Add logger utils (#2035)

* add logger utils

* replace get_root_logger() and logger.info() with print_log()

* fix a typo

* minor fix for the format of StreamHandler

mmdet/apis/train.py

-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):

mmdet/core/evaluation/mean_ap.py

-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)

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:

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:

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:

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')]:

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:

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,

mmdet/utils/__init__.py

 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'
+]

mmdet/utils/logger.py

+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))

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():