def test_label_studio_predictions_visualization():
"""Test creation of LabelStudioImageClassificationInput and Datamodule from images."""
download_data(
"https://label-studio-testdata.s3.us-east-2.amazonaws.com/lightning-flash/data.zip"
)
datamodule = ImageClassificationData.from_labelstudio(
train_export_json="data/project.json",
train_data_folder="data/upload/",
test_export_json="data/project.json",
test_data_folder="data/upload/",
val_split=0.5,
batch_size=1,
)
assert datamodule
app = launch_app(datamodule)
predictions = [[0, 1], [1, 0]]
vis_predictions = app.show_predictions(predictions)
assert len(vis_predictions) == 4
assert vis_predictions[0]["result"][0]["id"] != vis_predictions[3][
"result"][0]["id"]
assert vis_predictions[1]["result"][0]["id"] != vis_predictions[2][
"result"][0]["id"]
tasks_predictions = app.show_tasks(predictions)
assert len(tasks_predictions) == 4
tasks_predictions_json = app.show_tasks(predictions,
export_json="data/project.json")
assert tasks_predictions_json
def test_input_labelstudio_text():
"""Test creation of LabelStudioTextClassificationInput and Datamodule from text."""
download_data(
"https://label-studio-testdata.s3.us-east-2.amazonaws.com/lightning-flash/text_data.zip",
"./data/")
data = {
"data_folder": "data/upload/",
"export_json": "data/project.json",
"multi_label": False,
}
train_data, test_data = LabelStudioInput._split_train_test_data(data)
train_data, val_data = LabelStudioInput._split_train_val_data(train_data,
split=0.2)
train = LabelStudioTextClassificationInput(RunningStage.TRAINING,
train_data)
val = LabelStudioTextClassificationInput(RunningStage.VALIDATING,
val_data,
parameters=train.parameters)
test = LabelStudioTextClassificationInput(RunningStage.TESTING,
test_data,
parameters=train.parameters)
train_sample = train[0]
val_sample = val[0]
assert train_sample
assert val_sample
assert len(test) == 0
def test_pointcloud_object_detection_data(tmpdir):
seed_everything(52)
download_data("https://pl-flash-data.s3.amazonaws.com/KITTI_micro.zip",
tmpdir)
dm = PointCloudObjectDetectorData.from_folders(
train_folder=join(tmpdir, "KITTI_Micro", "Kitti", "train"))
class MockModel(PointCloudObjectDetector):
def training_step(self, batch, batch_idx: int):
assert isinstance(batch, ObjectDetectBatchCollator)
assert len(batch.point) == 2
assert batch.point[0][1].shape == torch.Size([4])
assert len(batch.bboxes) > 1
assert batch.attr[0]["name"] in ("000000.bin", "000001.bin")
assert batch.attr[1]["name"] in ("000000.bin", "000001.bin")
num_classes = 19
model = MockModel(backbone="pointpillars_kitti", num_classes=num_classes)
trainer = Trainer(max_epochs=1, limit_train_batches=1, limit_val_batches=0)
trainer.fit(model, dm)
predict_path = join(tmpdir, "KITTI_Micro", "Kitti", "predict")
model.eval()
predictions = model.predict([join(predict_path, "scans/000000.bin")])
assert predictions[0][DefaultDataKeys.INPUT].shape[1] == 4
assert len(predictions[0][DefaultDataKeys.PREDS]) == 158
def test_datamodule_labelstudio_text():
"""Test creation of LabelStudioTextClassificationInput and Datamodule from text."""
download_data(
"https://label-studio-testdata.s3.us-east-2.amazonaws.com/lightning-flash/text_data.zip",
"./data/")
datamodule = TextClassificationData.from_labelstudio(
train_export_json="data/project.json",
data_folder="data/upload/",
batch_size=4,
)
assert datamodule
def from_urban8k(
batch_size: int = 4,
**data_module_kwargs,
) -> AudioClassificationData:
"""Downloads and loads the Urban 8k sounds images data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/urban8k_images.zip",
"./data")
return AudioClassificationData.from_folders(
train_folder="data/urban8k_images/train",
val_folder="data/urban8k_images/val",
batch_size=batch_size,
**data_module_kwargs,
)
def from_kitti(
batch_size: int = 4,
**data_module_kwargs,
) -> PointCloudObjectDetectorData:
"""Downloads and loads the KITTI data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/KITTI_tiny.zip",
"data/")
return PointCloudObjectDetectorData.from_folders(
train_folder="data/KITTI_Tiny/Kitti/train",
val_folder="data/KITTI_Tiny/Kitti/val",
batch_size=batch_size,
**data_module_kwargs,
)
def from_kitti(
batch_size: int = 4,
**data_module_kwargs,
) -> PointCloudSegmentationData:
"""Downloads and loads the semantic KITTI data set."""
download_data(
"https://pl-flash-data.s3.amazonaws.com/SemanticKittiTiny.zip",
"data/")
return PointCloudSegmentationData.from_folders(
train_folder="data/SemanticKittiTiny/train",
val_folder="data/SemanticKittiTiny/val",
batch_size=batch_size,
**data_module_kwargs,
)
def test_datamodule_labelstudio_video():
"""Test creation of Datamodule from video."""
download_data(
"https://label-studio-testdata.s3.us-east-2.amazonaws.com/lightning-flash/video_data.zip"
)
datamodule = VideoClassificationData.from_labelstudio(
export_json="data/project.json",
data_folder="data/upload/",
clip_sampler="uniform",
clip_duration=1,
decode_audio=False,
batch_size=1,
)
assert datamodule
def from_squad(
batch_size: int = 4,
**data_module_kwargs,
) -> QuestionAnsweringData:
"""Downloads and loads a tiny subset of the squad V2 data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/squad_tiny.zip",
"./data/")
return QuestionAnsweringData.from_squad_v2(
train_file="./data/squad_tiny/train.json",
val_file="./data/squad_tiny/val.json",
batch_size=batch_size,
**data_module_kwargs,
)
def from_imdb(
batch_size: int = 4,
**data_module_kwargs,
) -> TextClassificationData:
"""Downloads and loads the IMDB sentiment classification data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/imdb.zip", "./data/")
return TextClassificationData.from_csv(
"review",
"sentiment",
train_file="data/imdb/train.csv",
val_file="data/imdb/valid.csv",
batch_size=batch_size,
**data_module_kwargs,
)
def from_hymenoptera(
batch_size: int = 4,
num_workers: int = 0,
**preprocess_kwargs,
) -> ImageClassificationData:
"""Downloads and loads the Hymenoptera (Ants, Bees) data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", "./data")
return ImageClassificationData.from_folders(
train_folder="data/hymenoptera_data/train/",
val_folder="data/hymenoptera_data/val/",
batch_size=batch_size,
num_workers=num_workers,
**preprocess_kwargs,
)
def test_datamodule_labelstudio_image():
"""Test creation of LabelStudioImageClassificationInput and Datamodule from images."""
download_data(
"https://label-studio-testdata.s3.us-east-2.amazonaws.com/lightning-flash/data.zip"
)
datamodule = ImageClassificationData.from_labelstudio(
train_export_json="data/project.json",
train_data_folder="data/upload/",
test_export_json="data/project.json",
test_data_folder="data/upload/",
val_split=0.5,
batch_size=1,
)
assert datamodule
def from_toxic(
val_split: float = 0.1,
batch_size: int = 4,
**data_module_kwargs,
) -> TextClassificationData:
"""Downloads and loads the Jigsaw toxic comments data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/jigsaw_toxic_comments.zip", "./data")
return TextClassificationData.from_csv(
"comment_text",
["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"],
train_file="data/jigsaw_toxic_comments/train.csv",
val_split=val_split,
batch_size=batch_size,
**data_module_kwargs,
)
def from_coco_128(
batch_size: int = 4,
num_workers: int = 0,
**preprocess_kwargs,
) -> StyleTransferData:
"""Downloads and loads the COCO 128 data set."""
download_data(
"https://github.com/zhiqwang/yolov5-rt-stack/releases/download/v0.3.0/coco128.zip",
"data/")
return StyleTransferData.from_folders(
train_folder="data/coco128/images/train2017/",
batch_size=batch_size,
num_workers=num_workers,
**preprocess_kwargs,
)
def titanic_data_download(path: str, predict_size: float = 0.1) -> None:
if not os.path.exists(path):
os.makedirs(path)
path_data = os.path.join(path, "titanic.csv")
download_data("https://pl-flash-data.s3.amazonaws.com/titanic.csv",
path_data)
if set(os.listdir(path)) != {"predict.csv", "titanic.csv"}:
assert 0 < predict_size < 1
df = pd.read_csv(path_data)
df_train, df_predict = train_test_split(df, test_size=predict_size)
df_train.to_csv(path_data)
df_predict = df_predict.drop(columns=["Survived"])
df_predict.to_csv(os.path.join(path, "predict.csv"))
def from_xsum(
batch_size: int = 4,
num_workers: int = 0,
**input_transform_kwargs,
) -> SummarizationData:
"""Downloads and loads the XSum data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/xsum.zip", "./data/")
return SummarizationData.from_csv(
"input",
"target",
train_file="data/xsum/train.csv",
val_file="data/xsum/valid.csv",
batch_size=batch_size,
num_workers=num_workers,
**input_transform_kwargs,
)
def from_movie_posters(
batch_size: int = 4,
num_workers: int = 0,
**preprocess_kwargs,
) -> ImageClassificationData:
"""Downloads and loads the movie posters genre classification data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/movie_posters.zip", "./data")
return ImageClassificationData.from_csv(
"Id",
["Action", "Romance", "Crime", "Thriller", "Adventure"],
train_file="data/movie_posters/train/metadata.csv",
val_file="data/movie_posters/val/metadata.csv",
batch_size=batch_size,
num_workers=num_workers,
**preprocess_kwargs,
)
def from_coco_128(
val_split: float = 0.1,
transform_kwargs: Optional[Dict[str, Any]] = None,
batch_size: int = 1,
**data_module_kwargs,
) -> ObjectDetectionData:
"""Downloads and loads the COCO 128 data set."""
download_data("https://github.com/zhiqwang/yolov5-rt-stack/releases/download/v0.3.0/coco128.zip", "data/")
return ObjectDetectionData.from_coco(
train_folder="data/coco128/images/train2017/",
train_ann_file="data/coco128/annotations/instances_train2017.json",
val_split=val_split,
transform_kwargs=dict(image_size=(128, 128)) if transform_kwargs is None else transform_kwargs,
batch_size=batch_size,
**data_module_kwargs,
)
def from_titanic(
batch_size: int = 4,
num_workers: int = 0,
**preprocess_kwargs,
) -> TabularClassificationData:
"""Downloads and loads the Titanic data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/titanic.zip", "./data")
return TabularClassificationData.from_csv(
["Sex", "Age", "SibSp", "Parch", "Ticket", "Cabin", "Embarked"],
"Fare",
target_fields="Survived",
train_file="data/titanic/titanic.csv",
val_split=0.1,
batch_size=batch_size,
num_workers=num_workers,
**preprocess_kwargs,
)
def from_wmt_en_ro(
batch_size: int = 4,
num_workers: int = 0,
**input_transform_kwargs,
) -> TranslationData:
"""Downloads and loads the WMT EN RO data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/wmt_en_ro.zip",
"./data")
return TranslationData.from_csv(
"input",
"target",
train_file="data/wmt_en_ro/train.csv",
val_file="data/wmt_en_ro/valid.csv",
batch_size=batch_size,
num_workers=num_workers,
**input_transform_kwargs,
)
def from_titanic(
val_split: float = 0.1,
batch_size: int = 4,
**data_module_kwargs,
) -> TabularRegressionData:
"""Downloads and loads the Titanic data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/titanic.zip",
"./data")
return TabularRegressionData.from_csv(
["Sex", "Age", "SibSp", "Parch", "Ticket", "Cabin", "Embarked"],
None,
target_field="Fare",
train_file="data/titanic/titanic.csv",
val_split=val_split,
batch_size=batch_size,
**data_module_kwargs,
)
def from_kinetics(
clip_sampler: str = "uniform",
clip_duration: int = 1,
decode_audio: bool = False,
batch_size=1,
**data_module_kwargs,
) -> VideoClassificationData:
"""Downloads and loads the Kinetics data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/kinetics.zip", "./data")
return VideoClassificationData.from_folders(
train_folder=os.path.join(os.getcwd(), "data/kinetics/train"),
val_folder=os.path.join(os.getcwd(), "data/kinetics/val"),
clip_sampler=clip_sampler,
clip_duration=clip_duration,
decode_audio=decode_audio,
batch_size=batch_size,
**data_module_kwargs,
)
def from_xsum(
backbone: str = "sshleifer/distilbart-xsum-1-1",
batch_size: int = 4,
num_workers: int = 0,
**preprocess_kwargs,
) -> SummarizationData:
"""Downloads and loads the XSum data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/xsum.zip", "./data/")
return SummarizationData.from_csv(
"input",
"target",
train_file="data/xsum/train.csv",
val_file="data/xsum/valid.csv",
backbone=backbone,
batch_size=batch_size,
num_workers=num_workers,
**preprocess_kwargs,
)
def from_imdb(
backbone: str = "prajjwal1/bert-medium",
batch_size: int = 4,
num_workers: int = 0,
**preprocess_kwargs,
) -> TextClassificationData:
"""Downloads and loads the IMDB sentiment classification data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/imdb.zip", "./data/")
return TextClassificationData.from_csv(
"review",
"sentiment",
train_file="data/imdb/train.csv",
val_file="data/imdb/valid.csv",
backbone=backbone,
batch_size=batch_size,
num_workers=num_workers,
**preprocess_kwargs,
)
def from_coco_128(
val_split: float = 0.1,
batch_size: int = 4,
num_workers: int = 0,
**preprocess_kwargs,
) -> ObjectDetectionData:
"""Downloads and loads the COCO 128 data set."""
download_data(
"https://github.com/zhiqwang/yolov5-rt-stack/releases/download/v0.3.0/coco128.zip",
"data/")
return ObjectDetectionData.from_coco(
train_folder="data/coco128/images/train2017/",
train_ann_file="data/coco128/annotations/instances_train2017.json",
val_split=val_split,
batch_size=batch_size,
num_workers=num_workers,
**preprocess_kwargs,
)
def from_toxic(
backbone: str = "unitary/toxic-bert",
val_split: float = 0.1,
batch_size: int = 4,
num_workers: int = 0,
**preprocess_kwargs,
) -> TextClassificationData:
"""Downloads and loads the Jigsaw toxic comments data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/jigsaw_toxic_comments.zip", "./data")
return TextClassificationData.from_csv(
"comment_text",
["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"],
train_file="data/jigsaw_toxic_comments/train.csv",
backbone=backbone,
val_split=val_split,
batch_size=batch_size,
num_workers=num_workers,
**preprocess_kwargs,
)
def from_carla(
num_classes: int = 21,
val_split: float = 0.1,
batch_size: int = 4,
**data_module_kwargs,
) -> SemanticSegmentationData:
"""Downloads and loads the CARLA capture data set."""
download_data(
"https://github.com/ongchinkiat/LyftPerceptionChallenge/releases/download/v0.1/carla-capture-20180513A.zip",
"./data",
)
return SemanticSegmentationData.from_folders(
train_folder="data/CameraRGB",
train_target_folder="data/CameraSeg",
val_split=val_split,
batch_size=batch_size,
num_classes=num_classes,
**data_module_kwargs,
)
def from_timit(
val_split: float = 0.1,
batch_size: int = 4,
num_workers: int = 0,
**input_transform_kwargs,
) -> SpeechRecognitionData:
"""Downloads and loads the timit data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/timit_data.zip",
"./data")
return SpeechRecognitionData.from_json(
"file",
"text",
train_file="data/timit/train.json",
test_file="data/timit/test.json",
val_split=val_split,
batch_size=batch_size,
num_workers=num_workers,
**input_transform_kwargs,
)
def from_wmt_en_ro(
backbone: str = "Helsinki-NLP/opus-mt-en-ro",
batch_size: int = 4,
num_workers: int = 0,
**preprocess_kwargs,
) -> TranslationData:
"""Downloads and loads the WMT EN RO data set."""
download_data("https://pl-flash-data.s3.amazonaws.com/wmt_en_ro.zip",
"./data")
return TranslationData.from_csv(
"input",
"target",
train_file="data/wmt_en_ro/train.csv",
val_file="data/wmt_en_ro/valid.csv",
backbone=backbone,
batch_size=batch_size,
num_workers=num_workers,
**preprocess_kwargs,
)
def test_pointcloud_segmentation_data(tmpdir):
seed_everything(52)
download_data(
"https://pl-flash-data.s3.amazonaws.com/SemanticKittiMicro.zip",
tmpdir)
datamodule = PointCloudSegmentationData.from_folders(
train_folder=join(tmpdir, "SemanticKittiMicro", "train"),
predict_folder=join(tmpdir, "SemanticKittiMicro", "predict"),
batch_size=4,
)
class MockModel(PointCloudSegmentation):
def training_step(self, batch, batch_idx: int):
assert batch[DataKeys.INPUT]["xyz"][0].shape == torch.Size(
[2, 45056, 3])
assert batch[DataKeys.INPUT]["xyz"][1].shape == torch.Size(
[2, 11264, 3])
assert batch[DataKeys.INPUT]["xyz"][2].shape == torch.Size(
[2, 2816, 3])
assert batch[DataKeys.INPUT]["xyz"][3].shape == torch.Size(
[2, 704, 3])
assert batch[DataKeys.INPUT]["labels"].shape == torch.Size(
[2, 45056])
assert batch[DataKeys.INPUT]["labels"].max() == 19
assert batch[DataKeys.INPUT]["labels"].min() == 0
assert batch[DataKeys.METADATA][0]["name"] in ("00_000000",
"00_000001")
assert batch[DataKeys.METADATA][1]["name"] in ("00_000000",
"00_000001")
num_classes = 19
model = MockModel(backbone="randlanet", num_classes=num_classes)
trainer = Trainer(max_epochs=1, limit_train_batches=1, limit_val_batches=0)
trainer.fit(model, datamodule=datamodule)
predictions = trainer.predict(model, datamodule=datamodule)[0]
assert predictions[0][DataKeys.INPUT].shape == torch.Size([45056, 3])
assert predictions[0][DataKeys.PREDS].shape == torch.Size([45056, 19])
assert predictions[0][DataKeys.TARGET].shape == torch.Size([45056])
