Merge branch 'main' into pr/53

.github/workflows/build.yml

@@ -223,3 +223,41 @@ jobs:
           env_vars: OS,PYTHON
           name: codecov-umbrella
           fail_ci_if_error: false
+
+  test_windows:
+      runs-on: ${{ matrix.os }}
+      strategy:
+        matrix:
+          os: [windows-2022]
+          python: [3.8]
+          platform: [cpu, cu102]
+      steps:
+        - uses: actions/checkout@v2
+        - name: Set up Python ${{ matrix.python }}
+          uses: actions/setup-python@v2
+          with:
+            python-version: ${{ matrix.python }}
+        - name: Upgrade pip
+          run: pip install pip --upgrade --user
+        - name: Install OpenCV
+          run: pip install opencv-python>=3
+        - name: Install PyTorch
+          # As a complement to Linux CI, we test on PyTorch LTS version
+          run: pip install torch==1.8.2+${{ matrix.platform }} torchvision==0.9.2+${{ matrix.platform }} -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
+        - name: Install MMCV
+          run: |
+            pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.8/index.html --only-binary mmcv-full
+        - name: Install MMCLS and MMDET
+          run: pip install mmcls mmdet
+        - name: Install unittest dependencies
+          run: pip install -r requirements/tests.txt -r requirements/optional.txt
+        - name: Build and install
+          run: pip install -e .
+        - name: Run unittests
+          run: |
+            python -m pip install timm
+            coverage run --branch --source mmfewshot -m pytest tests/
+        - name: Generate coverage report
+          run: |
+            coverage xml
+            coverage report -m

README.md

@@ -147,18 +147,21 @@ mmfewshot is an open source project that is contributed by researchers and engin
 
 ## Projects in OpenMMLab
 
-- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
+- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
-- [MIM](https://github.com/open-mmlab/mim): MIM Installs OpenMMLab Packages.
 - [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
 - [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
 - [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
+- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
 - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
+- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
+- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
+- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
+- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
+- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
+- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
 - [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
 - [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
-- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
-- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
-- [MMOCR](https://github.com/open-mmlab/mmocr): A comprehensive toolbox for text detection, recognition and understanding.
-- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab's next-generation toolbox for generative models.
 - [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
-- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
+- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
-- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
+- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.

README_zh-CN.md

@@ -147,21 +147,24 @@ MMFewShot 是一款由不同学校和公司共同贡献的开源项目。我们
 
 ## OpenMMLab 的其他项目
 
-- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
+- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
-- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMLab 项目、算法、模型的统一入口
+- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
-- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱与测试基准
+- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
-- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 检测工具箱与测试基准
+- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
-- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用3D目标检测平台
+- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
-- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱与测试基准
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
-- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱与测试基准
+- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具箱
-- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
+- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
-- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱与测试基准
-- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
-- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
-- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 新一代生成模型工具箱
-- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
-- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
 - [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
+- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
+- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
+- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
+- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
+- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
+- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
+- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
+- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
 
 ## 欢迎加入 OpenMMLab 社区
 

docs/en/get_started.md

@@ -1,6 +1,7 @@
 ## Test a model
 
 - single GPU
+- CPU
 - single node multiple GPU
 - multiple node
 
@@ -10,6 +11,10 @@ You can use the following commands to infer a dataset.
 # single-gpu
 python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]
 
+# CPU: disable GPUs and run single-gpu testing script
+export CUDA_VISIBLE_DEVICES=-1
+python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]
+
 # multi-gpu
 ./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [optional arguments]
 
@@ -46,6 +51,20 @@ 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 on 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
+python tools/train.py ${CONFIG_FILE} [optional arguments]
+```
+
+**Note**:
+
+We do not recommend users to use CPU for training because it is too slow. We support this feature to allow users to debug on machines without GPU for convenience.
+
+
 ### Train with multiple GPUs
 
 ```shell

docs/zh_cn/get_started.md

@@ -1,6 +1,7 @@
 ## Test a model
 
 - single GPU
+- CPU
 - single node multiple GPU
 - multiple node
 
@@ -10,6 +11,10 @@ You can use the following commands to infer a dataset.
 # single-gpu
 python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]
 
+# CPU: disable GPUs and run single-gpu testing script
+export CUDA_VISIBLE_DEVICES=-1
+python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]
+
 # multi-gpu
 ./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [optional arguments]
 
@@ -46,6 +51,20 @@ 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 on 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
+python tools/train.py ${CONFIG_FILE} [optional arguments]
+```
+
+**Note**:
+
+We do not recommend users to use CPU for training because it is too slow. We support this feature to allow users to debug on machines without GPU for convenience.
+
+
 ### Train with multiple GPUs
 
 ```shell

mmfewshot/__init__.py

@@ -32,7 +32,7 @@ assert (digit_version(mmcv_minimum_version) <= mmcv_version
     f'Please install mmcv>={mmcv_minimum_version}, <={mmcv_maximum_version}.'
 
 mmdet_minimum_version = '2.16.0'
-mmdet_maximum_version = '2.21.0'
+mmdet_maximum_version = '2.23.0'
 mmdet_version = digit_version(mmdet.__version__)
 
 

mmfewshot/classification/apis/train.py

@@ -1,4 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import warnings
 from typing import Dict, Union
 
 import torch
@@ -22,7 +23,7 @@ def train_model(model: Union[MMDataParallel, MMDistributedDataParallel],
                 distributed: bool = False,
                 validate: bool = False,
                 timestamp: str = None,
-                device: str = 'cuda',
+                device: str = None,
                 meta: Dict = None) -> None:
     logger = get_root_logger(log_level=cfg.log_level)
 
@@ -54,13 +55,14 @@ def train_model(model: Union[MMDataParallel, MMDistributedDataParallel],
             broadcast_buffers=False,
             find_unused_parameters=find_unused_parameters)
     else:
-        if device == 'cuda':
+        if device == 'cpu':
-            model = MMDataParallel(
+            warnings.warn(
-                model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids)
+                'The argument `device` is deprecated. To use cpu to train, '
-        elif device == 'cpu':
+                'please refers to https://mmclassification.readthedocs.io/en'
+                '/latest/getting_started.html#train-a-model')
             model = model.cpu()
         else:
-            raise ValueError(F'unsupported device name {device}.')
+            model = MMDataParallel(model, device_ids=cfg.gpu_ids)
 
     # build runner
     optimizer = build_optimizer(model, cfg.optimizer)

mmfewshot/detection/apis/train.py

@@ -55,8 +55,8 @@ def train_detector(model: nn.Module,
             broadcast_buffers=False,
             find_unused_parameters=find_unused_parameters)
     else:
-        model = MMDataParallel(
+        # Please use MMCV >= 1.4.4 for CPU training!
-            model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids)
+        model = MMDataParallel(model, device_ids=cfg.gpu_ids)
 
     # build runner
     optimizer = build_optimizer(model, cfg.optimizer)

tests/test_detection_data/test_datasets/test_few_shot_coco_dataset.py

@@ -1,5 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import copy
+import os
 import tempfile
 
 import numpy as np
@@ -144,10 +145,10 @@ def test_few_shot_coco_dataset():
 
     # test save and load dataset
     with tempfile.TemporaryDirectory() as tmpdir:
-        dataset.save_data_infos(tmpdir + 'ann.json')
+        dataset.save_data_infos(tmpdir + f'{os.sep}ann.json')
         data_config['ann_cfg'] = [{
             'type': 'saved_dataset',
-            'ann_file': tmpdir + 'ann.json'
+            'ann_file': tmpdir + f'{os.sep}ann.json'
         }]
         dataset = FewShotCocoDataset(**data_config)
         count = 0

tests/test_detection_data/test_datasets/test_few_shot_voc_dataset.py

@@ -1,5 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import copy
+import os
 import tempfile
 
 import numpy as np
@@ -108,10 +109,10 @@ def test_few_shot_voc_dataset():
         dataset.data_infos[1]['ann']['bboxes_ignore'] = np.array(
             [[11, 11, 100, 100]])
         dataset.data_infos[1]['ann']['labels_ignore'] = np.array([0])
-        dataset.save_data_infos(tmpdir + 'ann.json')
+        dataset.save_data_infos(tmpdir + f'{os.sep}ann.json')
         data_config['ann_cfg'] = [{
             'type': 'saved_dataset',
-            'ann_file': tmpdir + 'ann.json'
+            'ann_file': tmpdir + f'{os.sep}ann.json'
         }]
         dataset = FewShotVOCDataset(**data_config)
         count = 0

tests/test_detection_data/test_datasets/test_nway_kshot_dataset.py

@@ -1,4 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import os
 import tempfile
 
 import numpy as np
@@ -85,4 +86,4 @@ def test_nway_kshot_dataset():
                 assert count <= 1
         # test save dataset
         with tempfile.TemporaryDirectory() as tmpdir:
-            nway_kshot_dataset.save_data_infos(tmpdir + 'ann.json')
+            nway_kshot_dataset.save_data_infos(tmpdir + f'{os.sep}ann.json')

tests/test_detection_data/test_datasets/test_query_aware_dataset.py

@@ -1,4 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import os
 import tempfile
 
 import numpy as np
@@ -115,4 +116,4 @@ def test_query_aware_dataset():
 
     # test save dataset
     with tempfile.TemporaryDirectory() as tmpdir:
-        query_aware_dataset.save_data_infos(tmpdir + 'ann.json')
+        query_aware_dataset.save_data_infos(tmpdir + f'{os.sep}ann.json')

tests/test_detection_data/test_datasets/test_two_branch_dataset.py

@@ -1,4 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import os
 import tempfile
 
 from mmdet.apis import set_random_seed
@@ -45,4 +46,4 @@ def test_two_branch_dataset():
     assert len(two_branch_dataset) == 25
     # test save dataset
     with tempfile.TemporaryDirectory() as tmpdir:
-        two_branch_dataset.save_data_infos(tmpdir + 'ann.json')
+        two_branch_dataset.save_data_infos(tmpdir + f'{os.sep}ann.json')

tools/classification/test.py

@@ -3,6 +3,7 @@ import argparse
 import os
 import os.path as osp
 import time
+import warnings
 
 import mmcv
 import torch
@@ -64,10 +65,19 @@ def parse_args():
         help='whether to set deterministic options for CUDNN backend.')
     parser.add_argument('--local_rank', type=int, default=0)
     parser.add_argument(
-        '--device',
+        '--device', default=None, help='device used for testing. (Deprecated)')
-        choices=['cpu', 'cuda'],
+    parser.add_argument(
-        default='cuda',
+        '--gpu-ids',
-        help='device used for testing')
+        type=int,
+        nargs='+',
+        help='(Deprecated, please use --gpu-id) ids of gpus to use '
+        '(only applicable to non-distributed testing)')
+    parser.add_argument(
+        '--gpu-id',
+        type=int,
+        default=0,
+        help='id of gpu to use '
+        '(only applicable to non-distributed testing)')
     parser.add_argument(
         '--show_task_results',
         action='store_true',
@@ -75,6 +85,15 @@ def parse_args():
     args = parser.parse_args()
     if 'LOCAL_RANK' not in os.environ:
         os.environ['LOCAL_RANK'] = str(args.local_rank)
+
+    if args.device:
+        warnings.warn(
+            '--device is deprecated. To use cpu to test, please '
+            'refers to https://mmclassification.readthedocs.io/en/latest/'
+            'getting_started.html#inference-with-pretrained-models')
+
+    assert args.metrics or args.out, \
+        'Please specify at least one of output path and evaluation metrics.'
     return args
 
 
@@ -96,7 +115,14 @@ def main():
         # use config filename as default work_dir if cfg.work_dir is None
         cfg.work_dir = osp.join('./work_dirs',
                                 osp.splitext(osp.basename(args.config))[0])
-
+    if args.gpu_ids is not None:
+        cfg.gpu_ids = args.gpu_ids[0:1]
+        warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. '
+                      'Because we only support single GPU mode in '
+                      'non-distributed testing. Use the first GPU '
+                      'in `gpu_ids` now.')
+    else:
+        cfg.gpu_ids = [args.gpu_id]
     # init distributed env first, since logger depends on the dist info.
     if args.launcher == 'none':
         distributed = False

tools/classification/train.py

@@ -4,6 +4,7 @@ import copy
 import os
 import os.path as osp
 import time
+import warnings
 
 import cv2
 import mmcv
@@ -45,7 +46,13 @@ def parse_args():
         '--gpu-ids',
         type=int,
         nargs='+',
-        help='ids of gpus to use '
+        help='(Deprecated, please use --gpu-id) ids of gpus to use '
+        '(only applicable to non-distributed training)')
+    group_gpus.add_argument(
+        '--gpu-id',
+        type=int,
+        default=0,
+        help='id of gpu to use '
         '(only applicable to non-distributed training)')
     parser.add_argument('--seed', type=int, default=None, help='random seed')
     parser.add_argument(
@@ -87,10 +94,19 @@ def main():
                                 osp.splitext(osp.basename(args.config))[0])
     if args.resume_from is not None:
         cfg.resume_from = args.resume_from
+    if args.gpus is not None:
+        cfg.gpu_ids = range(1)
+        warnings.warn('`--gpus` is deprecated because we only support '
+                      'single GPU mode in non-distributed training. '
+                      'Use `gpus=1` now.')
     if args.gpu_ids is not None:
-        cfg.gpu_ids = args.gpu_ids
+        cfg.gpu_ids = args.gpu_ids[0:1]
-    else:
+        warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. '
-        cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)
+                      'Because we only support single GPU mode in '
+                      'non-distributed training. Use the first GPU '
+                      'in `gpu_ids` now.')
+    if args.gpus is None and args.gpu_ids is None:
+        cfg.gpu_ids = [args.gpu_id]
 
     # init distributed env first, since logger depends on the dist info.
     if args.launcher == 'none':

tools/detection/test.py

@@ -27,6 +27,18 @@ def parse_args():
         nargs='+',
         help='evaluation metrics, which depends on the dataset, e.g., "bbox",'
         ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC')
+    parser.add_argument(
+        '--gpu-ids',
+        type=int,
+        nargs='+',
+        help='(Deprecated, please use --gpu-id) ids of gpus to use '
+        '(only applicable to non-distributed training)')
+    parser.add_argument(
+        '--gpu-id',
+        type=int,
+        default=0,
+        help='id of gpu to use '
+        '(only applicable to non-distributed testing)')
     parser.add_argument('--show', action='store_true', help='show results')
     parser.add_argument(
         '--show-dir', help='directory where painted images will be saved')
@@ -116,7 +128,14 @@ def main():
     # currently only support single images testing
     samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1)
     assert samples_per_gpu == 1, 'currently only support single images testing'
-
+    if args.gpu_ids is not None:
+        cfg.gpu_ids = args.gpu_ids[0:1]
+        warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. '
+                      'Because we only support single GPU mode in '
+                      'non-distributed testing. Use the first GPU '
+                      'in `gpu_ids` now.')
+    else:
+        cfg.gpu_ids = [args.gpu_id]
     # init distributed env first, since logger depends on the dist info.
     if args.launcher == 'none':
         distributed = False
@@ -176,7 +195,8 @@ def main():
             shuffle=False)
 
     if not distributed:
-        model = MMDataParallel(model, device_ids=[0])
+        # Please use MMCV >= 1.4.4 for CPU testing!
+        model = MMDataParallel(model, device_ids=cfg.gpu_ids)
         show_kwargs = dict(show_score_thr=args.show_score_thr)
         if cfg.data.get('model_init', None) is not None:
             from mmfewshot.detection.apis import (single_gpu_model_init,

tools/detection/train.py

@@ -48,8 +48,14 @@ def parse_args():
         '--gpu-ids',
         type=int,
         nargs='+',
-        help='ids of gpus to use '
+        help='(Deprecated, please use --gpu-id) ids of gpus to use '
         '(only applicable to non-distributed training)')
+    parser.add_argument(
+        '--gpu-id',
+        type=int,
+        default=0,
+        help='id of gpu to use '
+        '(only applicable to non-distributed testing)')
     parser.add_argument('--seed', type=int, default=None, help='random seed')
     parser.add_argument(
         '--deterministic',
@@ -119,15 +125,24 @@ def main():
                                 osp.splitext(osp.basename(args.config))[0])
     if args.resume_from is not None:
         cfg.resume_from = args.resume_from
+    if args.gpus is not None:
+        cfg.gpu_ids = range(1)
+        warnings.warn('`--gpus` is deprecated because we only support '
+                      'single GPU mode in non-distributed training. '
+                      'Use `gpus=1` now.')
     if args.gpu_ids is not None:
-        cfg.gpu_ids = args.gpu_ids
+        cfg.gpu_ids = args.gpu_ids[0:1]
-    else:
+        warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. '
-        cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)
+                      'Because we only support single GPU mode in '
+                      'non-distributed training. Use the first GPU '
+                      'in `gpu_ids` now.')
+    if args.gpus is None and args.gpu_ids is None:
+        cfg.gpu_ids = [args.gpu_id]
 
     # init distributed env first, since logger depends on the dist info.
     if args.launcher == 'none':
         distributed = False
-        rank, world_size = get_dist_info()
+        rank = 0
     else:
         distributed = True
         init_dist(args.launcher, **cfg.dist_params)