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epic_kitchens_100 / epic_kitchens_100.py
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# coding=utf-8
# Copyright 2022 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.
# Lint as: python3
"""EPIC-KITCHENS-100 is a large-scale dataset in first-person (egocentric) vision; multi-faceted, audio-visual,
non-scripted recordings in native environments - i.e. the wearers' homes, capturing all daily activities
in the kitchen over multiple days. Annotations are collected using a novel 'Pause-and-Talk' narration interface.
EPIC-KITCHENS-100 is an extension of the EPIC-KITCHENS dataset released in 2018, to 100 hours of footage."""
import os
import csv
import datasets
_CITATION = """
@ARTICLE{Damen2021RESCALING,
title={Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100},
author={Damen, Dima and Doughty, Hazel and Farinella, Giovanni Maria and and Furnari, Antonino
and Ma, Jian and Kazakos, Evangelos and Moltisanti, Davide and Munro, Jonathan
and Perrett, Toby and Price, Will and Wray, Michael},
journal = {International Journal of Computer Vision (IJCV)},
year = {2021},
Url = {https://doi.org/10.1007/s11263-021-01531-2}
}
@INPROCEEDINGS{Damen2018EPICKITCHENS,
title={Scaling Egocentric Vision: The EPIC-KITCHENS Dataset},
author={Damen, Dima and Doughty, Hazel and Farinella, Giovanni Maria and Fidler, Sanja and
Furnari, Antonino and Kazakos, Evangelos and Moltisanti, Davide and Munro, Jonathan
and Perrett, Toby and Price, Will and Wray, Michael},
booktitle={European Conference on Computer Vision (ECCV)},
year={2018}
}
"""
_DESCRIPTION = """\
EPIC-KITCHENS-100 is a large-scale dataset in first-person (egocentric) vision; multi-faceted, audio-visual,
non-scripted recordings in native environments - i.e. the wearers' homes, capturing all daily activities
in the kitchen over multiple days. Annotations are collected using a novel 'Pause-and-Talk' narration interface.
EPIC-KITCHENS-100 is an extension of the EPIC-KITCHENS dataset released in 2018, to 100 hours of footage.
"""
_HOMEPAGE = "https://epic-kitchens.github.io/2022"
_LICENSE = "CC BY-NC 4.0"
_URL_BASE = "https://raw.githubusercontent.com/epic-kitchens/epic-kitchens-100-annotations/master/"
_VARIANTS = [
"action_recognition", # This split is used by four challenges: Action Recognition, Weakly supervised action recognition, Action detection, Action anticipation
"multi_instance_retrieval",
"unsupervised_domain_adaptation",
]
class EpicKitchens100(datasets.GeneratorBasedBuilder):
"""Epic Kitchens"""
BUILDER_CONFIGS = [datasets.BuilderConfig(name) for name in _VARIANTS]
DEFAULT_CONFIG_NAME = "action_recognition"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"extended": datasets.Value("bool"),
"narration_id": datasets.Value("string"),
"participant_id": datasets.Value("string"),
"video_id": datasets.Value("string"),
"path": datasets.Value("string"),
"narration_timestamp": datasets.Value("string"),
"start_timestamp": datasets.Value("string"),
"stop_timestamp": datasets.Value("string"),
"narration": datasets.Value("string"),
"verb": datasets.Value("string"),
"verb_class": datasets.Value("int32"),
# The mapping for `verb_class` is available at: https://github.com/epic-kitchens/epic-kitchens-100-annotations/blob/master/README.md#epic_100_noun_classescsv
"noun": datasets.Value("string"),
"noun_class": datasets.Value("string"),
# The mapping for `noun_class` is available at: https://github.com/epic-kitchens/epic-kitchens-100-annotations/blob/master/README.md#epic_100_noun_classescsv
"all_nouns": datasets.features.Sequence(datasets.Value("string")),
"all_noun_classes": datasets.features.Sequence(datasets.Value("int32")),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
license=_LICENSE
)
def _split_generators(self, dl_manager):
urls = {
"action_recognition": {
"train": os.path.join(_URL_BASE, "EPIC_100_train.csv"),
"validation": os.path.join(_URL_BASE, "EPIC_100_validation.csv"),
"test": os.path.join(_URL_BASE, "EPIC_100_test_timestamps.csv"),
},
"multi_instance_retrieval": {
"train": os.path.join(_URL_BASE, "retrieval_annotations/EPIC_100_retrieval_train.csv"),
"test": os.path.join(_URL_BASE, "retrieval_annotations/EPIC_100_retrieval_test.csv")
},
"unsupervised_domain_adaptation": {
"source_train": os.path.join(_URL_BASE, "UDA_annotations/EPIC_100_uda_source_train.csv"),
"target_train": os.path.join(_URL_BASE, "UDA_annotations/EPIC_100_uda_target_train_timestamps.csv"),
"source_test": os.path.join(_URL_BASE, "UDA_annotations/EPIC_100_uda_source_test_timestamps.csv"),
"target_test": os.path.join(_URL_BASE, "UDA_annotations/EPIC_100_uda_target_test_timestamps.csv"),
"source_val": os.path.join(_URL_BASE, "UDA_annotations/EPIC_100_uda_source_val.csv"),
"target_val": os.path.join(_URL_BASE, "UDA_annotations/EPIC_100_uda_target_val.csv"),
}
}
# Download data for all splits once for all since they are tiny csv files
files_path = dl_manager.download_and_extract(urls)
if self.config.name == "unsupervised_domain_adaptation":
splits = [
datasets.SplitGenerator(
name=datasets.Split(n_),
gen_kwargs={
"annotations": files_path[self.config.name][n_],
"split": n_,
},
)
for n_ in ["source_train", "target_train", "source_test", "target_test", "source_val", "target_val"]
]
return splits
else:
splits = [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"annotations": files_path[self.config.name]["train"],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"annotations": files_path[self.config.name]["test"],
"split": "test",
},
),
]
if self.config.name == "action_recognition":
splits.append(
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"annotations": files_path[self.config.name]["validation"],
"split": "validation",
},
),
)
return splits
def _generate_examples(self, annotations, split):
"""This function returns the examples."""
with open(annotations, encoding="utf-8") as csv_file:
csv_reader = csv.reader(csv_file, delimiter=",")
next(csv_reader) # Skip header
for idx, row in enumerate(csv_reader):
narration_id, participant_id, video_id, narration_timestamp, start_timestamp, stop_timestamp = row[:6]
if (self.config.name in ["action_recognition", "multi_instance_retrieval"] and split in ["train", "validation"]) or \
(self.config.name == "unsupervised_domain_adaptation" and split in ["source_train", "source_val", "target_val"]):
# The reason why it's jumping from 5 to 8 is that we are skipping `start_frame` and `stop_frame`
# since we are not exposing the frames, but just the videos
narration, verb, verb_class, noun, noun_class, all_nouns, all_noun_classes = row[8:15]
all_nouns = eval(all_nouns)
all_noun_classes = eval(all_noun_classes)
else:
narration = verb = noun = ""
verb_class = noun_class = -1
all_nouns = all_noun_classes = []
extended = len(narration_id.split("_")[1]) == 3
if extended:
path = f"EPIC-KITCHENS/{participant_id}/videos/{video_id}.MP4" #Paths for jz version
else:
path = f"EPIC_KITCHENS_2018/videos/{split}/{participant_id}/{video_id}.MP4" #Paths for jz version
yield idx, {
"extended": extended,
"narration_id": narration_id,
"participant_id": participant_id,
"video_id": video_id,
"path": path,
"narration_timestamp": narration_timestamp,
"start_timestamp": start_timestamp,
"stop_timestamp": stop_timestamp,
"narration": narration,
"verb": verb,
"verb_class": verb_class,
"noun": noun,
"noun_class": noun_class,
"all_nouns": all_nouns,
"all_noun_classes": all_noun_classes,
}