custom_dual.py

# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
# Obtained from: https://github.com/open-mmlab/mmsegmentation/tree/v0.16.0
# Modifications:
# - Additional dataset location logging

import os
import os.path as osp
from collections import OrderedDict
from functools import reduce

import mmcv
import numpy as np
from mmcv.utils import print_log
from prettytable import PrettyTable
from torch.utils.data import Dataset

from mmseg.core import eval_metrics
from mmseg.utils import get_root_logger
from .builder import DATASETS
from .pipelines import Compose


@DATASETS.register_module()
class CustomDatasetDual(Dataset):
    """Custom dataset for semantic segmentation. An example of file structure
    is as followed.

    .. code-block:: none

        ├── data
        │   ├── my_dataset
        │   │   ├── img_dir
        │   │   │   ├── train
        │   │   │   │   ├── xxx{img_suffix}
        │   │   │   │   ├── yyy{img_suffix}
        │   │   │   │   ├── zzz{img_suffix}
        │   │   │   ├── val
        │   │   ├── ann_dir
        │   │   │   ├── train
        │   │   │   │   ├── xxx{seg_map_suffix}
        │   │   │   │   ├── yyy{seg_map_suffix}
        │   │   │   │   ├── zzz{seg_map_suffix}
        │   │   │   ├── val

    The img/gt_semantic_seg pair of CustomDataset should be of the same
    except suffix. A valid img/gt_semantic_seg filename pair should be like
    ``xxx{img_suffix}`` and ``xxx{seg_map_suffix}`` (extension is also included
    in the suffix). If split is given, then ``xxx`` is specified in txt file.
    Otherwise, all files in ``img_dir/``and ``ann_dir`` will be loaded.
    Please refer to ``docs/tutorials/new_dataset.md`` for more details.


    Args:
        pipeline (list[dict]): Processing pipeline
        img_dir (str): Path to image directory
        img_suffix (str): Suffix of images. Default: '.jpg'
        ann_dir (str, optional): Path to annotation directory. Default: None
        seg_map_suffix (str): Suffix of segmentation maps. Default: '.png'
        split (str, optional): Split txt file. If split is specified, only
            file with suffix in the splits will be loaded. Otherwise, all
            images in img_dir/ann_dir will be loaded. Default: None
        data_root (str, optional): Data root for img_dir/ann_dir. Default:
            None.
        test_mode (bool): If test_mode=True, gt wouldn't be loaded.
        ignore_index (int): The label index to be ignored. Default: 255
        reduce_zero_label (bool): Whether to mark label zero as ignored.
            Default: False
        classes (str | Sequence[str], optional): Specify classes to load.
            If is None, ``cls.CLASSES`` will be used. Default: None.
        palette (Sequence[Sequence[int]]] | np.ndarray | None):
            The palette of segmentation map. If None is given, and
            self.PALETTE is None, random palette will be generated.
            Default: None
    """

    CLASSES = None

    PALETTE = None

    def __init__(self,
                 pipeline,
                 img_dir_source,
                 img_dir_target,
                 img_suffix_source='.jpg',
                 img_suffix_target='.jpg',
                 ann_dir_source=None,
                 ann_dir_target=None,
                 seg_map_suffix_source='.png',
                 seg_map_suffix_target='.png',
                 split_source=None,
                 split_target=None,
                 data_root_source=None,
                 data_root_target=None,
                 test_mode=False,
                 ignore_index=255,
                 crop_pseudo_margins_target=None,
                 valid_mask_size_target=None,
                 reduce_zero_label=False,
                 classes=None,
                 palette=None):
        self.pipeline = Compose(pipeline)
        self.img_dir_source = img_dir_source
        self.img_dir_target = img_dir_target
        self.img_suffix_source = img_suffix_source
        self.img_suffix_target = img_suffix_target
        self.ann_dir_source = ann_dir_source
        self.ann_dir_target = ann_dir_target
        self.seg_map_suffix_source = seg_map_suffix_source
        self.seg_map_suffix_target = seg_map_suffix_target
        self.split_source = split_source
        self.split_target = split_target
        self.data_root_source = data_root_source
        self.data_root_target = data_root_target
        self.test_mode = test_mode
        self.ignore_index = ignore_index
        self.crop_pseudo_margins_target = crop_pseudo_margins_target
        self.valid_mask_size_target = valid_mask_size_target
        self.reduce_zero_label = reduce_zero_label
        self.label_map = None
        self.CLASSES, self.PALETTE = self.get_classes_and_palette(
            classes, palette)

        # join paths if data_root_source is specified
        if self.data_root_source is not None:
            if not osp.isabs(self.img_dir_source):
                self.img_dir_source = osp.join(self.data_root_source, self.img_dir_source)
            if not (self.ann_dir_source is None or osp.isabs(self.ann_dir_source)):
                self.ann_dir_source = osp.join(self.data_root_source, self.ann_dir_source)
            if not (self.split_source is None or osp.isabs(self.split_source)):
                self.split_source = osp.join(self.data_root_source, self.split_source)

        # join paths if data_root_target is specified
        if self.data_root_target is not None:
            if not osp.isabs(self.img_dir_target):
                self.img_dir_target = osp.join(self.data_root_target, self.img_dir_target)
            if not (self.ann_dir_target is None or osp.isabs(self.ann_dir_target)):
                self.ann_dir_target = osp.join(self.data_root_target, self.ann_dir_target)
            if not (self.split_target is None or osp.isabs(self.split_target)):
                self.split_target = osp.join(self.data_root_target, self.split_target)
        
        # load annotations for source
        self.img_infos_source = self.load_annotations(self.img_dir_source, self.img_suffix_source,
                                               self.ann_dir_source,
                                               self.seg_map_suffix_source, self.split_source)

        # load annotations for target
        self.img_infos_target = self.load_annotations(self.img_dir_target, self.img_suffix_target,
                                               self.ann_dir_target,
                                               self.seg_map_suffix_target, self.split_target)

    def __len__(self):
        """Total number of samples of data."""
        return len(self.img_infos_source) * len(self.img_infos_target)

    def load_annotations(self, img_dir, img_suffix, ann_dir, seg_map_suffix,
                         split):
        """Load annotation from directory.

        Args:
            img_dir (str): Path to image directory
            img_suffix (str): Suffix of images.
            ann_dir (str|None): Path to annotation directory.
            seg_map_suffix (str|None): Suffix of segmentation maps.
            split (str|None): Split txt file. If split is specified, only file
                with suffix in the splits will be loaded. Otherwise, all images
                in img_dir/ann_dir will be loaded. Default: None

        Returns:
            list[dict]: All image info of dataset.
        """

        img_infos = []
        if split is not None:
            with open(split) as f:
                for line in f:
                    img_name = line.strip()
                    img_info = dict(filename=img_name + img_suffix)
                    if ann_dir is not None:
                        seg_map = img_name + seg_map_suffix
                        img_info['ann'] = dict(seg_map=seg_map)
                    img_infos.append(img_info)
        else:
            for img in mmcv.scandir(img_dir, img_suffix, recursive=True):
                img_info = dict(filename=img)
                if ann_dir is not None:
                    seg_map = img.replace(img_suffix, seg_map_suffix)
                    img_info['ann'] = dict(seg_map=seg_map)
                img_infos.append(img_info)

        print_log(
            f'Loaded {len(img_infos)} images from {img_dir}',
            logger=get_root_logger())
        return img_infos

    def get_ann_info_source(self, idx):
        """Get annotation for source image by index.

        Args:
            idx (int): Index of data.

        Returns:
            dict: Annotation info of specified index.
        """

        return self.img_infos_source[idx // len(self.img_infos_target)]['ann']

    def get_ann_info_target(self, idx):
        """Get annotation for target image by index.

        Args:
            idx (int): Index of data.

        Returns:
            dict: Annotation info of specified index.
        """

        return self.img_infos_target[idx % len(self.img_infos_target)]['ann']

    def pre_pipeline(self, results):
        """Prepare results dict for pipeline."""
        results['source']['seg_fields'] = []
        results['target']['seg_fields'] = []
        results['source']['img_prefix'] = self.img_dir_source
        results['target']['img_prefix'] = self.img_dir_target
        results['source']['seg_prefix'] = self.ann_dir_source
        results['target']['seg_prefix'] = self.ann_dir_target
        if self.custom_classes:
            results['source']['label_map'] = self.label_map
            results['target']['label_map'] = self.label_map

    def __getitem__(self, idx):
        """Get training/test data after pipeline.

        Args:
            idx (int): Index of data.

        Returns:
            dict: Training/test data (with annotation if `test_mode` is set
                False).
        """

        if self.test_mode:
            return self.prepare_test_img(idx)
        else:
            return self.prepare_train_img(idx)

    def prepare_train_img(self, idx):
        """Get training data and annotations after pipeline.

        Args:
            idx (int): Index of data.

        Returns:
            dict: Training data and annotation after pipeline with new keys
                introduced by pipeline.
        """

        img_info_source = self.img_infos_source[idx // len(self.img_infos_target)]
        img_info_target = self.img_infos_target[idx % len(self.img_infos_target)]
        ann_info_source = self.get_ann_info_source(idx)
        ann_info_target = self.get_ann_info_target(idx)
        results = dict(
            source=dict(
                img_info=img_info_source,
                ann_info=ann_info_source),
            target=dict(
                img_info=img_info_target,
                ann_info=ann_info_target))
        self.pre_pipeline(results)
        return self.pipeline(results)

    def prepare_test_img(self, idx):
        """Get testing data after pipeline.

        Args:
            idx (int): Index of data.

        Returns:
            dict: Testing data after pipeline with new keys introduced by
                pipeline.
        """

        img_info_source = self.img_infos_source[idx // len(self.img_infos_target)]
        img_info_target = self.img_infos_target[idx % len(self.img_infos_target)]
        results = dict(
            source=dict(img_info=img_info_source),
            target=dict(img_info=img_info_target))
        self.pre_pipeline(results)
        return self.pipeline(results)

    def format_results(self, results, **kwargs):
        """Place holder to format result to dataset specific output."""

    def get_gt_seg_maps(self, efficient_test=False):
        """Get ground truth segmentation maps for evaluation."""
        gt_seg_maps = []
        for img_info in self.img_infos:
            seg_map = osp.join(self.ann_dir, img_info['ann']['seg_map'])
            if efficient_test:
                gt_seg_map = seg_map
            else:
                gt_seg_map = mmcv.imread(
                    seg_map, flag='unchanged', backend='pillow')
            gt_seg_maps.append(gt_seg_map)
        return gt_seg_maps

    def get_classes_and_palette(self, classes=None, palette=None):
        """Get class names of current dataset.

        Args:
            classes (Sequence[str] | str | None): If classes is None, use
                default CLASSES defined by builtin dataset. If classes is a
                string, take it as a file name. The file contains the name of
                classes where each line contains one class name. If classes is
                a tuple or list, override the CLASSES defined by the dataset.
            palette (Sequence[Sequence[int]]] | np.ndarray | None):
                The palette of segmentation map. If None is given, random
                palette will be generated. Default: None
        """
        if classes is None:
            self.custom_classes = False
            return self.CLASSES, self.PALETTE

        self.custom_classes = True
        if isinstance(classes, str):
            # take it as a file path
            class_names = mmcv.list_from_file(classes)
        elif isinstance(classes, (tuple, list)):
            class_names = classes
        else:
            raise ValueError(f'Unsupported type {type(classes)} of classes.')

        if self.CLASSES:
            if not set(classes).issubset(self.CLASSES):
                raise ValueError('classes is not a subset of CLASSES.')

            # dictionary, its keys are the old label ids and its values
            # are the new label ids.
            # used for changing pixel labels in load_annotations.
            self.label_map = {}
            for i, c in enumerate(self.CLASSES):
                if c not in class_names:
                    self.label_map[i] = -1
                else:
                    self.label_map[i] = classes.index(c)

        palette = self.get_palette_for_custom_classes(class_names, palette)

        return class_names, palette

    def get_palette_for_custom_classes(self, class_names, palette=None):

        if self.label_map is not None:
            # return subset of palette
            palette = []
            for old_id, new_id in sorted(
                    self.label_map.items(), key=lambda x: x[1]):
                if new_id != -1:
                    palette.append(self.PALETTE[old_id])
            palette = type(self.PALETTE)(palette)

        elif palette is None:
            if self.PALETTE is None:
                palette = np.random.randint(0, 255, size=(len(class_names), 3))
            else:
                palette = self.PALETTE

        return palette

    def evaluate(self,
                 results,
                 metric='mIoU',
                 logger=None,
                 efficient_test=False,
                 **kwargs):
        """Evaluate the dataset.

        Args:
            results (list): Testing results of the dataset.
            metric (str | list[str]): Metrics to be evaluated. 'mIoU',
                'mDice' and 'mFscore' are supported.
            logger (logging.Logger | None | str): Logger used for printing
                related information during evaluation. Default: None.

        Returns:
            dict[str, float]: Default metrics.
        """

        if isinstance(metric, str):
            metric = [metric]
        allowed_metrics = ['mIoU', 'mDice', 'mFscore']
        if not set(metric).issubset(set(allowed_metrics)):
            raise KeyError('metric {} is not supported'.format(metric))
        eval_results = {}
        gt_seg_maps = self.get_gt_seg_maps(efficient_test)
        if self.CLASSES is None:
            num_classes = len(
                reduce(np.union1d, [np.unique(_) for _ in gt_seg_maps]))
        else:
            num_classes = len(self.CLASSES)
        ret_metrics = eval_metrics(
            results,
            gt_seg_maps,
            num_classes,
            self.ignore_index,
            metric,
            label_map=self.label_map,
            reduce_zero_label=self.reduce_zero_label)

        if self.CLASSES is None:
            class_names = tuple(range(num_classes))
        else:
            class_names = self.CLASSES

        # summary table
        ret_metrics_summary = OrderedDict({
            ret_metric: np.round(np.nanmean(ret_metric_value) * 100, 2)
            for ret_metric, ret_metric_value in ret_metrics.items()
        })

        # each class table
        ret_metrics.pop('aAcc', None)
        ret_metrics_class = OrderedDict({
            ret_metric: np.round(ret_metric_value * 100, 2)
            for ret_metric, ret_metric_value in ret_metrics.items()
        })
        ret_metrics_class.update({'Class': class_names})
        ret_metrics_class.move_to_end('Class', last=False)

        # for logger
        class_table_data = PrettyTable()
        for key, val in ret_metrics_class.items():
            class_table_data.add_column(key, val)

        summary_table_data = PrettyTable()
        for key, val in ret_metrics_summary.items():
            if key == 'aAcc':
                summary_table_data.add_column(key, [val])
            else:
                summary_table_data.add_column('m' + key, [val])

        print_log('per class results:', logger)
        print_log('\n' + class_table_data.get_string(), logger=logger)
        print_log('Summary:', logger)
        print_log('\n' + summary_table_data.get_string(), logger=logger)

        # each metric dict
        for key, value in ret_metrics_summary.items():
            if key == 'aAcc':
                eval_results[key] = value / 100.0
            else:
                eval_results['m' + key] = value / 100.0

        ret_metrics_class.pop('Class', None)
        for key, value in ret_metrics_class.items():
            eval_results.update({
                key + '.' + str(name): value[idx] / 100.0
                for idx, name in enumerate(class_names)
            })

        if mmcv.is_list_of(results, str):
            for file_name in results:
                os.remove(file_name)
        return eval_results