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import os
import os.path as osp
from collections import OrderedDict
from functools import reduce
import annotator.mmpkg.mmcv as mmcv
import numpy as np
from annotator.mmpkg.mmcv.utils import print_log
from torch.utils.data import Dataset
from annotator.mmpkg.mmseg.core import eval_metrics
from annotator.mmpkg.mmseg.utils import get_root_logger
from .builder import DATASETS
from .pipelines import Compose
@DATASETS.register_module()
class CustomDataset(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,
img_suffix='.jpg',
ann_dir=None,
seg_map_suffix='.png',
split=None,
data_root=None,
test_mode=False,
ignore_index=255,
reduce_zero_label=False,
classes=None,
palette=None):
self.pipeline = Compose(pipeline)
self.img_dir = img_dir
self.img_suffix = img_suffix
self.ann_dir = ann_dir
self.seg_map_suffix = seg_map_suffix
self.split = split
self.data_root = data_root
self.test_mode = test_mode
self.ignore_index = ignore_index
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 is specified
if self.data_root is not None:
if not osp.isabs(self.img_dir):
self.img_dir = osp.join(self.data_root, self.img_dir)
if not (self.ann_dir is None or osp.isabs(self.ann_dir)):
self.ann_dir = osp.join(self.data_root, self.ann_dir)
if not (self.split is None or osp.isabs(self.split)):
self.split = osp.join(self.data_root, self.split)
# load annotations
self.img_infos = self.load_annotations(self.img_dir, self.img_suffix,
self.ann_dir,
self.seg_map_suffix, self.split)
def __len__(self):
"""Total number of samples of data."""
return len(self.img_infos)
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', logger=get_root_logger())
return img_infos
def get_ann_info(self, idx):
"""Get annotation by index.
Args:
idx (int): Index of data.
Returns:
dict: Annotation info of specified index.
"""
return self.img_infos[idx]['ann']
def pre_pipeline(self, results):
"""Prepare results dict for pipeline."""
results['seg_fields'] = []
results['img_prefix'] = self.img_dir
results['seg_prefix'] = self.ann_dir
if self.custom_classes:
results['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 = self.img_infos[idx]
ann_info = self.get_ann_info(idx)
results = dict(img_info=img_info, ann_info=ann_info)
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 = self.img_infos[idx]
results = dict(img_info=img_info)
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)
try:
from prettytable import PrettyTable
# 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)
except ImportError: # prettytable is not installed
pass
# 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
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