Integration of the WandbLogger with the latest changes in the PaddleOCR integrations

README.md

@@ -49,6 +49,7 @@ PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools
 - Support user-defined training, provides rich predictive inference deployment solutions
 - Support PIP installation, easy to use
 - Support Linux, Windows, MacOS and other systems
+- Supports metric logging to [VisualDL](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/03_VisualDL/visualdl_usage_en.html) and [Weights & Biases](docs.wandb.ai)
 
 ## Visualization
 

doc/doc_en/config_en.md

@@ -36,6 +36,7 @@ Take rec_chinese_lite_train_v2.0.yml as an example
 |      pretrained_model    |    Set the path of the pre-trained model      |  ./pretrain_models/CRNN/best_accuracy  |  \          |
 |      checkpoints         |    set model parameter path            |       None        |   Used to load parameters after interruption to continue training|
 |      use_visualdl  |    Set whether to enable visualdl for visual log display |          False        |    [Tutorial](https://www.paddlepaddle.org.cn/paddle/visualdl) |
+|      use_wandb     |    Set whether to enable W&B for visual log display      | False | [Documentation](https://docs.wandb.ai/)
 |      infer_img            |    Set inference image path or folder path     |       ./infer_img | \||
 |      character_dict_path |    Set dictionary path            |  ./ppocr/utils/ppocr_keys_v1.txt  | If the character_dict_path is None, model can only recognize number and lower letters |
 |      max_text_length     |    Set the maximum length of text        |       25          |                \                 |
@@ -66,7 +67,7 @@ In PaddleOCR, the network is divided into four stages: Transform, Backbone, Neck
 | :---------------------: |  :---------------------:   | :--------------:  |   :--------------------:   |
 |      model_type        |         Network Type          |  rec  |  Currently support`rec`,`det`,`cls`  |
 |      algorithm           |    Model name  |       CRNN         |               See [algorithm_overview](./algorithm_overview_en.md) for the support list             |
-|      **Transform**           |    Set the transformation method  |       -       |               Currently only recognition algorithms are supported, see [ppocr/modeling/transforms](../../ppocr/modeling/transforms) for details            |
+|      **Transform**           |    Set the transformation method  |       -       |               Currently only recognition algorithms are supported, see [ppocr/modeling/transform](../../ppocr/modeling/transform) for details            |
 |        name    |      Transformation class name   |         TPS       | Currently supports `TPS` |
 |        num_fiducial      |   Number of TPS control points        |       20      |  Ten on the top and bottom       |
 |        loc_lr      |    Localization network learning rate        |       0.1      |  \      |
@@ -130,6 +131,17 @@ In PaddleOCR, the network is divided into four stages: Transform, Backbone, Neck
 |      drop_last        |        Whether to discard the last incomplete mini-batch because the number of samples in the data set cannot be divisible by batch_size        |  True | \  |
 |      num_workers        |        The number of sub-processes used to load data, if it is 0, the sub-process is not started, and the data is loaded in the main process       |  8 | \  |
 
+### Weights & Biases ([W&B](../../ppocr/utils/loggers/wandb_logger.py))
+|         Parameter             |            Use            |      Defaults        |            Note             |
+| :---------------------: |  :---------------------:   | :--------------:  |   :--------------------:   |
+|          project              |     Project to which the run is to be logged | uncategorized | \
+|          name                 |     Alias/Name of the run | Randomly generated by wandb | \ 
+|          id                   |     ID of the run    | Randomly generated by wandb     | \
+|          entity               | User or team to which the run is being logged         | The logged in user | \
+|          save_dir             | local directory in which all the models and other data is saved | wandb | \
+|          config               | model configuration | None | \
+
+
 <a name="3-multilingual-config-file-generation"></a>
 
 ## 3. Multilingual Config File Generation
@@ -233,4 +245,4 @@ For more supported languages, please refer to : [Multi-language model](https://g
 
 The multi-language model training method is the same as the Chinese model. The training data set is 100w synthetic data. A small amount of fonts and test data can be downloaded using the following two methods.
 * [Baidu Netdisk](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA),Extraction code:frgi.
-* [Google drive](https://drive.google.com/file/d/18cSWX7wXSy4G0tbKJ0d9PuIaiwRLHpjA/view)
+* [Google drive](https://drive.google.com/file/d/18cSWX7wXSy4G0tbKJ0d9PuIaiwRLHpjA/view)
\ No newline at end of file

ppocr/utils/loggers/__init__.py

+from .vdl_logger import VDLLogger
+from .wandb_logger import WandbLogger
\ No newline at end of file

ppocr/utils/loggers/base_logger.py

+import os
+from abc import ABC, abstractmethod
+
+class BaseLogger(ABC):
+    def __init__(self, save_dir):
+        self.save_dir = save_dir
+        os.makedirs(self.save_dir, exist_ok=True)
+
+    @abstractmethod
+    def log_metrics(self, metrics, prefix=None):
+        pass
+
+    @abstractmethod
+    def close(self):
+        pass
\ No newline at end of file

ppocr/utils/loggers/vdl_logger.py

+from .base_logger import BaseLogger
+from visualdl import LogWriter
+
+class VDLLogger(BaseLogger):
+    def __init__(self, save_dir):
+        super().__init__(save_dir)
+        self.vdl_writer = LogWriter(logdir=save_dir)
+
+    def log_metrics(self, metrics, prefix=None, step=None):
+        if not prefix:
+            prefix = ""
+        updated_metrics = {prefix + "/" + k: v for k, v in metrics.items()}
+
+        for k, v in updated_metrics.items():
+            self.vdl_writer.add_scalar(k, v, step)
+
+    def close(self):
+        self.vdl_writer.close() 
\ No newline at end of file

ppocr/utils/loggers/wandb_logger.py

+import os
+from .base_logger import BaseLogger
+
+class WandbLogger(BaseLogger):
+    def __init__(self, 
+        project=None, 
+        name=None, 
+        id=None, 
+        entity=None, 
+        save_dir=None, 
+        config=None,
+        **kwargs):
+        try:
+            import wandb
+            self.wandb = wandb
+        except ModuleNotFoundError:
+            raise ModuleNotFoundError(
+                "Please install wandb using `pip install wandb`"
+                )
+
+        self.project = project
+        self.name = name
+        self.id = id
+        self.save_dir = save_dir
+        self.config = config
+        self.kwargs = kwargs
+        self.entity = entity
+        self._run = None
+        self._wandb_init = dict(
+            project=self.project,
+            name=self.name,
+            id=self.id,
+            entity=self.entity,
+            dir=self.save_dir,
+            resume="allow"
+        )
+        self._wandb_init.update(**kwargs)
+
+        _ = self.run
+
+        if self.config:
+            self.run.config.update(self.config)
+
+    @property
+    def run(self):
+        if self._run is None:
+            if self.wandb.run is not None:
+                logger.info(
+                    "There is a wandb run already in progress "
+                    "and newly created instances of `WandbLogger` will reuse"
+                    " this run. If this is not desired, call `wandb.finish()`"
+                    "before instantiating `WandbLogger`."
+                )
+                self._run = self.wandb.run
+            else:
+                self._run = self.wandb.init(**self._wandb_init)
+        return self._run
+
+    def log_metrics(self, metrics, prefix=None, step=None):
+        if not prefix:
+            prefix = ""
+        updated_metrics = {prefix + "/" + k: v for k, v in metrics.items()}
+
+        self.run.log(updated_metrics, step=step)
+
+    def log_model(self, is_best, prefix, metadata=None):
+        model_path = os.path.join(self.save_dir, prefix + '.pdparams')
+        artifact = self.wandb.Artifact('model-{}'.format(self.run.id), type='model', metadata=metadata)
+        artifact.add_file(model_path, name="model_ckpt.pdparams")
+
+        aliases = [prefix]
+        if is_best:
+            aliases.append("best")
+
+        self.run.log_artifact(artifact, aliases=aliases)
+
+    def close(self):
+        self.run.finish()
\ No newline at end of file

tools/program.py

@@ -31,6 +31,7 @@ from ppocr.utils.stats import TrainingStats
 from ppocr.utils.save_load import save_model
 from ppocr.utils.utility import print_dict, AverageMeter
 from ppocr.utils.logging import get_logger
+from ppocr.utils.loggers import VDLLogger, WandbLogger
 from ppocr.utils import profiler
 from ppocr.data import build_dataloader
 
@@ -161,7 +162,7 @@ def train(config,
           eval_class,
           pre_best_model_dict,
           logger,
-          vdl_writer=None,
+          log_writer=None,
           scaler=None):
     cal_metric_during_train = config['Global'].get('cal_metric_during_train',
                                                    False)
@@ -288,10 +289,8 @@ def train(config,
             stats['lr'] = lr
             train_stats.update(stats)
 
-            if vdl_writer is not None and dist.get_rank() == 0:
-                for k, v in train_stats.get().items():
-                    vdl_writer.add_scalar('TRAIN/{}'.format(k), v, global_step)
-                vdl_writer.add_scalar('TRAIN/lr', lr, global_step)
+            if log_writer is not None and dist.get_rank() == 0:
+                log_writer.log_metrics(metrics=train_stats.get(), prefix="TRAIN", step=global_step)
 
             if dist.get_rank() == 0 and (
                 (global_step > 0 and global_step % print_batch_step == 0) or
@@ -337,11 +336,9 @@ def train(config,
                 logger.info(cur_metric_str)
 
                 # logger metric
-                if vdl_writer is not None:
-                    for k, v in cur_metric.items():
-                        if isinstance(v, (float, int)):
-                            vdl_writer.add_scalar('EVAL/{}'.format(k),
-                                                  cur_metric[k], global_step)
+                if log_writer is not None:
+                    log_writer.log_metrics(metrics=cur_metric, prefix="EVAL", step=global_step)
+
                 if cur_metric[main_indicator] >= best_model_dict[
                         main_indicator]:
                     best_model_dict.update(cur_metric)
@@ -362,10 +359,13 @@ def train(config,
                 ]))
                 logger.info(best_str)
                 # logger best metric
-                if vdl_writer is not None:
-                    vdl_writer.add_scalar('EVAL/best_{}'.format(main_indicator),
-                                          best_model_dict[main_indicator],
-                                          global_step)
+                if log_writer is not None:
+                    log_writer.log_metrics(metrics={
+                        "best_{}".format(main_indicator): best_model_dict[main_indicator]
+                    }, prefix="EVAL", step=global_step)
+
+                    if isinstance(log_writer, WandbLogger):
+                        log_writer.log_model(is_best=True, prefix="best_accuracy", metadata=best_model_dict)
 
             reader_start = time.time()
         if dist.get_rank() == 0:
@@ -380,6 +380,10 @@ def train(config,
                 best_model_dict=best_model_dict,
                 epoch=epoch,
                 global_step=global_step)
+
+            if isinstance(log_writer, WandbLogger):
+                log_writer.log_model(is_best=False, prefix="latest")
+
         if dist.get_rank() == 0 and epoch > 0 and epoch % save_epoch_step == 0:
             save_model(
                 model,
@@ -392,11 +396,15 @@ def train(config,
                 best_model_dict=best_model_dict,
                 epoch=epoch,
                 global_step=global_step)
+            
+            if isinstance(log_writer, WandbLogger):
+                log_writer.log_model(is_best=False, prefix='iter_epoch_{}'.format(epoch))
+
     best_str = 'best metric, {}'.format(', '.join(
         ['{}: {}'.format(k, v) for k, v in best_model_dict.items()]))
     logger.info(best_str)
-    if dist.get_rank() == 0 and vdl_writer is not None:
-        vdl_writer.close()
+    if dist.get_rank() == 0 and log_writer is not None:
+        log_writer.close()
     return
 
 
@@ -553,15 +561,22 @@ def preprocess(is_train=False):
 
     config['Global']['distributed'] = dist.get_world_size() != 1
 
-    if config['Global']['use_visualdl'] and dist.get_rank() == 0:
-        from visualdl import LogWriter
+    if "use_visualdl" in config['Global'] and config['Global']['use_visualdl'] and dist.get_rank() == 0:
         save_model_dir = config['Global']['save_model_dir']
         vdl_writer_path = '{}/vdl/'.format(save_model_dir)
-        os.makedirs(vdl_writer_path, exist_ok=True)
-        vdl_writer = LogWriter(logdir=vdl_writer_path)
+        log_writer = VDLLogger(save_model_dir)
+    elif ("use_wandb" in config['Global'] and config['Global']['use_wandb']) or "wandb" in config:
+        save_dir = config['Global']['save_model_dir']
+        wandb_writer_path = "{}/wandb".format(save_dir)
+        if "wandb" in config:
+            wandb_params = config['wandb']
+        else:
+            wandb_params = dict()
+        wandb_params.update({'save_dir': save_model_dir})
+        log_writer = WandbLogger(**wandb_params, config=config)
     else:
-        vdl_writer = None
+        log_writer = None
     print_dict(config, logger)
     logger.info('train with paddle {} and device {}'.format(paddle.__version__,
                                                             device))
-    return config, device, logger, vdl_writer
+    return config, device, logger, log_writer