Integration of the WandbLogger with the latest changes in the PaddleOCR integrations
Manan Goel
parent
e4ab0ebe86
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dde60d69be
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@ -49,6 +49,7 @@ PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools
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- Support user-defined training, provides rich predictive inference deployment solutions
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- Support PIP installation, easy to use
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- Support Linux, Windows, MacOS and other systems
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- 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)
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## Visualization
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@ -36,6 +36,7 @@ Take rec_chinese_lite_train_v2.0.yml as an example
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| pretrained_model | Set the path of the pre-trained model | ./pretrain_models/CRNN/best_accuracy | \ |
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| checkpoints | set model parameter path | None | Used to load parameters after interruption to continue training|
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| use_visualdl | Set whether to enable visualdl for visual log display | False | [Tutorial](https://www.paddlepaddle.org.cn/paddle/visualdl) |
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| use_wandb | Set whether to enable W&B for visual log display | False | [Documentation](https://docs.wandb.ai/)
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| infer_img | Set inference image path or folder path | ./infer_img | \||
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| 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 |
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| max_text_length | Set the maximum length of text | 25 | \ |
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@ -66,7 +67,7 @@ In PaddleOCR, the network is divided into four stages: Transform, Backbone, Neck
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| :---------------------: | :---------------------: | :--------------: | :--------------------: |
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| model_type | Network Type | rec | Currently support`rec`,`det`,`cls` |
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| algorithm | Model name | CRNN | See [algorithm_overview](./algorithm_overview_en.md) for the support list |
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| **Transform** | Set the transformation method | - | Currently only recognition algorithms are supported, see [ppocr/modeling/transforms](../../ppocr/modeling/transforms) for details |
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| **Transform** | Set the transformation method | - | Currently only recognition algorithms are supported, see [ppocr/modeling/transform](../../ppocr/modeling/transform) for details |
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| name | Transformation class name | TPS | Currently supports `TPS` |
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| num_fiducial | Number of TPS control points | 20 | Ten on the top and bottom |
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| loc_lr | Localization network learning rate | 0.1 | \ |
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@ -130,6 +131,17 @@ In PaddleOCR, the network is divided into four stages: Transform, Backbone, Neck
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| 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 | \ |
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| 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 | \ |
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### Weights & Biases ([W&B](../../ppocr/utils/loggers/wandb_logger.py))
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| Parameter | Use | Defaults | Note |
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| :---------------------: | :---------------------: | :--------------: | :--------------------: |
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| project | Project to which the run is to be logged | uncategorized | \
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| name | Alias/Name of the run | Randomly generated by wandb | \
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| id | ID of the run | Randomly generated by wandb | \
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| entity | User or team to which the run is being logged | The logged in user | \
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| save_dir | local directory in which all the models and other data is saved | wandb | \
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| config | model configuration | None | \
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<a name="3-multilingual-config-file-generation"></a>
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## 3. Multilingual Config File Generation
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@ -0,0 +1,2 @@
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from .vdl_logger import VDLLogger
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from .wandb_logger import WandbLogger
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@ -0,0 +1,15 @@
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import os
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from abc import ABC, abstractmethod
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class BaseLogger(ABC):
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def __init__(self, save_dir):
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self.save_dir = save_dir
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os.makedirs(self.save_dir, exist_ok=True)
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@abstractmethod
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def log_metrics(self, metrics, prefix=None):
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pass
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@abstractmethod
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def close(self):
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pass
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@ -0,0 +1,18 @@
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from .base_logger import BaseLogger
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from visualdl import LogWriter
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class VDLLogger(BaseLogger):
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def __init__(self, save_dir):
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super().__init__(save_dir)
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self.vdl_writer = LogWriter(logdir=save_dir)
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def log_metrics(self, metrics, prefix=None, step=None):
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if not prefix:
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prefix = ""
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updated_metrics = {prefix + "/" + k: v for k, v in metrics.items()}
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for k, v in updated_metrics.items():
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self.vdl_writer.add_scalar(k, v, step)
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def close(self):
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self.vdl_writer.close()
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@ -0,0 +1,78 @@
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import os
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from .base_logger import BaseLogger
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class WandbLogger(BaseLogger):
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def __init__(self,
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project=None,
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name=None,
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id=None,
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entity=None,
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save_dir=None,
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config=None,
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**kwargs):
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try:
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import wandb
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self.wandb = wandb
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except ModuleNotFoundError:
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raise ModuleNotFoundError(
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"Please install wandb using `pip install wandb`"
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)
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self.project = project
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self.name = name
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self.id = id
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self.save_dir = save_dir
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self.config = config
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self.kwargs = kwargs
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self.entity = entity
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self._run = None
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self._wandb_init = dict(
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project=self.project,
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name=self.name,
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id=self.id,
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entity=self.entity,
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dir=self.save_dir,
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resume="allow"
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)
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self._wandb_init.update(**kwargs)
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_ = self.run
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if self.config:
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self.run.config.update(self.config)
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@property
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def run(self):
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if self._run is None:
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if self.wandb.run is not None:
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logger.info(
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"There is a wandb run already in progress "
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"and newly created instances of `WandbLogger` will reuse"
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" this run. If this is not desired, call `wandb.finish()`"
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"before instantiating `WandbLogger`."
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)
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self._run = self.wandb.run
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else:
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self._run = self.wandb.init(**self._wandb_init)
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return self._run
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def log_metrics(self, metrics, prefix=None, step=None):
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if not prefix:
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prefix = ""
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updated_metrics = {prefix + "/" + k: v for k, v in metrics.items()}
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self.run.log(updated_metrics, step=step)
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def log_model(self, is_best, prefix, metadata=None):
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model_path = os.path.join(self.save_dir, prefix + '.pdparams')
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artifact = self.wandb.Artifact('model-{}'.format(self.run.id), type='model', metadata=metadata)
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artifact.add_file(model_path, name="model_ckpt.pdparams")
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aliases = [prefix]
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if is_best:
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aliases.append("best")
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self.run.log_artifact(artifact, aliases=aliases)
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def close(self):
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self.run.finish()
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@ -31,6 +31,7 @@ from ppocr.utils.stats import TrainingStats
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from ppocr.utils.save_load import save_model
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from ppocr.utils.utility import print_dict, AverageMeter
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from ppocr.utils.logging import get_logger
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from ppocr.utils.loggers import VDLLogger, WandbLogger
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from ppocr.utils import profiler
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from ppocr.data import build_dataloader
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@ -161,7 +162,7 @@ def train(config,
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eval_class,
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pre_best_model_dict,
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logger,
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vdl_writer=None,
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log_writer=None,
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scaler=None):
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cal_metric_during_train = config['Global'].get('cal_metric_during_train',
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False)
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@ -288,10 +289,8 @@ def train(config,
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stats['lr'] = lr
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train_stats.update(stats)
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if vdl_writer is not None and dist.get_rank() == 0:
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for k, v in train_stats.get().items():
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vdl_writer.add_scalar('TRAIN/{}'.format(k), v, global_step)
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vdl_writer.add_scalar('TRAIN/lr', lr, global_step)
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if log_writer is not None and dist.get_rank() == 0:
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log_writer.log_metrics(metrics=train_stats.get(), prefix="TRAIN", step=global_step)
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if dist.get_rank() == 0 and (
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(global_step > 0 and global_step % print_batch_step == 0) or
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@ -337,11 +336,9 @@ def train(config,
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logger.info(cur_metric_str)
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# logger metric
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if vdl_writer is not None:
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for k, v in cur_metric.items():
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if isinstance(v, (float, int)):
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vdl_writer.add_scalar('EVAL/{}'.format(k),
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cur_metric[k], global_step)
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if log_writer is not None:
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log_writer.log_metrics(metrics=cur_metric, prefix="EVAL", step=global_step)
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if cur_metric[main_indicator] >= best_model_dict[
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main_indicator]:
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best_model_dict.update(cur_metric)
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@ -362,10 +359,13 @@ def train(config,
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]))
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logger.info(best_str)
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# logger best metric
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if vdl_writer is not None:
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vdl_writer.add_scalar('EVAL/best_{}'.format(main_indicator),
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best_model_dict[main_indicator],
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global_step)
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if log_writer is not None:
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log_writer.log_metrics(metrics={
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"best_{}".format(main_indicator): best_model_dict[main_indicator]
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}, prefix="EVAL", step=global_step)
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if isinstance(log_writer, WandbLogger):
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log_writer.log_model(is_best=True, prefix="best_accuracy", metadata=best_model_dict)
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reader_start = time.time()
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if dist.get_rank() == 0:
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@ -380,6 +380,10 @@ def train(config,
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best_model_dict=best_model_dict,
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epoch=epoch,
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global_step=global_step)
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if isinstance(log_writer, WandbLogger):
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log_writer.log_model(is_best=False, prefix="latest")
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if dist.get_rank() == 0 and epoch > 0 and epoch % save_epoch_step == 0:
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save_model(
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model,
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@ -392,11 +396,15 @@ def train(config,
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best_model_dict=best_model_dict,
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epoch=epoch,
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global_step=global_step)
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if isinstance(log_writer, WandbLogger):
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log_writer.log_model(is_best=False, prefix='iter_epoch_{}'.format(epoch))
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best_str = 'best metric, {}'.format(', '.join(
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['{}: {}'.format(k, v) for k, v in best_model_dict.items()]))
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logger.info(best_str)
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if dist.get_rank() == 0 and vdl_writer is not None:
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vdl_writer.close()
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if dist.get_rank() == 0 and log_writer is not None:
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log_writer.close()
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return
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@ -553,15 +561,22 @@ def preprocess(is_train=False):
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config['Global']['distributed'] = dist.get_world_size() != 1
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if config['Global']['use_visualdl'] and dist.get_rank() == 0:
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from visualdl import LogWriter
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if "use_visualdl" in config['Global'] and config['Global']['use_visualdl'] and dist.get_rank() == 0:
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save_model_dir = config['Global']['save_model_dir']
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vdl_writer_path = '{}/vdl/'.format(save_model_dir)
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os.makedirs(vdl_writer_path, exist_ok=True)
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vdl_writer = LogWriter(logdir=vdl_writer_path)
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log_writer = VDLLogger(save_model_dir)
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elif ("use_wandb" in config['Global'] and config['Global']['use_wandb']) or "wandb" in config:
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save_dir = config['Global']['save_model_dir']
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wandb_writer_path = "{}/wandb".format(save_dir)
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if "wandb" in config:
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wandb_params = config['wandb']
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else:
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wandb_params = dict()
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wandb_params.update({'save_dir': save_model_dir})
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log_writer = WandbLogger(**wandb_params, config=config)
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else:
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vdl_writer = None
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log_writer = None
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print_dict(config, logger)
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logger.info('train with paddle {} and device {}'.format(paddle.__version__,
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device))
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return config, device, logger, vdl_writer
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return config, device, logger, log_writer
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