[Fix] Convert SyncBN to BN when training on DP

.dev/gather_models.py

@@ -75,7 +75,7 @@ def get_final_results(log_json_path, iter_num):
 def parse_args():
     parser = argparse.ArgumentParser(description='Gather benchmarked models')
     parser.add_argument(
-        '-c', '--config-name', type=str, help='Process the selected config.')
+        '-f', '--config-name', type=str, help='Process the selected config.')
     parser.add_argument(
         '-w',
         '--work-dir',

docker/Dockerfile

@@ -1,7 +1,7 @@
 ARG PYTORCH="1.6.0"
 ARG CUDA="10.1"
 ARG CUDNN="7"
-ARG MMCV="1.3.12"
+ARG MMCV="1.3.13"
 
 FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
 

docker/serve/Dockerfile

@@ -3,7 +3,7 @@ ARG CUDA="10.1"
 ARG CUDNN="7"
 FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
 
-ARG MMCV="1.3.12"
+ARG MMCV="1.3.13"
 ARG MMSEG="0.17.0"
 
 ENV PYTHONUNBUFFERED TRUE

docs/get_started.md

@@ -11,7 +11,7 @@ The compatible MMSegmentation and MMCV versions are as below. Please install the
 
 | MMSegmentation version |    MMCV version     |
 |:-------------------:|:-------------------:|
-| master              | mmcv-full>=1.3.7, <1.4.0 |
+| master              | mmcv-full>=1.3.13, <1.4.0 |
 | 0.17.0              | mmcv-full>=1.3.7, <1.4.0 |
 | 0.16.0              | mmcv-full>=1.3.7, <1.4.0 |
 | 0.15.0              | mmcv-full>=1.3.7, <1.4.0 |

docs/train.md

@@ -19,6 +19,14 @@ To trade speed with GPU memory, you may pass in `--options model.backbone.with_c
 
 ### Train with a single GPU
 
+official support:
+
+```shell
+./tools/dist_train.sh ${CONFIG_FILE} 1 [optional arguments]
+```
+
+experimental support (Convert SyncBN to BN):
+
 ```shell
 python tools/train.py ${CONFIG_FILE} [optional arguments]
 ```

docs_zh-CN/get_started.md

@@ -11,7 +11,7 @@
 
 | MMSegmentation 版本 |    MMCV 版本     |
 |:-------------------:|:-------------------:|
-| master              | mmcv-full>=1.3.7, <1.4.0 |
+| master              | mmcv-full>=1.3.13, <1.4.0 |
 | 0.17.0              | mmcv-full>=1.3.7, <1.4.0 |
 | 0.16.0              | mmcv-full>=1.3.7, <1.4.0 |
 | 0.15.0              | mmcv-full>=1.3.7, <1.4.0 |

mmseg/__init__.py

@@ -6,7 +6,7 @@
 
 from .version import __version__, version_info
 
-MMCV_MIN = '1.3.7'
+MMCV_MIN = '1.3.13'
 MMCV_MAX = '1.4.0'
 
 

tests/test_models/test_forward.py

@@ -8,7 +8,7 @@
 import pytest
 import torch
 import torch.nn as nn
-from mmcv.utils.parrots_wrapper import SyncBatchNorm, _BatchNorm
+from mmcv.cnn.utils import revert_sync_batchnorm
 
 
 def _demo_mm_inputs(input_shape=(2, 3, 8, 16), num_classes=10):
@@ -189,28 +189,6 @@ def _check_input_dim(self, inputs):
     pass
 
 
-def _convert_batchnorm(module):
-    module_output = module
-    if isinstance(module, SyncBatchNorm):
-        # to be consistent with SyncBN, we hack dim check function in BN
-        module_output = _BatchNorm(module.num_features, module.eps,
-                                   module.momentum, module.affine,
-                                   module.track_running_stats)
-        if module.affine:
-            module_output.weight.data = module.weight.data.clone().detach()
-            module_output.bias.data = module.bias.data.clone().detach()
-            # keep requires_grad unchanged
-            module_output.weight.requires_grad = module.weight.requires_grad
-            module_output.bias.requires_grad = module.bias.requires_grad
-        module_output.running_mean = module.running_mean
-        module_output.running_var = module.running_var
-        module_output.num_batches_tracked = module.num_batches_tracked
-    for name, child in module.named_children():
-        module_output.add_module(name, _convert_batchnorm(child))
-    del module
-    return module_output
-
-
 @patch('torch.nn.modules.batchnorm._BatchNorm._check_input_dim',
        _check_input_dim)
 @patch('torch.distributed.get_world_size', get_world_size)
@@ -241,7 +219,7 @@ def _test_encoder_decoder_forward(cfg_file):
         imgs = imgs.cuda()
         gt_semantic_seg = gt_semantic_seg.cuda()
     else:
-        segmentor = _convert_batchnorm(segmentor)
+        segmentor = revert_sync_batchnorm(segmentor)
 
     # Test forward train
     losses = segmentor.forward(

tools/train.py

@@ -4,9 +4,11 @@
 import os
 import os.path as osp
 import time
+import warnings
 
 import mmcv
 import torch
+from mmcv.cnn.utils import revert_sync_batchnorm
 from mmcv.runner import get_dist_info, init_dist
 from mmcv.utils import Config, DictAction, get_git_hash
 
@@ -137,6 +139,14 @@ def main():
         test_cfg=cfg.get('test_cfg'))
     model.init_weights()
 
+    # SyncBN is not support for DP
+    if not distributed:
+        warnings.warn(
+            'SyncBN is only supported with DDP. To be compatible with DP, '
+            'we convert SyncBN to BN. Please use dist_train.sh which can '
+            'avoid this error.')
+        model = revert_sync_batchnorm(model)
+
     logger.info(model)
 
     datasets = [build_dataset(cfg.data.train)]