Colorlog and task/config.__repr__ (#253)

* colorlog

* optimize __repr__

paddlehub/common/logger.py

@@ -17,6 +17,7 @@ from __future__ import print_function
 from __future__ import division
 from __future__ import print_function
 
+import colorlog
 import logging
 import math
 import os
@@ -26,21 +27,40 @@ from paddlehub.common.dir import CONF_HOME
 
 
 class Logger(object):
+    DEBUG = 10
+    INFO = 20
+    WARNING = 30
+    ERROR = 40
+    CRITICAL = 50
+    TRAIN = 21
+    EVAL = 22
     PLACEHOLDER = '%'
     NOLOG = "NOLOG"
+    logging.addLevelName(TRAIN, 'TRAIN')
+    logging.addLevelName(EVAL, 'EVAL')
 
     def __init__(self, name=None):
         if not name:
             name = "PaddleHub"
         self.logger = logging.getLogger(name)
         self.handler = logging.StreamHandler()
-        self.format = logging.Formatter(
-            '[%(asctime)-15s] [%(levelname)8s] - %(message)s')
-        self.handler.setFormatter(self.format)
 
+        self.format = colorlog.ColoredFormatter(
+            '%(log_color)s[%(asctime)-15s] [%(levelname)8s] - %(message)s',
+            log_colors={
+                'DEBUG': 'purple',
+                'INFO': 'green',
+                'WARNING': 'yellow',
+                'ERROR': 'red',
+                'CRITICAL': 'bold_red',
+                'TRAIN': 'cyan',
+                'EVAL': 'blue',
+            })
+        self.handler.setFormatter(self.format)
         self.logger.addHandler(self.handler)
         self.logLevel = "DEBUG"
-        self.logger.setLevel(self._get_logging_level())
+        self.logger.setLevel(logging.DEBUG)
+        self.logger.propagate = False
         if os.path.exists(os.path.join(CONF_HOME, "config.json")):
             with open(os.path.join(CONF_HOME, "config.json"), "r") as fp:
                 level = json.load(fp).get("log_level", "DEBUG")
@@ -50,9 +70,6 @@ class Logger(object):
     def _is_no_log(self):
         return self.getLevel() == Logger.NOLOG
 
-    def _get_logging_level(self):
-        return eval("logging.%s" % self.logLevel)
-
     def setLevel(self, logLevel):
         self.logLevel = logLevel.upper()
         if not self._is_no_log():
@@ -62,7 +79,7 @@ class Logger(object):
     def getLevel(self):
         return self.logLevel
 
-    def __call__(self, type, msg):
+    def __call__(self, level, msg):
         def _get_log_arr(msg, len_limit=30):
             ph = Logger.PLACEHOLDER
             lrspace = 2
@@ -109,24 +126,29 @@ class Logger(object):
         if self._is_no_log():
             return
 
-        func = eval("self.logger.%s" % type)
         for msg in _get_log_arr(msg):
-            func(msg)
+            self.logger.log(level, msg)
 
     def debug(self, msg):
-        self("debug", msg)
+        self(logger.DEBUG, msg)
 
     def info(self, msg):
-        self("info", msg)
-
-    def error(self, msg):
-        self("error", msg)
+        self(logger.INFO, msg)
 
     def warning(self, msg):
-        self("warning", msg)
+        self(logger.WARNING, msg)
+
+    def error(self, msg):
+        self(logger.ERROR, msg)
 
     def critical(self, msg):
-        self("critical", msg)
+        self(logger.CRITICAL, msg)
+
+    def train(self, msg):
+        self(logger.TRAIN, msg)
+
+    def eval(self, msg):
+        self(logger.EVAL, msg)
 
 
 logger = Logger()

paddlehub/finetune/config.py

@@ -109,3 +109,8 @@ class RunConfig(object):
     @property
     def use_data_parallel(self):
         return self._use_data_parallel
+
+    def __repr__(self):
+        return "config with num_epoch=%s, batch_size=%s, use_cuda=%s, checkpoint_dir=%s and %s" % (
+            self.num_epoch, self.batch_size, self.use_cuda, self.checkpoint_dir,
+            self.strategy)

paddlehub/finetune/task/basic_task.py

@@ -290,11 +290,6 @@ class BasicTask(object):
                     build_strategy=self.build_strategy)
 
         self.exe.run(self.env.startup_program)
-
-        # to avoid to print logger two times in result of the logger usage of paddle-fluid 1.5
-        for handler in logging.root.handlers[:]:
-            logging.root.removeHandler(handler)
-
         self._build_env_end_event()
 
     @property
@@ -454,7 +449,7 @@ class BasicTask(object):
             self.env.score_scalar = {}
 
     def _finetune_start_event(self):
-        logger.info("PaddleHub finetune start")
+        logger.train("PaddleHub finetune start")
 
     def _finetune_end_event(self, run_states):
         logger.info("PaddleHub finetune finished.")
@@ -466,7 +461,7 @@ class BasicTask(object):
         logger.info("PaddleHub predict finished.")
 
     def _eval_start_event(self):
-        logger.info("Evaluation on {} dataset start".format(self.phase))
+        logger.eval("Evaluation on {} dataset start".format(self.phase))
 
     def _eval_end_event(self, run_states):
         eval_scores, eval_loss, run_speed = self._calculate_metrics(run_states)
@@ -484,7 +479,7 @@ class BasicTask(object):
                     scalar_value=eval_scores[metric],
                     global_step=self._envs['train'].current_step)
             log_scores += "%s=%.5f " % (metric, eval_scores[metric])
-        logger.info(
+        logger.eval(
             "[%s dataset evaluation result] loss=%.5f %s[step/sec: %.2f]" %
             (self.phase, eval_loss, log_scores, run_speed))
 
@@ -502,7 +497,7 @@ class BasicTask(object):
             self.best_score = main_value
             model_saved_dir = os.path.join(self.config.checkpoint_dir,
                                            "best_model")
-            logger.info("best model saved to %s [best %s=%.5f]" %
+            logger.eval("best model saved to %s [best %s=%.5f]" %
                         (model_saved_dir, main_metric, main_value))
 
             save_result = fluid.io.save_persistables(
@@ -523,9 +518,9 @@ class BasicTask(object):
                 scalar_value=scores[metric],
                 global_step=self._envs['train'].current_step)
             log_scores += "%s=%.5f " % (metric, scores[metric])
-        logger.info("step %d / %d: loss=%.5f %s[step/sec: %.2f]" %
-                    (self.current_step, self.max_train_steps, avg_loss,
-                     log_scores, run_speed))
+        logger.train("step %d / %d: loss=%.5f %s[step/sec: %.2f]" %
+                     (self.current_step, self.max_train_steps, avg_loss,
+                      log_scores, run_speed))
 
     def _save_ckpt_interval_event(self):
         self.save_checkpoint()
@@ -760,3 +755,8 @@ class BasicTask(object):
                 break
 
         return global_run_states
+
+    def __repr__(self):
+        return "Task: %s with metrics_choices: %s， reader: %s, %s" % (
+            self.__class__.__name__, self.metrics_choices,
+            self._base_data_reader.__class__.__name__, self.config)

requirements.txt

@@ -16,3 +16,4 @@ tensorboard >= 1.15
 cma == 2.7.0
 sentencepiece
 nltk
+colorlog

requirements_py2.txt

@@ -15,3 +15,4 @@ tensorboard >= 1.15
 cma == 2.7.0
 sentencepiece
 nltk
+colorlog

setup.py

@@ -33,7 +33,7 @@ max_version, mid_version, min_version = python_version()
 REQUIRED_PACKAGES = [
     'six >= 1.10.0', 'protobuf >= 3.6.0', 'pyyaml', 'Pillow', 'requests',
     'tb-paddle', 'tensorboard >= 1.15', 'cma == 2.7.0', 'flask >= 1.1.0',
-    'sentencepiece', 'nltk'
+    'sentencepiece', 'nltk', 'colorlog'
 ]
 
 if max_version < 3: