Datasets, remove some usages of num_output (#1601)

* CombinedDataset: do not depend on num_outputs for dtype inference
* OggZipDataset: define `get_data_dim` to avoid reliance on `num_outputs`
* LmDataset: do not rely on num_outputs for data keys and data dim

returnn/datasets/audio.py

@@ -406,6 +406,21 @@ def get_total_num_seqs(self, *, fast: bool = False) -> int:
         self._lazy_init()
         return len(self._data)
 
+    def get_data_dim(self, key: str) -> int:
+        """:return: dim of data entry with `key`"""
+        if key == "data":
+            assert self.feature_extractor is not None
+            return self.feature_extractor.get_feature_dimension()
+        elif key == "classes":
+            assert self.targets is not None
+            return self.targets.num_labels
+        elif key == "raw":
+            return 0
+        elif key == "orth":
+            return 256
+        else:
+            raise ValueError(f"{self}: unknown data key: {key}")
+
     def get_data_dtype(self, key: str) -> str:
         """:return: dtype of data entry with `key`"""
         if key == "data":

returnn/datasets/lm.py

@@ -7,7 +7,7 @@
 
 from __future__ import annotations
 
-from typing import Optional, Union, Callable, List, Tuple, BinaryIO, cast
+from typing import Optional, Union, Callable, Iterator, List, Tuple, BinaryIO, cast
 import typing
 import os
 import sys
@@ -394,7 +394,16 @@ def get_data_keys(self):
         """
         :rtype: list[str]
         """
-        return sorted(self.num_outputs.keys())
+
+        def _data_keys() -> Iterator[str]:
+            # return keys in alphabetically sorted
+            if self.add_delayed_seq_data:
+                yield "delayed"
+            yield "data"
+            for i in range(self.add_random_phone_seqs):
+                yield f"random{i}"
+
+        return list(_data_keys())
 
     def get_target_list(self):
         """
@@ -1626,6 +1635,12 @@ def is_data_sparse(self, key):
         """
         return True  # all is sparse
 
+    def get_data_dim(self, _key: str) -> int:
+        """
+        :return: the data dim of data entry `_key`
+        """
+        return self.num_inputs  # same dim for all keys
+
     def get_data_dtype(self, key):
         """
         :param str key:

returnn/datasets/meta.py

@@ -1001,7 +1001,8 @@ def __init__(
         self.num_outputs = self.data_dims
 
         self.data_dtypes = {
-            data_key: _select_dtype(data_key, self.data_dims, data_dtypes) for data_key in self.data_keys
+            data_key: _select_dtype(data_key, dset_data_key, self.datasets[dset_key], data_dtypes)
+            for (dset_key, dset_data_key), data_key in data_map.items()
         }
 
         self.dataset_seq_idx_boundaries = None  # type: typing.Optional[typing.List[int]]
@@ -2086,13 +2087,16 @@ def is_data_sparse(self, key: str) -> bool:
         return self._data_keys[key].get("sparse", False)
 
 
-def _select_dtype(key, data_dims, data_dtypes):
-    if data_dtypes and key in data_dtypes:
-        v = data_dtypes[key]
+def _select_dtype(
+    data_key: str,
+    dataset_data_key: str,
+    dataset: Dataset,
+    existing_dtype_map: Optional[Dict[str, str]],
+):
+    if existing_dtype_map and data_key in existing_dtype_map:
+        v = existing_dtype_map[data_key]
         assert isinstance(v, str)  # e.g. "int32" or "float32"
         return v
-    assert key in data_dims
-    if data_dims[key][1] == 1:  # sparse
-        return "int32"  # standard for 1-of-k
-    else:
-        return "float32"  # standard otherwise
+    if dataset.is_data_sparse(dataset_data_key):
+        return "int32"  # standard 1-of-k
+    return "float32"  # default float32 otherwise