[tune/sgd] Import ABC from collections.abc instead of collectio… (#7982)

* Import ABC from collections.abc instead of collections for Python 3 compatibility.

* Fix linter errors.

python/ray/tune/progress_reporter.py

@@ -7,6 +7,11 @@ from ray.tune.result import (EPISODE_REWARD_MEAN, MEAN_ACCURACY, MEAN_LOSS,
                              TRAINING_ITERATION, TIME_TOTAL_S, TIMESTEPS_TOTAL)
 from ray.tune.utils import flatten_dict
 
+try:
+    from collections.abc import Mapping
+except ImportError:
+    from collections import Mapping
+
 try:
     from tabulate import tabulate
 except ImportError:
@@ -101,7 +106,7 @@ class TuneReporterBase(ProgressReporter):
         if metric in self._metric_columns:
             raise ValueError("Column {} already exists.".format(metric))
 
-        if isinstance(self._metric_columns, collections.Mapping):
+        if isinstance(self._metric_columns, Mapping):
             representation = representation or metric
             self._metric_columns[metric] = representation
         else:
@@ -291,7 +296,7 @@ def trial_progress_str(trials, metric_columns, fmt="psql", max_rows=None):
         num_trials, ", ".join(num_trials_strs)))
 
     # Pre-process trials to figure out what columns to show.
-    if isinstance(metric_columns, collections.Mapping):
+    if isinstance(metric_columns, Mapping):
         keys = list(metric_columns.keys())
     else:
         keys = metric_columns
@@ -303,7 +308,7 @@ def trial_progress_str(trials, metric_columns, fmt="psql", max_rows=None):
     params = sorted(set().union(*[t.evaluated_params for t in trials]))
     trial_table = [_get_trial_info(trial, params, keys) for trial in trials]
     # Format column headings
-    if isinstance(metric_columns, collections.Mapping):
+    if isinstance(metric_columns, Mapping):
         formatted_columns = [metric_columns[k] for k in keys]
     else:
         formatted_columns = keys

python/ray/util/sgd/torch/torch_runner.py

@@ -1,4 +1,3 @@
-import collections
 from filelock import FileLock
 import logging
 import inspect
@@ -17,6 +16,11 @@ from ray.util.sgd import utils
 logger = logging.getLogger(__name__)
 amp = None
 
+try:
+    from collections.abc import Iterable
+except ImportError:
+    from collections import Iterable
+
 try:
     from apex import amp
 except ImportError:
@@ -133,7 +137,7 @@ class TorchRunner:
         self.schedulers = self.scheduler_creator(self.given_optimizers,
                                                  self.config)
 
-        if not isinstance(self.schedulers, collections.Iterable):
+        if not isinstance(self.schedulers, Iterable):
             self.schedulers = [self.schedulers]
 
     def _try_setup_apex(self):
@@ -153,7 +157,7 @@ class TorchRunner:
         self._initialize_dataloaders()
         logger.debug("Creating model")
         self.models = self.model_creator(self.config)
-        if not isinstance(self.models, collections.Iterable):
+        if not isinstance(self.models, Iterable):
             self.models = [self.models]
         assert all(isinstance(model, nn.Module) for model in self.models), (
             "All models must be PyTorch models: {}.".format(self.models))
@@ -163,7 +167,7 @@ class TorchRunner:
         logger.debug("Creating optimizer.")
         self.optimizers = self.optimizer_creator(self.given_models,
                                                  self.config)
-        if not isinstance(self.optimizers, collections.Iterable):
+        if not isinstance(self.optimizers, Iterable):
             self.optimizers = [self.optimizers]
 
         self._create_schedulers_if_available()

python/ray/util/sgd/torch/training_operator.py

@@ -1,4 +1,3 @@
-import collections
 import torch
 
 from ray.util.sgd.utils import (TimerCollection, AverageMeterCollection,
@@ -8,6 +7,11 @@ from ray.util.sgd.torch.constants import (SCHEDULER_STEP_EPOCH, NUM_STEPS,
 
 amp = None
 
+try:
+    from collections.abc import Iterable
+except ImportError:
+    from collections import Iterable
+
 try:
     from apex import amp
 except ImportError:
@@ -25,7 +29,7 @@ except ImportError:
 
 def _is_multiple(component):
     """Checks if a component (optimizer, model, etc) is not singular."""
-    return isinstance(component, collections.Iterable) and len(component) > 1
+    return isinstance(component, Iterable) and len(component) > 1
 
 
 class TrainingOperator:
@@ -66,19 +70,24 @@ class TrainingOperator:
                  use_tqdm=False):
         # You are not expected to override this method.
         self._models = models  # List of models
-        assert isinstance(models, collections.Iterable), (
-            "Components need to be iterable. Got: {}".format(type(models)))
+        assert isinstance(
+            models,
+            Iterable), ("Components need to be iterable. Got: {}".format(
+                type(models)))
         self._optimizers = optimizers  # List of optimizers
-        assert isinstance(optimizers, collections.Iterable), (
-            "Components need to be iterable. Got: {}".format(type(optimizers)))
+        assert isinstance(
+            optimizers,
+            Iterable), ("Components need to be iterable. Got: {}".format(
+                type(optimizers)))
         self._train_loader = train_loader
         self._validation_loader = validation_loader
         self._world_rank = world_rank
         self._criterion = criterion
         self._schedulers = schedulers
         if schedulers:
-            assert isinstance(schedulers, collections.Iterable), (
-                "Components need to be iterable. Got: {}".format(
+            assert isinstance(
+                schedulers,
+                Iterable), ("Components need to be iterable. Got: {}".format(
                     type(schedulers)))
         self._config = config
         self._use_fp16 = use_fp16