Fix - Fix imports for python 3.10 (#1)

**💬 Feature description:**
Similar to JDAI-CV#713

**🧪 How to test:**
Tested in
https://github.gsissc.myatos.net/GLB-BDS-AILAB-CV/innov-tools-tracklet_annotations/pull/30

Author: Natuu

fastreid/data/build.py

@@ -8,15 +8,16 @@
 import os
 
 import torch
-TORCH_MAJOR = int(torch.__version__.split('.')[0])
-TORCH_MINOR = int(torch.__version__.split('.')[1])
+
+TORCH_MAJOR = int(torch.__version__.split(".")[0])
+TORCH_MINOR = int(torch.__version__.split(".")[1])
 
 if TORCH_MAJOR == 1 and TORCH_MINOR < 8:
     from torch._six import string_classes
 else:
     string_classes = str
 
-from collections import Mapping
+from collections.abc import Mapping
 
 from fastreid.config import configurable
 from fastreid.utils import comm
@@ -26,15 +27,14 @@
 from .datasets import DATASET_REGISTRY
 from .transforms import build_transforms
 
-__all__ = [
-    "build_reid_train_loader",
-    "build_reid_test_loader"
-]
+__all__ = ["build_reid_train_loader", "build_reid_test_loader"]
 
 _root = os.getenv("FASTREID_DATASETS", "datasets")
 
 
-def _train_loader_from_config(cfg, *, train_set=None, transforms=None, sampler=None, **kwargs):
+def _train_loader_from_config(
+    cfg, *, train_set=None, transforms=None, sampler=None, **kwargs
+):
     if transforms is None:
         transforms = build_transforms(cfg, is_train=True)
 
@@ -58,12 +58,18 @@ def _train_loader_from_config(cfg, *, train_set=None, transforms=None, sampler=N
         if sampler_name == "TrainingSampler":
             sampler = samplers.TrainingSampler(len(train_set))
         elif sampler_name == "NaiveIdentitySampler":
-            sampler = samplers.NaiveIdentitySampler(train_set.img_items, mini_batch_size, num_instance)
+            sampler = samplers.NaiveIdentitySampler(
+                train_set.img_items, mini_batch_size, num_instance
+            )
         elif sampler_name == "BalancedIdentitySampler":
-            sampler = samplers.BalancedIdentitySampler(train_set.img_items, mini_batch_size, num_instance)
+            sampler = samplers.BalancedIdentitySampler(
+                train_set.img_items, mini_batch_size, num_instance
+            )
         elif sampler_name == "SetReWeightSampler":
             set_weight = cfg.DATALOADER.SET_WEIGHT
-            sampler = samplers.SetReWeightSampler(train_set.img_items, mini_batch_size, num_instance, set_weight)
+            sampler = samplers.SetReWeightSampler(
+                train_set.img_items, mini_batch_size, num_instance, set_weight
+            )
         elif sampler_name == "ImbalancedDatasetSampler":
             sampler = samplers.ImbalancedDatasetSampler(train_set.img_items)
         else:
@@ -79,7 +85,11 @@ def _train_loader_from_config(cfg, *, train_set=None, transforms=None, sampler=N
 
 @configurable(from_config=_train_loader_from_config)
 def build_reid_train_loader(
-        train_set, *, sampler=None, total_batch_size, num_workers=0,
+    train_set,
+    *,
+    sampler=None,
+    total_batch_size,
+    num_workers=0,
 ):
     """
     Build a dataloader for object re-identification with some default features.
@@ -91,7 +101,9 @@ def build_reid_train_loader(
 
     mini_batch_size = total_batch_size // comm.get_world_size()
 
-    batch_sampler = torch.utils.data.sampler.BatchSampler(sampler, mini_batch_size, True)
+    batch_sampler = torch.utils.data.sampler.BatchSampler(
+        sampler, mini_batch_size, True
+    )
 
     train_loader = DataLoaderX(
         comm.get_local_rank(),
@@ -105,12 +117,16 @@ def build_reid_train_loader(
     return train_loader
 
 
-def _test_loader_from_config(cfg, *, dataset_name=None, test_set=None, num_query=0, transforms=None, **kwargs):
+def _test_loader_from_config(
+    cfg, *, dataset_name=None, test_set=None, num_query=0, transforms=None, **kwargs
+):
     if transforms is None:
         transforms = build_transforms(cfg, is_train=False)
 
     if test_set is None:
-        assert dataset_name is not None, "dataset_name must be explicitly passed in when test_set is not provided"
+        assert (
+            dataset_name is not None
+        ), "dataset_name must be explicitly passed in when test_set is not provided"
         data = DATASET_REGISTRY.get(dataset_name)(root=_root, **kwargs)
         if comm.is_main_process():
             data.show_test()
@@ -184,7 +200,9 @@ def fast_batch_collator(batched_inputs):
         return out
 
     elif isinstance(elem, Mapping):
-        return {key: fast_batch_collator([d[key] for d in batched_inputs]) for key in elem}
+        return {
+            key: fast_batch_collator([d[key] for d in batched_inputs]) for key in elem
+        }
 
     elif isinstance(elem, float):
         return torch.tensor(batched_inputs, dtype=torch.float64)

fastreid/evaluation/testing.py

@@ -2,7 +2,8 @@
 import logging
 import pprint
 import sys
-from collections import Mapping, OrderedDict
+from collections import OrderedDict
+from collections.abc import Mapping
 
 import numpy as np
 from tabulate import tabulate
@@ -20,7 +21,7 @@ def print_csv_format(results):
     assert isinstance(results, OrderedDict) or not len(results), results
     logger = logging.getLogger(__name__)
 
-    dataset_name = results.pop('dataset')
+    dataset_name = results.pop("dataset")
     metrics = ["Dataset"] + [k for k in results]
     csv_results = [(dataset_name, *list(results.values()))]