From a55dc5bc9c1f5408a014bcb6ae34fcd1958efe59 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rapha=C3=ABl=20Bournhonesque?= Date: Mon, 9 Dec 2024 18:42:37 +0100 Subject: [PATCH] fix: use openfoodfacts.ml module for proof classification --- open_prices/proofs/ml/image_classifier.py | 177 +- poetry.lock | 2531 +++++++++++---------- pyproject.toml | 3 +- 3 files changed, 1347 insertions(+), 1364 deletions(-) diff --git a/open_prices/proofs/ml/image_classifier.py b/open_prices/proofs/ml/image_classifier.py index a6bf875e..4b831902 100644 --- a/open_prices/proofs/ml/image_classifier.py +++ b/open_prices/proofs/ml/image_classifier.py @@ -1,16 +1,9 @@ -import functools import logging -import math -import time -import typing from pathlib import Path -import grpc -import numpy as np from django.conf import settings -from PIL import Image, ImageOps -from tritonclient.grpc import service_pb2, service_pb2_grpc -from tritonclient.grpc.service_pb2_grpc import GRPCInferenceServiceStub +from openfoodfacts.ml.image_classification import ImageClassifier +from PIL import Image from .. import constants from ..models import Proof, ProofPrediction @@ -28,171 +21,25 @@ ] PROOF_CLASSIFICATION_MODEL_NAME = "price_proof_classification" PROOF_CLASSIFICATION_MODEL_VERSION = "price_proof_classification-1.0" +MODEL_VERSION = "1" -@functools.cache -def get_triton_inference_stub( - triton_uri: str | None = None, -) -> GRPCInferenceServiceStub: - """Return a gRPC stub for Triton Inference Server. - - If `triton_uri` is not provided, the default value from settings is used. - - :param triton_uri: URI of the Triton Inference Server, defaults to None - :return: gRPC stub for Triton Inference Server - """ - triton_uri = triton_uri or settings.TRITON_URI - channel = grpc.insecure_channel(triton_uri) - return service_pb2_grpc.GRPCInferenceServiceStub(channel) - - -def classify_transforms( - img: Image.Image, - size: int = 224, - mean: tuple[float, float, float] = (0.0, 0.0, 0.0), - std: tuple[float, float, float] = (1.0, 1.0, 1.0), - interpolation: Image.Resampling = Image.Resampling.BILINEAR, - crop_fraction: float = 1.0, -) -> np.ndarray: - """ - Applies a series of image transformations including resizing, center - cropping, normalization, and conversion to a NumPy array. - - Transformation steps is based on the one used in the Ultralytics library: - https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/augment.py#L2319 - - :param img: Input Pillow image. - :param size: The target size for the transformed image (shortest edge). - :param mean: Mean values for each RGB channel used in normalization. - :param std: Standard deviation values for each RGB channel used in - normalization. - :param interpolation: Interpolation method from PIL ( - Image.Resampling.NEAREST, Image.Resampling.BILINEAR, - Image.Resampling.BICUBIC). - :param crop_fraction: Fraction of the image to be cropped. - :return: The transformed image as a NumPy array. - """ - if img.mode != "RGB": - img = img.convert("RGB") - - # Rotate the image based on the EXIF orientation if needed - img = typing.cast(Image.Image, ImageOps.exif_transpose(img)) - - # Step 1: Resize while preserving the aspect ratio - width, height = img.size - - # Calculate scale size while preserving aspect ratio - scale_size = math.floor(size / crop_fraction) - - aspect_ratio = width / height - if width < height: - new_width = scale_size - new_height = int(new_width / aspect_ratio) - else: - new_height = scale_size - new_width = int(new_height * aspect_ratio) - - img = img.resize((new_width, new_height), interpolation) - - # Step 2: Center crop - left = (new_width - size) // 2 - top = (new_height - size) // 2 - right = left + size - bottom = top + size - img = img.crop((left, top, right, bottom)) - - # Step 3: Convert the image to a NumPy array and scale pixel values to - # [0, 1] - img_array = np.array(img).astype(np.float32) / 255.0 - - # Step 4: Normalize the image - mean_np = np.array(mean, dtype=np.float32).reshape(1, 1, 3) - std_np = np.array(std, dtype=np.float32).reshape(1, 1, 3) - img_array = (img_array - mean_np) / std_np - - # Step 5: Change the order of dimensions from (H, W, C) to (C, H, W) - img_array = np.transpose(img_array, (2, 0, 1)) - return img_array - - -class ImageClassifier: - def __init__(self, model_name: str, label_names: list[str]): - self.model_name: str = model_name - self.label_names = label_names - - def predict( - self, - image: Image.Image, - triton_uri: str | None = None, - ) -> list[tuple[str, float]]: - """Run an image classification model on an image. - - The model is expected to have been trained with Ultralytics library - (Yolov8). - - :param image: the input Pillow image - :param triton_uri: URI of the Triton Inference Server, defaults to - None. If not provided, the default value from settings is used. - :return: the prediction results as a list of tuples (label, confidence) - """ - image_array = classify_transforms(image) - image_array = np.expand_dims(image_array, axis=0) - - grpc_stub = get_triton_inference_stub(triton_uri) - request = service_pb2.ModelInferRequest() - request.model_name = self.model_name - - image_input = service_pb2.ModelInferRequest().InferInputTensor() - image_input.name = "images" - - image_input.datatype = "FP32" - - image_input.shape.extend([1, 3, 224, 224]) - request.inputs.extend([image_input]) - - output = service_pb2.ModelInferRequest().InferRequestedOutputTensor() - output.name = "output0" - request.outputs.extend([output]) - - request.raw_input_contents.extend([image_array.tobytes()]) - start_time = time.monotonic() - response = grpc_stub.ModelInfer(request) - latency = time.monotonic() - start_time - - logger.debug("Inference time for %s: %s", self.model_name, latency) - - start_time = time.monotonic() - if len(response.outputs) != 1: - raise Exception(f"expected 1 output, got {len(response.outputs)}") - - if len(response.raw_output_contents) != 1: - raise Exception( - f"expected 1 raw output content, got {len(response.raw_output_contents)}" - ) - - output_index = {output.name: i for i, output in enumerate(response.outputs)} - output = np.frombuffer( - response.raw_output_contents[output_index["output0"]], - dtype=np.float32, - ).reshape((1, len(self.label_names)))[0] - - score_indices = np.argsort(-output) - - latency = time.monotonic() - start_time - logger.debug("Post-processing time for %s: %s", self.model_name, latency) - return [(self.label_names[i], float(output[i])) for i in score_indices] - - -def predict_proof_type(image: Image.Image) -> list[tuple[str, float]]: +def predict_proof_type( + image: Image.Image, model_version: str = MODEL_VERSION +) -> list[tuple[str, float]]: """Predict the type of a proof image. :param image: the input Pillow image + :param model_version: the version of the model to use :return: the prediction results as a list of tuples (label, confidence) """ classifier = ImageClassifier( - PROOF_CLASSIFICATION_MODEL_NAME, PROOF_CLASSIFICATION_LABEL_NAMES + model_name=PROOF_CLASSIFICATION_MODEL_NAME, + label_names=PROOF_CLASSIFICATION_LABEL_NAMES, + ) + return classifier.predict( + image, triton_uri=settings.TRITON_URI, model_version=model_version ) - return classifier.predict(image) def run_and_save_proof_prediction(proof_id: int) -> None: diff --git a/poetry.lock b/poetry.lock index 23c2a22b..2de91072 100644 --- a/poetry.lock +++ b/poetry.lock @@ -24,23 +24,24 @@ files = [ [[package]] name = "anyio" -version = "4.4.0" +version = "4.7.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false -python-versions = ">=3.8" +python-versions = ">=3.9" files = [ - {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"}, - {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"}, + {file = "anyio-4.7.0-py3-none-any.whl", hash = "sha256:ea60c3723ab42ba6fff7e8ccb0488c898ec538ff4df1f1d5e642c3601d07e352"}, + {file = "anyio-4.7.0.tar.gz", hash = "sha256:2f834749c602966b7d456a7567cafcb309f96482b5081d14ac93ccd457f9dd48"}, ] [package.dependencies] idna = ">=2.8" sniffio = ">=1.1" +typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""} [package.extras] -doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] -test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"] -trio = ["trio (>=0.23)"] +doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"] +test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"] +trio = ["trio (>=0.26.1)"] [[package]] name = "asgiref" @@ -58,21 +59,18 @@ tests = ["mypy (>=0.800)", "pytest", "pytest-asyncio"] [[package]] name = "asttokens" -version = "2.4.1" +version = "3.0.0" description = "Annotate AST trees with source code positions" optional = false -python-versions = "*" +python-versions = ">=3.8" files = [ - 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