omegaconf 2.2.2 compatibility

Summary: OmegaConf 2.2.2 doesn't like heterogenous tuples or Sequence or Set members. Workaround this.

Reviewed By: shapovalov

Differential Revision: D37278736

fbshipit-source-id: 123e6657947f5b27514910e4074c92086a457a2a

Author: bottler

pytorch3d/implicitron/dataset/data_loader_map_provider.py

@@ -5,7 +5,7 @@
 # LICENSE file in the root directory of this source tree.
 
 from dataclasses import dataclass
-from typing import Optional, Sequence
+from typing import Optional, Tuple
 
 import torch
 from pytorch3d.implicitron.tools.config import registry, ReplaceableBase
@@ -86,7 +86,7 @@ class SequenceDataLoaderMapProvider(DataLoaderMapProviderBase):
     num_workers: int = 0
     dataset_len: int = 1000
     dataset_len_val: int = 1
-    images_per_seq_options: Sequence[int] = (2,)
+    images_per_seq_options: Tuple[int, ...] = (2,)
     sample_consecutive_frames: bool = False
     consecutive_frames_max_gap: int = 0
     consecutive_frames_max_gap_seconds: float = 0.1

pytorch3d/implicitron/dataset/json_index_dataset.py

@@ -122,9 +122,9 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
     subsets: Optional[List[str]] = None
     limit_to: int = 0
     limit_sequences_to: int = 0
-    pick_sequence: Sequence[str] = ()
-    exclude_sequence: Sequence[str] = ()
-    limit_category_to: Sequence[int] = ()
+    pick_sequence: Tuple[str, ...] = ()
+    exclude_sequence: Tuple[str, ...] = ()
+    limit_category_to: Tuple[int, ...] = ()
     dataset_root: str = ""
     load_images: bool = True
     load_depths: bool = True

pytorch3d/implicitron/dataset/json_index_dataset_map_provider.py

@@ -7,7 +7,7 @@
 
 import json
 import os
-from typing import Dict, List, Sequence, Tuple, Type
+from typing import Dict, List, Tuple, Type
 
 from omegaconf import DictConfig, open_dict
 from pytorch3d.implicitron.tools.config import (
@@ -98,7 +98,7 @@ class JsonIndexDatasetMapProvider(DatasetMapProviderBase):  # pyre-ignore [13]
     dataset_root: str = _CO3D_DATASET_ROOT
     n_frames_per_sequence: int = -1
     test_on_train: bool = False
-    restrict_sequence_name: Sequence[str] = ()
+    restrict_sequence_name: Tuple[str, ...] = ()
     test_restrict_sequence_id: int = -1
     assert_single_seq: bool = False
     only_test_set: bool = False

pytorch3d/implicitron/models/implicit_function/idr_feature_field.py

@@ -3,7 +3,7 @@
 #              implicit_differentiable_renderer.py
 # Copyright (c) 2020 Lior Yariv
 import math
-from typing import Sequence
+from typing import Tuple
 
 import torch
 from pytorch3d.implicitron.tools.config import registry
@@ -53,10 +53,10 @@ class IdrFeatureField(ImplicitFunctionBase, torch.nn.Module):
     feature_vector_size: int = 3
     d_in: int = 3
     d_out: int = 1
-    dims: Sequence[int] = (512, 512, 512, 512, 512, 512, 512, 512)
+    dims: Tuple[int, ...] = (512, 512, 512, 512, 512, 512, 512, 512)
     geometric_init: bool = True
     bias: float = 1.0
-    skip_in: Sequence[int] = ()
+    skip_in: Tuple[int, ...] = ()
     weight_norm: bool = True
     n_harmonic_functions_xyz: int = 0
     pooled_feature_dim: int = 0

pytorch3d/implicitron/models/view_pooler/feature_aggregator.py

@@ -6,7 +6,7 @@
 
 from abc import ABC, abstractmethod
 from enum import Enum
-from typing import Dict, Optional, Sequence, Union
+from typing import Dict, Optional, Sequence, Tuple, Union
 
 import torch
 import torch.nn.functional as F
@@ -176,7 +176,7 @@ class ReductionFeatureAggregator(torch.nn.Module, FeatureAggregatorBase):
             the stack of source-view-specific features to a single feature.
     """
 
-    reduction_functions: Sequence[ReductionFunction] = (
+    reduction_functions: Tuple[ReductionFunction, ...] = (
         ReductionFunction.AVG,
         ReductionFunction.STD,
     )
@@ -269,7 +269,7 @@ class AngleWeightedReductionFeatureAggregator(torch.nn.Module, FeatureAggregator
             used when calculating the angle-based aggregation weights.
     """
 
-    reduction_functions: Sequence[ReductionFunction] = (
+    reduction_functions: Tuple[ReductionFunction, ...] = (
         ReductionFunction.AVG,
         ReductionFunction.STD,
     )

tests/implicitron/test_config.py

@@ -9,7 +9,7 @@
 import unittest
 from dataclasses import dataclass, field, is_dataclass
 from enum import Enum
-from typing import Any, Dict, List, Optional, Set, Tuple
+from typing import Any, Dict, List, Optional, Tuple
 
 from omegaconf import DictConfig, ListConfig, OmegaConf, ValidationError
 from pytorch3d.implicitron.tools.config import (
@@ -484,16 +484,14 @@ class MyDataclass:
             none_field: Optional[int] = None
             float_field: float = 9.3
             bool_field: bool = True
-            tuple_field: tuple = (3, True, "j")
+            tuple_field: Tuple[int, ...] = (3,)
 
         class SimpleClass:
             def __init__(
                 self,
                 tuple_member_: Tuple[int, int] = (3, 4),
-                set_member_: Set[int] = {2},  # noqa
             ):
                 self.tuple_member = tuple_member_
-                self.set_member = set_member_
 
             def get_tuple(self):
                 return self.tuple_member
@@ -524,11 +522,9 @@ class C(Configurable):
         self.assertEqual(simple.get_tuple(), [3, 4])
         self.assertTrue(isinstance(simple.get_tuple(), ListConfig))
         # get_default_args converts sets to ListConfigs (which act like lists).
-        self.assertEqual(simple.set_member, [2])
-        self.assertTrue(isinstance(simple.set_member, ListConfig))
         self.assertEqual(c.a_tuple, [4.0, 3.0])
         self.assertTrue(isinstance(c.a_tuple, ListConfig))
-        self.assertEqual(mydata.tuple_field, (3, True, "j"))
+        self.assertEqual(mydata.tuple_field, (3,))
         self.assertTrue(isinstance(mydata.tuple_field, ListConfig))
         f(**c.f_args)