PaddleX/paddlex/inference/models/image_feature/predictor.py

# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import Any, Union, Dict, List, Tuple
import numpy as np

from ....utils.func_register import FuncRegister
from ....modules.general_recognition.model_list import MODELS
from ...common.batch_sampler import ImageBatchSampler
from ...common.reader import ReadImage
from ..common import (
    Resize,
    ResizeByShort,
    Normalize,
    ToCHWImage,
    ToBatch,
    StaticInfer,
)
from ..base import BasicPredictor
from .processors import NormalizeFeatures
from .result import IdentityResult


class ImageFeaturePredictor(BasicPredictor):
    """ImageFeaturePredictor that inherits from BasicPredictor."""

    entities = MODELS

    _FUNC_MAP = {}
    register = FuncRegister(_FUNC_MAP)

    def __init__(self, *args: List, **kwargs: Dict) -> None:
        """Initializes ClasPredictor.

        Args:
            *args: Arbitrary positional arguments passed to the superclass.
            **kwargs: Arbitrary keyword arguments passed to the superclass.
        """
        super().__init__(*args, **kwargs)
        self.preprocessors, self.infer, self.postprocessors = self._build()

    def _build_batch_sampler(self) -> ImageBatchSampler:
        """Builds and returns an ImageBatchSampler instance.

        Returns:
            ImageBatchSampler: An instance of ImageBatchSampler.
        """
        return ImageBatchSampler()

    def _get_result_class(self) -> type:
        """Returns the result class, IdentityResult.

        Returns:
            type: The IdentityResult class.
        """
        return IdentityResult

    def _build(self) -> Tuple:
        """Build the preprocessors, inference engine, and postprocessors based on the configuration.

        Returns:
            tuple: A tuple containing the preprocessors, inference engine, and postprocessors.
        """
        preprocessors = {"Read": ReadImage(format="RGB")}
        for cfg in self.config["PreProcess"]["transform_ops"]:
            tf_key = list(cfg.keys())[0]
            func = self._FUNC_MAP[tf_key]
            args = cfg.get(tf_key, {})
            if args is not None and "return_numpy" in args:
                args.pop("return_numpy")
            name, op = func(self, **args) if args else func(self)
            preprocessors[name] = op
        preprocessors["ToBatch"] = ToBatch()

        infer = StaticInfer(
            model_dir=self.model_dir,
            model_prefix=self.MODEL_FILE_PREFIX,
            option=self.pp_option,
        )

        postprocessors = {}
        for key in self.config["PostProcess"]:
            func = self._FUNC_MAP.get(key)
            args = self.config["PostProcess"].get(key, {})
            name, op = func(self, **args) if args else func(self)
            postprocessors[name] = op
        return preprocessors, infer, postprocessors

    def process(self, batch_data: List[Union[str, np.ndarray]]) -> Dict[str, Any]:
        """
        Process a batch of data through the preprocessing, inference, and postprocessing.

        Args:
            batch_data (List[Union[str, np.ndarray], ...]): A batch of input data (e.g., image file paths).

        Returns:
            dict: A dictionary containing the input path, raw image, class IDs, scores, and label names for every instance of the batch. Keys include 'input_path', 'input_img', 'class_ids', 'scores', and 'label_names'.
        """
        batch_raw_imgs = self.preprocessors["Read"](imgs=batch_data.instances)
        batch_imgs = self.preprocessors["Resize"](imgs=batch_raw_imgs)
        batch_imgs = self.preprocessors["Normalize"](imgs=batch_imgs)
        batch_imgs = self.preprocessors["ToCHW"](imgs=batch_imgs)
        x = self.preprocessors["ToBatch"](imgs=batch_imgs)
        batch_preds = self.infer(x=x)
        features = self.postprocessors["NormalizeFeatures"](batch_preds)
        return {
            "input_path": batch_data.input_paths,
            "page_index": batch_data.page_indexes,
            "input_img": batch_raw_imgs,
            "feature": features,
        }

    @register("ResizeImage")
    # TODO(gaotingquan): backend & interpolation
    def build_resize(
        self, resize_short=None, size=None, backend="cv2", interpolation="LINEAR"
    ):
        assert resize_short or size
        if resize_short:
            op = ResizeByShort(
                target_short_edge=resize_short, size_divisor=None, interp="LINEAR"
            )
        else:
            op = Resize(target_size=size)
        return "Resize", op

    @register("NormalizeImage")
    def build_normalize(
        self,
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225],
        scale=1 / 255,
        order="",
        channel_num=3,
    ):
        assert channel_num == 3
        return "Normalize", Normalize(scale=scale, mean=mean, std=std)

    @register("ToCHWImage")
    def build_to_chw(self):
        return "ToCHW", ToCHWImage()

    @register("NormalizeFeatures")
    def build_normalize_features(self):
        return "NormalizeFeatures", NormalizeFeatures()