replacing all instances of torch.distributed.deprecated with torch.distributed (#248)

* make pixel indexes 0-based for bounding box in pascal voc dataset

* replacing all instances of torch.distributed.deprecated with torch.distributed

* replacing all instances of torch.distributed.deprecated with torch.distributed

maskrcnn_benchmark/data/samplers/distributed.py

 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# Code is copy-pasted exactly as in torch.utils.data.distributed,
-# with a modification in the import to use the deprecated backend
+# Code is copy-pasted exactly as in torch.utils.data.distributed.
 # FIXME remove this once c10d fixes the bug it has
 import math
 import torch
-import torch.distributed.deprecated as dist
+import torch.distributed as dist
 from torch.utils.data.sampler import Sampler
 
 

maskrcnn_benchmark/engine/inference.py

@@ -65,8 +65,8 @@ def inference(
     # convert to a torch.device for efficiency
     device = torch.device(device)
     num_devices = (
-        torch.distributed.deprecated.get_world_size()
-        if torch.distributed.deprecated.is_initialized()
+        torch.distributed.get_world_size()
+        if torch.distributed.is_initialized()
         else 1
     )
     logger = logging.getLogger("maskrcnn_benchmark.inference")

maskrcnn_benchmark/engine/trainer.py

@@ -4,7 +4,7 @@ import logging
 import time
 
 import torch
-from torch.distributed import deprecated as dist
+import torch.distributed as dist
 
 from maskrcnn_benchmark.utils.comm import get_world_size
 from maskrcnn_benchmark.utils.metric_logger import MetricLogger

maskrcnn_benchmark/utils/comm.py

@@ -13,21 +13,21 @@ import torch
 
 
 def get_world_size():
-    if not torch.distributed.deprecated.is_initialized():
+    if not torch.distributed.is_initialized():
         return 1
-    return torch.distributed.deprecated.get_world_size()
+    return torch.distributed.get_world_size()
 
 
 def get_rank():
-    if not torch.distributed.deprecated.is_initialized():
+    if not torch.distributed.is_initialized():
         return 0
-    return torch.distributed.deprecated.get_rank()
+    return torch.distributed.get_rank()
 
 
 def is_main_process():
-    if not torch.distributed.deprecated.is_initialized():
+    if not torch.distributed.is_initialized():
         return True
-    return torch.distributed.deprecated.get_rank() == 0
+    return torch.distributed.get_rank() == 0
 
 
 def synchronize():
@@ -35,10 +35,10 @@ def synchronize():
     Helper function to synchronize between multiple processes when
     using distributed training
     """
-    if not torch.distributed.deprecated.is_initialized():
+    if not torch.distributed.is_initialized():
         return
-    world_size = torch.distributed.deprecated.get_world_size()
-    rank = torch.distributed.deprecated.get_rank()
+    world_size = torch.distributed.get_world_size()
+    rank = torch.distributed.get_rank()
     if world_size == 1:
         return
 
@@ -47,7 +47,7 @@ def synchronize():
             tensor = torch.tensor(0, device="cuda")
         else:
             tensor = torch.tensor(1, device="cuda")
-        torch.distributed.deprecated.broadcast(tensor, r)
+        torch.distributed.broadcast(tensor, r)
         while tensor.item() == 1:
             time.sleep(1)
 
@@ -103,11 +103,11 @@ def scatter_gather(data):
     # each process will then serialize the data to the folder defined by
     # the main process, and then the main process reads all of the serialized
     # files and returns them in a list
-    if not torch.distributed.deprecated.is_initialized():
+    if not torch.distributed.is_initialized():
         return [data]
     synchronize()
     # get rank of the current process
-    rank = torch.distributed.deprecated.get_rank()
+    rank = torch.distributed.get_rank()
 
     # the data to communicate should be small
     data_to_communicate = torch.empty(256, dtype=torch.uint8, device="cuda")
@@ -119,7 +119,7 @@ def scatter_gather(data):
 
     synchronize()
     # the main process (rank=0) communicates the data to all processes
-    torch.distributed.deprecated.broadcast(data_to_communicate, 0)
+    torch.distributed.broadcast(data_to_communicate, 0)
 
     # get the data that was communicated
     tmp_dir = _decode(data_to_communicate)
@@ -135,7 +135,7 @@ def scatter_gather(data):
     # only the master process returns the data
     if rank == 0:
         data_list = []
-        world_size = torch.distributed.deprecated.get_world_size()
+        world_size = torch.distributed.get_world_size()
         for r in range(world_size):
             file_path = os.path.join(tmp_dir, file_template.format(r))
             d = torch.load(file_path)

tools/test_net.py

@@ -41,7 +41,7 @@ def main():
 
     if distributed:
         torch.cuda.set_device(args.local_rank)
-        torch.distributed.deprecated.init_process_group(
+        torch.distributed.init_process_group(
             backend="nccl", init_method="env://"
         )
 

tools/train_net.py

@@ -35,7 +35,7 @@ def train(cfg, local_rank, distributed):
     scheduler = make_lr_scheduler(cfg, optimizer)
 
     if distributed:
-        model = torch.nn.parallel.deprecated.DistributedDataParallel(
+        model = torch.nn.parallel.DistributedDataParallel(
             model, device_ids=[local_rank], output_device=local_rank,
             # this should be removed if we update BatchNorm stats
             broadcast_buffers=False,
@@ -136,7 +136,7 @@ def main():
 
     if args.distributed:
         torch.cuda.set_device(args.local_rank)
-        torch.distributed.deprecated.init_process_group(
+        torch.distributed.init_process_group(
             backend="nccl", init_method="env://"
         )