PaddleX/paddlex/modules/semantic_segmentation/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.predictor.transforms import image_common
from ...base import BasePredictor
from .keys import SegKeys as K
from . import transforms as T
from .utils import InnerConfig
from ..support_models import SUPPORT_MODELS


class SegPredictor(BasePredictor):
    """ SegPredictor """
    support_models = SUPPORT_MODELS

    def __init__(self,
                 model_dir,
                 kernel_option,
                 pre_transforms=None,
                 post_transforms=None,
                 has_prob_map=False):
        super().__init__(
            model_dir=model_dir,
            kernel_option=kernel_option,
            pre_transforms=pre_transforms,
            post_transforms=post_transforms)
        self.has_prob_map = has_prob_map

    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.IM_PATH]]

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

    def _run(self, batch_input):
        """ run """
        images = [data[K.IMAGE] for data in batch_input]
        input_ = np.stack(images, axis=0)
        if input_.ndim == 3:
            input_ = input_[:, np.newaxis]
        input_ = input_.astype(dtype=np.float32, copy=False)
        outputs = self._predictor.predict([input_])
        out_maps = outputs[0]
        # In-place update
        pred = batch_input
        for dict_, out_map in zip(pred, out_maps):
            if self.has_prob_map:
                # `out_map` is prob map
                dict_[K.PROB_MAP] = out_map
                dict_[K.SEG_MAP] = np.argmax(out_map, axis=1)
            else:
                # `out_map` is seg map
                dict_[K.SEG_MAP] = out_map
        return pred

    def _get_pre_transforms_for_data(self, data):
        """ _get_pre_transforms_for_data """
        # If `K.IMAGE` (the decoded image) is found, return a default list of
        # transformation operators for the input (if possible).
        # If `K.IMAGE` (the decoded image) is not found, `K.IM_PATH` (the image
        # path) must be contained in the input. In this case, we infer
        # transformation operators from the config file.
        # In cases where the input contains both `K.IMAGE` and `K.IM_PATH`,
        # `K.IMAGE` takes precedence over `K.IM_PATH`.
        if K.IMAGE not in data:
            if K.IM_PATH not in data:
                raise KeyError(
                    f"Key {repr(K.IM_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())
            pre_transforms.append(image_common.ToCHWImage())
        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."
            )
        return pre_transforms

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