Merge pull request #781 from ufal/tf-data-2

Towards TF dataset, part III

.travis.yml

@@ -14,11 +14,7 @@ env:
     - TEST_SUITE=mypy
 
 python:
-  #- "2.7"
-  #- "3.4"
-  - "3.5"
-  #- "3.5-dev" # 3.5 development branch
-  #- "nightly" # currently points to 3.6-dev
+  - "3.6"
 
 # commands to install dependencies
 before_install:

docs/requirements.txt

@@ -10,4 +10,4 @@ python_speech_features
 pygments
 typeguard
 sacrebleu
-tensorflow>=1.10.0,<1.11
+tensorflow>=1.12.0,<1.13

neuralmonkey/checking.py

@@ -4,64 +4,14 @@
 constructing the computational graph.
 """
 
-
-from typing import List, Optional, Iterable
-
+from typing import List, Optional
 import tensorflow as tf
 
-from neuralmonkey.logging import log, debug
-from neuralmonkey.dataset import Dataset
-from neuralmonkey.runners.base_runner import BaseRunner
-
 
 class CheckingException(Exception):
     pass
 
 
-def check_dataset_and_coders(dataset: Dataset,
-                             runners: Iterable[BaseRunner]) -> None:
-    # pylint: disable=protected-access
-
-    data_list = []
-    for runner in runners:
-        for c in runner.feedables:
-            if hasattr(c, "data_id"):
-                data_list.append((getattr(c, "data_id"), c))
-            elif hasattr(c, "data_ids"):
-                data_list.extend([(d, c) for d in getattr(c, "data_ids")])
-            elif hasattr(c, "input_sequence"):
-                inpseq = getattr(c, "input_sequence")
-                if hasattr(inpseq, "data_id"):
-                    data_list.append((getattr(inpseq, "data_id"), c))
-                elif hasattr(inpseq, "data_ids"):
-                    data_list.extend(
-                        [(d, c) for d in getattr(inpseq, "data_ids")])
-                else:
-                    log("Input sequence: {} does not have a data attribute"
-                        .format(str(inpseq)))
-            else:
-                log(("Coder: {} has neither an input sequence attribute nor a "
-                     "a data attribute.").format(c))
-
-    debug("Found series: {}".format(str(data_list)), "checking")
-    missing = []
-
-    for (serie, coder) in data_list:
-        if serie not in dataset:
-            log("dataset {} does not have serie {}".format(
-                dataset.name, serie))
-            missing.append((coder, serie))
-
-    if missing:
-        formated = ["{} ({}, {}.{})" .format(serie, str(cod),
-                                             cod.__class__.__module__,
-                                             cod.__class__.__name__)
-                    for cod, serie in missing]
-
-        raise CheckingException("Dataset '{}' is mising series {}:"
-                                .format(dataset.name, ", ".join(formated)))
-
-
 def assert_shape(tensor: tf.Tensor,
                  expected_shape: List[Optional[int]]) -> None:
     """Check shape of a tensor.

neuralmonkey/checkpython.py

@@ -1,7 +1,7 @@
 import sys
 
-if sys.version_info[0] < 3 or sys.version_info[1] < 5:
+if sys.version_info[0] < 3 or sys.version_info[1] < 6:
     print("Error:", file=sys.stderr)
-    print("Neural Monkey must use Python >= 3.5", file=sys.stderr)
+    print("Neural Monkey must use Python >= 3.6", file=sys.stderr)
     print("Your Python is", sys.version, sys.executable, file=sys.stderr)
     sys.exit(1)

neuralmonkey/config/normalize.py

@@ -14,7 +14,6 @@
 
 import numpy as np
 
-from neuralmonkey.dataset import BatchingScheme
 from neuralmonkey.logging import warn
 from neuralmonkey.tf_manager import get_default_tf_manager
 from neuralmonkey.trainers.delayed_update_trainer import DelayedUpdateTrainer
@@ -34,25 +33,6 @@ def normalize_configuration(cfg: Namespace, train_mode: bool) -> None:
     if cfg.tf_manager is None:
         cfg.tf_manager = get_default_tf_manager()
 
-    if (cfg.batch_size is None) == (cfg.batching_scheme is None):
-        raise ValueError("You must specify either batch_size or "
-                         "batching_scheme (not both).")
-
-    if cfg.batch_size is not None:
-        assert cfg.batching_scheme is None
-        cfg.batching_scheme = BatchingScheme(batch_size=cfg.batch_size)
-    else:
-        assert cfg.batching_scheme is not None
-        cfg.batch_size = cfg.batching_scheme.batch_size
-
-    if cfg.runners_batch_size is None:
-        cfg.runners_batch_size = cfg.batching_scheme.batch_size
-
-    cfg.runners_batching_scheme = BatchingScheme(
-        batch_size=cfg.runners_batch_size,
-        token_level_batching=cfg.batching_scheme.token_level_batching,
-        use_leftover_buckets=True)
-
     cfg.evaluation = [(e[0], e[0], e[1]) if len(e) == 2 else e
                       for e in cfg.evaluation]
 

neuralmonkey/config/parsing.py

@@ -150,12 +150,13 @@ def _parse_class_name(string: str, vars_dict: VarsDict) -> ClassSymbol:
 
 
 def _parse_value(string: str, vars_dict: VarsDict) -> Any:
-    """Parse the value recursively according to the Nerualmonkey grammar.
+    """Parse the value recursively according to the Nerual Monkey grammar.
 
     Arguments:
         string: the string to be parsed
         vars_dict: a dictionary of variables for substitution
     """
+    string = string.strip()
 
     if string in CONSTANTS:
         return CONSTANTS[string]