PaddleX/paddlex/modules/semantic_segmentation/predictor/utils.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 codecs
import yaml

from ....utils import logging
from ...base.predictor.transforms import image_common


class InnerConfig(object):
    """Inner Config"""

    def __init__(self, config_path):
        self.inner_cfg = self.load(config_path)

    def load(self, config_path):
        """load config"""
        with codecs.open(config_path, "r", "utf-8") as file:
            dic = yaml.load(file, Loader=yaml.FullLoader)
        return dic

    @property
    def pre_transforms(self):
        """read preprocess transforms from  config file"""

        def _process_incompct_args(cfg, arg_names, action):
            for name in arg_names:
                if name in cfg:
                    if action == "ignore":
                        logging.warning(f"Ignoring incompatible argument: {name}")
                    elif action == "raise":
                        raise RuntimeError(f"Incompatible argument detected: {name}")
                    else:
                        raise ValueError(f"Unknown action: {action}")

        tfs_cfg = self.inner_cfg["Deploy"]["transforms"]
        tfs = []
        for cfg in tfs_cfg:
            if cfg["type"] == "Normalize":
                tf = image_common.Normalize(
                    mean=cfg.get("mean", 0.5), std=cfg.get("std", 0.5)
                )
            elif cfg["type"] == "Resize":
                tf = image_common.Resize(
                    target_size=cfg.get("target_size", (512, 512)),
                    keep_ratio=cfg.get("keep_ratio", False),
                    size_divisor=cfg.get("size_divisor", None),
                    interp=cfg.get("interp", "LINEAR"),
                )
            elif cfg["type"] == "ResizeByLong":
                tf = image_common.ResizeByLong(
                    target_long_edge=cfg["long_size"],
                    size_divisor=None,
                    interp="LINEAR",
                )
            elif cfg["type"] == "ResizeByShort":
                _process_incompct_args(cfg, ["max_size"], action="raise")
                tf = image_common.ResizeByShort(
                    target_short_edge=cfg["short_size"],
                    size_divisor=None,
                    interp="LINEAR",
                )
            elif cfg["type"] == "Padding":
                _process_incompct_args(cfg, ["label_padding_value"], action="ignore")
                tf = image_common.Pad(
                    target_size=cfg["target_size"],
                    val=cfg.get("im_padding_value", 127.5),
                )
            else:
                raise RuntimeError(f"Unsupported type: {cfg['type']}")
            tfs.append(tf)
        return tfs