fix tests & review

neuralmonkey/encoders/numpy_encoder.py → neuralmonkey/encoders/numpy_stateful_filler.py

@@ -13,7 +13,7 @@
 # pylint: disable=too-few-public-methods
 
 
-class StatefulNumberEncoder(ModelPart, Stateful):
+class StatefulFiller(ModelPart, Stateful):
 
     def __init__(self,
                  name: str,
@@ -59,13 +59,12 @@ def feed_dict(self, dataset: Dataset, train: bool = False) -> FeedDict:
         return {self.vector: dataset.get_series(self.data_id)}
 
 
-class SpatialNumpyEncoder(ModelPart, SpatialStatefulWithOutput):
+class SpatialFiller(ModelPart, SpatialStatefulWithOutput):
     # pylint: disable=too-many-arguments
     def __init__(self,
                  name: str,
                  input_shape: List[int],
                  data_id: str,
-                 output_shape: int = None,
                  save_checkpoint: Optional[str] = None,
                  load_checkpoint: Optional[str] = None,
                  initializers: InitializerSpecs = None) -> None:
@@ -74,8 +73,6 @@ def __init__(self,
                            initializers)
 
         assert len(input_shape) == 3
-        if output_shape is not None and output_shape <= 0:
-            raise ValueError("Output vector dimension must be postive.")
 
         self.data_id = data_id
         self.input_shape = input_shape

neuralmonkey/readers/numpy_reader.py

@@ -1,11 +1,11 @@
-from typing import List
+from typing import List, Callable, Iterable
 import os
 
 from typeguard import check_argument_types
 import numpy as np
 
 
-def single_tensor(files: List[str]):
+def single_tensor(files: List[str]) -> np.ndarray:
     """Load a single tensor from a numpy file."""
     check_argument_types()
     if len(files) == 1:
@@ -14,20 +14,20 @@ def single_tensor(files: List[str]):
     return np.concatenate([np.load(f) for f in files], axis=0)
 
 
-def from_file_list(prefix: str, default_tensor_name: str = "arr_0"):
-    """Load list of numpy arrays according to a list of files.
+def from_file_list(prefix: str,
+                   default_tensor_name: str = "arr_0") -> Callable:
+    """Load a list of numpy arrays from a list of .npz numpy files.
 
     Args:
-        prefix: A common prefix of the files in lists of relative paths.
-        default_tensor_name: Key of the tensors to load in the npz files.
+        prefix: A common prefix for the files in the list.
+        default_tensor_name: Key of the tensors to load from the npz files.
 
-    Return:
-        A reader function that loads numpy arrays from files on path writen
-        path relatively to the given prefix.
+    Returns:
+        A generator function that yields the loaded arryas.
     """
     check_argument_types()
 
-    def load(files: List[str]):
+    def load(files: List[str]) -> Iterable[np.ndarray]:
         for list_file in files:
             with open(list_file, encoding="utf-8") as f_list:
                 for line in f_list:
@@ -39,4 +39,4 @@ def load(files: List[str]):
 
 
 # pylint: disable=invalid-name
-numpy_file_list_reader = from_file_list("")
+numpy_file_list_reader = from_file_list(prefix="")

neuralmonkey/tests/test_encoders_init.py

@@ -6,8 +6,6 @@
 
 from typing import Dict, List, Any, Iterable
 
-from neuralmonkey.encoders.numpy_encoder import (VectorEncoder,
-                                                 PostCNNImageEncoder)
 from neuralmonkey.encoders.recurrent import SentenceEncoder
 from neuralmonkey.encoders.sentence_cnn_encoder import SentenceCNNEncoder
 from neuralmonkey.model.sequence import EmbeddedSequence
@@ -80,37 +78,6 @@
     "use_noisy_activations": [None, SentenceEncoder]
 }
 
-VECTOR_ENCODER_GOOD = {
-    "name": ["vector_encoder"],
-    "dimension": [10],
-    "data_id": ["marmelade"],
-    "output_shape": [1, None, 100]
-}
-
-VECTOR_ENCODER_BAD = {
-    "nonexistent": ["ahoj"],
-    "name": [None, 1],
-    "dimension": [0, -1, "ahoj", 3.14, VOCABULARY, SentenceEncoder, None],
-    "data_id": [3.14, VOCABULARY, None],
-    "output_shape": [0, -1, "ahoj", 3.14, VOCABULARY]
-}
-
-POST_CNN_IMAGE_ENCODER_GOOD = {
-    "name": ["vector_encoder"],
-    "input_shape": [[1, 2, 3], [10, 20, 3]],
-    "output_shape": [10],
-    "data_id": ["marmelade"],
-}
-
-POST_CNN_IMAGE_ENCODER_BAD = {
-    "nonexistent": ["ahoj"],
-    "name": [None, 1],
-    "data_id": [3.14, VOCABULARY, None],
-    "output_shape": [0, -1, "hoj", 3.14, None, VOCABULARY, SentenceEncoder],
-    "input_shape": [3, [10, 20], [-1, 10, 20], "123", "ahoj", 3.14,
-                    VOCABULARY, []]
-}
-
 
 def traverse_combinations(
         params: Dict[str, List[Any]],
@@ -187,28 +154,6 @@ def test_sentence_cnn_encoder(self):
                                SENTENCE_CNN_ENCODER_GOOD,
                                SENTENCE_CNN_ENCODER_BAD)
 
-    def test_vector_encoder(self):
-        with self.assertRaises(Exception):
-            # pylint: disable=no-value-for-parameter
-            # on purpose, should fail
-            VectorEncoder()
-            # pylint: enable=no-value-for-parameter
-
-        self._run_constructors(VectorEncoder,
-                               VECTOR_ENCODER_GOOD,
-                               VECTOR_ENCODER_BAD)
-
-    def test_post_cnn_encoder(self):
-        with self.assertRaises(Exception):
-            # pylint: disable=no-value-for-parameter
-            # on purpose, should fail
-            PostCNNImageEncoder()
-            # pylint: enable=no-value-for-parameter
-
-        self._run_constructors(PostCNNImageEncoder,
-                               POST_CNN_IMAGE_ENCODER_GOOD,
-                               POST_CNN_IMAGE_ENCODER_BAD)
-
 
 if __name__ == "__main__":
     unittest.main()