From 824be950b9a36d97a71ba5ef34c2bdf8887f921f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Haian=20Huang=28=E6=B7=B1=E5=BA=A6=E7=9C=B8=29?=
<1286304229@qq.com>
Date: Mon, 7 Mar 2022 22:39:25 +0800
Subject: [PATCH] Add writer (#74)
* add writer
* update
* update
* update docstr
* update unittest
* update unittest
* fix comment
* update unittest
* fix comment
* fix comment
* fix comment
* fix comment
* update
---
mmengine/registry/__init__.py | 4 +-
mmengine/registry/root.py | 3 +-
mmengine/visualization/__init__.py | 7 +-
mmengine/visualization/writer.py | 822 +++++++++++++++++++++++++++
tests/test_visualizer/test_writer.py | 505 +++++++++++++---
5 files changed, 1256 insertions(+), 85 deletions(-)
create mode 100644 mmengine/visualization/writer.py
diff --git a/mmengine/registry/__init__.py b/mmengine/registry/__init__.py
index 0cfb1e9e..2142fae3 100644
--- a/mmengine/registry/__init__.py
+++ b/mmengine/registry/__init__.py
@@ -3,11 +3,11 @@ from .registry import Registry, build_from_cfg
from .root import (DATA_SAMPLERS, DATASETS, EVALUATORS, HOOKS, MODEL_WRAPPERS,
MODELS, OPTIMIZER_CONSTRUCTORS, OPTIMIZERS,
PARAM_SCHEDULERS, RUNNER_CONSTRUCTORS, RUNNERS, TASK_UTILS,
- TRANSFORMS, VISUALIZERS, WEIGHT_INITIALIZERS)
+ TRANSFORMS, VISUALIZERS, WEIGHT_INITIALIZERS, WRITERS)
__all__ = [
'Registry', 'build_from_cfg', 'RUNNERS', 'RUNNER_CONSTRUCTORS', 'HOOKS',
'DATASETS', 'DATA_SAMPLERS', 'TRANSFORMS', 'MODELS', 'WEIGHT_INITIALIZERS',
'OPTIMIZERS', 'OPTIMIZER_CONSTRUCTORS', 'TASK_UTILS', 'PARAM_SCHEDULERS',
- 'EVALUATORS', 'MODEL_WRAPPERS', 'VISUALIZERS'
+ 'EVALUATORS', 'MODEL_WRAPPERS', 'WRITERS', 'VISUALIZERS'
]
diff --git a/mmengine/registry/root.py b/mmengine/registry/root.py
index 39fad795..988cb855 100644
--- a/mmengine/registry/root.py
+++ b/mmengine/registry/root.py
@@ -39,6 +39,7 @@ TASK_UTILS = Registry('task util')
# manage all kinds of evaluators for computing metrics
EVALUATORS = Registry('evaluator')
-
# manage visualizer
VISUALIZERS = Registry('visualizer')
+# manage writer
+WRITERS = Registry('writer')
diff --git a/mmengine/visualization/__init__.py b/mmengine/visualization/__init__.py
index f1cf58e1..892c3daa 100644
--- a/mmengine/visualization/__init__.py
+++ b/mmengine/visualization/__init__.py
@@ -1,4 +1,9 @@
# Copyright (c) OpenMMLab. All rights reserved.
from .visualizer import Visualizer
+from .writer import (BaseWriter, ComposedWriter, LocalWriter,
+ TensorboardWriter, WandbWriter)
-__all__ = ['Visualizer']
+__all__ = [
+ 'Visualizer', 'BaseWriter', 'LocalWriter', 'WandbWriter',
+ 'TensorboardWriter', 'ComposedWriter'
+]
diff --git a/mmengine/visualization/writer.py b/mmengine/visualization/writer.py
new file mode 100644
index 00000000..31ab3e08
--- /dev/null
+++ b/mmengine/visualization/writer.py
@@ -0,0 +1,822 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+import os.path as osp
+import time
+from abc import ABCMeta, abstractmethod
+from typing import Any, List, Optional, Union
+
+import cv2
+import numpy as np
+import torch
+
+from mmengine.data import BaseDataSample
+from mmengine.fileio import dump
+from mmengine.logging import BaseGlobalAccessible
+from mmengine.registry import VISUALIZERS, WRITERS
+from mmengine.utils import TORCH_VERSION
+from .visualizer import Visualizer
+
+
+class BaseWriter(metaclass=ABCMeta):
+ """Base class for writer.
+
+ Each writer can inherit ``BaseWriter`` and implement
+ the required functions.
+
+ Args:
+ visualizer (dict, :obj:`Visualizer`, optional):
+ Visualizer instance or dictionary. Default to None.
+ save_dir (str, optional): The root directory to save
+ the files produced by the writer. Default to None.
+ """
+
+ def __init__(self,
+ visualizer: Optional[Union[dict, 'Visualizer']] = None,
+ save_dir: Optional[str] = None):
+ self._save_dir = save_dir
+ if self._save_dir:
+ timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
+ self._save_dir = osp.join(
+ self._save_dir, f'write_data_{timestamp}') # type: ignore
+ self._visualizer = visualizer
+ if visualizer:
+ if isinstance(visualizer, dict):
+ self._visualizer = VISUALIZERS.build(visualizer)
+ else:
+ assert isinstance(visualizer, Visualizer), \
+ 'visualizer should be an instance of Visualizer, ' \
+ f'but got {type(visualizer)}'
+
+ @property
+ def visualizer(self) -> 'Visualizer':
+ """Return the visualizer object.
+
+ You can get the drawing backend through the visualizer property for
+ custom drawing.
+ """
+ return self._visualizer # type: ignore
+
+ @property
+ @abstractmethod
+ def experiment(self) -> Any:
+ """Return the experiment object associated with this writer.
+
+ The experiment attribute can get the write backend, such as wandb,
+ tensorboard. If you want to write other data, such as writing a table,
+ you can directly get the write backend through experiment.
+ """
+ pass
+
+ def add_params(self, params_dict: dict, **kwargs) -> None:
+ """Record a set of parameters.
+
+ Args:
+ params_dict (dict): Each key-value pair in the dictionary is the
+ name of the parameters and it's corresponding value.
+ """
+ pass
+
+ def add_graph(self, model: torch.nn.Module,
+ input_tensor: Union[torch.Tensor,
+ List[torch.Tensor]], **kwargs) -> None:
+ """Record graph.
+
+ Args:
+ model (torch.nn.Module): Model to draw.
+ input_tensor (torch.Tensor, list[torch.Tensor]): A variable
+ or a tuple of variables to be fed.
+ """
+ pass
+
+ def add_image(self,
+ name: str,
+ image: Optional[np.ndarray] = None,
+ gt_sample: Optional['BaseDataSample'] = None,
+ pred_sample: Optional['BaseDataSample'] = None,
+ draw_gt: bool = True,
+ draw_pred: bool = True,
+ step: int = 0,
+ **kwargs) -> None:
+ """Record image.
+
+ Args:
+ name (str): The unique identifier for the image to save.
+ image (np.ndarray, optional): The image to be saved. The format
+ should be RGB. Default to None.
+ gt_sample (:obj:`BaseDataSample`, optional): The ground truth data
+ structure of OpenMMlab. Default to None.
+ pred_sample (:obj:`BaseDataSample`, optional): The predicted result
+ data structure of OpenMMlab. Default to None.
+ draw_gt (bool): Whether to draw the ground truth. Default: True.
+ draw_pred (bool): Whether to draw the predicted result.
+ Default to True.
+ step (int): Global step value to record. Default to 0.
+ """
+ pass
+
+ def add_scalar(self,
+ name: str,
+ value: Union[int, float],
+ step: int = 0,
+ **kwargs) -> None:
+ """Record scalar.
+
+ Args:
+ name (str): The unique identifier for the scalar to save.
+ value (float, int): Value to save.
+ step (int): Global step value to record. Default to 0.
+ """
+ pass
+
+ def add_scalars(self,
+ scalar_dict: dict,
+ step: int = 0,
+ file_path: Optional[str] = None,
+ **kwargs) -> None:
+ """Record scalars' data.
+
+ Args:
+ scalar_dict (dict): Key-value pair storing the tag and
+ corresponding values.
+ step (int): Global step value to record. Default to 0.
+ file_path (str, optional): The scalar's data will be
+ saved to the `file_path` file at the same time
+ if the `file_path` parameter is specified.
+ Default to None.
+ """
+ pass
+
+ def close(self) -> None:
+ """close an opened object."""
+ pass
+
+
+@WRITERS.register_module()
+class LocalWriter(BaseWriter):
+ """Local write class.
+
+ It can write image, hyperparameters, scalars, etc.
+ to the local hard disk. You can get the drawing backend
+ through the visualizer property for custom drawing.
+
+ Examples:
+ >>> from mmengine.visualization import LocalWriter
+ >>> import numpy as np
+ >>> local_writer = LocalWriter(dict(type='DetVisualizer'),\
+ save_dir='temp_dir')
+ >>> img=np.random.randint(0, 256, size=(10, 10, 3))
+ >>> local_writer.add_image('img', img)
+ >>> local_writer.add_scaler('mAP', 0.6)
+ >>> local_writer.add_scalars({'loss': [1, 2, 3], 'acc': 0.8})
+ >>> local_writer.add_params(dict(lr=0.1, mode='linear'))
+
+ >>> local_writer.visualizer.draw_bboxes(np.array([0, 0, 1, 1]), \
+ edgecolors='g')
+ >>> local_writer.add_image('img', \
+ local_writer.visualizer.get_image())
+
+ Args:
+ save_dir (str): The root directory to save the files
+ produced by the writer.
+ visualizer (dict, :obj:`Visualizer`, optional): Visualizer
+ instance or dictionary. Default to None
+ img_save_dir (str): The directory to save images.
+ Default to 'writer_image'.
+ params_save_file (str): The file to save parameters.
+ Default to 'parameters.yaml'.
+ scalar_save_file (str): The file to save scalar values.
+ Default to 'scalars.json'.
+ img_show (bool): Whether to show the image when calling add_image.
+ Default to False.
+ """
+
+ def __init__(self,
+ save_dir: str,
+ visualizer: Optional[Union[dict, 'Visualizer']] = None,
+ img_save_dir: str = 'writer_image',
+ params_save_file: str = 'parameters.yaml',
+ scalar_save_file: str = 'scalars.json',
+ img_show: bool = False):
+ assert params_save_file.split('.')[-1] == 'yaml'
+ assert scalar_save_file.split('.')[-1] == 'json'
+ super(LocalWriter, self).__init__(visualizer, save_dir)
+ os.makedirs(self._save_dir, exist_ok=True) # type: ignore
+ self._img_save_dir = osp.join(
+ self._save_dir, # type: ignore
+ img_save_dir)
+ self._scalar_save_file = osp.join(
+ self._save_dir, # type: ignore
+ scalar_save_file)
+ self._params_save_file = osp.join(
+ self._save_dir, # type: ignore
+ params_save_file)
+ self._img_show = img_show
+
+ @property
+ def experiment(self) -> 'LocalWriter':
+ """Return the experiment object associated with this writer."""
+ return self
+
+ def add_params(self, params_dict: dict, **kwargs) -> None:
+ """Record parameters to disk.
+
+ Args:
+ params_dict (dict): The dict of parameters to save.
+ """
+ assert isinstance(params_dict, dict)
+ self._dump(params_dict, self._params_save_file, 'yaml')
+
+ def add_image(self,
+ name: str,
+ image: Optional[np.ndarray] = None,
+ gt_sample: Optional['BaseDataSample'] = None,
+ pred_sample: Optional['BaseDataSample'] = None,
+ draw_gt: bool = True,
+ draw_pred: bool = True,
+ step: int = 0,
+ **kwargs) -> None:
+ """Record image to disk.
+
+ Args:
+ name (str): The unique identifier for the image to save.
+ image (np.ndarray, optional): The image to be saved. The format
+ should be RGB. Default to None.
+ gt_sample (:obj:`BaseDataSample`, optional): The ground truth data
+ structure of OpenMMlab. Default to None.
+ pred_sample (:obj:`BaseDataSample`, optional): The predicted result
+ data structure of OpenMMlab. Default to None.
+ draw_gt (bool): Whether to draw the ground truth. Default to True.
+ draw_pred (bool): Whether to draw the predicted result.
+ Default to True.
+ step (int): Global step value to record. Default to 0.
+ """
+ assert self.visualizer, 'Please instantiate the visualizer ' \
+ 'object with initialization parameters.'
+ self.visualizer.draw(image, gt_sample, pred_sample, draw_gt, draw_pred)
+ if self._img_show:
+ self.visualizer.show()
+ else:
+ drawn_image = cv2.cvtColor(self.visualizer.get_image(),
+ cv2.COLOR_RGB2BGR)
+ os.makedirs(self._img_save_dir, exist_ok=True)
+ save_file_name = f'{name}_{step}.png'
+ cv2.imwrite(
+ osp.join(self._img_save_dir, save_file_name), drawn_image)
+
+ def add_scalar(self,
+ name: str,
+ value: Union[int, float],
+ step: int = 0,
+ **kwargs) -> None:
+ """Add scalar data to disk.
+
+ Args:
+ name (str): The unique identifier for the scalar to save.
+ value (float, int): Value to save.
+ step (int): Global step value to record. Default to 0.
+ """
+ self._dump({name: value, 'step': step}, self._scalar_save_file, 'json')
+
+ def add_scalars(self,
+ scalar_dict: dict,
+ step: int = 0,
+ file_path: Optional[str] = None,
+ **kwargs) -> None:
+ """Record scalars. The scalar dict will be written to the default and
+ specified files if ``file_name`` is specified.
+
+ Args:
+ scalar_dict (dict): Key-value pair storing the tag and
+ corresponding values.
+ step (int): Global step value to record. Default to 0.
+ file_path (str, optional): The scalar's data will be
+ saved to the ``file_path`` file at the same time
+ if the ``file_path`` parameter is specified.
+ Default to None.
+ """
+ assert isinstance(scalar_dict, dict)
+ scalar_dict.setdefault('step', step)
+ if file_path is not None:
+ assert file_path.split('.')[-1] == 'json'
+ new_save_file_path = osp.join(
+ self._save_dir, # type: ignore
+ file_path)
+ assert new_save_file_path != self._scalar_save_file, \
+ '"file_path" and "scalar_save_file" have the same name, ' \
+ 'please set "file_path" to another value'
+ self._dump(scalar_dict, new_save_file_path, 'json')
+ self._dump(scalar_dict, self._scalar_save_file, 'json')
+
+ def _dump(self, value_dict: dict, file_path: str,
+ file_format: str) -> None:
+ """dump dict to file.
+
+ Args:
+ value_dict (dict) : Save dict data.
+ file_path (str): The file path to save data.
+ file_format (str): The file format to save data.
+ """
+ with open(file_path, 'a+') as f:
+ dump(value_dict, f, file_format=file_format)
+ f.write('\n')
+
+
+@WRITERS.register_module()
+class WandbWriter(BaseWriter):
+ """Write various types of data to wandb.
+
+ Examples:
+ >>> from mmengine.visualization import WandbWriter
+ >>> import numpy as np
+ >>> wandb_writer = WandbWriter(dict(type='DetVisualizer'))
+ >>> img=np.random.randint(0, 256, size=(10, 10, 3))
+ >>> wandb_writer.add_image('img', img)
+ >>> wandb_writer.add_scaler('mAP', 0.6)
+ >>> wandb_writer.add_scalars({'loss': [1, 2, 3],'acc': 0.8})
+ >>> wandb_writer.add_params(dict(lr=0.1, mode='linear'))
+
+ >>> wandb_writer.visualizer.draw_bboxes(np.array([0, 0, 1, 1]), \
+ edgecolors='g')
+ >>> wandb_writer.add_image('img', \
+ wandb_writer.visualizer.get_image())
+
+ >>> wandb_writer = WandbWriter()
+ >>> assert wandb_writer.visualizer is None
+ >>> wandb_writer.add_image('img', img)
+
+ Args:
+ init_kwargs (dict, optional): wandb initialization
+ input parameters. Default to None.
+ commit: (bool, optional) Save the metrics dict to the wandb server
+ and increment the step. If false `wandb.log` just
+ updates the current metrics dict with the row argument
+ and metrics won't be saved until `wandb.log` is called
+ with `commit=True`. Default to True.
+ visualizer (dict, :obj:`Visualizer`, optional):
+ Visualizer instance or dictionary. Default to None.
+ save_dir (str, optional): The root directory to save the files
+ produced by the writer. Default to None.
+ """
+
+ def __init__(self,
+ init_kwargs: Optional[dict] = None,
+ commit: Optional[bool] = True,
+ visualizer: Optional[Union[dict, 'Visualizer']] = None,
+ save_dir: Optional[str] = None):
+ super(WandbWriter, self).__init__(visualizer, save_dir)
+ self._commit = commit
+ self._wandb = self._setup_env(init_kwargs)
+
+ @property
+ def experiment(self):
+ """Return wandb object.
+
+ The experiment attribute can get the wandb backend, If you want to
+ write other data, such as writing a table, you can directly get the
+ wandb backend through experiment.
+ """
+ return self._wandb
+
+ def _setup_env(self, init_kwargs: Optional[dict] = None) -> Any:
+ """Setup env.
+
+ Args:
+ init_kwargs (dict): The init args.
+
+ Return:
+ :obj:`wandb`
+ """
+ try:
+ import wandb
+ except ImportError:
+ raise ImportError(
+ 'Please run "pip install wandb" to install wandb')
+ if init_kwargs:
+ wandb.init(**init_kwargs)
+ else:
+ wandb.init()
+
+ return wandb
+
+ def add_params(self, params_dict: dict, **kwargs) -> None:
+ """Record a set of parameters to be compared in wandb.
+
+ Args:
+ params_dict (dict): Each key-value pair in the dictionary
+ is the name of the parameters and it's
+ corresponding value.
+ """
+ assert isinstance(params_dict, dict)
+ self._wandb.log(params_dict, commit=self._commit)
+
+ def add_image(self,
+ name: str,
+ image: Optional[np.ndarray] = None,
+ gt_sample: Optional['BaseDataSample'] = None,
+ pred_sample: Optional['BaseDataSample'] = None,
+ draw_gt: bool = True,
+ draw_pred: bool = True,
+ step: int = 0,
+ **kwargs) -> None:
+ """Record image to wandb.
+
+ Args:
+ name (str): The unique identifier for the image to save.
+ image (np.ndarray, optional): The image to be saved. The format
+ should be RGB. Default to None.
+ gt_sample (:obj:`BaseDataSample`, optional): The ground truth data
+ structure of OpenMMlab. Default to None.
+ pred_sample (:obj:`BaseDataSample`, optional): The predicted result
+ data structure of OpenMMlab. Default to None.
+ draw_gt (bool): Whether to draw the ground truth. Default: True.
+ draw_pred (bool): Whether to draw the predicted result.
+ Default to True.
+ step (int): Global step value to record. Default to 0.
+ """
+ if self.visualizer:
+ self.visualizer.draw(image, gt_sample, pred_sample, draw_gt,
+ draw_pred)
+ self._wandb.log({name: self.visualizer.get_image()},
+ commit=self._commit,
+ step=step)
+ else:
+ self.add_image_to_wandb(name, image, gt_sample, pred_sample,
+ draw_gt, draw_pred, step, **kwargs)
+
+ def add_scalar(self,
+ name: str,
+ value: Union[int, float],
+ step: int = 0,
+ **kwargs) -> None:
+ """Record scalar data to wandb.
+
+ Args:
+ name (str): The unique identifier for the scalar to save.
+ value (float, int): Value to save.
+ step (int): Global step value to record. Default to 0.
+ """
+ self._wandb.log({name: value}, commit=self._commit, step=step)
+
+ def add_scalars(self,
+ scalar_dict: dict,
+ step: int = 0,
+ file_path: Optional[str] = None,
+ **kwargs) -> None:
+ """Record scalar's data to wandb.
+
+ Args:
+ scalar_dict (dict): Key-value pair storing the tag and
+ corresponding values.
+ step (int): Global step value to record. Default to 0.
+ file_path (str, optional): Useless parameter. Just for
+ interface unification. Default to None.
+ """
+ self._wandb.log(scalar_dict, commit=self._commit, step=step)
+
+ def add_image_to_wandb(self,
+ name: str,
+ image: np.ndarray,
+ gt_sample: Optional['BaseDataSample'] = None,
+ pred_sample: Optional['BaseDataSample'] = None,
+ draw_gt: bool = True,
+ draw_pred: bool = True,
+ step: int = 0,
+ **kwargs) -> None:
+ """Record image to wandb.
+
+ Args:
+ name (str): The unique identifier for the image to save.
+ image (np.ndarray): The image to be saved. The format
+ should be BGR.
+ gt_sample (:obj:`BaseDataSample`, optional): The ground truth data
+ structure of OpenMMlab. Default to None.
+ pred_sample (:obj:`BaseDataSample`, optional): The predicted result
+ data structure of OpenMMlab. Default to None.
+ draw_gt (bool): Whether to draw the ground truth. Default to True.
+ draw_pred (bool): Whether to draw the predicted result.
+ Default to True.
+ step (int): Global step value to record. Default to 0.
+ """
+ raise NotImplementedError()
+
+ def close(self) -> None:
+ """close an opened wandb object."""
+ if hasattr(self, '_wandb'):
+ self._wandb.join()
+
+
+@WRITERS.register_module()
+class TensorboardWriter(BaseWriter):
+ """Tensorboard write class. It can write images, hyperparameters, scalars,
+ etc. to a tensorboard file.
+
+ Its drawing function is provided by Visualizer.
+
+ Examples:
+ >>> from mmengine.visualization import TensorboardWriter
+ >>> import numpy as np
+ >>> tensorboard_writer = TensorboardWriter(dict(type='DetVisualizer'),\
+ save_dir='temp_dir')
+ >>> img=np.random.randint(0, 256, size=(10, 10, 3))
+ >>> tensorboard_writer.add_image('img', img)
+ >>> tensorboard_writer.add_scaler('mAP', 0.6)
+ >>> tensorboard_writer.add_scalars({'loss': 0.1,'acc':0.8})
+ >>> tensorboard_writer.add_params(dict(lr=0.1, mode='linear'))
+
+ >>> tensorboard_writer.visualizer.draw_bboxes(np.array([0, 0, 1, 1]), \
+ edgecolors='g')
+ >>> tensorboard_writer.add_image('img', \
+ tensorboard_writer.visualizer.get_image())
+
+ Args:
+ save_dir (str): The root directory to save the files
+ produced by the writer.
+ visualizer (dict, :obj:`Visualizer`, optional): Visualizer instance
+ or dictionary. Default to None.
+ log_dir (str): Save directory location. Default to 'tf_writer'.
+ """
+
+ def __init__(self,
+ save_dir: str,
+ visualizer: Optional[Union[dict, 'Visualizer']] = None,
+ log_dir: str = 'tf_logs'):
+ super(TensorboardWriter, self).__init__(visualizer, save_dir)
+ self._tensorboard = self._setup_env(log_dir)
+
+ def _setup_env(self, log_dir: str):
+ """Setup env.
+
+ Args:
+ log_dir (str): Save directory location. Default 'tf_writer'.
+
+ Return:
+ :obj:`SummaryWriter`
+ """
+ if TORCH_VERSION == 'parrots':
+ try:
+ from tensorboardX import SummaryWriter
+ except ImportError:
+ raise ImportError('Please install tensorboardX to use '
+ 'TensorboardLoggerHook.')
+ else:
+ try:
+ from torch.utils.tensorboard import SummaryWriter
+ except ImportError:
+ raise ImportError(
+ 'Please run "pip install future tensorboard" to install '
+ 'the dependencies to use torch.utils.tensorboard '
+ '(applicable to PyTorch 1.1 or higher)')
+
+ self.log_dir = osp.join(self._save_dir, log_dir) # type: ignore
+ return SummaryWriter(self.log_dir)
+
+ @property
+ def experiment(self):
+ """Return Tensorboard object."""
+ return self._tensorboard
+
+ def add_graph(self, model: torch.nn.Module,
+ input_tensor: Union[torch.Tensor,
+ List[torch.Tensor]], **kwargs) -> None:
+ """Record graph data to tensorboard.
+
+ Args:
+ model (torch.nn.Module): Model to draw.
+ input_tensor (torch.Tensor, list[torch.Tensor]): A variable
+ or a tuple of variables to be fed.
+ """
+ if isinstance(input_tensor, list):
+ for array in input_tensor:
+ assert array.ndim == 4
+ assert isinstance(array, torch.Tensor)
+ else:
+ assert isinstance(input_tensor,
+ torch.Tensor) and input_tensor.ndim == 4
+ self._tensorboard.add_graph(model, input_tensor)
+
+ def add_params(self, params_dict: dict, **kwargs) -> None:
+ """Record a set of hyperparameters to be compared in TensorBoard.
+
+ Args:
+ params_dict (dict): Each key-value pair in the dictionary is the
+ name of the hyper parameter and it's corresponding value.
+ The type of the value can be one of `bool`, `string`,
+ `float`, `int`, or `None`.
+ """
+ assert isinstance(params_dict, dict)
+ self._tensorboard.add_hparams(params_dict, {})
+
+ def add_image(self,
+ name: str,
+ image: Optional[np.ndarray] = None,
+ gt_sample: Optional['BaseDataSample'] = None,
+ pred_sample: Optional['BaseDataSample'] = None,
+ draw_gt: bool = True,
+ draw_pred: bool = True,
+ step: int = 0,
+ **kwargs) -> None:
+ """Record image to tensorboard.
+
+ Args:
+ name (str): The unique identifier for the image to save.
+ image (np.ndarray, optional): The image to be saved. The format
+ should be RGB. Default to None.
+ gt_sample (:obj:`BaseDataSample`, optional): The ground truth data
+ structure of OpenMMlab. Default to None.
+ pred_sample (:obj:`BaseDataSample`, optional): The predicted result
+ data structure of OpenMMlab. Default to None.
+ draw_gt (bool): Whether to draw the ground truth. Default to True.
+ draw_pred (bool): Whether to draw the predicted result.
+ Default to True.
+ step (int): Global step value to record. Default to 0.
+ """
+ assert self.visualizer, 'Please instantiate the visualizer ' \
+ 'object with initialization parameters.'
+ self.visualizer.draw(image, gt_sample, pred_sample, draw_gt, draw_pred)
+ self._tensorboard.add_image(
+ name, self.visualizer.get_image(), step, dataformats='HWC')
+
+ def add_scalar(self,
+ name: str,
+ value: Union[int, float],
+ step: int = 0,
+ **kwargs) -> None:
+ """Record scalar data to summary.
+
+ Args:
+ name (str): The unique identifier for the scalar to save.
+ value (float, int): Value to save.
+ step (int): Global step value to record. Default to 0.
+ """
+ self._tensorboard.add_scalar(name, value, step)
+
+ def add_scalars(self,
+ scalar_dict: dict,
+ step: int = 0,
+ file_path: Optional[str] = None,
+ **kwargs) -> None:
+ """Record scalar's data to summary.
+
+ Args:
+ scalar_dict (dict): Key-value pair storing the tag and
+ corresponding values.
+ step (int): Global step value to record. Default to 0.
+ file_path (str, optional): Useless parameter. Just for
+ interface unification. Default to None.
+ """
+ assert isinstance(scalar_dict, dict)
+ assert 'step' not in scalar_dict, 'Please set it directly ' \
+ 'through the step parameter'
+ for key, value in scalar_dict.items():
+ self.add_scalar(key, value, step)
+
+ def close(self):
+ """close an opened tensorboard object."""
+ if hasattr(self, '_tensorboard'):
+ self._tensorboard.close()
+
+
+class ComposedWriter(BaseGlobalAccessible):
+ """Wrapper class to compose multiple a subclass of :class:`BaseWriter`
+ instances. By inheriting BaseGlobalAccessible, it can be accessed anywhere
+ once instantiated.
+
+ Examples:
+ >>> from mmengine.visualization import ComposedWriter
+ >>> import numpy as np
+ >>> composed_writer= ComposedWriter.create_instance( \
+ 'composed_writer', writers=[dict(type='LocalWriter', \
+ visualizer=dict(type='DetVisualizer'), \
+ save_dir='temp_dir'), dict(type='WandbWriter')])
+ >>> img=np.random.randint(0, 256, size=(10, 10, 3))
+ >>> composed_writer.add_image('img', img)
+ >>> composed_writer.add_scalar('mAP', 0.6)
+ >>> composed_writer.add_scalars({'loss': 0.1,'acc':0.8})
+ >>> composed_writer.add_params(dict(lr=0.1, mode='linear'))
+
+ Args:
+ name (str): The name of the instance. Defaults: 'composed_writer'.
+ writers (list, optional): The writers to compose. Default to None
+ """
+
+ def __init__(self,
+ name: str = 'composed_writer',
+ writers: Optional[List[Union[dict, 'BaseWriter']]] = None):
+ super().__init__(name)
+ self._writers = []
+ if writers is not None:
+ assert isinstance(writers, list)
+ for writer in writers:
+ if isinstance(writer, dict):
+ self._writers.append(WRITERS.build(writer))
+ else:
+ assert isinstance(writer, BaseWriter), \
+ f'writer should be an instance of a subclass of ' \
+ f'BaseWriter, but got {type(writer)}'
+ self._writers.append(writer)
+
+ def __len__(self):
+ return len(self._writers)
+
+ def get_writer(self, index: int) -> 'BaseWriter':
+ """Returns the writer object corresponding to the specified index."""
+ return self._writers[index]
+
+ def get_experiment(self, index: int) -> Any:
+ """Returns the writer's experiment object corresponding to the
+ specified index."""
+ return self._writers[index].experiment
+
+ def get_visualizer(self, index: int) -> 'Visualizer':
+ """Returns the writer's visualizer object corresponding to the
+ specified index."""
+ return self._writers[index].visualizer
+
+ def add_params(self, params_dict: dict, **kwargs):
+ """Record parameters.
+
+ Args:
+ params_dict (dict): The dictionary of parameters to save.
+ """
+ for writer in self._writers:
+ writer.add_params(params_dict, **kwargs)
+
+ def add_graph(self, model: torch.nn.Module,
+ input_array: Union[torch.Tensor,
+ List[torch.Tensor]], **kwargs) -> None:
+ """Record graph data.
+
+ Args:
+ model (torch.nn.Module): Model to draw.
+ input_array (torch.Tensor, list[torch.Tensor]): A variable
+ or a tuple of variables to be fed.
+ """
+ for writer in self._writers:
+ writer.add_graph(model, input_array, **kwargs)
+
+ def add_image(self,
+ name: str,
+ image: Optional[np.ndarray] = None,
+ gt_sample: Optional['BaseDataSample'] = None,
+ pred_sample: Optional['BaseDataSample'] = None,
+ draw_gt: bool = True,
+ draw_pred: bool = True,
+ step: int = 0,
+ **kwargs) -> None:
+ """Record image.
+
+ Args:
+ name (str): The unique identifier for the image to save.
+ image (np.ndarray, optional): The image to be saved. The format
+ should be RGB. Default to None.
+ gt_sample (:obj:`BaseDataSample`, optional): The ground truth data
+ structure of OpenMMlab. Default to None.
+ pred_sample (:obj:`BaseDataSample`, optional): The predicted result
+ data structure of OpenMMlab. Default to None.
+ draw_gt (bool): Whether to draw the ground truth. Default to True.
+ draw_pred (bool): Whether to draw the predicted result.
+ Default to True.
+ step (int): Global step value to record. Default to 0.
+ """
+ for writer in self._writers:
+ writer.add_image(name, image, gt_sample, pred_sample, draw_gt,
+ draw_pred, step, **kwargs)
+
+ def add_scalar(self,
+ name: str,
+ value: Union[int, float],
+ step: int = 0,
+ **kwargs) -> None:
+ """Record scalar data.
+
+ Args:
+ name (str): The unique identifier for the scalar to save.
+ value (float, int): Value to save.
+ step (int): Global step value to record. Default to 0.
+ """
+ for writer in self._writers:
+ writer.add_scalar(name, value, step, **kwargs)
+
+ def add_scalars(self,
+ scalar_dict: dict,
+ step: int = 0,
+ file_path: Optional[str] = None,
+ **kwargs) -> None:
+ """Record scalars' data.
+
+ Args:
+ scalar_dict (dict): Key-value pair storing the tag and
+ corresponding values.
+ step (int): Global step value to record. Default to 0.
+ file_path (str, optional): The scalar's data will be
+ saved to the `file_path` file at the same time
+ if the `file_path` parameter is specified.
+ Default to None.
+ """
+ for writer in self._writers:
+ writer.add_scalars(scalar_dict, step, file_path, **kwargs)
+
+ def close(self) -> None:
+ """close an opened object."""
+ for writer in self._writers:
+ writer.close()
diff --git a/tests/test_visualizer/test_writer.py b/tests/test_visualizer/test_writer.py
index 718ff8da..5219a2a4 100644
--- a/tests/test_visualizer/test_writer.py
+++ b/tests/test_visualizer/test_writer.py
@@ -1,141 +1,484 @@
# Copyright (c) OpenMMLab. All rights reserved.
-import random
+import os
+import shutil
import sys
-from unittest.mock import MagicMock
+from unittest.mock import MagicMock, Mock, patch
import numpy as np
import pytest
import torch
+import torch.nn as nn
-from mmengine.data import BaseDataElement, BaseDataSample
-from mmengine.visualizer import (VISUALIZERS, LocalWriter, TensorboardWriter,
- WandbWriter)
+from mmengine.fileio import load
+from mmengine.registry import VISUALIZERS, WRITERS
+from mmengine.visualization import (ComposedWriter, LocalWriter,
+ TensorboardWriter, WandbWriter)
-def get_demo_datasample():
- metainfo = dict(
- img_id=random.randint(0, 100),
- img_shape=(random.randint(400, 600), random.randint(400, 600)))
- gt_instances = BaseDataElement(
- data=dict(bboxes=torch.rand((5, 4)), labels=torch.rand((5, ))))
- pred_instances = BaseDataElement(
- data=dict(bboxes=torch.rand((5, 4)), scores=torch.rand((5, ))))
- data = dict(gt_instances=gt_instances, pred_instances=pred_instances)
- data_sample = BaseDataSample(data=data, metainfo=metainfo)
- return data_sample
+def draw(self, image, gt_sample, pred_sample, show_gt=True, show_pred=True):
+ self.set_image(image)
class TestLocalWriter:
+ def test_init(self):
+ # visuailzer must be a dictionary or an instance
+ # of Visualizer and its subclasses
+ with pytest.raises(AssertionError):
+ LocalWriter('temp_dir', [dict(type='Visualizer')])
+
+ # 'params_save_file' format must be yaml
+ with pytest.raises(AssertionError):
+ LocalWriter('temp_dir', params_save_file='a.txt')
+
+ # 'scalar_save_file' format must be json
+ with pytest.raises(AssertionError):
+ LocalWriter('temp_dir', scalar_save_file='a.yaml')
+
+ local_writer = LocalWriter('temp_dir')
+ assert os.path.exists(local_writer._save_dir)
+ shutil.rmtree('temp_dir')
+
+ local_writer = WRITERS.build(
+ dict(
+ type='LocalWriter',
+ visualizer=dict(type='Visualizer'),
+ save_dir='temp_dir'))
+ assert os.path.exists(local_writer._save_dir)
+ shutil.rmtree('temp_dir')
+
+ def test_experiment(self):
+ local_writer = LocalWriter('temp_dir')
+ assert local_writer.experiment == local_writer
+ shutil.rmtree('temp_dir')
+
+ def test_add_params(self):
+ local_writer = LocalWriter('temp_dir')
+
+ # 'params_dict' must be dict
+ with pytest.raises(AssertionError):
+ local_writer.add_params(['lr', 0])
+
+ params_dict = dict(lr=0.1, wd=[1.0, 0.1, 0.001], mode='linear')
+ local_writer.add_params(params_dict)
+ out_dict = load(local_writer._params_save_file, 'yaml')
+ assert out_dict == params_dict
+ shutil.rmtree('temp_dir')
+
+ @patch('mmengine.visualization.visualizer.Visualizer.draw', draw)
def test_add_image(self):
- image = np.random.randint(0, 256, size=(10, 10, 3))
- data_sample = get_demo_datasample()
+ image = np.random.randint(0, 256, size=(10, 10, 3)).astype(np.uint8)
+
+ # The visuailzer parameter must be set when
+ # the local_writer object is instantiated and
+ # the `add_image` method is called.
+ with pytest.raises(AssertionError):
+ local_writer = LocalWriter('temp_dir')
+ local_writer.add_image('img', image)
- local_writer = LocalWriter(visuailzer=dict(type='Visualizer'))
+ local_writer = LocalWriter('temp_dir', dict(type='Visualizer'))
local_writer.add_image('img', image)
- local_writer.add_image('img', image, data_sample)
+ assert os.path.exists(
+ os.path.join(local_writer._img_save_dir, 'img_0.png'))
bboxes = np.array([[1, 1, 2, 2], [1, 1.5, 1, 2.5]])
local_writer.visualizer.draw_bboxes(bboxes)
- local_writer.add_image('img', local_writer.visualizer.get_image())
+ local_writer.add_image(
+ 'img', local_writer.visualizer.get_image(), step=2)
+ assert os.path.exists(
+ os.path.join(local_writer._img_save_dir, 'img_2.png'))
visuailzer = VISUALIZERS.build(dict(type='Visualizer'))
- local_writer = LocalWriter(visuailzer=visuailzer)
+ local_writer = LocalWriter('temp_dir', visuailzer)
local_writer.add_image('img', image)
- local_writer.add_image('img', image, data_sample)
+ assert os.path.exists(
+ os.path.join(local_writer._img_save_dir, 'img_0.png'))
+
+ shutil.rmtree('temp_dir')
+
+ def test_add_scalar(self):
+ local_writer = LocalWriter('temp_dir')
+ local_writer.add_scalar('map', 0.9)
+ out_dict = load(local_writer._scalar_save_file, 'json')
+ assert out_dict == {'map': 0.9, 'step': 0}
+ shutil.rmtree('temp_dir')
+
+ # test append mode
+ local_writer = LocalWriter('temp_dir')
+ local_writer.add_scalar('map', 0.9, step=0)
+ local_writer.add_scalar('map', 0.95, step=1)
+ with open(local_writer._scalar_save_file) as f:
+ out_dict = f.read()
+ assert out_dict == '{"map": 0.9, "step": 0}\n{"map": ' \
+ '0.95, "step": 1}\n'
+ shutil.rmtree('temp_dir')
+
+ def test_add_scalars(self):
+ local_writer = LocalWriter('temp_dir')
+ input_dict = {'map': 0.7, 'acc': 0.9}
+ local_writer.add_scalars(input_dict)
+ out_dict = load(local_writer._scalar_save_file, 'json')
+ assert out_dict == {'map': 0.7, 'acc': 0.9, 'step': 0}
+
+ # test append mode
+ local_writer.add_scalars({'map': 0.8, 'acc': 0.8}, step=1)
+ with open(local_writer._scalar_save_file) as f:
+ out_dict = f.read()
+ assert out_dict == '{"map": 0.7, "acc": 0.9, ' \
+ '"step": 0}\n{"map": 0.8, "acc": 0.8, "step": 1}\n'
+
+ # test file_path
+ local_writer = LocalWriter('temp_dir')
+ local_writer.add_scalars(input_dict, file_path='temp.json')
+ assert os.path.exists(local_writer._scalar_save_file)
+ assert os.path.exists(
+ os.path.join(local_writer._save_dir, 'temp.json'))
+
+ # file_path and scalar_save_file cannot be the same
+ with pytest.raises(AssertionError):
+ local_writer.add_scalars(input_dict, file_path='scalars.json')
+
+ shutil.rmtree('temp_dir')
- # test `visuailzer` parameter
- # `visuailzer` parameter which must be either Visualizer instance
- # or valid dictionary.
+
+class TestTensorboardWriter:
+ sys.modules['torch.utils.tensorboard'] = MagicMock()
+ sys.modules['tensorboardX'] = MagicMock()
+
+ def test_init(self):
+ # visuailzer must be a dictionary or an instance
+ # of Visualizer and its subclasses
with pytest.raises(AssertionError):
+ LocalWriter('temp_dir', [dict(type='Visualizer')])
+
+ TensorboardWriter('temp_dir')
+ WRITERS.build(
+ dict(
+ type='TensorboardWriter',
+ visualizer=dict(type='Visualizer'),
+ save_dir='temp_dir'))
+
+ def test_experiment(self):
+ tensorboard_writer = TensorboardWriter('temp_dir')
+ assert tensorboard_writer.experiment == tensorboard_writer._tensorboard
+
+ def test_add_graph(self):
- class A:
- pass
+ class Model(nn.Module):
+
+ def __init__(self):
+ super().__init__()
+ self.conv = nn.Conv2d(1, 2, 1)
+
+ def forward(self, x, y=None):
+ return self.conv(x)
+
+ tensorboard_writer = TensorboardWriter('temp_dir')
+
+ # input must be tensor
+ with pytest.raises(AssertionError):
+ tensorboard_writer.add_graph(Model(), np.zeros([1, 1, 3, 3]))
- LocalWriter(visuailzer=A())
+ # input must be 4d tensor
with pytest.raises(AssertionError):
- LocalWriter(visuailzer=dict(a='Visualizer'))
+ tensorboard_writer.add_graph(Model(), torch.zeros([1, 3, 3]))
+
+ # If the input is a list, the inner element must be a 4d tensor
+ with pytest.raises(AssertionError):
+ tensorboard_writer.add_graph(
+ Model(), [torch.zeros([1, 1, 3, 3]),
+ torch.zeros([1, 3, 3])])
+
+ tensorboard_writer.add_graph(Model(), torch.zeros([1, 1, 3, 3]))
+ tensorboard_writer.add_graph(
+ Model(), [torch.zeros([1, 1, 3, 3]),
+ torch.zeros([1, 1, 3, 3])])
+
+ def test_add_params(self):
+ tensorboard_writer = TensorboardWriter('temp_dir')
+
+ # 'params_dict' must be dict
+ with pytest.raises(AssertionError):
+ tensorboard_writer.add_params(['lr', 0])
+
+ params_dict = dict(lr=0.1, wd=0.2, mode='linear')
+ tensorboard_writer.add_params(params_dict)
+
+ @patch('mmengine.visualization.visualizer.Visualizer.draw', draw)
+ def test_add_image(self):
+ image = np.random.randint(0, 256, size=(10, 10, 3)).astype(np.uint8)
- # test not visuailzer
# The visuailzer parameter must be set when
# the local_writer object is instantiated and
# the `add_image` method is called.
with pytest.raises(AssertionError):
- local_writer = LocalWriter()
- local_writer.add_image('img', image)
+ tensorboard_writer = TensorboardWriter('temp_dir')
+ tensorboard_writer.add_image('img', image)
+
+ tensorboard_writer = TensorboardWriter('temp_dir',
+ dict(type='Visualizer'))
+ tensorboard_writer.add_image('img', image)
- def test_add_scaler(self):
- local_writer = LocalWriter()
- local_writer.add_scaler('map', 0.9)
+ bboxes = np.array([[1, 1, 2, 2], [1, 1.5, 1, 2.5]])
+ tensorboard_writer.visualizer.draw_bboxes(bboxes)
+ tensorboard_writer.add_image(
+ 'img', tensorboard_writer.visualizer.get_image(), step=2)
- def test_add_hyperparams(self):
- local_writer = LocalWriter()
- local_writer.add_hyperparams('hyper', dict(lr=0.01))
+ visuailzer = VISUALIZERS.build(dict(type='Visualizer'))
+ tensorboard_writer = TensorboardWriter('temp_dir', visuailzer)
+ tensorboard_writer.add_image('img', image)
+
+ def test_add_scalar(self):
+ tensorboard_writer = TensorboardWriter('temp_dir')
+ tensorboard_writer.add_scalar('map', 0.9)
+ # test append mode
+ tensorboard_writer.add_scalar('map', 0.9, step=0)
+ tensorboard_writer.add_scalar('map', 0.95, step=1)
+
+ def test_add_scalars(self):
+ tensorboard_writer = TensorboardWriter('temp_dir')
+ # The step value must be passed through the parameter
+ with pytest.raises(AssertionError):
+ tensorboard_writer.add_scalars({'map': 0.7, 'acc': 0.9, 'step': 1})
+
+ input_dict = {'map': 0.7, 'acc': 0.9}
+ tensorboard_writer.add_scalars(input_dict)
+ # test append mode
+ tensorboard_writer.add_scalars({'map': 0.8, 'acc': 0.8}, step=1)
class TestWandbWriter:
sys.modules['wandb'] = MagicMock()
- def test_add_image(self):
- image = np.random.randint(0, 256, size=(10, 10, 3))
- data_sample = get_demo_datasample()
+ def test_init(self):
+ WandbWriter()
+ WRITERS.build(
+ dict(
+ type='WandbWriter',
+ visualizer=dict(type='Visualizer'),
+ save_dir='temp_dir'))
+ def test_experiment(self):
wandb_writer = WandbWriter()
- assert not wandb_writer.visualizer
- wandb_writer.add_image('img', image, data_sample)
+ assert wandb_writer.experiment == wandb_writer._wandb
- wandb_writer = WandbWriter(visuailzer=dict(type='Visualizer'))
- assert wandb_writer.visualizer
+ def test_add_params(self):
+ wandb_writer = WandbWriter()
+
+ # 'params_dict' must be dict
+ with pytest.raises(AssertionError):
+ wandb_writer.add_params(['lr', 0])
+
+ params_dict = dict(lr=0.1, wd=0.2, mode='linear')
+ wandb_writer.add_params(params_dict)
+
+ @patch('mmengine.visualization.visualizer.Visualizer.draw', draw)
+ @patch('mmengine.visualization.writer.WandbWriter.add_image_to_wandb',
+ Mock)
+ def test_add_image(self):
+ image = np.random.randint(0, 256, size=(10, 10, 3)).astype(np.uint8)
+
+ wandb_writer = WandbWriter()
wandb_writer.add_image('img', image)
- wandb_writer.add_image('img', image, data_sample)
+ wandb_writer = WandbWriter(visualizer=dict(type='Visualizer'))
+ bboxes = np.array([[1, 1, 2, 2], [1, 1.5, 1, 2.5]])
wandb_writer.visualizer.set_image(image)
- wandb_writer.add_image('img', wandb_writer.visualizer.get_image())
+ wandb_writer.visualizer.draw_bboxes(bboxes)
+ wandb_writer.add_image(
+ 'img', wandb_writer.visualizer.get_image(), step=2)
- # TODO test file exist
+ visuailzer = VISUALIZERS.build(dict(type='Visualizer'))
+ wandb_writer = WandbWriter(visualizer=visuailzer)
+ wandb_writer.add_image('img', image)
- def test_add_scaler(self):
+ def test_add_scalar(self):
wandb_writer = WandbWriter()
- wandb_writer.add_scaler('map', 0.9)
+ wandb_writer.add_scalar('map', 0.9)
+ # test append mode
+ wandb_writer.add_scalar('map', 0.9, step=0)
+ wandb_writer.add_scalar('map', 0.95, step=1)
- def test_add_hyperparams(self):
+ def test_add_scalars(self):
wandb_writer = WandbWriter()
- wandb_writer.add_hyperparams('hyper', dict(lr=0.01))
+ input_dict = {'map': 0.7, 'acc': 0.9}
+ wandb_writer.add_scalars(input_dict)
+ # test append mode
+ wandb_writer.add_scalars({'map': 0.8, 'acc': 0.8}, step=1)
-class TestTensorboardWriter:
- sys.modules['torch.utils.tensorboard.SummaryWriter'] = MagicMock()
+class TestComposedWriter:
+ sys.modules['torch.utils.tensorboard'] = MagicMock()
+ sys.modules['tensorboardX'] = MagicMock()
+ sys.modules['wandb'] = MagicMock()
- def test_add_image(self):
- image = np.random.randint(0, 256, size=(10, 10, 3))
- data_sample = get_demo_datasample()
+ def test_init(self):
- tensorboard_writer = TensorboardWriter()
- assert not tensorboard_writer.visualizer
- tensorboard_writer.add_image('img', image, data_sample)
+ class A:
+ pass
- tensorboard_writer = TensorboardWriter(
- visuailzer=dict(type='Visualizer'))
- assert tensorboard_writer.visualizer
- tensorboard_writer.add_image('img', image)
- tensorboard_writer.add_image('img', image, data_sample)
+ # The writers inner element must be a dictionary or a
+ # subclass of Writer.
+ with pytest.raises(AssertionError):
+ ComposedWriter(writers=[A()])
+
+ composed_writer = ComposedWriter(writers=[
+ WandbWriter(),
+ dict(
+ type='TensorboardWriter',
+ visualizer=dict(type='Visualizer'),
+ save_dir='temp_dir')
+ ])
+ assert len(composed_writer._writers) == 2
+
+ # test global
+ composed_writer = ComposedWriter.create_instance(
+ 'composed_writer',
+ writers=[
+ WandbWriter(),
+ dict(
+ type='TensorboardWriter',
+ visualizer=dict(type='Visualizer'),
+ save_dir='temp_dir')
+ ])
+ assert len(composed_writer._writers) == 2
+ composed_writer_any = ComposedWriter.get_instance('composed_writer')
+ assert composed_writer_any == composed_writer
+
+ def test_get_writer(self):
+ composed_writer = ComposedWriter(writers=[
+ WandbWriter(),
+ dict(
+ type='TensorboardWriter',
+ visualizer=dict(type='Visualizer'),
+ save_dir='temp_dir')
+ ])
+ assert isinstance(composed_writer.get_writer(0), WandbWriter)
+ assert isinstance(composed_writer.get_writer(1), TensorboardWriter)
+
+ def test_get_experiment(self):
+ composed_writer = ComposedWriter(writers=[
+ WandbWriter(),
+ dict(
+ type='TensorboardWriter',
+ visualizer=dict(type='Visualizer'),
+ save_dir='temp_dir')
+ ])
+ assert composed_writer.get_experiment(
+ 0) == composed_writer._writers[0].experiment
+ assert composed_writer.get_experiment(
+ 1) == composed_writer._writers[1].experiment
+
+ def test_get_visualizer(self):
+ composed_writer = ComposedWriter(writers=[
+ WandbWriter(),
+ dict(
+ type='TensorboardWriter',
+ visualizer=dict(type='Visualizer'),
+ save_dir='temp_dir')
+ ])
+ assert composed_writer.get_visualizer(
+ 0) == composed_writer._writers[0].visualizer
+ assert composed_writer.get_visualizer(
+ 1) == composed_writer._writers[1].visualizer
+
+ def test_add_params(self):
+ composed_writer = ComposedWriter(writers=[
+ WandbWriter(),
+ dict(
+ type='TensorboardWriter',
+ visualizer=dict(type='Visualizer'),
+ save_dir='temp_dir')
+ ])
+
+ # 'params_dict' must be dict
+ with pytest.raises(AssertionError):
+ composed_writer.add_params(['lr', 0])
- tensorboard_writer.visualizer.set_image(image)
- tensorboard_writer.add_image('img',
- tensorboard_writer.visualizer.get_image())
+ params_dict = dict(lr=0.1, wd=0.2, mode='linear')
+ composed_writer.add_params(params_dict)
- # test no visualizer
- # The visuailzer parameter must be set when
- # the tensorboard_writer object is instantiated and
- # the `add_image` method is called.
+ def test_add_graph(self):
+ composed_writer = ComposedWriter(writers=[
+ WandbWriter(),
+ dict(
+ type='TensorboardWriter',
+ visualizer=dict(type='Visualizer'),
+ save_dir='temp_dir')
+ ])
+
+ class Model(nn.Module):
+
+ def __init__(self):
+ super().__init__()
+ self.conv = nn.Conv2d(1, 2, 1)
+
+ def forward(self, x, y=None):
+ return self.conv(x)
+
+ # input must be tensor
with pytest.raises(AssertionError):
- tensorboard_writer = TensorboardWriter()
- tensorboard_writer.add_image('img', image)
+ composed_writer.add_graph(Model(), np.zeros([1, 1, 3, 3]))
+
+ # input must be 4d tensor
+ with pytest.raises(AssertionError):
+ composed_writer.add_graph(Model(), torch.zeros([1, 3, 3]))
- def test_add_scaler(self):
- tensorboard_writer = TensorboardWriter()
- tensorboard_writer.add_scaler('map', 0.9)
+ # If the input is a list, the inner element must be a 4d tensor
+ with pytest.raises(AssertionError):
+ composed_writer.add_graph(
+ Model(), [torch.zeros([1, 1, 3, 3]),
+ torch.zeros([1, 3, 3])])
+
+ composed_writer.add_graph(Model(), torch.zeros([1, 1, 3, 3]))
+ composed_writer.add_graph(
+ Model(), [torch.zeros([1, 1, 3, 3]),
+ torch.zeros([1, 1, 3, 3])])
+
+ @patch('mmengine.visualization.visualizer.Visualizer.draw', draw)
+ @patch('mmengine.visualization.writer.WandbWriter.add_image_to_wandb',
+ Mock)
+ def test_add_image(self):
+ composed_writer = ComposedWriter(writers=[
+ WandbWriter(),
+ dict(
+ type='TensorboardWriter',
+ visualizer=dict(type='Visualizer'),
+ save_dir='temp_dir')
+ ])
- def test_add_hyperparams(self):
- tensorboard_writer = TensorboardWriter()
- tensorboard_writer.add_hyperparams('hyper', dict(lr=0.01))
+ image = np.random.randint(0, 256, size=(10, 10, 3)).astype(np.uint8)
+ composed_writer.add_image('img', image)
+
+ bboxes = np.array([[1, 1, 2, 2], [1, 1.5, 1, 2.5]])
+ composed_writer.get_writer(1).visualizer.draw_bboxes(bboxes)
+ composed_writer.get_writer(1).add_image(
+ 'img',
+ composed_writer.get_writer(1).visualizer.get_image(),
+ step=2)
+
+ def test_add_scalar(self):
+ composed_writer = ComposedWriter(writers=[
+ WandbWriter(),
+ dict(
+ type='TensorboardWriter',
+ visualizer=dict(type='Visualizer'),
+ save_dir='temp_dir')
+ ])
+ composed_writer.add_scalar('map', 0.9)
+ # test append mode
+ composed_writer.add_scalar('map', 0.9, step=0)
+ composed_writer.add_scalar('map', 0.95, step=1)
+
+ def test_add_scalars(self):
+ composed_writer = ComposedWriter(writers=[
+ WandbWriter(),
+ dict(
+ type='TensorboardWriter',
+ visualizer=dict(type='Visualizer'),
+ save_dir='temp_dir')
+ ])
+ input_dict = {'map': 0.7, 'acc': 0.9}
+ composed_writer.add_scalars(input_dict)
+ # test append mode
+ composed_writer.add_scalars({'map': 0.8, 'acc': 0.8}, step=1)
--
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