Source code for tensorbay.opendataset.AnimalsWithAttributes2.loader
#!/usr/bin/env python3
#
# Copyright 2021 Graviti. Licensed under MIT License.
#
# pylint: disable=invalid-name
"""Dataloader of AnimalsWithAttributes2 dataset."""
import os
from tensorbay.dataset import Data, Dataset
from tensorbay.label import Classification
from tensorbay.opendataset._utility import glob
DATASET_NAME = "AnimalsWithAttributes2"
[docs]def AnimalsWithAttributes2(path: str) -> Dataset:
"""`Animals with attributes 2 <https://cvml.ist.ac.at/AwA2/>`_ dataset.
The file structure should be like::
<path>
classes.txt
predicates.txt
predicate-matrix-binary.txt
JPEGImages/
<classname>/
<imagename>.jpg
...
...
Arguments:
path: The root directory of the dataset.
Returns:
Loaded :class:`~tensorbay.dataset.dataset.Dataset` instance.
"""
root_path = os.path.abspath(os.path.expanduser(path))
dataset = Dataset(DATASET_NAME)
dataset.load_catalog(os.path.join(os.path.dirname(__file__), "catalog.json"))
segment = dataset.create_segment()
with open(os.path.join(root_path, "classes.txt"), encoding="utf-8") as fp:
class_names = [line[:-1].split("\t", 1)[-1] for line in fp]
with open(os.path.join(root_path, "predicates.txt"), encoding="utf-8") as fp:
attribute_keys = [line[:-1].split("\t", 1)[-1] for line in fp]
with open(os.path.join(root_path, "predicate-matrix-binary.txt"), encoding="utf-8") as fp:
attribute_values = [line[:-1].split(" ") for line in fp]
attribute_mapping = {}
for class_name, values in zip(class_names, attribute_values):
attribute_mapping[class_name] = Classification(
category=class_name,
attributes=dict(zip(attribute_keys, (bool(int(value)) for value in values))),
)
for class_name in sorted(os.listdir(os.path.join(root_path, "JPEGImages"))):
image_paths = glob(os.path.join(root_path, "JPEGImages", class_name, "*.jpg"))
label = attribute_mapping[class_name]
for image_path in image_paths:
data = Data(image_path)
data.label.classification = label
segment.append(data)
return dataset