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You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.

Source code for mmcls.core.hook.wandblogger_hook

# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp

import numpy as np
from mmcv.runner import HOOKS, BaseRunner
from mmcv.runner.dist_utils import get_dist_info, master_only
from mmcv.runner.hooks.checkpoint import CheckpointHook
from mmcv.runner.hooks.evaluation import DistEvalHook, EvalHook
from mmcv.runner.hooks.logger.wandb import WandbLoggerHook


[docs]@HOOKS.register_module() class MMClsWandbHook(WandbLoggerHook): """Enhanced Wandb logger hook for classification. Comparing with the :cls:`mmcv.runner.WandbLoggerHook`, this hook can not only automatically log all information in ``log_buffer`` but also log the following extra information. - **Checkpoints**: If ``log_checkpoint`` is True, the checkpoint saved at every checkpoint interval will be saved as W&B Artifacts. This depends on the : class:`mmcv.runner.CheckpointHook` whose priority is higher than this hook. Please refer to https://docs.wandb.ai/guides/artifacts/model-versioning to learn more about model versioning with W&B Artifacts. - **Checkpoint Metadata**: If ``log_checkpoint_metadata`` is True, every checkpoint artifact will have a metadata associated with it. The metadata contains the evaluation metrics computed on validation data with that checkpoint along with the current epoch/iter. It depends on :class:`EvalHook` whose priority is higher than this hook. - **Evaluation**: At every interval, this hook logs the model prediction as interactive W&B Tables. The number of samples logged is given by ``num_eval_images``. Currently, this hook logs the predicted labels along with the ground truth at every evaluation interval. This depends on the :class:`EvalHook` whose priority is higher than this hook. Also note that the data is just logged once and subsequent evaluation tables uses reference to the logged data to save memory usage. Please refer to https://docs.wandb.ai/guides/data-vis to learn more about W&B Tables. Here is a config example: .. code:: python checkpoint_config = dict(interval=10) # To log checkpoint metadata, the interval of checkpoint saving should # be divisible by the interval of evaluation. evaluation = dict(interval=5) log_config = dict( ... hooks=[ ... dict(type='MMClsWandbHook', init_kwargs={ 'entity': "YOUR_ENTITY", 'project': "YOUR_PROJECT_NAME" }, log_checkpoint=True, log_checkpoint_metadata=True, num_eval_images=100) ]) Args: init_kwargs (dict): A dict passed to wandb.init to initialize a W&B run. Please refer to https://docs.wandb.ai/ref/python/init for possible key-value pairs. interval (int): Logging interval (every k iterations). Defaults to 10. log_checkpoint (bool): Save the checkpoint at every checkpoint interval as W&B Artifacts. Use this for model versioning where each version is a checkpoint. Defaults to False. log_checkpoint_metadata (bool): Log the evaluation metrics computed on the validation data with the checkpoint, along with current epoch as a metadata to that checkpoint. Defaults to True. num_eval_images (int): The number of validation images to be logged. If zero, the evaluation won't be logged. Defaults to 100. """ def __init__(self, init_kwargs=None, interval=10, log_checkpoint=False, log_checkpoint_metadata=False, num_eval_images=100, **kwargs): super(MMClsWandbHook, self).__init__(init_kwargs, interval, **kwargs) self.log_checkpoint = log_checkpoint self.log_checkpoint_metadata = ( log_checkpoint and log_checkpoint_metadata) self.num_eval_images = num_eval_images self.log_evaluation = (num_eval_images > 0) self.ckpt_hook: CheckpointHook = None self.eval_hook: EvalHook = None @master_only def before_run(self, runner: BaseRunner): super(MMClsWandbHook, self).before_run(runner) # Inspect CheckpointHook and EvalHook for hook in runner.hooks: if isinstance(hook, CheckpointHook): self.ckpt_hook = hook if isinstance(hook, (EvalHook, DistEvalHook)): self.eval_hook = hook # Check conditions to log checkpoint if self.log_checkpoint: if self.ckpt_hook is None: self.log_checkpoint = False self.log_checkpoint_metadata = False runner.logger.warning( 'To log checkpoint in MMClsWandbHook, `CheckpointHook` is' 'required, please check hooks in the runner.') else: self.ckpt_interval = self.ckpt_hook.interval # Check conditions to log evaluation if self.log_evaluation or self.log_checkpoint_metadata: if self.eval_hook is None: self.log_evaluation = False self.log_checkpoint_metadata = False runner.logger.warning( 'To log evaluation or checkpoint metadata in ' 'MMClsWandbHook, `EvalHook` or `DistEvalHook` in mmcls ' 'is required, please check whether the validation ' 'is enabled.') else: self.eval_interval = self.eval_hook.interval self.val_dataset = self.eval_hook.dataloader.dataset if (self.log_evaluation and self.num_eval_images > len(self.val_dataset)): self.num_eval_images = len(self.val_dataset) runner.logger.warning( f'The num_eval_images ({self.num_eval_images}) is ' 'greater than the total number of validation samples ' f'({len(self.val_dataset)}). The complete validation ' 'dataset will be logged.') # Check conditions to log checkpoint metadata if self.log_checkpoint_metadata: assert self.ckpt_interval % self.eval_interval == 0, \ 'To log checkpoint metadata in MMClsWandbHook, the interval ' \ f'of checkpoint saving ({self.ckpt_interval}) should be ' \ 'divisible by the interval of evaluation ' \ f'({self.eval_interval}).' # Initialize evaluation table if self.log_evaluation: # Initialize data table self._init_data_table() # Add ground truth to the data table self._add_ground_truth() # Log ground truth data self._log_data_table() @master_only def after_train_epoch(self, runner): super(MMClsWandbHook, self).after_train_epoch(runner) if not self.by_epoch: return # Save checkpoint and metadata if (self.log_checkpoint and self.every_n_epochs(runner, self.ckpt_interval) or (self.ckpt_hook.save_last and self.is_last_epoch(runner))): if self.log_checkpoint_metadata and self.eval_hook: metadata = { 'epoch': runner.epoch + 1, **self._get_eval_results() } else: metadata = None aliases = [f'epoch_{runner.epoch+1}', 'latest'] model_path = osp.join(self.ckpt_hook.out_dir, f'epoch_{runner.epoch+1}.pth') self._log_ckpt_as_artifact(model_path, aliases, metadata) # Save prediction table if self.log_evaluation and self.eval_hook._should_evaluate(runner): results = self.eval_hook.latest_results # Initialize evaluation table self._init_pred_table() # Add predictions to evaluation table self._add_predictions(results, runner.epoch + 1) # Log the evaluation table self._log_eval_table(runner.epoch + 1) def after_train_iter(self, runner): if self.get_mode(runner) == 'train': # An ugly patch. The iter-based eval hook will call the # `after_train_iter` method of all logger hooks before evaluation. # Use this trick to skip that call. # Don't call super method at first, it will clear the log_buffer return super(MMClsWandbHook, self).after_train_iter(runner) else: super(MMClsWandbHook, self).after_train_iter(runner) rank, _ = get_dist_info() if rank != 0: return if self.by_epoch: return # Save checkpoint and metadata if (self.log_checkpoint and self.every_n_iters(runner, self.ckpt_interval) or (self.ckpt_hook.save_last and self.is_last_iter(runner))): if self.log_checkpoint_metadata and self.eval_hook: metadata = { 'iter': runner.iter + 1, **self._get_eval_results() } else: metadata = None aliases = [f'iter_{runner.iter+1}', 'latest'] model_path = osp.join(self.ckpt_hook.out_dir, f'iter_{runner.iter+1}.pth') self._log_ckpt_as_artifact(model_path, aliases, metadata) # Save prediction table if self.log_evaluation and self.eval_hook._should_evaluate(runner): results = self.eval_hook.latest_results # Initialize evaluation table self._init_pred_table() # Log predictions self._add_predictions(results, runner.iter + 1) # Log the table self._log_eval_table(runner.iter + 1) @master_only def after_run(self, runner): self.wandb.finish() def _log_ckpt_as_artifact(self, model_path, aliases, metadata=None): """Log model checkpoint as W&B Artifact. Args: model_path (str): Path of the checkpoint to log. aliases (list): List of the aliases associated with this artifact. metadata (dict, optional): Metadata associated with this artifact. """ model_artifact = self.wandb.Artifact( f'run_{self.wandb.run.id}_model', type='model', metadata=metadata) model_artifact.add_file(model_path) self.wandb.log_artifact(model_artifact, aliases=aliases) def _get_eval_results(self): """Get model evaluation results.""" results = self.eval_hook.latest_results eval_results = self.val_dataset.evaluate( results, logger='silent', **self.eval_hook.eval_kwargs) return eval_results def _init_data_table(self): """Initialize the W&B Tables for validation data.""" columns = ['image_name', 'image', 'ground_truth'] self.data_table = self.wandb.Table(columns=columns) def _init_pred_table(self): """Initialize the W&B Tables for model evaluation.""" columns = ['epoch'] if self.by_epoch else ['iter'] columns += ['image_name', 'image', 'ground_truth', 'prediction' ] + list(self.val_dataset.CLASSES) self.eval_table = self.wandb.Table(columns=columns) def _add_ground_truth(self): # Get image loading pipeline from mmcls.datasets.pipelines import LoadImageFromFile img_loader = None for t in self.val_dataset.pipeline.transforms: if isinstance(t, LoadImageFromFile): img_loader = t CLASSES = self.val_dataset.CLASSES self.eval_image_indexs = np.arange(len(self.val_dataset)) # Set seed so that same validation set is logged each time. np.random.seed(42) np.random.shuffle(self.eval_image_indexs) self.eval_image_indexs = self.eval_image_indexs[:self.num_eval_images] for idx in self.eval_image_indexs: img_info = self.val_dataset.data_infos[idx] if img_loader is not None: img_info = img_loader(img_info) # Get image and convert from BGR to RGB image = img_info['img'][..., ::-1] else: # For CIFAR dataset. image = img_info['img'] image_name = img_info.get('filename', f'img_{idx}') gt_label = img_info.get('gt_label').item() self.data_table.add_data(image_name, self.wandb.Image(image), CLASSES[gt_label]) def _add_predictions(self, results, idx): table_idxs = self.data_table_ref.get_index() assert len(table_idxs) == len(self.eval_image_indexs) for ndx, eval_image_index in enumerate(self.eval_image_indexs): result = results[eval_image_index] self.eval_table.add_data( idx, self.data_table_ref.data[ndx][0], self.data_table_ref.data[ndx][1], self.data_table_ref.data[ndx][2], self.val_dataset.CLASSES[np.argmax(result)], *tuple(result)) def _log_data_table(self): """Log the W&B Tables for validation data as artifact and calls `use_artifact` on it so that the evaluation table can use the reference of already uploaded images. This allows the data to be uploaded just once. """ data_artifact = self.wandb.Artifact('val', type='dataset') data_artifact.add(self.data_table, 'val_data') self.wandb.run.use_artifact(data_artifact) data_artifact.wait() self.data_table_ref = data_artifact.get('val_data') def _log_eval_table(self, idx): """Log the W&B Tables for model evaluation. The table will be logged multiple times creating new version. Use this to compare models at different intervals interactively. """ pred_artifact = self.wandb.Artifact( f'run_{self.wandb.run.id}_pred', type='evaluation') pred_artifact.add(self.eval_table, 'eval_data') if self.by_epoch: aliases = ['latest', f'epoch_{idx}'] else: aliases = ['latest', f'iter_{idx}'] self.wandb.run.log_artifact(pred_artifact, aliases=aliases)
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