def instance_segmentation():
"""Segment object instances in images."""
cli = FlashCLI(
InstanceSegmentation,
InstanceSegmentationData,
default_datamodule_builder=from_pets,
default_arguments={
"trainer.max_epochs": 3,
},
)
cli.trainer.save_checkpoint("instance_segmentation_model.pt")
def semantic_segmentation():
"""Segment objects in images."""
cli = FlashCLI(
SemanticSegmentation,
SemanticSegmentationData,
default_datamodule_builder=from_carla,
default_arguments={
"trainer.max_epochs": 3,
},
)
cli.trainer.save_checkpoint("semantic_segmentation_model.pt")
def video_classification():
"""Classify videos."""
cli = FlashCLI(
VideoClassifier,
VideoClassificationData,
default_datamodule_builder=from_kinetics,
default_arguments={
"trainer.max_epochs": 3,
},
)
cli.trainer.save_checkpoint("video_classification.pt")
def face_detection():
"""Detect faces in images."""
cli = FlashCLI(
FaceDetector,
FaceDetectionData,
default_datamodule_builder=from_fddb,
default_arguments={
"trainer.max_epochs": 3,
},
)
cli.trainer.save_checkpoint("face_detection_model.pt")
def object_detection():
"""Detect objects in images."""
cli = FlashCLI(
ObjectDetector,
ObjectDetectionData,
default_datamodule_builder=from_coco_128,
default_arguments={
"trainer.max_epochs": 3,
},
)
cli.trainer.save_checkpoint("object_detection_model.pt")
def audio_classification():
"""Classify audio spectrograms."""
cli = FlashCLI(
ImageClassifier,
AudioClassificationData,
default_datamodule_builder=from_urban8k,
default_arguments={
"trainer.max_epochs": 3,
},
)
cli.trainer.save_checkpoint("audio_classification_model.pt")
def summarization():
"""Summarize text."""
cli = FlashCLI(
SummarizationTask,
SummarizationData,
default_datamodule_builder=from_xsum,
default_arguments={
"trainer.max_epochs": 3,
"model.backbone": "sshleifer/distilbart-xsum-1-1",
},
)
cli.trainer.save_checkpoint("summarization_model_xsum.pt")
def pointcloud_detection():
"""Detect objects in point clouds."""
cli = FlashCLI(
PointCloudObjectDetector,
PointCloudObjectDetectorData,
default_datamodule_builder=from_kitti,
default_arguments={
"trainer.max_epochs": 3,
},
finetune=False,
)
cli.trainer.save_checkpoint("pointcloud_detection_model.pt")
def speech_recognition():
"""Speech recognition."""
cli = FlashCLI(
SpeechRecognition,
SpeechRecognitionData,
default_datamodule_builder=from_timit,
default_arguments={
"trainer.max_epochs": 3,
},
finetune=False,
)
cli.trainer.save_checkpoint("speech_recognition_model.pt")
def translation():
"""Translate text."""
cli = FlashCLI(
TranslationTask,
TranslationData,
default_datamodule_builder=from_wmt_en_ro,
default_arguments={
"trainer.max_epochs": 3,
"model.backbone": "Helsinki-NLP/opus-mt-en-ro",
},
)
cli.trainer.save_checkpoint("translation_model_en_ro.pt")
def keypoint_detection():
"""Detect keypoints in images."""
cli = FlashCLI(
KeypointDetector,
KeypointDetectionData,
default_datamodule_builder=from_biwi,
default_arguments={
"model.num_keypoints": 1,
"trainer.max_epochs": 3,
},
)
cli.trainer.save_checkpoint("keypoint_detection_model.pt")
def question_answering():
"""Extractive Question Answering."""
cli = FlashCLI(
QuestionAnsweringTask,
QuestionAnsweringData,
default_datamodule_builder=from_squad,
default_arguments={
"trainer.max_epochs": 3,
"model.backbone": "distilbert-base-uncased",
},
)
cli.trainer.save_checkpoint("question_answering_model.pt")
def text_classification():
"""Classify text."""
cli = FlashCLI(
TextClassifier,
TextClassificationData,
default_datamodule_builder=from_imdb,
additional_datamodule_builders=[from_toxic],
default_arguments={
"trainer.max_epochs": 3,
},
datamodule_attributes={"num_classes", "labels", "multi_label"},
)
cli.trainer.save_checkpoint("text_classification_model.pt")
def graph_classification():
"""Classify graphs."""
cli = FlashCLI(
GraphClassifier,
GraphClassificationData,
default_datamodule_builder=from_tu_dataset,
default_arguments={
"trainer.max_epochs": 3,
},
finetune=False,
datamodule_attributes={"num_classes", "num_features"},
)
cli.trainer.save_checkpoint("graph_classification.pt")
def tabular_classification():
"""Classify tabular data."""
cli = FlashCLI(
TabularClassifier,
TabularClassificationData,
default_datamodule_builder=from_titanic,
default_arguments={
"trainer.max_epochs": 3,
},
finetune=False,
datamodule_attributes={"num_features", "num_classes", "embedding_sizes"},
)
cli.trainer.save_checkpoint("tabular_classification_model.pt")
def pointcloud_segmentation():
"""Segment objects in point clouds."""
cli = FlashCLI(
PointCloudSegmentation,
PointCloudSegmentationData,
default_datamodule_builder=from_kitti,
default_arguments={
"trainer.max_epochs": 3,
"model.backbone": "randlanet_semantic_kitti",
},
finetune=False,
)
cli.trainer.save_checkpoint("pointcloud_segmentation_model.pt")
def image_classification():
"""Classify images."""
cli = FlashCLI(
ImageClassifier,
ImageClassificationData,
default_datamodule_builder=from_hymenoptera,
additional_datamodule_builders=[from_movie_posters],
default_arguments={
"trainer.max_epochs": 3,
},
datamodule_attributes={"num_classes", "labels", "multi_label"},
)
cli.trainer.save_checkpoint("image_classification_model.pt")
def style_transfer():
"""Image style transfer."""
cli = FlashCLI(
StyleTransfer,
StyleTransferData,
default_datamodule_builder=from_coco_128,
default_arguments={
"trainer.max_epochs":
3,
"model.style_image":
os.path.join(flash.ASSETS_ROOT, "starry_night.jpg"),
},
finetune=False,
)
cli.trainer.save_checkpoint("style_transfer_model.pt")
def tabular_forecasting():
"""Timeseries forecasting."""
cli = FlashCLI(
TabularForecaster,
TabularForecastingData,
default_datamodule_builder=from_synthetic_ar_data,
default_arguments={
"trainer.max_epochs": 1,
"model.backbone": "n_beats",
"model.backbone_kwargs": {
"widths": [32, 512],
"backcast_loss_ratio": 0.1
},
},
finetune=False,
datamodule_attributes={"parameters"},
)
cli.trainer.save_checkpoint("tabular_forecasting_model.pt")
def tabular_regression():
"""Classify tabular data."""
cli = FlashCLI(
TabularRegressor,
TabularRegressionData,
default_datamodule_builder=from_titanic,
default_arguments={
"trainer.max_epochs": 3,
"model.backbone": "tabnet",
},
finetune=False,
datamodule_attributes={
"embedding_sizes",
"categorical_fields",
"num_features",
"cat_dims",
},
)
cli.trainer.save_checkpoint("tabular_regression_model.pt")
