Merge branch 'dev' into single-stage

mmdet/datasets/utils/data_container.py

-import functools
-
-import torch
-
-
-def assert_tensor_type(func):
-
-    @functools.wraps(func)
-    def wrapper(*args, **kwargs):
-        if not isinstance(args[0].data, torch.Tensor):
-            raise AttributeError('{} has no attribute {} for type {}'.format(
-                args[0].__class__.__name__, func.__name__, args[0].datatype))
-        return func(*args, **kwargs)
-
-    return wrapper
-
-
-class DataContainer(object):
-
-    def __init__(self, data, stack=False, padding_value=0, cpu_only=False):
-        self._data = data
-        self._cpu_only = cpu_only
-        self._stack = stack
-        self._padding_value = padding_value
-
-    def __repr__(self):
-        return '{}({})'.format(self.__class__.__name__, repr(self.data))
-
-    @property
-    def data(self):
-        return self._data
-
-    @property
-    def datatype(self):
-        if isinstance(self.data, torch.Tensor):
-            return self.data.type()
-        else:
-            return type(self.data)
-
-    @property
-    def cpu_only(self):
-        return self._cpu_only
-
-    @property
-    def stack(self):
-        return self._stack
-
-    @property
-    def padding_value(self):
-        return self._padding_value
-
-    @assert_tensor_type
-    def size(self, *args, **kwargs):
-        return self.data.size(*args, **kwargs)
-
-    @assert_tensor_type
-    def dim(self):
-        return self.data.dim()

tools/dist_train.sh

@@ -2,4 +2,4 @@
 
 PYTHON=${PYTHON:-"python"}
 
-$PYTHON -m torch.distributed.launch --nproc_per_node=$2 train.py $1 --launcher pytorch ${@:3}
+$PYTHON -m torch.distributed.launch --nproc_per_node=$2 $(dirname "$0")/train.py $1 --launcher pytorch ${@:3}

tools/test.py

@@ -3,9 +3,10 @@ import argparse
 import torch
 import mmcv
 from mmcv.runner import load_checkpoint, parallel_test, obj_from_dict
+from mmcv.parallel import scatter, MMDataParallel
 
 from mmdet import datasets
-from mmdet.core import scatter, MMDataParallel, results2json, coco_eval
+from mmdet.core import results2json, coco_eval
 from mmdet.datasets import collate, build_dataloader
 from mmdet.models import build_detector, detectors
 
@@ -44,17 +45,16 @@ def parse_args():
         '--eval',
         type=str,
         nargs='+',
-        choices=['proposal', 'bbox', 'segm', 'keypoints'],
+        choices=['proposal', 'proposal_fast', 'bbox', 'segm', 'keypoints'],
         help='eval types')
     parser.add_argument('--show', action='store_true', help='show results')
     args = parser.parse_args()
     return args
 
 
-args = parse_args()
-
-
 def main():
+    args = parse_args()
+
     cfg = mmcv.Config.fromfile(args.config)
     cfg.model.pretrained = None
     cfg.data.test.test_mode = True

tools/train.py

@@ -2,17 +2,18 @@ from __future__ import division
 
 import argparse
 import logging
+import random
 from collections import OrderedDict
 
 import numpy as np
 import torch
 from mmcv import Config
-from mmcv.runner import Runner, obj_from_dict
+from mmcv.runner import Runner, obj_from_dict, DistSamplerSeedHook
+from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
 
 from mmdet import datasets, __version__
-from mmdet.core import (init_dist, DistOptimizerHook, DistSamplerSeedHook,
-                        MMDataParallel, MMDistributedDataParallel,
-                        CocoDistEvalRecallHook, CocoDistEvalmAPHook)
+from mmdet.core import (init_dist, DistOptimizerHook, CocoDistEvalRecallHook,
+                        CocoDistEvalmAPHook)
 from mmdet.datasets import build_dataloader
 from mmdet.models import build_detector, RPN
 
@@ -55,6 +56,7 @@ def get_logger(log_level):
 
 
 def set_random_seed(seed):
+    random.seed(seed)
     np.random.seed(seed)
     torch.manual_seed(seed)
     torch.cuda.manual_seed_all(seed)
@@ -89,7 +91,7 @@ def main():
     if args.work_dir is not None:
         cfg.work_dir = args.work_dir
     cfg.gpus = args.gpus
-    # add mmdet version to checkpoint as meta data
+    # save mmdet version in checkpoint as meta data
     cfg.checkpoint_config.meta = dict(
         mmdet_version=__version__, config=cfg.text)
 
@@ -103,13 +105,13 @@ def main():
     # init distributed environment if necessary
     if args.launcher == 'none':
         dist = False
-        logger.info('Disabled distributed training.')
+        logger.info('Non-distributed training.')
     else:
         dist = True
         init_dist(args.launcher, **cfg.dist_params)
         if torch.distributed.get_rank() != 0:
             logger.setLevel('ERROR')
-        logger.info('Enabled distributed training.')
+        logger.info('Distributed training.')
 
     # prepare data loaders
     train_dataset = obj_from_dict(cfg.data.train, datasets)