`mmagic.engine.runner`¶

Package Contents¶

Classes¶

`LogProcessor`	LogProcessor inherits from `mmengine.runner.LogProcessor` and
`MultiTestLoop`	Test loop for MMagic models which support evaluate multiply dataset at
`MultiValLoop`	Validation loop for MMagic models which support evaluate multiply

class mmagic.engine.runner.LogProcessor(window_size=10, by_epoch=True, custom_cfg: Optional[List[dict]] = None, num_digits: int = 4, log_with_hierarchy: bool = False, mean_pattern='.*(loss|time|data_time|grad_norm).*')[source]¶

Bases: mmengine.runner.LogProcessor

LogProcessor inherits from mmengine.runner.LogProcessor and overwrites self.get_log_after_iter().

This log processor should be used along with mmagic.engine.runner.MultiValLoop and mmagic.engine.runner.MultiTestLoop.

_get_dataloader_size(runner, mode) → int[source]¶

Get dataloader size of current loop. In MultiValLoop and MultiTestLoop, we use total_length instead of len(dataloader) to denote the total number of iterations.

Parameters

runner (Runner) – The runner of the training/validation/testing
mode (str) – Current mode of runner.

Returns

The dataloader size of current loop.

Return type

int

class mmagic.engine.runner.MultiTestLoop(runner, dataloader, evaluator, fp16=False)[source]¶

Bases: mmengine.runner.base_loop.BaseLoop

Test loop for MMagic models which support evaluate multiply dataset at the same time. This class support evaluate:

Metrics (metric) on a single dataset (e.g. PSNR and SSIM on DIV2K dataset)
Different metrics on different datasets (e.g. PSNR on DIV2K and SSIM and PSNR on SET5)

Use cases:

Case 1: metrics on a single dataset

>>> # add the following lines in your config
>>> # 1. use `MultiTestLoop` instead of `TestLoop` in MMEngine
>>> val_cfg = dict(type='MultiTestLoop')
>>> # 2. specific MultiEvaluator instead of Evaluator in MMEngine
>>> test_evaluator = dict(
>>>     type='MultiEvaluator',
>>>     metrics=[
>>>         dict(type='PSNR', crop_border=2, prefix='Set5'),
>>>         dict(type='SSIM', crop_border=2, prefix='Set5'),
>>>     ])
>>> # 3. define dataloader
>>> test_dataloader = dict(...)

Case 2: different metrics on different datasets

>>> # add the following lines in your config
>>> # 1. use `MultiTestLoop` instead of `TestLoop` in MMEngine
>>> Test_cfg = dict(type='MultiTestLoop')
>>> # 2. specific a list MultiEvaluator
>>> # do not forget to add prefix for each metric group
>>> div2k_evaluator = dict(
>>>     type='MultiEvaluator',
>>>     metrics=dict(type='SSIM', crop_border=2, prefix='DIV2K'))
>>> set5_evaluator = dict(
>>>     type='MultiEvaluator',
>>>     metrics=[
>>>         dict(type='PSNR', crop_border=2, prefix='Set5'),
>>>         dict(type='SSIM', crop_border=2, prefix='Set5'),
>>>     ])
>>> # define evaluator config
>>> test_evaluator = [div2k_evaluator, set5_evaluator]
>>> # 3. specific a list dataloader for each metric groups
>>> div2k_dataloader = dict(...)
>>> set5_dataloader = dict(...)
>>> # define dataloader config
>>> test_dataloader = [div2k_dataloader, set5_dataloader]

Parameters

runner (Runner) – A reference of runner.
dataloader (Dataloader or dict or list) – A dataloader object or a dict to build a dataloader a list of dataloader object or a list of config dicts.
evaluator (Evaluator or dict or list) – A evaluator object or a dict to build the evaluator or a list of evaluator object or a list of config dicts.

property total_length: int¶

_build_dataloaders(dataloader: DATALOADER_TYPE) → List[torch.utils.data.DataLoader][source]¶

Build dataloaders.

Parameters: dataloader (Dataloader or dict or list) – A dataloader object or a dict to build a dataloader a list of dataloader object or a list of config dict.
Returns: List of dataloaders for compute metrics.
Return type: List[Dataloader]

_build_evaluators(evaluator: EVALUATOR_TYPE) → List[mmengine.evaluator.Evaluator][source]¶

Build evaluators.

Parameters: evaluator (Evaluator or dict or list) – A evaluator object or a dict to build the evaluator or a list of evaluator object or a list of config dicts.
Returns: List of evaluators for compute metrics.
Return type: List[Evaluator]

run()[source]¶

Launch validation. The evaluation process consists of four steps.

Prepare pre-calculated items for all metrics by calling self.evaluator.prepare_metrics().
Get a list of metrics-sampler pair. Each pair contains a list of metrics with the same sampler mode and a shared sampler.
Generate images for the each metrics group. Loop for elements in each sampler and feed to the model as input by calling self.run_iter().
Evaluate all metrics by calling self.evaluator.evaluate().

run_iter(idx, data_batch: dict, metrics: Sequence[mmengine.evaluator.BaseMetric])[source]¶

Iterate one mini-batch and feed the output to corresponding metrics.

Parameters

idx (int) – Current idx for the input data.
data_batch (dict) – Batch of data from dataloader.
metrics (Sequence[BaseMetric]) – Specific metrics to evaluate.

class mmagic.engine.runner.MultiValLoop(runner, dataloader: DATALOADER_TYPE, evaluator: EVALUATOR_TYPE, fp16: bool = False)[source]¶

Bases: mmengine.runner.base_loop.BaseLoop

Validation loop for MMagic models which support evaluate multiply dataset at the same time. This class support evaluate:

Metrics (metric) on a single dataset (e.g. PSNR and SSIM on DIV2K dataset)
Different metrics on different datasets (e.g. PSNR on DIV2K and SSIM and PSNR on SET5)

Use cases:

Case 1: metrics on a single dataset

>>> # add the following lines in your config
>>> # 1. use `MultiValLoop` instead of `ValLoop` in MMEngine
>>> val_cfg = dict(type='MultiValLoop')
>>> # 2. specific MultiEvaluator instead of Evaluator in MMEngine
>>> val_evaluator = dict(
>>>     type='MultiEvaluator',
>>>     metrics=[
>>>         dict(type='PSNR', crop_border=2, prefix='Set5'),
>>>         dict(type='SSIM', crop_border=2, prefix='Set5'),
>>>     ])
>>> # 3. define dataloader
>>> val_dataloader = dict(...)

Case 2: different metrics on different datasets

>>> # add the following lines in your config
>>> # 1. use `MultiValLoop` instead of `ValLoop` in MMEngine
>>> val_cfg = dict(type='MultiValLoop')
>>> # 2. specific a list MultiEvaluator
>>> # do not forget to add prefix for each metric group
>>> div2k_evaluator = dict(
>>>     type='MultiEvaluator',
>>>     metrics=dict(type='SSIM', crop_border=2, prefix='DIV2K'))
>>> set5_evaluator = dict(
>>>     type='MultiEvaluator',
>>>     metrics=[
>>>         dict(type='PSNR', crop_border=2, prefix='Set5'),
>>>         dict(type='SSIM', crop_border=2, prefix='Set5'),
>>>     ])
>>> # define evaluator config
>>> val_evaluator = [div2k_evaluator, set5_evaluator]
>>> # 3. specific a list dataloader for each metric groups
>>> div2k_dataloader = dict(...)
>>> set5_dataloader = dict(...)
>>> # define dataloader config
>>> val_dataloader = [div2k_dataloader, set5_dataloader]

Parameters

runner (Runner) – A reference of runner.
dataloader (Dataloader or dict or list) – A dataloader object or a dict to build a dataloader a list of dataloader object or a list of config dicts.
evaluator (Evaluator or dict or list) – A evaluator object or a dict to build the evaluator or a list of evaluator object or a list of config dicts.

property total_length: int¶

_build_dataloaders(dataloader: DATALOADER_TYPE) → List[torch.utils.data.DataLoader][source]¶

Build dataloaders.

Parameters: dataloader (Dataloader or dict or list) – A dataloader object or a dict to build a dataloader a list of dataloader object or a list of config dict.
Returns: List of dataloaders for compute metrics.
Return type: List[Dataloader]

_build_evaluators(evaluator: EVALUATOR_TYPE) → List[mmengine.evaluator.Evaluator][source]¶

Build evaluators.

Parameters: evaluator (Evaluator or dict or list) – A evaluator object or a dict to build the evaluator or a list of evaluator object or a list of config dicts.
Returns: List of evaluators for compute metrics.
Return type: List[Evaluator]

run()[source]¶

Launch validation. The evaluation process consists of four steps.

Prepare pre-calculated items for all metrics by calling self.evaluator.prepare_metrics().
Get a list of metrics-sampler pair. Each pair contains a list of metrics with the same sampler mode and a shared sampler.
Generate images for the each metrics group. Loop for elements in each sampler and feed to the model as input by calling self.run_iter().
Evaluate all metrics by calling self.evaluator.evaluate().

run_iter(idx, data_batch: dict, metrics: Sequence[mmengine.evaluator.BaseMetric])[source]¶

Iterate one mini-batch and feed the output to corresponding metrics.

Parameters

idx (int) – Current idx for the input data.
data_batch (dict) – Batch of data from dataloader.
metrics (Sequence[BaseMetric]) – Specific metrics to evaluate.

mmagic.engine.runner¶

Package Contents¶

Classes¶

`mmagic.engine.runner`¶