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import tempfile |
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from pathlib import Path |
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import pytest |
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import yaml |
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from dotenv import dotenv_values |
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from torch.utils.data import Subset |
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from tqdm import tqdm |
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from s3prl.downstream.emotion.expert import DownstreamExpert |
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from s3prl.problem import SuperbER |
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@pytest.mark.corpus |
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@pytest.mark.parametrize("fold_id", [0, 1, 2, 3, 4]) |
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def test_er_dataset(fold_id): |
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v3_er_folder = Path(__file__).parent.parent / "s3prl" / "downstream" / "emotion" |
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IEMOCAP = dotenv_values()["IEMOCAP"] |
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with (v3_er_folder / "config.yaml").open() as file: |
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config = yaml.load(file, Loader=yaml.FullLoader)["downstream_expert"] |
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config["datarc"]["root"] = IEMOCAP |
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config["datarc"]["meta_data"] = v3_er_folder / "meta_data" |
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config["datarc"]["test_fold"] = f"fold{fold_id + 1}" |
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with tempfile.TemporaryDirectory() as tempdir: |
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expert = DownstreamExpert(320, config, tempdir) |
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train_dataset_v3 = expert.get_dataloader("train").dataset |
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valid_dataset_v3 = expert.get_dataloader("dev").dataset |
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test_dataset_v3 = expert.get_dataloader("test").dataset |
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with tempfile.TemporaryDirectory() as tempdir: |
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default_config = SuperbER().default_config() |
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train_csv, valid_csv, test_csvs = SuperbER().prepare_data( |
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{"iemocap": IEMOCAP, "test_fold": fold_id}, tempdir, tempdir |
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) |
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encoder_path = SuperbER().build_encoder( |
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default_config["build_encoder"], |
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tempdir, |
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tempdir, |
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train_csv, |
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valid_csv, |
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test_csvs, |
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) |
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train_dataset_v4 = SuperbER().build_dataset( |
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default_config["build_dataset"], |
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tempdir, |
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tempdir, |
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"train", |
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train_csv, |
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encoder_path, |
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None, |
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) |
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valid_dataset_v4 = SuperbER().build_dataset( |
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default_config["build_dataset"], |
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tempdir, |
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tempdir, |
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"valid", |
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valid_csv, |
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encoder_path, |
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None, |
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) |
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test_dataset_v4 = SuperbER().build_dataset( |
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default_config["build_dataset"], |
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tempdir, |
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tempdir, |
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"test", |
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test_csvs[0], |
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encoder_path, |
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None, |
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) |
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def compare_dataset(v3, v4): |
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data_v3 = {} |
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for wav, label, name in tqdm(v3, desc="v3"): |
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if isinstance(v3, Subset): |
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v3 = v3.dataset |
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label_name = [k for k, v in v3.class_dict.items() if v == label][0] |
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data_v3[name] = label_name |
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data_v4 = {} |
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for batch in tqdm(v4, desc="v4"): |
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data_v4[batch["unique_name"]] = batch["label"] |
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assert sorted(data_v3.keys()) == sorted(data_v4.keys()) |
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for key in data_v3: |
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value_v3 = data_v3[key] |
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value_v4 = data_v4[key] |
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assert value_v3 == value_v4 |
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compare_dataset(train_dataset_v3, train_dataset_v4) |
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compare_dataset(valid_dataset_v3, valid_dataset_v4) |
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compare_dataset(test_dataset_v3, test_dataset_v4) |
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