From dde60d69be5d58f687194d5dbc7f3e06132d633c Mon Sep 17 00:00:00 2001 From: Manan Goel Date: Tue, 12 Apr 2022 15:47:54 +0000 Subject: [PATCH] Integration of the WandbLogger with the latest changes in the PaddleOCR integrations --- README.md | 1 + doc/doc_en/config_en.md | 16 +++++- ppocr/utils/loggers/__init__.py | 2 + ppocr/utils/loggers/base_logger.py | 15 ++++++ ppocr/utils/loggers/vdl_logger.py | 18 +++++++ ppocr/utils/loggers/wandb_logger.py | 78 +++++++++++++++++++++++++++++ tools/program.py | 59 ++++++++++++++-------- 7 files changed, 165 insertions(+), 24 deletions(-) create mode 100644 ppocr/utils/loggers/__init__.py create mode 100644 ppocr/utils/loggers/base_logger.py create mode 100644 ppocr/utils/loggers/vdl_logger.py create mode 100644 ppocr/utils/loggers/wandb_logger.py diff --git a/README.md b/README.md index 99d20357..9e79d773 100644 --- a/README.md +++ b/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 diff --git a/doc/doc_en/config_en.md b/doc/doc_en/config_en.md index d7bf5ead..7cdd3c49 100644 --- a/doc/doc_en/config_en.md +++ b/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 | \ + + ## 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 diff --git a/ppocr/utils/loggers/__init__.py b/ppocr/utils/loggers/__init__.py new file mode 100644 index 00000000..0c99035c --- /dev/null +++ b/ppocr/utils/loggers/__init__.py @@ -0,0 +1,2 @@ +from .vdl_logger import VDLLogger +from .wandb_logger import WandbLogger \ No newline at end of file diff --git a/ppocr/utils/loggers/base_logger.py b/ppocr/utils/loggers/base_logger.py new file mode 100644 index 00000000..3a7fc359 --- /dev/null +++ b/ppocr/utils/loggers/base_logger.py @@ -0,0 +1,15 @@ +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 diff --git a/ppocr/utils/loggers/vdl_logger.py b/ppocr/utils/loggers/vdl_logger.py new file mode 100644 index 00000000..00cabd17 --- /dev/null +++ b/ppocr/utils/loggers/vdl_logger.py @@ -0,0 +1,18 @@ +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 diff --git a/ppocr/utils/loggers/wandb_logger.py b/ppocr/utils/loggers/wandb_logger.py new file mode 100644 index 00000000..5571a703 --- /dev/null +++ b/ppocr/utils/loggers/wandb_logger.py @@ -0,0 +1,78 @@ +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 diff --git a/tools/program.py b/tools/program.py index 8ec152bb..37ce0010 100755 --- a/tools/program.py +++ b/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 -- GitLab