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import os |
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import datasets |
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import numpy as np |
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_DESCRIPTION = """\ |
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GTEA is composed of 50 recorded videos of 25 participants making two different mixed salads. The videos are captured by a camera with a top-down view onto the work-surface. The participants are provided with recipe steps which are randomly sampled from a statistical recipe model. |
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""" |
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_CITATION = """\ |
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@inproceedings{stein2013combining, |
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title={Combining embedded accelerometers with computer vision for recognizing food preparation activities}, |
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author={Stein, Sebastian and McKenna, Stephen J}, |
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booktitle={Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing}, |
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pages={729--738}, |
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year={2013} |
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} |
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""" |
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_HOMEPAGE = "" |
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_LICENSE = "xxx" |
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_URLS = {"full": "https://huggingface.co/datasets/dinggd/gtea/resolve/main/gtea.zip"} |
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class GTEA(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name="split1", version=VERSION, description="Cross Validation Split1" |
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), |
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datasets.BuilderConfig( |
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name="split2", version=VERSION, description="Cross Validation Split2" |
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), |
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datasets.BuilderConfig( |
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name="split3", version=VERSION, description="Cross Validation Split3" |
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), |
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datasets.BuilderConfig( |
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name="split4", version=VERSION, description="Cross Validation Split4" |
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), |
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] |
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DEFAULT_CONFIG_NAME = "1" |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"video_id": datasets.Value("string"), |
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"video_feature": datasets.Array2D(shape=(None, 2048), dtype="float32"), |
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"video_label": datasets.Sequence(datasets.Value(dtype="int32")), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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urls_to_download = _URLS |
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data_dir = dl_manager.download_and_extract(urls_to_download)["full"] |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": os.path.join( |
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data_dir, f"gtea/splits/train.{self.config.name}.bundle" |
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), |
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"featurefolder": os.path.join(data_dir, "gtea/features"), |
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"gtfolder": os.path.join(data_dir, "gtea/groundTruth"), |
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"mappingpath": os.path.join(data_dir, "gtea/mapping.txt"), |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": os.path.join( |
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data_dir, f"gtea/splits/test.{self.config.name}.bundle" |
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), |
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"featurefolder": os.path.join(data_dir, "gtea/features"), |
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"gtfolder": os.path.join(data_dir, "gtea/groundTruth"), |
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"mappingpath": os.path.join(data_dir, "gtea/mapping.txt"), |
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}, |
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), |
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] |
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def _generate_examples(self, filepath, featurefolder, gtfolder, mappingpath): |
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with open(mappingpath, "r") as f: |
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actions = f.read().splitlines() |
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actions_dict = {} |
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for a in actions: |
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actions_dict[a.split()[1]] = int(a.split()[0]) |
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with open(filepath, "r") as f: |
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lines = f.read().splitlines() |
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for key, line in enumerate(lines): |
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vid = line[:-4] |
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featurepath = os.path.join(featurefolder, f"{vid}.npy") |
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gtpath = os.path.join(gtfolder, line) |
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feature = np.load(featurepath).T |
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with open(gtpath, "r") as f: |
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content = f.read().splitlines() |
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label = np.zeros(min(np.shape(feature)[1], len(content))) |
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for i in range(len(label)): |
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label[i] = actions_dict[content[i]] |
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yield key, { |
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"video_id": vid, |
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"video_feature": feature, |
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"video_label": label, |
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} |
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