use mmcv.init_dist (open-mmlab#1851)

.pre-commit-config.yaml

@@ -8,11 +8,11 @@ repos:
   hooks:
       - id: isort
 - repo: https://github.com/pre-commit/mirrors-yapf
-  rev: 80b9cd2f0f3b1f3456a77eff3ddbaf08f18c08ae
+  rev: v0.29.0
   hooks:
     - id: yapf
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v2.3.0
+  rev: v2.4.0
   hooks:
     - id: flake8
     - id: trailing-whitespace

mmdet/apis/__init__.py

@@ -1,10 +1,9 @@
-from .env import get_root_logger, init_dist, set_random_seed
 from .inference import (async_inference_detector, inference_detector,
                         init_detector, show_result, show_result_pyplot)
-from .train import train_detector
+from .train import get_root_logger, set_random_seed, train_detector
 
 __all__ = [
-    'async_inference_detector', 'init_dist', 'get_root_logger',
-    'set_random_seed', 'train_detector', 'init_detector', 'inference_detector',
-    'show_result', 'show_result_pyplot'
+    'get_root_logger', 'set_random_seed', 'train_detector', 'init_detector',
+    'async_inference_detector', 'inference_detector', 'show_result',
+    'show_result_pyplot'
 ]

mmdet/apis/train.py

@@ -1,18 +1,39 @@
-from __future__ import division
+import logging
+import random
 import re
 from collections import OrderedDict
 
+import numpy as np
 import torch
 import torch.distributed as dist
 from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
-from mmcv.runner import DistSamplerSeedHook, Runner, obj_from_dict
+from mmcv.runner import (DistSamplerSeedHook, Runner, get_dist_info,
+                         obj_from_dict)
 
 from mmdet import datasets
 from mmdet.core import (CocoDistEvalmAPHook, CocoDistEvalRecallHook,
                         DistEvalmAPHook, DistOptimizerHook, Fp16OptimizerHook)
 from mmdet.datasets import DATASETS, build_dataloader
 from mmdet.models import RPN
-from .env import get_root_logger
+
+
+def set_random_seed(seed):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+
+def get_root_logger(log_level=logging.INFO):
+    logger = logging.getLogger()
+    if not logger.hasHandlers():
+        logging.basicConfig(
+            format='%(asctime)s - %(levelname)s - %(message)s',
+            level=log_level)
+    rank, _ = get_dist_info()
+    if rank != 0:
+        logger.setLevel('ERROR')
+    return logger
 
 
 def parse_losses(losses):

requirements.txt

@@ -1,10 +1,10 @@
-mmcv>=0.2.10
-numpy
+albumentations>=0.3.2
+imagecorruptions
 matplotlib
+mmcv>=0.2.15
+numpy
+pycocotools
 six
 terminaltables
-pycocotools
 torch>=1.1
 torchvision
-imagecorruptions
-albumentations>=0.3.2

tools/test.py

@@ -9,9 +9,8 @@
 import torch
 import torch.distributed as dist
 from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
-from mmcv.runner import get_dist_info, load_checkpoint
+from mmcv.runner import get_dist_info, init_dist, load_checkpoint
 
-from mmdet.apis import init_dist
 from mmdet.core import coco_eval, results2json, wrap_fp16_model
 from mmdet.datasets import build_dataloader, build_dataset
 from mmdet.models import build_detector

tools/test_robustness.py

@@ -10,13 +10,13 @@
 import torch
 import torch.distributed as dist
 from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
-from mmcv.runner import get_dist_info, load_checkpoint
+from mmcv.runner import get_dist_info, init_dist, load_checkpoint
 from pycocotools.coco import COCO
 from pycocotools.cocoeval import COCOeval
 from robustness_eval import get_results
 
 from mmdet import datasets
-from mmdet.apis import init_dist, set_random_seed
+from mmdet.apis import set_random_seed
 from mmdet.core import (eval_map, fast_eval_recall, results2json,
                         wrap_fp16_model)
 from mmdet.datasets import build_dataloader, build_dataset

tools/train.py

@@ -4,10 +4,10 @@
 
 import torch
 from mmcv import Config
+from mmcv.runner import init_dist
 
 from mmdet import __version__
-from mmdet.apis import (get_root_logger, init_dist, set_random_seed,
-                        train_detector)
+from mmdet.apis import get_root_logger, set_random_seed, train_detector
 from mmdet.datasets import build_dataset
 from mmdet.models import build_detector