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import io |
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import itertools as it |
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import numpy as np |
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import datasets as d |
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_DESCRIPTION = """\ |
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The Dropjects dataset was created at the Chair of Cyber-Physical Systems in Production \ |
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Engineering at the Technical University of Munich. |
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""" |
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SUBSETS = [ |
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"omni", |
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"cps", |
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"linemod", |
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"ycbv", |
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"homebreweddb", |
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"hope", |
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"tless", |
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] |
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NUM_SHARDS = { |
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"cps": 1000, |
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"ycbv": 1000, |
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"linemod": 1000, |
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"tless": 1000, |
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"omni": 10_000, |
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} |
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BASE_PATH = "https://huggingface.co/datasets/LukasDb/dropjects/resolve/main/data/train/{subset}/{shard}.tar" |
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h = 1440 |
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w = 2560 |
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class Dropjects(d.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = list(d.BuilderConfig(name=x) for x in SUBSETS) |
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def _info(self): |
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features = d.Features( |
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{ |
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"rgb": d.Array3D((h, w, 3), dtype="uint8"), |
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"rgb_R": d.Array3D((h, w, 3), dtype="uint8"), |
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"depth": d.Array2D((h, w), dtype="float32"), |
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"depth_R": d.Array2D((h, w), dtype="float32"), |
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"mask": d.Array2D((h, w), dtype="int32"), |
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"obj_ids": d.Sequence(d.Value("int32")), |
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"obj_classes": d.Sequence(d.Value("string")), |
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"obj_pos": d.Sequence(d.Sequence(d.Value("float32"))), |
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"obj_rot": d.Sequence(d.Sequence(d.Value("float32"))), |
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"obj_bbox_obj": d.Sequence(d.Sequence(d.Value("int32"))), |
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"obj_bbox_visib": d.Sequence(d.Sequence(d.Value("int32"))), |
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"cam_location": d.Sequence(d.Value("float32")), |
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"cam_rotation": d.Sequence(d.Value("float32")), |
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"cam_matrix": d.Array2D((3, 3), dtype="float32"), |
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"obj_px_count_all": d.Sequence(d.Value("int32")), |
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"obj_px_count_valid": d.Sequence(d.Value("int32")), |
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"obj_px_count_visib": d.Sequence(d.Value("int32")), |
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"obj_visib_fract": d.Sequence(d.Value("float32")), |
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} |
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) |
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return d.DatasetInfo( |
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description=_DESCRIPTION, |
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citation="", |
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homepage="", |
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license="cc", |
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features=features, |
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) |
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def _split_generators(self, dl_manager): |
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subset = self.config.name |
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archive_paths = [ |
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BASE_PATH.format(subset=subset, shard=i) for i in range(NUM_SHARDS[subset]) |
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] |
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downloaded = dl_manager.download(archive_paths) |
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return [ |
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d.SplitGenerator( |
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name=d.Split.TRAIN, |
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gen_kwargs={"tars": [dl_manager.iter_archive(d) for d in downloaded]}, |
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), |
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] |
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def _generate_examples(self, tars): |
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sample = {} |
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id = None |
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for tar in tars: |
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for file_path, file_obj in tar: |
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new_id = file_path.split(".")[0] |
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if id is None: |
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id = new_id |
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else: |
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if id != new_id: |
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yield id, sample |
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sample = {} |
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id = new_id |
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key = file_path.split(".")[1] |
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bytes = io.BytesIO(file_obj.read()) |
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value = np.load(bytes, allow_pickle=False) |
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sample[key] = value |
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