def _info(self):
return tfds.core.DatasetInfo(
builder=self,
description=_DESCRIPTION,
features=tfds_features.FeaturesDict({
'trimesh': tfg_features.TriangleMesh(),
'label': tfds_features.ClassLabel(num_classes=353),
'model_id': tfds_features.Text(),
}),
supervised_keys=('trimesh', 'label'),
# Homepage of the dataset for documentation
homepage='https://shapenet.org/',
citation=_CITATION,
)
def _info(self):
return tfds.core.DatasetInfo(
builder=self,
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# tfds.features.FeatureConnectors
features=tfds.features.FeaturesDict({
'image':
tfds_features.Image(shape=(None, None, 3), dtype=tf.uint8),
'image/filename':
tfds_features.Text(),
'image/source':
tfds_features.Text(),
'2d_keypoints':
tfds_features.FeaturesDict({
'num_annotators':
tf.uint8,
'num_keypoints':
tf.uint8,
'keypoints':
tfds_features.Tensor(shape=(None, ), dtype=tf.float32),
}),
'mask':
tfds_features.Image(shape=(None, None, 1), dtype=tf.uint8),
'model':
tfg_features.TriangleMesh(),
'model/source':
tfds_features.Text(),
'3d_keypoints':
tfds_features.Tensor(shape=(None, 3), dtype=tf.float32),
'voxel':
tfg_features.VoxelGrid(shape=(128, 128, 128)),
'pose':
tfg_features.Pose(), # pose of object w.r.t to world.
'camera':
tfds_features.FeaturesDict({
'parameters':
tfg_features.Camera(),
'position_with_respect_to_object':
tfds_features.Tensor(shape=(3, ), dtype=tf.float32),
'inplane_rotation':
tf.float32,
}),
'category':
tfds_features.ClassLabel(num_classes=len(self.CLASS_INDEX)),
'bbox':
tfds_features.BBoxFeature(),
'truncated':
tf.bool,
'occluded':
tf.bool,
'slightly_occluded':
tf.bool,
}),
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage='http://pix3d.csail.mit.edu/',
citation=_CITATION,
)