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Add dataset reporting example (#1180)
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pollfly
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examples/datasets/urbansounds_dataset_preprocessing.py
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163
examples/datasets/urbansounds_dataset_preprocessing.py
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import os.path
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from pathlib import Path
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import matplotlib as mpl
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import numpy as np
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from tqdm import tqdm
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import torchaudio
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import torch
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from clearml import Task, Dataset
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task = Task.init(project_name="examples/Urbansounds", task_name="preprocessing")
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# Let's preprocess the data and create a new ClearML dataset from it, so we can track it around
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# The cool thing is, we can easily debug, by using, you guessed it: debug samples! We can log both
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# the original sound and its processed mel spectrogram as debug samples, so we can manually check
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# if everything went as planned.
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class PreProcessor:
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def __init__(self):
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self.configuration = {"number_of_mel_filters": 64, "resample_freq": 22050}
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task.connect(self.configuration)
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def preprocess_sample(self, sample, original_sample_freq):
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if self.configuration["resample_freq"] > 0:
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resample_transform = torchaudio.transforms.Resample(
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orig_freq=original_sample_freq,
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new_freq=self.configuration["resample_freq"],
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)
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sample = resample_transform(sample)
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# This will convert audio files with two channels into one
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sample = torch.mean(sample, dim=0, keepdim=True)
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# Convert audio to log-scale Mel spectrogram
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melspectrogram_transform = torchaudio.transforms.MelSpectrogram(
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sample_rate=self.configuration["resample_freq"],
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n_mels=self.configuration["number_of_mel_filters"],
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)
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melspectrogram = melspectrogram_transform(sample)
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melspectogram_db = torchaudio.transforms.AmplitudeToDB()(melspectrogram)
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# Make sure all spectrograms are the same size
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fixed_length = 3 * (self.configuration["resample_freq"] // 200)
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if melspectogram_db.shape[2] < fixed_length:
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melspectogram_db = torch.nn.functional.pad(
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melspectogram_db, (0, fixed_length - melspectogram_db.shape[2])
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)
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else:
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melspectogram_db = melspectogram_db[:, :, :fixed_length]
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return melspectogram_db
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class DataSetBuilder:
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def __init__(self):
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self.configuration = {"dataset_path": "dataset"}
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task.connect(self.configuration)
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self.original_dataset = Dataset.get(
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dataset_project="examples/Urbansounds",
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dataset_name="UrbanSounds example",
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dataset_tags=["raw"],
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alias="Raw Dataset",
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)
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# This will return the pandas dataframe we added in the previous task
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self.metadata = (
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Task.get_task(task_id=self.original_dataset._task.id)
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.artifacts["metadata"]
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.get()
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)
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# This will download the data and return a local path to the data
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self.original_dataset_path = Path(
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self.original_dataset.get_mutable_local_copy(
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self.configuration["dataset_path"], overwrite=True
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)
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)
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# Prepare a preprocessor that will handle each sample one by one
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self.preprocessor = PreProcessor()
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# Get ready for the new one
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self.preprocessed_dataset = None
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def log_dataset_statistics(self):
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histogram_data = self.metadata["label"].value_counts()
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self.preprocessed_dataset.get_logger().report_table(
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title="Raw Dataset Metadata",
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series="Raw Dataset Metadata",
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table_plot=self.metadata,
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)
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self.preprocessed_dataset.get_logger().report_histogram(
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title="Class distribution",
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series="Class distribution",
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values=histogram_data,
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iteration=0,
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xlabels=histogram_data.index.tolist(),
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yaxis="Amount of samples",
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)
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def build_dataset(self):
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# Let's create a new dataset that is a child of the original one
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# We'll add the preprocessed samples to the original dataset, leading to a new version
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# Providing the parent dataset allows us to keep a clear lineage of our data
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self.preprocessed_dataset = Dataset.create(
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dataset_name="UrbanSounds example",
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dataset_project="examples/Urbansounds",
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dataset_tags=["preprocessed"],
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parent_datasets=[self.original_dataset.id],
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)
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# loop through the metadata entries and preprocess each sample, then add some of them as debug samples to
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# manually double check in the UI that everything has worked (you can watch the spectrogram and listen to the
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# audio side by side in the debug sample UI)
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for i, (_, data) in tqdm(enumerate(self.metadata.iterrows())):
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_, audio_file_path, label = data.tolist()
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sample, sample_freq = torchaudio.load(
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self.original_dataset_path / audio_file_path, normalize=True
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)
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spectrogram = self.preprocessor.preprocess_sample(sample, sample_freq)
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# Get only the filename and replace the extension, we're saving an image here
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new_file_name = os.path.basename(audio_file_path).replace(".wav", ".npy")
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# Get the correct folder, basically the original dataset folder + the new filename
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spectrogram_path = (
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self.original_dataset_path
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/ os.path.dirname(audio_file_path)
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/ new_file_name
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)
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# Save the numpy array to disk
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np.save(spectrogram_path, spectrogram)
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# Log every 10th sample as a debug sample to the UI, so we can manually check it
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if i % 10 == 0:
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# Convert the numpy array to a viewable JPEG
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rgb_image = mpl.colormaps["viridis"](
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spectrogram[0, :, :].detach().numpy() * 255
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)[:, :, :3]
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title = os.path.splitext(os.path.basename(audio_file_path))[0]
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# Report the image and the original sound, so they can be viewed side by side
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self.preprocessed_dataset.get_logger().report_image(
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title=title, series="spectrogram", image=rgb_image
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)
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self.preprocessed_dataset.get_logger().report_media(
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title=title,
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series="original_audio",
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local_path=self.original_dataset_path / audio_file_path,
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)
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# The original data path will now also have the spectrograms in its filetree.
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# So that's why we add it here to fill up the new dataset with.
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self.preprocessed_dataset.add_files(self.original_dataset_path)
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# Again add some visualizations to the task
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self.log_dataset_statistics()
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# We still want the metadata
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self.preprocessed_dataset._task.upload_artifact(
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name="metadata", artifact_object=self.metadata
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)
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self.preprocessed_dataset.finalize(auto_upload=True)
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if __name__ == "__main__":
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datasetbuilder = DataSetBuilder()
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datasetbuilder.build_dataset()
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96
examples/datasets/urbansounds_get_data.py
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96
examples/datasets/urbansounds_get_data.py
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import pandas as pd
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from pathlib import Path
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from clearml import Task, Dataset, StorageManager
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task = Task.init(project_name="examples/Urbansounds", task_name="download data")
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configuration = {
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"selected_classes": [
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"air_conditioner",
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"car_horn",
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"children_playing",
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"dog_bark",
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"drilling",
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"engine_idling",
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"gun_shot",
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"jackhammer",
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"siren",
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"street_music",
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]
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}
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task.connect(configuration)
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def get_urbansound8k():
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# Download UrbanSound8K dataset (https://urbansounddataset.weebly.com/urbansound8k.html)
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# For simplicity we will use here a subset of that dataset using clearml StorageManager
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path_to_urbansound8k = StorageManager.get_local_copy(
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"https://allegro-datasets.s3.amazonaws.com/clearml/UrbanSound8K.zip",
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extract_archive=True,
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)
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path_to_urbansound8k_csv = (
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Path(path_to_urbansound8k) / "UrbanSound8K" / "metadata" / "UrbanSound8K.csv"
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)
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path_to_urbansound8k_audio = Path(path_to_urbansound8k) / "UrbanSound8K" / "audio"
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return path_to_urbansound8k_csv, path_to_urbansound8k_audio
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def log_dataset_statistics(dataset, metadata):
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histogram_data = metadata["class"].value_counts()
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dataset.get_logger().report_table(
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title="Raw Dataset Metadata", series="Raw Dataset Metadata", table_plot=metadata
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)
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dataset.get_logger().report_histogram(
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title="Class distribution",
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series="Class distribution",
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values=histogram_data,
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iteration=0,
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xlabels=histogram_data.index.tolist(),
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yaxis="Amount of samples",
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)
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def build_clearml_dataset():
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# Get a local copy of both the data and the labels
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path_to_urbansound8k_csv, path_to_urbansound8k_audio = get_urbansound8k()
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urbansound8k_metadata = pd.read_csv(path_to_urbansound8k_csv)
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# Subset the data to only include the classes we want
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urbansound8k_metadata = urbansound8k_metadata[
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urbansound8k_metadata["class"].isin(configuration["selected_classes"])
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]
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# Create a pandas dataframe containing labels and other info we need later (fold is for train test split)
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metadata = pd.DataFrame(
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{
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"fold": urbansound8k_metadata.loc[:, "fold"],
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"filepath": (
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"fold"
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+ urbansound8k_metadata.loc[:, "fold"].astype(str)
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+ "/"
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+ urbansound8k_metadata.loc[:, "slice_file_name"].astype(str)
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),
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"label": urbansound8k_metadata.loc[:, "classID"],
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}
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)
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# Now create a clearml dataset to start versioning our changes and make it much easier to get the right data
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# in other tasks as well as on different machines
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dataset = Dataset.create(
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dataset_name="UrbanSounds example",
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dataset_project="examples/Urbansounds",
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dataset_tags=["raw"],
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)
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# Add the local files we downloaded earlier
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dataset.add_files(path_to_urbansound8k_audio)
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# Add the metadata in pandas format, we can now see it in the webUI and have it be easily accessible
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dataset._task.upload_artifact(name="metadata", artifact_object=metadata)
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# Let's add some cool graphs as statistics in the plots section!
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log_dataset_statistics(dataset, urbansound8k_metadata)
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# Finalize and upload the data and labels of the dataset
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dataset.finalize(auto_upload=True)
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if __name__ == "__main__":
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build_clearml_dataset()
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