PaddleX/paddlex/modules/object_detection/predictor/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.



import os

import numpy as np

from ....utils import logging
from ...base import BasePredictor
from ...base.predictor.transforms import image_common
from . import transforms as T
from .keys import DetKeys as K
from .utils import InnerConfig
from ..support_models import SUPPORT_MODELS


class DetPredictor(BasePredictor):
    """ Detection Predictor """
    support_models = SUPPORT_MODELS

    def load_other_src(self):
        """ load the inner config file """
        infer_cfg_file_path = os.path.join(self.model_dir, 'inference.yml')
        if not os.path.exists(infer_cfg_file_path):
            raise FileNotFoundError(
                f"Cannot find config file: {infer_cfg_file_path}")
        return InnerConfig(infer_cfg_file_path)

    @classmethod
    def get_input_keys(cls):
        """ get input keys """
        return [[K.IMAGE], [K.IMAGE_PATH]]

    @classmethod
    def get_output_keys(cls):
        """ get output keys """
        return [K.BOXES]

    def _run(self, batch_input):
        """ run """
        input_dict = {}
        input_dict["image"] = np.stack(
            [data[K.IMAGE] for data in batch_input], axis=0).astype(
                dtype=np.float32, copy=False)
        input_dict["scale_factor"] = np.stack(
            [data[K.SCALE_FACTOR][::-1] for data in batch_input],
            axis=0).astype(
                dtype=np.float32, copy=False)
        input_dict["im_shape"] = np.stack(
            [data[K.IMAGE_SHAPE][::-1] for data in batch_input], axis=0).astype(
                dtype=np.float32, copy=False)

        input_ = [input_dict[i] for i in self._predictor.get_input_names()]

        batch_np_boxes, batch_np_boxes_num = self._predictor.predict(input_)

        pred = batch_input
        box_idx_start = 0
        for idx in range(len(batch_input)):
            np_boxes_num = batch_np_boxes_num[idx]
            box_idx_end = box_idx_start + np_boxes_num
            np_boxes = batch_np_boxes[box_idx_start:box_idx_end]
            box_idx_start = box_idx_end

            batch_input[idx][K.BOXES] = np_boxes
        return pred

    def _get_pre_transforms_for_data(self, data):
        """ get preprocess transforms """
        if K.IMAGE not in data:
            if K.IMAGE_PATH not in data:
                raise KeyError(
                    f"Key {repr(K.IMAGE_PATH)} is required, but not found.")
            logging.info(
                f"Transformation operators for data preprocessing will be inferred from config file."
            )
            pre_transforms = self.other_src.pre_transforms
            pre_transforms.insert(0, image_common.ReadImage(format='RGB'))
        else:
            raise RuntimeError(
                f"`{self.__class__.__name__}` does not have default transformation operators to preprocess the input. "
                f"Please set `pre_transforms` when using the {repr(K.IMAGE)} key in input dict."
            )
        pre_transforms.insert(0, T.LoadLabels(self.other_src.labels))
        return pre_transforms

    def _get_post_transforms_for_data(self, data):
        """ get postprocess transforms """
        if data.get('cli_flag', False):
            output_dir = data.get("output_dir", "./")
            return [T.SaveDetResults(output_dir), T.PrintResult()]
        return []