h5py instead of bob.io.base H5File

bob/bio/gmm/bioalgorithm/GMM.py

@@ -17,11 +17,11 @@ from typing import Callable
 import dask.array as da
 import numpy as np
 import dask
+from h5py import File as HDF5File
 
 from sklearn.base import BaseEstimator
 
 import bob.core
-import bob.io.base
 
 from bob.bio.base.pipelines.vanilla_biometrics.abstract_classes import BioAlgorithm
 from bob.learn.em.mixture import GMMMachine
@@ -150,13 +150,13 @@ class GMM(BioAlgorithm, BaseEstimator):
 
         hdf5 = (
             ubm_file
-            if isinstance(ubm_file, bob.io.base.HDF5File)
-            else bob.io.base.HDF5File(ubm_file, "w")
+            if isinstance(ubm_file, HDF5File)
+            else HDF5File(ubm_file, "w")
         )
         self.ubm.save(hdf5)
 
     def load_ubm(self, ubm_file):
-        hdf5file = bob.io.base.HDF5File(ubm_file)
+        hdf5file = HDF5File(ubm_file)
         logger.debug("Loading model from file '%s'", ubm_file)
         # read UBM
         self.ubm = GMMMachine.from_hdf5(hdf5file)
@@ -177,7 +177,7 @@ class GMM(BioAlgorithm, BaseEstimator):
 
     def read_feature(self, feature_file):
         """Read the type of features that we require, namely GMM_Stats"""
-        return GMMStats.from_hdf5(bob.io.base.HDF5File(feature_file))
+        return GMMStats.from_hdf5(HDF5File(feature_file))
 
     def write_feature(self, feature, feature_file):
         """Write the features (GMM_Stats)"""
@@ -213,7 +213,7 @@ class GMM(BioAlgorithm, BaseEstimator):
 
     def read_model(self, model_file):
         """Reads the model, which is a GMM machine"""
-        return GMMMachine.from_hdf5(bob.io.base.HDF5File(model_file), ubm=self.ubm)
+        return GMMMachine.from_hdf5(HDF5File(model_file), ubm=self.ubm)
 
     def write_model(self, model, model_file):
         """Write the features (GMM_Stats)"""
@@ -232,7 +232,6 @@ class GMM(BioAlgorithm, BaseEstimator):
             The probe data to compare to the model.
         """
 
-        # import ipdb; ipdb.set_trace()
         assert isinstance(biometric_reference, GMMMachine)
         stats = self.project(data)
         return self.scoring_function(
@@ -287,9 +286,6 @@ class GMM(BioAlgorithm, BaseEstimator):
 
     def fit(self, X, y=None, **kwargs):
         """Trains the UBM."""
-        # TODO: Delayed to dask array
-        if not all(isinstance(x, da.Array) for x in X):
-            raise ValueError(f"This function only supports dask arrays, {type(X[0])}")
 
         # Stack all the samples in a 2D array of features
         array = da.vstack(X)
@@ -332,7 +328,6 @@ def delayed_to_da(delayed, meta=None):
     """Converts one dask.delayed object to a dask.array"""
     if meta is None:
         meta = np.array(delayed.data.compute())
-        print(meta.shape)
 
     darray = da.from_delayed(delayed.data, meta.shape, dtype=meta.dtype, name=False)
     return darray, meta