diff --git a/mmdet/apis/train.py b/mmdet/apis/train.py
index f68de4f2f6078017d080fae196cbd07a7a6b99f2..97c0dc69ebf1b1815ef99bfd51aa7f60ca4e9fd0 100644
--- a/mmdet/apis/train.py
+++ b/mmdet/apis/train.py
@@ -1,4 +1,3 @@
-import logging
import random
import re
from collections import OrderedDict
@@ -7,35 +6,14 @@ import numpy as np
import torch
import torch.distributed as dist
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
-from mmcv.runner import (DistSamplerSeedHook, Runner, get_dist_info,
- obj_from_dict)
+from mmcv.runner import DistSamplerSeedHook, Runner, 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
-
-
-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
+from mmdet.utils import get_root_logger
def set_random_seed(seed, deterministic=False):
diff --git a/mmdet/core/evaluation/mean_ap.py b/mmdet/core/evaluation/mean_ap.py
index eb877ee916dd2bbd5c7dbdb594ffa8d3a808ce0b..4e3cd5d07654d9cd7b9ba4f7b55b71f2f34f31c9 100644
--- a/mmdet/core/evaluation/mean_ap.py
+++ b/mmdet/core/evaluation/mean_ap.py
@@ -1,10 +1,10 @@
-import logging
from multiprocessing import Pool
import mmcv
import numpy as np
from terminaltables import AsciiTable
+from mmdet.utils import print_log
from .bbox_overlaps import bbox_overlaps
from .class_names import get_classes
@@ -268,7 +268,7 @@ def eval_map(det_results,
scale_ranges=None,
iou_thr=0.5,
dataset=None,
- logger='default',
+ logger=None,
nproc=4):
"""Evaluate mAP of a dataset.
@@ -291,11 +291,8 @@ def eval_map(det_results,
dataset (list[str] | str | None): Dataset name or dataset classes,
there are minor differences in metrics for different datsets, e.g.
"voc07", "imagenet_det", etc. Default: None.
- logger (logging.Logger | 'print' | None): The way to print the mAP
- summary. If a Logger is specified, then the summary will be logged
- with `logger.info()`; if set to "print", then it will be simply
- printed to stdout; if set to None, then no information will be
- printed. Default: 'print'.
+ logger (logging.Logger | str | None): The way to print the mAP
+ summary. See `mmdet.utils.print_log()` for details. Default: None.
nproc (int): Processes used for computing TP and FP.
Default: 4.
@@ -383,9 +380,9 @@ def eval_map(det_results,
if cls_result['num_gts'] > 0:
aps.append(cls_result['ap'])
mean_ap = np.array(aps).mean().item() if aps else 0.0
- if logger is not None:
- print_map_summary(
- mean_ap, eval_results, dataset, area_ranges, logger=logger)
+
+ print_map_summary(
+ mean_ap, eval_results, dataset, area_ranges, logger=logger)
return mean_ap, eval_results
@@ -405,18 +402,12 @@ def print_map_summary(mean_ap,
results (list[dict]): Calculated from `eval_map()`.
dataset (list[str] | str | None): Dataset name or dataset classes.
scale_ranges (list[tuple] | None): Range of scales to be evaluated.
- logger (logging.Logger | 'print' | None): The way to print the mAP
- summary. If a Logger is specified, then the summary will be logged
- with `logger.info()`; if set to "print", then it will be simply
- printed to stdout; if set to None, then no information will be
- printed. Default: 'print'.
+ logger (logging.Logger | str | None): The way to print the mAP
+ summary. See `mmdet.utils.print_log()` for details. Default: None.
"""
- def _print(content):
- if logger == 'print':
- print(content)
- elif isinstance(logger, logging.Logger):
- logger.info(content)
+ if logger == 'silent':
+ return
if isinstance(results[0]['ap'], np.ndarray):
num_scales = len(results[0]['ap'])
@@ -426,9 +417,6 @@ def print_map_summary(mean_ap,
if scale_ranges is not None:
assert len(scale_ranges) == num_scales
- assert logger is None or logger == 'print' or isinstance(
- logger, logging.Logger)
-
num_classes = len(results)
recalls = np.zeros((num_scales, num_classes), dtype=np.float32)
@@ -453,7 +441,7 @@ def print_map_summary(mean_ap,
header = ['class', 'gts', 'dets', 'recall', 'ap']
for i in range(num_scales):
if scale_ranges is not None:
- _print('Scale range ', scale_ranges[i])
+ print_log('Scale range {}'.format(scale_ranges[i]), logger=logger)
table_data = [header]
for j in range(num_classes):
row_data = [
@@ -464,4 +452,4 @@ def print_map_summary(mean_ap,
table_data.append(['mAP', '', '', '', '{:.3f}'.format(mean_ap[i])])
table = AsciiTable(table_data)
table.inner_footing_row_border = True
- _print('\n' + table.table)
+ print_log('\n' + table.table, logger=logger)
diff --git a/mmdet/models/backbones/hrnet.py b/mmdet/models/backbones/hrnet.py
index 7b7b469041b8ccb497f4bd9426e85699b2067e6f..0f7a082cf0e5b00afcd67f078daf0a819f63ee8c 100644
--- a/mmdet/models/backbones/hrnet.py
+++ b/mmdet/models/backbones/hrnet.py
@@ -3,6 +3,7 @@ from mmcv.cnn import constant_init, kaiming_init
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
+from mmdet.utils import get_root_logger
from ..registry import BACKBONES
from ..utils import build_conv_layer, build_norm_layer
from .resnet import BasicBlock, Bottleneck
@@ -460,7 +461,6 @@ class HRNet(nn.Module):
def init_weights(self, pretrained=None):
if isinstance(pretrained, str):
- from mmdet.apis import get_root_logger
logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
diff --git a/mmdet/models/backbones/resnet.py b/mmdet/models/backbones/resnet.py
index d2f3b7c517eae3c0c6efeb9d7e4ab65389c840aa..ab6913e82720477940b2dfbfb6d144a1529740a2 100644
--- a/mmdet/models/backbones/resnet.py
+++ b/mmdet/models/backbones/resnet.py
@@ -6,6 +6,7 @@ from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.models.plugins import GeneralizedAttention
from mmdet.ops import ContextBlock
+from mmdet.utils import get_root_logger
from ..registry import BACKBONES
from ..utils import build_conv_layer, build_norm_layer
@@ -468,7 +469,6 @@ class ResNet(nn.Module):
def init_weights(self, pretrained=None):
if isinstance(pretrained, str):
- from mmdet.apis import get_root_logger
logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
diff --git a/mmdet/models/backbones/ssd_vgg.py b/mmdet/models/backbones/ssd_vgg.py
index 8cbe42cca9cb37855b1433a45a5a8110465f543c..c7615e2a70dcd3f4a153a4dd01038412acdb94bf 100644
--- a/mmdet/models/backbones/ssd_vgg.py
+++ b/mmdet/models/backbones/ssd_vgg.py
@@ -4,6 +4,7 @@ import torch.nn.functional as F
from mmcv.cnn import VGG, constant_init, kaiming_init, normal_init, xavier_init
from mmcv.runner import load_checkpoint
+from mmdet.utils import get_root_logger
from ..registry import BACKBONES
@@ -73,7 +74,6 @@ class SSDVGG(VGG):
def init_weights(self, pretrained=None):
if isinstance(pretrained, str):
- from mmdet.apis import get_root_logger
logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
diff --git a/mmdet/models/detectors/base.py b/mmdet/models/detectors/base.py
index 60662929450a60e456e224f012986c05357cc6b6..8d38b23fc5eb01db1e62384fa52f8ada4b795113 100644
--- a/mmdet/models/detectors/base.py
+++ b/mmdet/models/detectors/base.py
@@ -6,6 +6,7 @@ import pycocotools.mask as maskUtils
import torch.nn as nn
from mmdet.core import auto_fp16, get_classes, tensor2imgs
+from mmdet.utils import print_log
class BaseDetector(nn.Module, metaclass=ABCMeta):
@@ -71,9 +72,7 @@ class BaseDetector(nn.Module, metaclass=ABCMeta):
def init_weights(self, pretrained=None):
if pretrained is not None:
- from mmdet.apis import get_root_logger
- logger = get_root_logger()
- logger.info('load model from: {}'.format(pretrained))
+ print_log('load model from: {}'.format(pretrained), logger='root')
async def aforward_test(self, *, img, img_meta, **kwargs):
for var, name in [(img, 'img'), (img_meta, 'img_meta')]:
diff --git a/mmdet/models/shared_heads/res_layer.py b/mmdet/models/shared_heads/res_layer.py
index 33b962bb7a12d521587803253d46dcb2a787a74c..e1a1ba0d76b34d6199ba397916e6b29ade8e0a74 100644
--- a/mmdet/models/shared_heads/res_layer.py
+++ b/mmdet/models/shared_heads/res_layer.py
@@ -3,6 +3,7 @@ from mmcv.cnn import constant_init, kaiming_init
from mmcv.runner import load_checkpoint
from mmdet.core import auto_fp16
+from mmdet.utils import get_root_logger
from ..backbones import ResNet, make_res_layer
from ..registry import SHARED_HEADS
@@ -45,7 +46,6 @@ class ResLayer(nn.Module):
def init_weights(self, pretrained=None):
if isinstance(pretrained, str):
- from mmdet.apis import get_root_logger
logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
diff --git a/mmdet/ops/dcn/deform_conv.py b/mmdet/ops/dcn/deform_conv.py
index 24e5b08cbfd2a9581fae59fe56ed419ea9441e31..5ba5a5e8fc0c810006282b6072227480b759c1f5 100644
--- a/mmdet/ops/dcn/deform_conv.py
+++ b/mmdet/ops/dcn/deform_conv.py
@@ -6,6 +6,7 @@ from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair, _single
+from mmdet.utils import print_log
from . import deform_conv_cuda
@@ -297,10 +298,10 @@ class DeformConvPack(DeformConv):
'_offset.bias')
if version is not None and version > 1:
- from mmdet.apis import get_root_logger
- logger = get_root_logger()
- logger.info('DeformConvPack {} is upgraded to version 2.'.format(
- prefix.rstrip('.')))
+ print_log(
+ 'DeformConvPack {} is upgraded to version 2.'.format(
+ prefix.rstrip('.')),
+ logger='root')
super()._load_from_state_dict(state_dict, prefix, local_metadata,
strict, missing_keys, unexpected_keys,
@@ -420,11 +421,10 @@ class ModulatedDeformConvPack(ModulatedDeformConv):
'_offset.bias')
if version is not None and version > 1:
- from mmdet.apis import get_root_logger
- logger = get_root_logger()
- logger.info(
+ print_log(
'ModulatedDeformConvPack {} is upgraded to version 2.'.format(
- prefix.rstrip('.')))
+ prefix.rstrip('.')),
+ logger='root')
super()._load_from_state_dict(state_dict, prefix, local_metadata,
strict, missing_keys, unexpected_keys,
diff --git a/mmdet/utils/__init__.py b/mmdet/utils/__init__.py
index f65e3b2fbcf1d87c6c184f4ef63e9ad40537a23e..537a34a13ac6f7b210fce536a3f38aec0c83e203 100644
--- a/mmdet/utils/__init__.py
+++ b/mmdet/utils/__init__.py
@@ -1,4 +1,8 @@
from .flops_counter import get_model_complexity_info
+from .logger import get_root_logger, print_log
from .registry import Registry, build_from_cfg
-__all__ = ['Registry', 'build_from_cfg', 'get_model_complexity_info']
+__all__ = [
+ 'Registry', 'build_from_cfg', 'get_model_complexity_info',
+ 'get_root_logger', 'print_log'
+]
diff --git a/mmdet/utils/logger.py b/mmdet/utils/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e6a1396b95fe3e66530cfd4144a241b0c692b6f
--- /dev/null
+++ b/mmdet/utils/logger.py
@@ -0,0 +1,66 @@
+import logging
+
+from mmcv.runner import get_dist_info
+
+
+def get_root_logger(log_file=None, log_level=logging.INFO):
+ """Get the root logger.
+
+ The logger will be initialized if it has not been initialized. By default a
+ StreamHandler will be added. If `log_file` is specified, a FileHandler will
+ also be added. The name of the root logger is the top-level package name,
+ e.g., "mmdet".
+
+ Args:
+ log_file (str | None): The log filename. If specified, a FileHandler
+ will be added to the root logger.
+ log_level (int): The root logger level. Note that only the process of
+ rank 0 is affected, while other processes will set the level to
+ "Error" and be silent most of the time.
+
+ Returns:
+ logging.Logger: The root logger.
+ """
+ logger = logging.getLogger(__name__.split('.')[0]) # i.e., mmdet
+ # if the logger has been initialized, just return it
+ if logger.hasHandlers():
+ return logger
+
+ format_str = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+ logging.basicConfig(format=format_str, 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(format_str))
+ file_handler.setLevel(log_level)
+ logger.addHandler(file_handler)
+
+ return logger
+
+
+def print_log(msg, logger=None, level=logging.INFO):
+ """Print a log message.
+
+ Args:
+ msg (str): The message to be logged.
+ logger (logging.Logger | str | None): The logger to be used. Some
+ special loggers are:
+ - "root": the root logger obtained with `get_root_logger()`.
+ - "silent": no message will be printed.
+ - None: The `print()` method will be used to print log messages.
+ level (int): Logging level. Only available when `logger` is a Logger
+ object or "root".
+ """
+ if logger is None:
+ print(msg)
+ elif logger == 'root':
+ _logger = get_root_logger()
+ _logger.log(level, msg)
+ elif isinstance(logger, logging.Logger):
+ logger.log(level, msg)
+ elif logger != 'silent':
+ raise TypeError(
+ 'logger should be either a logging.Logger object, "root", '
+ '"silent" or None, but got {}'.format(logger))
diff --git a/tools/train.py b/tools/train.py
index 2931a28c92858bdd3cbb4fe866e7a291bd5f7f7d..7f89795d5d6a2660db2b4bfb04123134cd210481 100644
--- a/tools/train.py
+++ b/tools/train.py
@@ -10,9 +10,10 @@ from mmcv import Config
from mmcv.runner import init_dist
from mmdet import __version__
-from mmdet.apis import get_root_logger, set_random_seed, train_detector
+from mmdet.apis import set_random_seed, train_detector
from mmdet.datasets import build_dataset
from mmdet.models import build_detector
+from mmdet.utils import get_root_logger
def parse_args():