# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Animals with Attributes v2 (AwA2)""" import csv import json import os import datasets _CITATION = """\ @article{xian2018zero, title={Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly}, author={Xian, Yongqin and Lampert, Christoph H and Schiele, Bernt and Akata, Zeynep}, journal={IEEE transactions on pattern analysis and machine intelligence}, volume={41}, number={9}, pages={2251--2265}, year={2018}, publisher={IEEE} } """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ **Homepage:** https://cvml.ista.ac.at/AwA2/ **IMPORTANT NOTES** - This HF dataset loads the instances with class-level annotations. - Images and License can be downloaded from: https://cvml.ista.ac.at/AwA2/AwA2-data.zip """ # TODO: Add a link to an official homepage for the dataset here _HOMEPAGE = "https://cvml.ista.ac.at/AwA2/" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "" # TODO: Add link to the official dataset URLs here # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) # _URLS = { # # "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip", # # "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip", # } _URLS = { "data": "https://cvml.ista.ac.at/AwA2/AwA2-data.zip", # including images # "annotation": "https://cvml.ista.ac.at/AwA2/AwA2-base.zip", # "features": "http://cvml.ist.ac.at/AwA2/AwA2-features.zip", } def _load_AwA2_dataset(datadir): image_dir = os.path.join(datadir, "JPEGImages") classes_path = os.path.join(datadir, "classes.txt") predicates_path = os.path.join(datadir, "predicates.txt") annotation_binary = os.path.join(datadir, "predicate-matrix-binary.txt") annotation_continuous = os.path.join(datadir, "predicate-matrix-continuous.txt") # load classes classes = [] with open(classes_path, "r") as f: for line in f: classes.append(line.split("\t")[1].strip()) # load predicates predicates = [] with open(predicates_path, "r") as f: for line in f: predicates.append(line.split("\t")[1].strip()) # class to annotation binary annotation_binary_list = [] with open(annotation_binary, "r") as f: for line in f: ann = [int(x) for x in line.strip().split(" ")] assert len(ann) == len(predicates) annotation_binary_list.append(ann) class_to_annotation_binary = dict(zip(classes, annotation_binary_list)) # class to annotation continuous annotation_continuous_list = [] with open(annotation_continuous, "r") as f: for line in f: ann = [float(x) for x in line.strip().split(" ") if x != ""] assert len(ann) == len(predicates) annotation_continuous_list.append(ann) class_to_annotation_continuous = dict(zip(classes, annotation_continuous_list)) print("classes:", len(classes), classes) print("attribute types:", len(predicates), predicates) data = [] # list all images in image dir for class_type in os.listdir(image_dir): image_class_dir = os.path.join(image_dir, class_type) for img_name in os.listdir(image_class_dir): data.append( { "image_id": img_name, "image_path": os.path.join(image_class_dir, img_name), "class": class_type, "attributes_binary": class_to_annotation_binary[class_type], "attributes_continuous": class_to_annotation_continuous[class_type], "attribute_types": predicates } ) return data # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case class AwA2(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" VERSION = datasets.Version("1.0.0") # This is an example of a dataset with multiple configurations. # If you don't want/need to define several sub-sets in your dataset, # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. # If you need to make complex sub-parts in the datasets with configurable options # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig # BUILDER_CONFIG_CLASS = MyBuilderConfig # You will be able to load one or the other configurations in the following list with # data = datasets.load_dataset('my_dataset', 'first_domain') # data = datasets.load_dataset('my_dataset', 'second_domain') # BUILDER_CONFIGS = [ # datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"), # datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"), # ] # DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset # if self.config.name == "first_domain": # This is the name of the configuration selected in BUILDER_CONFIGS above # features = datasets.Features( # { # "sentence": datasets.Value("string"), # "option1": datasets.Value("string"), # "answer": datasets.Value("string") # # These are the features of your dataset like images, labels ... # } # ) # else: # This is an example to show how to have different features for "first_domain" and "second_domain" # features = datasets.Features( # { # "sentence": datasets.Value("string"), # "option2": datasets.Value("string"), # "second_domain_answer": datasets.Value("string") # # These are the features of your dataset like images, labels ... # } # ) features = datasets.Features( { "image_id": datasets.Value("string"), "image_path": datasets.Value("string"), "class": datasets.Value("string"), "attributes_binary": datasets.features.Sequence(datasets.Value("int32")), "attributes_continuous": datasets.features.Sequence(datasets.Value("float32")), "attribute_types": datasets.features.Sequence(datasets.Value("string")), } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=features, # Here we define them above because they are different between the two configurations # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and # specify them. They'll be used if as_supervised=True in builder.as_dataset. # supervised_keys=("sentence", "label"), # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive downloaded_files = dl_manager.download_and_extract(_URLS) # downloaded_files = { # "data": "/shared/nas/data/m1/shared-resource/vision-language/data/raw/AwA2/Animals_with_Attributes2", # } print("downloaded_files: ", downloaded_files) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": downloaded_files["data"], "split": "train", }, ) ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath, split): # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. data = _load_AwA2_dataset(filepath) for key, row in enumerate(data): yield key, { "image_id": row["image_id"], "image_path": row["image_path"], "class": row["class"], "attributes_binary": row["attributes_binary"], "attributes_continuous": row["attributes_continuous"], "attribute_types": row["attribute_types"], }