Upload 3 files

config.py

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+import argparse
+import sys
+import torch
+from multiprocessing import cpu_count
+
+class Config:
+    def __init__(self):
+        self.device = "cuda:0"
+        self.is_half = True
+        self.n_cpu = 0
+        self.gpu_name = None
+        self.gpu_mem = None
+        (
+            self.python_cmd,
+            self.listen_port,
+            self.colab,
+            self.noparallel,
+            self.noautoopen,
+            self.api
+        ) = self.arg_parse()
+        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
+
+    @staticmethod
+    def arg_parse() -> tuple:
+        exe = sys.executable or "python"
+        parser = argparse.ArgumentParser()
+        parser.add_argument("--port", type=int, default=7865, help="Listen port")
+        parser.add_argument("--pycmd", type=str, default=exe, help="Python command")
+        parser.add_argument("--colab", action="store_true", help="Launch in colab")
+        parser.add_argument(
+            "--noparallel", action="store_true", help="Disable parallel processing"
+        )
+        parser.add_argument(
+            "--noautoopen",
+            action="store_true",
+            help="Do not open in browser automatically",
+        )
+        parser.add_argument("--api", action="store_true", help="Launch with api")
+        cmd_opts = parser.parse_args()
+
+        cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865
+
+        return (
+            cmd_opts.pycmd,
+            cmd_opts.port,
+            cmd_opts.colab,
+            cmd_opts.noparallel,
+            cmd_opts.noautoopen,
+            cmd_opts.api
+        )
+
+    # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
+    # check `getattr` and try it for compatibility
+    @staticmethod
+    def has_mps() -> bool:
+        if not torch.backends.mps.is_available():
+            return False
+        try:
+            torch.zeros(1).to(torch.device("mps"))
+            return True
+        except Exception:
+            return False
+
+    def device_config(self) -> tuple:
+        if torch.cuda.is_available():
+            i_device = int(self.device.split(":")[-1])
+            self.gpu_name = torch.cuda.get_device_name(i_device)
+            if (
+                ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
+                or "P40" in self.gpu_name.upper()
+                or "1060" in self.gpu_name
+                or "1070" in self.gpu_name
+                or "1080" in self.gpu_name
+            ):
+                print("Found GPU", self.gpu_name, ", force to fp32")
+                self.is_half = False
+            else:
+                print("Found GPU", self.gpu_name)
+            self.gpu_mem = int(
+                torch.cuda.get_device_properties(i_device).total_memory
+                / 1024
+                / 1024
+                / 1024
+                + 0.4
+            )
+        elif self.has_mps():
+            print("No supported Nvidia GPU found, use MPS instead")
+            self.device = "mps"
+            self.is_half = False
+        else:
+            print("No supported Nvidia GPU found, use CPU instead")
+            self.device = "cpu"
+            self.is_half = False
+
+        if self.n_cpu == 0:
+            self.n_cpu = cpu_count()
+
+        if self.is_half:
+            # 6G显存配置
+            x_pad = 3
+            x_query = 10
+            x_center = 60
+            x_max = 65
+        else:
+            # 5G显存配置
+            x_pad = 1
+            x_query = 6
+            x_center = 38
+            x_max = 41
+
+        if self.gpu_mem != None and self.gpu_mem <= 4:
+            x_pad = 1
+            x_query = 5
+            x_center = 30
+            x_max = 32
+
+        return x_pad, x_query, x_center, x_max

dataset-script.py

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+from datasets import load_dataset
+load_dataset("path/to/my_dataset")
+datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "title": datasets.Value("string"),
+        "context": datasets.Value("string"),
+        "question": datasets.Value("string"),
+        "answers": datasets.Sequence(
+            {
+                "text": datasets.Value("string"),
+                "answer_start": datasets.Value("int32"),
+            }
+        ),
+    }
+)
+def _info(self):
+    return datasets.DatasetInfo(
+        description=_DESCRIPTION,
+        features=datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "title": datasets.Value("string"),
+                "context": datasets.Value("string"),
+                "question": datasets.Value("string"),
+                "answers": datasets.features.Sequence(
+                    {"text": datasets.Value("string"), "answer_start": datasets.Value("int32"),}
+                ),
+            }
+        ),
+        # No default supervised_keys (as we have to pass both question
+        # and context as input).
+        supervised_keys=None,
+        homepage="https://rajpurkar.github.io/SQuAD-explorer/",
+        citation=_CITATION,
+    )
+class SuperGlueConfig(datasets.BuilderConfig):
+    """BuilderConfig for SuperGLUE."""
+
+    def __init__(self, features, data_url, citation, url, label_classes=("False", "True"), **kwargs):
+        """BuilderConfig for SuperGLUE.
+
+        Args:
+        features: *list[string]*, list of the features that will appear in the
+            feature dict. Should not include "label".
+        data_url: *string*, url to download the zip file from.
+        citation: *string*, citation for the data set.
+        url: *string*, url for information about the data set.
+        label_classes: *list[string]*, the list of classes for the label if the
+            label is present as a string. Non-string labels will be cast to either
+            'False' or 'True'.
+        **kwargs: keyword arguments forwarded to super.
+        """
+        # Version history:
+        # 1.0.2: Fixed non-nondeterminism in ReCoRD.
+        # 1.0.1: Change from the pre-release trial version of SuperGLUE (v1.9) to
+        #        the full release (v2.0).
+        # 1.0.0: S3 (new shuffling, sharding and slicing mechanism).
+        # 0.0.2: Initial version.
+        super(SuperGlueConfig, self).__init__(version=datasets.Version("1.0.2"), **kwargs)
+        self.features = features
+        self.label_classes = label_classes
+        self.data_url = data_url
+        self.citation = citation
+        self.url = url
+class SuperGlue(datasets.GeneratorBasedBuilder):
+    """The SuperGLUE benchmark."""
+
+    BUILDER_CONFIGS = [
+        SuperGlueConfig(
+            name="boolq",
+            description=_BOOLQ_DESCRIPTION,
+            features=["question", "passage"],
+            data_url="https://dl.fbaipublicfiles.com/glue/superglue/data/v2/BoolQ.zip",
+            citation=_BOOLQ_CITATION,
+            url="https://github.com/google-research-datasets/boolean-questions",
+        ),
+        ...
+        ...
+        SuperGlueConfig(
+            name="axg",
+            description=_AXG_DESCRIPTION,
+            features=["premise", "hypothesis"],
+            label_classes=["entailment", "not_entailment"],
+            data_url="https://dl.fbaipublicfiles.com/glue/superglue/data/v2/AX-g.zip",
+            citation=_AXG_CITATION,
+            url="https://github.com/rudinger/winogender-schemas",
+        ),
+from datasets import load_dataset
+dataset = load_dataset('super_glue', 'boolq')
+class NewDataset(datasets.GeneratorBasedBuilder):
+
+VERSION = datasets.Version("1.1.0")
+
+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"
+_URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
+_URLS = {
+    "train": _URL + "train-v1.1.json",
+    "dev": _URL + "dev-v1.1.json",
+}
+def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+    urls_to_download = self._URLS
+    downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+    return [
+        datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+        datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+    ]
+def _generate_examples(self, filepath):
+    """This function returns the examples in the raw (text) form."""
+    logger.info("generating examples from = %s", filepath)
+    with open(filepath) as f:
+        squad = json.load(f)
+        for article in squad["data"]:
+            title = article.get("title", "").strip()
+            for paragraph in article["paragraphs"]:
+                context = paragraph["context"].strip()
+                for qa in paragraph["qas"]:
+                    question = qa["question"].strip()
+                    id_ = qa["id"]
+
+                    answer_starts = [answer["answer_start"] for answer in qa["answers"]]
+                    answers = [answer["text"].strip() for answer in qa["answers"]]
+
+                    # Features currently used are "context", "question", and "answers".
+                    # Others are extracted here for the ease of future expansions.
+                    yield id_, {
+                        "title": title,
+                        "context": context,
+                        "question": question,
+                        "id": id_,
+                        "answers": {"answer_start": answer_starts, "text": answers,},
+                    }
+datasets-cli test datasets/<your-dataset-folder> --save_infos --all_configs
+datasets-cli dummy_data datasets/<your-dataset-folder> --auto_generate
+datasets-cli dummy_data datasets/<your-dataset-folder>
+
+==============================DUMMY DATA INSTRUCTIONS==============================
+- In order to create the dummy data for my-dataset, please go into the folder './datasets/my-dataset/dummy/1.1.0' with *cd ./datasets/my-dataset/dummy/1.1.0* .
+
+- Please create the following dummy data files 'dummy_data/TREC_10.label, dummy_data/train_5500.label' from the folder './datasets/my-dataset/dummy/1.1.0'
+
+- For each of the splits 'train, test', make sure that one or more of the dummy data files provide at least one example
+
+- If the method *_generate_examples(...)* includes multiple *open()* statements, you might have to create other files in addition to 'dummy_data/TREC_10.label, dummy_data/train_5500.label'. In this case please refer to the *_generate_examples(...)* method
+
+- After all dummy data files are created, they should be zipped recursively to 'dummy_data.zip' with the command *zip -r dummy_data.zip dummy_data/*
+
+- You can now delete the folder 'dummy_data' with the command *rm -r dummy_data*
+
+- To get the folder 'dummy_data' back for further changes to the dummy data, simply unzip dummy_data.zip with the command *unzip dummy_data.zip*
+
+- Make sure you have created the file 'dummy_data.zip' in './datasets/my-dataset/dummy/1.1.0'
+===================================================================================
+RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_<your_dataset_name>
+RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<your_dataset_name>

dataset-train.py

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+my_dataset_repository/
+├── README.md
+├── data.csv
+└── holdout.csv
+---
+configs:
+- config_name: default data_files:
+- split: train
+path: "data.csv"
+- split: test
+path: "holdout.csv"
+---
+my_dataset_repository/
+├── README.md
+├── data/
+│   ├── abc.csv
+│   └── def.csv
+└── holdout/
+    └── ghi.csv
+---
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path:
+    - "data/abc.csv"
+    - "data/def.csv"
+  - split: test
+    path: "holdout/ghi.csv"
+---
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: "data/*.csv"
+  - split: test
+    path: "holdout/*.csv"
+---
+from datasets import load_dataset
+
+main_data = load_dataset("my_dataset_repository", "main_data")
+additional_data = load_dataset("my_dataset_repository", "additional_data")
+---
+configs:
+- config_name: tab
+  data_files: "main_data.csv"
+  sep: "\t"
+- config_name: comma
+  data_files: "additional_data.csv"
+  sep: ","
+---
+configs:
+- config_name: tab
+  data_files: "main_data.csv"
+  sep: "\t"
+- config_name: comma
+  data_files: "additional_data.csv"
+  sep: ","
+---
+- config_name: main_data
+  data_files: "main_data.csv"
+  default: true
+my_dataset_repository/
+├── README.md
+└── data/
+    ├── train-00000-of-00003.csv
+    ├── train-00001-of-00003.csv
+    ├── train-00002-of-00003.csv
+    ├── test-00000-of-00001.csv
+    ├── random-00000-of-00003.csv
+    ├── random-00001-of-00003.csv
+    └── random-00002-of-00003.csv
+my_dataset_repository/
+├── README.md
+├── train_0.csv
+├── train_1.csv
+├── train_2.csv
+├── train_3.csv
+├── test_0.csv
+└── test_1.csv
+my_dataset_repository/
+├── README.md
+└── data/
+    ├── train/
+    │   ├── shard_0.csv
+    │   ├── shard_1.csv
+    │   ├── shard_2.csv
+    │   └── shard_3.csv
+    └── test/
+        ├── shard_0.csv
+        └── shard_1.csv