Revert "Remove dense Tensor transformation"

This reverts commit 0912600fdc.

src/preprocessing.py

@@ -1,4 +1,4 @@
-from typing import List, Tuple
+from typing import Dict, List, Tuple
 
 from Bio.pairwise2 import align
 from Bio.SeqIO import parse
@@ -6,6 +6,7 @@ from numpy.random import random
 from tensorflow import Tensor, int64
 from tensorflow.data import TFRecordDataset
 from tensorflow.io import TFRecordWriter, VarLenFeature, parse_single_example
+from tensorflow.sparse import to_dense
 from tensorflow.train import Example, Feature, Features, Int64List
 
 from constants import *
@@ -49,7 +50,10 @@ def read_fastq(data_file, label_file) -> List[bytes]:
     examples = []
     with open(data_file) as data, open(label_file) as labels:
         for element, label in zip(parse(data, "fastq"), parse(labels, "fastq")):
-            example = generate_example(sequence=str(element.seq), label=str(label.seq))
+            example = generate_example(
+                sequence=str(element.seq),
+                label=str(label.seq),
+            )
             examples.append(example)
     return examples
 
@@ -71,6 +75,17 @@ def create_dataset(data_file, label_file, dataset_split=[0.8, 0.1, 0.1]) -> None
                 test.write(element)
 
 
+def transform_features(parsed_features) -> Dict[str, Tensor]:
+    """
+    Transform the parsed features of an Example into a list of dense Tensors
+    """
+    features = {}
+    sparse_features = ["sequence", "label"]
+    for element in sparse_features:
+        features[element] = to_dense(parsed_features[element])
+    return features
+
+
 def process_input(byte_string) -> Tuple[Tensor, Tensor]:
     """
     Parse a byte-string into an Example object
@@ -79,7 +94,8 @@ def process_input(byte_string) -> Tuple[Tensor, Tensor]:
         "sequence": VarLenFeature(dtype=int64),
         "label": VarLenFeature(dtype=int64),
     }
-    features = parse_single_example(byte_string, features=schema)
+    parsed_features = parse_single_example(byte_string, features=schema)
+    features = transform_features(parsed_features)
     return features["sequence"], features["label"]