Add ADE20K dataset

chainercv/datasets/__init__.py

@@ -1,3 +1,7 @@
+from chainercv.datasets.ade20k.ade20k_semantic_segmentation_dataset import ADE20KSemanticSegmentationDataset  # NOQA
+from chainercv.datasets.ade20k.ade20k_test_image_dataset import ADE20KTestImageDataset  # NOQA
+from chainercv.datasets.ade20k.ade20k_utils import ade20k_label_colors  # NOQA
+from chainercv.datasets.ade20k.ade20k_utils import ade20k_label_names  # NOQA
 from chainercv.datasets.camvid.camvid_dataset import camvid_ignore_label_color  # NOQA
 from chainercv.datasets.camvid.camvid_dataset import camvid_label_colors  # NOQA
 from chainercv.datasets.camvid.camvid_dataset import camvid_label_names  # NOQA

chainercv/datasets/ade20k/ade20k_semantic_segmentation_dataset.py

@@ -0,0 +1,81 @@
+import glob
+import os
+
+import numpy as np
+
+from chainer import dataset
+from chainer.dataset import download
+from chainercv import utils
+from chainercv.utils import read_image
+
+root = 'pfnet/chainercv/ade20k'
+url = 'http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip'
+
+
+def get_ade20k():
+    data_root = download.get_dataset_directory(root)
+    cache_path = utils.cached_download(url)
+    utils.extractall(cache_path, data_root, os.path.splitext(url)[1])
+    return data_root
+
+
+class ADE20KSemanticSegmentationDataset(dataset.DatasetMixin):
+
+    """Semantic segmentation dataset for `ADE20K`_.
+
+    This is ADE20K dataset distributed in MIT Scene Parsing Benchmark website.
+    It has 20,210 training images and 2,000 validation images.
+
+    .. _`MIT Scene Parsing Benchmark`: http://sceneparsing.csail.mit.edu/
+
+    Args:
+        data_dir (string): Path to the dataset directory. The directory should
+            contain the :obj:`ADEChallengeData2016` directory. And that
+            directory should contain at least :obj:`images` and
+            :obj:`annotations` directries. If :obj:`auto` is given, the dataset
+            is automatically downloaded into
+            :obj:`$CHAINER_DATASET_ROOT/pfnet/chainercv/ade20k`.
+        split ({'train', 'val'}): Select from dataset splits used in
+            MIT Scene Parsing Benchmark dataset (ADE20K).
+
+    """
+
+    def __init__(self, data_dir='auto', split='train'):
+        if data_dir is 'auto':
+            data_dir = get_ade20k()
+
+        if split == 'train' or split == 'val':
+            img_dir = os.path.join(
+                data_dir, 'ADEChallengeData2016', 'images',
+                'training' if split == 'train' else 'validation')
+            label_dir = os.path.join(
+                data_dir, 'ADEChallengeData2016', 'annotations',
+                'training' if split == 'train' else 'validation')
+        else:
+            raise ValueError(
+                'Please give \'split\' argument with either \'train\' or '
+                '\'val\'.')
+
+        self.img_paths = sorted(glob.glob(os.path.join(img_dir, '*.jpg')))
+        self.label_paths = sorted(glob.glob(os.path.join(label_dir, '*.png')))
+
+    def __len__(self):
+        return len(self.img_paths)
+
+    def get_example(self, i):
+        """Returns the i-th example.
+
+        Args:
+            i (int): The index of the example.
+
+        Returns:
+            Returns a tuple consited of a color image and a label whose shapes
+            are (3, H, W) and (H, W), respectively. H and W are height and
+            width of the image. The dtype of the color image is
+            :obj:`numpy.float32` and the dtype of the label image is
+            :obj:`numpy.int32`.
+
+        """
+        img = read_image(self.img_paths[i])
+        label = read_image(self.label_paths[i], dtype=np.int32, color=False)[0]
+        return img, label

chainercv/datasets/ade20k/ade20k_test_image_dataset.py

@@ -0,0 +1,58 @@
+import glob
+import os
+
+from chainer import dataset
+from chainer.dataset import download
+from chainercv import utils
+from chainercv.utils import read_image
+
+root = 'pfnet/chainercv/ade20k'
+url = 'http://data.csail.mit.edu/places/ADEchallenge/release_test.zip'
+
+
+def get_ade20k():
+    data_root = download.get_dataset_directory(root)
+    cache_path = utils.cached_download(url)
+    utils.extractall(cache_path, data_root, os.path.splitext(url)[1])
+    return data_root
+
+
+class ADE20KTestImageDataset(dataset.DatasetMixin):
+
+    """Image dataset for test split of `ADE20K`_.
+
+    This is an image dataset of test split in ADE20K dataset distributed at
+    MIT Scene Parsing Benchmark website. It has 3,352 test images.
+
+    .. _`MIT Scene Parsing Benchmark`: http://sceneparsing.csail.mit.edu/
+
+    Args:
+        data_dir (string): Path to the dataset directory. The directory should
+            contain the :obj:`release_test` dir. If :obj:`auto` is given, the
+            dataset is automatically downloaded into
+            :obj:`$CHAINER_DATASET_ROOT/pfnet/chainercv/ade20k`.
+
+    """
+
+    def __init__(self, data_dir='auto'):
+        if data_dir is 'auto':
+            data_dir = get_ade20k()
+        img_dir = os.path.join(data_dir, 'release_test', 'testing')
+        self.img_paths = sorted(glob.glob(os.path.join(img_dir, '*.jpg')))
+
+    def __len__(self):
+        return len(self.img_paths)
+
+    def get_example(self, i):
+        """Returns the i-th example.
+
+        Args:
+            i (int): The index of the example.
+
+        Returns:
+            Returns a color image whose shape is (3, H, W). H and W are height
+            and width of the image. The dtype of the image is
+            :obj:`numpy.float32`.
+
+        """
+        return read_image(self.img_paths[i])