[Enhance] New-style CPU training and inference. (open-mmlab#1251)

* [Enhance] New-style CPU training and inference.

* assert mmcv version

* SyncBN to BN in training and testing

* SyncBN to BN in training and testing

* upload untracked files to this branch

* delete gpu_ids

* fix bugs

* assert args.gpu_id in train.py

* use cfg.gpu_ids = [args.gpu_id]

* use cfg.gpu_ids = [args.gpu_id]

* fix typo

* fix typo

* fix typos

Author: MengzhangLI

docs/en/inference.md

@@ -6,6 +6,7 @@ and also some high-level apis for easier integration to other projects.
 ### Test a dataset
 
 - single GPU
+- CPU
 - single node multiple GPU
 - multiple node
 
@@ -15,6 +16,10 @@ You can use the following commands to test a dataset.
 # single-gpu testing
 python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] [--show]
 
+# CPU: disable GPUs and run single-gpu testing script
+export CUDA_VISIBLE_DEVICES=-1
+python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] [--show]
+
 # multi-gpu testing
 ./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}]
 ```

docs/en/train.md

@@ -33,6 +33,20 @@ python tools/train.py ${CONFIG_FILE} [optional arguments]
 
 If you want to specify the working directory in the command, you can add an argument `--work-dir ${YOUR_WORK_DIR}`.
 
+### Train with CPU
+
+The process of training on the CPU is consistent with single GPU training. We just need to disable GPUs before the training process.
+
+```shell
+export CUDA_VISIBLE_DEVICES=-1
+```
+
+And then run the script [above](#train-with-a-single-gpu).
+
+```{warning}
+The process of training on the CPU is consistent with single GPU training. We just need to disable GPUs before the training process.
+```
+
 ### Train with multiple GPUs
 
 ```shell

docs/zh_cn/inference.md

@@ -5,6 +5,7 @@
 ### 测试一个数据集
 
 - 单卡 GPU
+- CPU
 - 单节点多卡 GPU
 - 多节点
 
@@ -14,6 +15,10 @@
 # 单卡 GPU 测试
 python tools/test.py ${配置文件} ${检查点文件} [--out ${结果文件}] [--eval ${评估指标}] [--show]
 
+# CPU: 禁用 GPU 并运行单 GPU 测试脚本
+export CUDA_VISIBLE_DEVICES=-1
+python tools/test.py ${配置文件} ${检查点文件} [--out ${结果文件}] [--eval ${评估指标}] [--show]
+
 # 多卡GPU 测试
 ./tools/dist_test.sh ${配置文件} ${检查点文件} ${GPU数目} [--out ${结果文件}] [--eval ${评估指标}]
 ```

docs/zh_cn/train.md

@@ -23,6 +23,20 @@ python tools/train.py ${配置文件} [可选参数]
 
 如果您想在命令里定义工作文件夹路径，您可以添加一个参数`--work-dir ${YOUR_WORK_DIR}`。
 
+### 使用 CPU 训练
+
+使用 CPU 训练的流程和使用单 GPU 训练的流程一致，我们仅需要在训练流程开始前禁用 GPU。
+
+```shell
+export CUDA_VISIBLE_DEVICES=-1
+```
+
+之后运行单 GPU 训练脚本即可。
+
+```{warning}
+我们不推荐用户使用 CPU 进行训练，这太过缓慢。我们支持这个功能是为了方便用户在没有 GPU 的机器上进行调试。
+```
+
 ### 使用多卡 GPU 训练
 
 ```shell

mmseg/apis/train.py

@@ -2,13 +2,15 @@
 import random
 import warnings
 
+import mmcv
 import numpy as np
 import torch
 import torch.distributed as dist
 from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
 from mmcv.runner import HOOKS, build_optimizer, build_runner, get_dist_info
 from mmcv.utils import build_from_cfg
 
+from mmseg import digit_version
 from mmseg.core import DistEvalHook, EvalHook
 from mmseg.datasets import build_dataloader, build_dataset
 from mmseg.utils import find_latest_checkpoint, get_root_logger
@@ -99,9 +101,10 @@ def train_segmentor(model,
             broadcast_buffers=False,
             find_unused_parameters=find_unused_parameters)
     else:
-        model = MMDataParallel(
-            model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids)
-
+        if not torch.cuda.is_available():
+            assert digit_version(mmcv.__version__) >= digit_version('1.4.4'), \
+                'Please use MMCV >= 1.4.4 for CPU training!'
+        model = MMDataParallel(model, device_ids=cfg.gpu_ids)
     # build runner
     optimizer = build_optimizer(model, cfg.optimizer)
 

tools/test.py

@@ -8,11 +8,13 @@
 
 import mmcv
 import torch
+from mmcv.cnn.utils import revert_sync_batchnorm
 from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
 from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
                          wrap_fp16_model)
 from mmcv.utils import DictAction
 
+from mmseg import digit_version
 from mmseg.apis import multi_gpu_test, single_gpu_test
 from mmseg.datasets import build_dataloader, build_dataset
 from mmseg.models import build_segmentor
@@ -147,11 +149,18 @@ def main():
     cfg.model.pretrained = None
     cfg.data.test.test_mode = True
 
-    cfg.gpu_ids = [args.gpu_id]
+    if args.gpu_id is not None:
+        cfg.gpu_ids = [args.gpu_id]
 
     # init distributed env first, since logger depends on the dist info.
     if args.launcher == 'none':
+        cfg.gpu_ids = [args.gpu_id]
         distributed = False
+        if len(cfg.gpu_ids) > 1:
+            warnings.warn(f'The gpu-ids is reset from {cfg.gpu_ids} to '
+                          f'{cfg.gpu_ids[0:1]} to avoid potential error in '
+                          'non-distribute testing time.')
+            cfg.gpu_ids = cfg.gpu_ids[0:1]
     else:
         distributed = True
         init_dist(args.launcher, **cfg.dist_params)
@@ -236,7 +245,15 @@ def main():
         tmpdir = None
 
     if not distributed:
-        model = MMDataParallel(model, device_ids=[0])
+        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.')
+        if not torch.cuda.is_available():
+            assert digit_version(mmcv.__version__) >= digit_version('1.4.4'), \
+                'Please use MMCV >= 1.4.4 for CPU training!'
+        model = revert_sync_batchnorm(model)
+        model = MMDataParallel(model, device_ids=cfg.gpu_ids)
         results = single_gpu_test(
             model,
             data_loader,