Thinh Huynh Nguyen Truong
commited on
Commit
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6896071
1
Parent(s):
cf18101
Update
Browse files- requirements.txt +2 -0
- test.py +47 -35
requirements.txt
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datasets
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# Python 3.10.6
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datasets
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Pillow
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test.py
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import csv
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import json
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import os
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import datasets
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from datasets import DownloadManager
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@@ -56,6 +58,19 @@ _URLS = {
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_BASE_URL = ""
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# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
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class Test(datasets.GeneratorBasedBuilder):
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"""TODO: Short description of my dataset."""
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"depth": datasets.Image(),
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"rgb": datasets.Image(),
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"gt": datasets.Image(),
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# These are the features of your dataset like images, labels ...
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}
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), # Here we define them above because they are different between the two configurations
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# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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urls = _URLS[self.config.name]
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data_dir = dl_manager.download_and_extract(urls)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": os.path.join(data_dir, "train.jsonl"),
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"split": "train",
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": os.path.join(data_dir, "dev.jsonl"),
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"split": "dev",
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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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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": os.path.join(data_dir, "test.jsonl"),
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"split": "test",
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},
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),
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]
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# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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def _generate_examples(self,
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# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
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# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
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with open(
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"second_domain_answer": ""
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if split == "test"
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else data["second_domain_answer"],
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}
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import csv
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import json
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import os
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from typing import Dict, List
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from PIL import Image
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import datasets
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from datasets import DownloadManager
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_BASE_URL = ""
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def get_download_url(config_name: str, partition: str) -> str:
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"""Get download URL based on config name and parition (train/dev/test)
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Args:
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config_name (str): can be "v1", "v2",...
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partition (str): can be "train", "dev" or "test"
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Returns:
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str: URL to download file
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"""
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return f"https://huggingface.co/datasets/RGBD-SOD/test/tree/main/data{config_name}/{partition}.zip"
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# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
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class Test(datasets.GeneratorBasedBuilder):
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"""TODO: Short description of my dataset."""
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"depth": datasets.Image(),
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"rgb": datasets.Image(),
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"gt": datasets.Image(),
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"name": datasets.Value("string"),
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# These are the features of your dataset like images, labels ...
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}
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), # Here we define them above because they are different between the two configurations
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# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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urls = _URLS[self.config.name]
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data_dir = dl_manager.download_and_extract(urls)
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train_dir = dl_manager.download_and_extract(
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get_download_url(self.config.name, "train")
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)
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dev_dir = dl_manager.download_and_extract(
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get_download_url(self.config.name, "dev")
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)
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# test_dir = dl_manager.download_and_extract(
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# get_download_url(self.config.name, "test")
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# )
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"dir_path": train_dir},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"dir_path": dev_dir},
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),
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# datasets.SplitGenerator(
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# name=datasets.Split.TEST,
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# # These kwargs will be passed to _generate_examples
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# gen_kwargs={"dir_path": test_dir},
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# ),
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]
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# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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def _generate_examples(self, dir_path: str):
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# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
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# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
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with open(os.path.join(dir_path, "metadata.json"), "r") as f:
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json_object = json.load(f)
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metadata: List[Dict[str, str]] = json_object["metadata"]
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for key, row in enumerate(metadata):
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yield key, {
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"name": row["name"],
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"rgb": Image.open(os.path.join(dir_path, metadata["rgb"]), mode="RGB"),
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"gt": Image.open(os.path.join(dir_path, metadata["gt"]), mode="L"),
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"depth": Image.open(
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os.path.join(dir_path, metadata["depth"]), mode="L"
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),
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}
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