_rgb(image), dtype=np.float32), new_size, interpolation=cv2.INTER_CUBIC) + return normalize(np_image, DATASET_MEAN, DATASET_STD) class ImageEncoder(MultiModalEncoder): diff --git a/tests/test_multimodal.py b/tests/test_multimodal.py index b10d8a1..c385e45 100644 --- a/tests/test_multimodal.py +++ b/tests/test_multimodal.py @@ -1,6 +1,8 @@ import base64 from io import BytesIO +from typing import Tuple +import numpy as np import pytest import requests from mistral_common.protocol.instruct.messages import ( @@ -70,6 +72,44 @@ def test_image_encoder(mm_config: MultimodalConfig, special_token_ids: SpecialIm assert len(tokens) == (w + 1) * h +@pytest.mark.parametrize("size", [(200, 311), (300, 212), (251, 1374), (1475, 477), (1344, 1544), (2133, 3422)]) +def test_image_processing( + mm_config: MultimodalConfig, special_token_ids: SpecialImageIDs, size: Tuple[int, int] +) -> None: + mm_config.max_image_size = 1024 + mm_encoder = ImageEncoder(mm_config, special_token_ids) + + # all images with w,h >= 1024 should be resized to 1024 + # else round to nearest multiple of 16 + # all while keeping the aspect ratio + EXP_IMG_SIZES = { + (200, 311): (208, 320), + (300, 212): (304, 224), + (251, 1374): (192, 1024), + (1475, 477): (1024, 336), + (1344, 1544): (896, 1024), + (2133, 3422): (640, 1024), + } + # integration test to make sure the img processing stays 100% the same + EXP_IMG_SUM = { + (200, 311): 232038.65023772235, + (300, 212): 182668.98900347573, + (251, 1374): 726925.9371541862, + (1475, 477): 985935.4162606588, + (1344, 1544): 2982953.705365115, + (2133, 3422): 2304438.4010818982, + } + + url = f"https://picsum.photos/id/237/{size[0]}/{size[1]}" + + content = ImageURLChunk(image_url=url) + + image = mm_encoder(content).image + + assert image.transpose().shape[:2] == EXP_IMG_SIZES[size], image.transpose().shape[:2] + assert np.abs(image).sum() - EXP_IMG_SUM[size] < 1e-5, np.abs(image).sum() + + def test_image_encoder_formats(mm_config: MultimodalConfig, special_token_ids: SpecialImageIDs) -> None: mm_encoder = ImageEncoder(mm_config, special_token_ids)