Resolved #315, #316, #317

nobrainer/dataset.py

@@ -69,6 +69,9 @@ def __init__(
         self.volume_shape = volume_shape
         self.n_classes = n_classes
 
+        if self.n_classes < 1:
+            raise ValueError("n_classes must be > 0.")
+
     @classmethod
     def from_tfrecords(
         cls,
@@ -80,6 +83,7 @@ def from_tfrecords(
         n_classes=1,
         tf_dataset_options=None,
         num_parallel_calls=1,
+        label_mapping=None,
     ):
         """Function to retrieve a saved tf record as a nobrainer Dataset
 
@@ -123,6 +127,7 @@ def from_tfrecords(
 
         if not n_volumes:
             n_volumes = block_length * len(files)
+        print(f"n_volumes: {n_volumes}")
 
         dataset = dataset.interleave(
             map_func=lambda x: tf.data.TFRecordDataset(
@@ -138,7 +143,9 @@ def from_tfrecords(
         if block_shape:
             ds_obj.block(block_shape)
         if not scalar_labels:
-            ds_obj.map_labels()
+            ds_obj.map_labels(
+                label_mapping=label_mapping, num_parallel_calls=num_parallel_calls
+            )
         # TODO automatically determine batch size
         ds_obj.batch(1)
 
@@ -158,6 +165,7 @@ def from_files(
         n_classes=1,
         block_shape=None,
         tf_dataset_options=None,
+        label_mapping=None,
     ):
         """Create Nobrainer datasets from data
         filepaths: List(str), list of paths to individual input data files.
@@ -221,6 +229,7 @@ def from_files(
             block_shape=block_shape,
             tf_dataset_options=tf_dataset_options,
             num_parallel_calls=num_parallel_calls,
+            label_mapping=label_mapping,
         )
         ds_eval = None
         if n_eval > 0:
@@ -234,6 +243,7 @@ def from_files(
                 block_shape=block_shape,
                 tf_dataset_options=tf_dataset_options,
                 num_parallel_calls=num_parallel_calls,
+                label_mapping=label_mapping,
             )
         return ds_train, ds_eval
 
@@ -315,19 +325,31 @@ def _f(x, y):
         self.dataset = self.dataset.unbatch()
         return self
 
-    def map_labels(self, label_mapping=None):
-        if self.n_classes < 1:
-            raise ValueError("n_classes must be > 0.")
-
+    def map_labels(self, label_mapping=None, num_parallel_calls=1):
         if label_mapping is not None:
-            self.map(lambda x, y: (x, replace(y, label_mapping=label_mapping)))
+            self.map(lambda x, y: (x, replace(y, mapping=label_mapping)))
 
         if self.n_classes == 1:
-            self.map(lambda x, y: (x, tf.expand_dims(binarize(y), -1)))
+            self.map(
+                lambda x, y: (x, tf.expand_dims(binarize(y), -1)),
+                num_parallel_calls=num_parallel_calls,
+            )
         elif self.n_classes == 2:
-            self.map(lambda x, y: (x, tf.one_hot(binarize(y), self.n_classes)))
+            self.map(
+                lambda x, y: (
+                    x,
+                    tf.one_hot(tf.cast(binarize(y), dtype=tf.int32), self.n_classes),
+                ),
+                num_parallel_calls=num_parallel_calls,
+            )
         elif self.n_classes > 2:
-            self.map(lambda x, y: (x, tf.one_hot(y, self.n_classes)))
+            self.map(
+                lambda x, y: (
+                    x,
+                    tf.one_hot(tf.cast(y, dtype=tf.int32), self.n_classes),
+                ),
+                num_parallel_calls=num_parallel_calls,
+            )
 
         return self