WIP: Port to dask pipelines

bob/pad/face/config/vanilla_pad/qm_svm.py

+# Legacy imports
+from bob.bio.face.helpers import face_crop_solver
+from bob.bio.video import VideoLikeContainer
+from bob.bio.video.transformer import Wrapper as TransformerWrapper
+from bob.pad.face.extractor import ImageQualityMeasure
+
+# new imports
+from sklearn.svm import SVC
+from sklearn.model_selection import GridSearchCV
+from sklearn.pipeline import make_pipeline
+from bob.pad.base.pipelines.vanilla_pad import FrameContainersToFrames
+import bob.pipelines as mario
+
+database = globals().get("database")
+if database is not None:
+    annotation_type = database.annotation_type
+    fixed_positions = database.fixed_positions
+else:
+    annotation_type = None
+    fixed_positions = None
+
+# Preprocessor #
+cropper = face_crop_solver(cropped_image_size=64, cropped_positions=annotation_type)
+preprocessor = TransformerWrapper(cropper)
+preprocessor = mario.wrap(
+    ["sample", "checkpoint"],
+    preprocessor,
+    transform_extra_arguments=(("annotations", "annotations"),),
+    features_dir="temp/faces-64",
+    save_func=VideoLikeContainer.save,
+    load_func=VideoLikeContainer.load,
+)
+
+# Legacy extractor #
+extractor = TransformerWrapper(ImageQualityMeasure(galbally=True, msu=True, dtype=None))
+extractor = mario.wrap(
+    ["sample", "checkpoint"],
+    extractor,
+    features_dir="temp/iqm-features",
+    save_func=VideoLikeContainer.save,
+    load_func=VideoLikeContainer.load,
+)
+
+# new stuff #
+frame_cont_to_array = FrameContainersToFrames()
+
+param_grid = [
+    {"C": [1, 10, 100, 1000], "kernel": ["linear"]},
+    {"C": [1, 10, 100, 1000], "gamma": [0.001, 0.0001], "kernel": ["rbf"]},
+]
+classifier = GridSearchCV(SVC(), param_grid=param_grid, cv=3)
+classifier = mario.wrap(
+    ["sample", "checkpoint"],
+    classifier,
+    fit_extra_arguments=[("y", "is_bonafide")],
+    model_path="temp/svm.pkl",
+)
+
+
+# pipeline #
+# stateless_pipeline = mario.transformers.StatelessPipeline(
+#     [
+#         ("preprocessor", preprocessor),
+#         ("extractor", extractor),
+#         ("frame_cont_to_array", frame_cont_to_array),
+#     ]
+# )
+frames_classifier = make_pipeline(frame_cont_to_array, classifier)
+pipeline = make_pipeline(preprocessor, extractor, frames_classifier)