diff --git a/mmdet/apis/train.py b/mmdet/apis/train.py
index 47320c69eaa2557730aa3d9c34a82c3913bf6df0..c146b04feab9e354fb7953a253a2711e7c778ca2 100644
--- a/mmdet/apis/train.py
+++ b/mmdet/apis/train.py
@@ -24,15 +24,24 @@ def set_random_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)
+def get_root_logger(log_file=None, log_level=logging.INFO):
+ logger = logging.getLogger('mmdet')
+ # if the logger has been initialized, just return it
+ if logger.hasHandlers():
+ return logger
+
+ logging.basicConfig(
+ format='%(asctime)s - %(levelname)s - %(message)s', level=log_level)
rank, _ = get_dist_info()
if rank != 0:
logger.setLevel('ERROR')
+ elif log_file is not None:
+ file_handler = logging.FileHandler(log_file, 'w')
+ file_handler.setFormatter(
+ logging.Formatter('%(asctime)s - %(levelname)s - %(message)s'))
+ file_handler.setLevel(log_level)
+ logger.addHandler(file_handler)
+
return logger
@@ -75,15 +84,26 @@ def train_detector(model,
cfg,
distributed=False,
validate=False,
- logger=None):
- if logger is None:
- logger = get_root_logger(cfg.log_level)
+ timestamp=None):
+ logger = get_root_logger(cfg.log_level)
# start training
if distributed:
- _dist_train(model, dataset, cfg, validate=validate)
+ _dist_train(
+ model,
+ dataset,
+ cfg,
+ validate=validate,
+ logger=logger,
+ timestamp=timestamp)
else:
- _non_dist_train(model, dataset, cfg, validate=validate)
+ _non_dist_train(
+ model,
+ dataset,
+ cfg,
+ validate=validate,
+ logger=logger,
+ timestamp=timestamp)
def build_optimizer(model, optimizer_cfg):
@@ -166,7 +186,12 @@ def build_optimizer(model, optimizer_cfg):
return optimizer_cls(params, **optimizer_cfg)
-def _dist_train(model, dataset, cfg, validate=False):
+def _dist_train(model,
+ dataset,
+ cfg,
+ validate=False,
+ logger=None,
+ timestamp=None):
# prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
data_loaders = [
@@ -179,8 +204,10 @@ def _dist_train(model, dataset, cfg, validate=False):
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
- runner = Runner(model, batch_processor, optimizer, cfg.work_dir,
- cfg.log_level)
+ runner = Runner(
+ model, batch_processor, optimizer, cfg.work_dir, logger=logger)
+ # an ugly walkaround to make the .log and .log.json filenames the same
+ runner.timestamp = timestamp
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
@@ -218,7 +245,12 @@ def _dist_train(model, dataset, cfg, validate=False):
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
-def _non_dist_train(model, dataset, cfg, validate=False):
+def _non_dist_train(model,
+ dataset,
+ cfg,
+ validate=False,
+ logger=None,
+ timestamp=None):
if validate:
raise NotImplementedError('Built-in validation is not implemented '
'yet in not-distributed training. Use '
@@ -239,8 +271,10 @@ def _non_dist_train(model, dataset, cfg, validate=False):
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
- runner = Runner(model, batch_processor, optimizer, cfg.work_dir,
- cfg.log_level)
+ runner = Runner(
+ model, batch_processor, optimizer, cfg.work_dir, logger=logger)
+ # an ugly walkaround to make the .log and .log.json filenames the same
+ runner.timestamp = timestamp
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
diff --git a/mmdet/models/backbones/hrnet.py b/mmdet/models/backbones/hrnet.py
index c73fed01d22cc0f174f349e5d097b1ceda9b2867..7b7b469041b8ccb497f4bd9426e85699b2067e6f 100644
--- a/mmdet/models/backbones/hrnet.py
+++ b/mmdet/models/backbones/hrnet.py
@@ -1,5 +1,3 @@
-import logging
-
import torch.nn as nn
from mmcv.cnn import constant_init, kaiming_init
from mmcv.runner import load_checkpoint
@@ -462,7 +460,8 @@ class HRNet(nn.Module):
def init_weights(self, pretrained=None):
if isinstance(pretrained, str):
- logger = logging.getLogger()
+ from mmdet.apis import get_root_logger
+ logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.modules():
diff --git a/mmdet/models/backbones/resnet.py b/mmdet/models/backbones/resnet.py
index ac14bc2d2d39cf27ffd1d0893ae1dbe1831b0e0a..3343c5c504e39b72dcd118bbcc513148e39a7e5b 100644
--- a/mmdet/models/backbones/resnet.py
+++ b/mmdet/models/backbones/resnet.py
@@ -1,5 +1,3 @@
-import logging
-
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import constant_init, kaiming_init
@@ -495,7 +493,8 @@ class ResNet(nn.Module):
def init_weights(self, pretrained=None):
if isinstance(pretrained, str):
- logger = logging.getLogger()
+ from mmdet.apis import get_root_logger
+ logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.modules():
diff --git a/mmdet/models/backbones/ssd_vgg.py b/mmdet/models/backbones/ssd_vgg.py
index b199444b9c128935517f00d113f4e478e71bea77..8cbe42cca9cb37855b1433a45a5a8110465f543c 100644
--- a/mmdet/models/backbones/ssd_vgg.py
+++ b/mmdet/models/backbones/ssd_vgg.py
@@ -1,5 +1,3 @@
-import logging
-
import torch
import torch.nn as nn
import torch.nn.functional as F
@@ -75,7 +73,8 @@ class SSDVGG(VGG):
def init_weights(self, pretrained=None):
if isinstance(pretrained, str):
- logger = logging.getLogger()
+ from mmdet.apis import get_root_logger
+ logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.features.modules():
diff --git a/mmdet/models/detectors/base.py b/mmdet/models/detectors/base.py
index 6a33dab4c59ec8551dc3fa5e5a8965954c063d56..7d0929a760b742662563e88b6a0d8d20e6a3693d 100644
--- a/mmdet/models/detectors/base.py
+++ b/mmdet/models/detectors/base.py
@@ -1,4 +1,3 @@
-import logging
from abc import ABCMeta, abstractmethod
import mmcv
@@ -9,11 +8,9 @@ import torch.nn as nn
from mmdet.core import auto_fp16, get_classes, tensor2imgs
-class BaseDetector(nn.Module):
+class BaseDetector(nn.Module, metaclass=ABCMeta):
"""Base class for detectors"""
- __metaclass__ = ABCMeta
-
def __init__(self):
super(BaseDetector, self).__init__()
self.fp16_enabled = False
@@ -61,9 +58,8 @@ class BaseDetector(nn.Module):
"""
pass
- @abstractmethod
async def async_simple_test(self, img, img_meta, **kwargs):
- pass
+ raise NotImplementedError
@abstractmethod
def simple_test(self, img, img_meta, **kwargs):
@@ -75,7 +71,8 @@ class BaseDetector(nn.Module):
def init_weights(self, pretrained=None):
if pretrained is not None:
- logger = logging.getLogger()
+ from mmdet.apis import get_root_logger
+ logger = get_root_logger()
logger.info('load model from: {}'.format(pretrained))
async def aforward_test(self, *, img, img_meta, **kwargs):
diff --git a/mmdet/models/shared_heads/res_layer.py b/mmdet/models/shared_heads/res_layer.py
index cbc77ac98d72b2a3c28c06e8feb4cc9aa3a109c7..33b962bb7a12d521587803253d46dcb2a787a74c 100644
--- a/mmdet/models/shared_heads/res_layer.py
+++ b/mmdet/models/shared_heads/res_layer.py
@@ -1,5 +1,3 @@
-import logging
-
import torch.nn as nn
from mmcv.cnn import constant_init, kaiming_init
from mmcv.runner import load_checkpoint
@@ -47,7 +45,8 @@ class ResLayer(nn.Module):
def init_weights(self, pretrained=None):
if isinstance(pretrained, str):
- logger = logging.getLogger()
+ from mmdet.apis import get_root_logger
+ logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.modules():
diff --git a/tools/train.py b/tools/train.py
index e3bbbde6a0b76d399a04dac256f9747f480fdb64..5958d2409b810c344c867a59ff173a58d8b881d3 100644
--- a/tools/train.py
+++ b/tools/train.py
@@ -1,7 +1,10 @@
from __future__ import division
import argparse
import os
+import os.path as osp
+import time
+import mmcv
import torch
from mmcv import Config
from mmcv.runner import init_dist
@@ -71,11 +74,17 @@ def main():
distributed = True
init_dist(args.launcher, **cfg.dist_params)
- # init logger before other steps
- logger = get_root_logger(cfg.log_level)
+ # create work_dir
+ mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
+ # init the logger before other steps
+ timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
+ log_file = osp.join(cfg.work_dir, '{}.log'.format(timestamp))
+ logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
+
+ # log some basic info
logger.info('Distributed training: {}'.format(distributed))
logger.info('MMDetection Version: {}'.format(__version__))
- logger.info('Config: {}'.format(cfg.text))
+ logger.info('Config:\n{}'.format(cfg.text))
# set random seeds
if args.seed is not None:
@@ -103,7 +112,7 @@ def main():
cfg,
distributed=distributed,
validate=args.validate,
- logger=logger)
+ timestamp=timestamp)
if __name__ == '__main__':