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mmedit.evaluation.metrics.fid

Module Contents

Classes

FrechetInceptionDistance

FID metric. In this metric, we calculate the distance between real

TransFID

FID metric. In this metric, we calculate the distance between real

class mmedit.evaluation.metrics.fid.FrechetInceptionDistance(fake_nums: int, real_nums: int = - 1, inception_style='StyleGAN', inception_path: Optional[str] = None, inception_pkl: Optional[str] = None, fake_key: Optional[str] = None, real_key: Optional[str] = 'img', need_cond_input: bool = False, sample_model: str = 'orig', collect_device: str = 'cpu', prefix: Optional[str] = None, sample_kwargs: dict = dict())[源代码]

Bases: mmedit.evaluation.metrics.base_gen_metric.GenerativeMetric

FID metric. In this metric, we calculate the distance between real distributions and fake distributions. The distributions are modeled by the real samples and fake samples, respectively. Inception_v3 is adopted as the feature extractor, which is widely used in StyleGAN and BigGAN.

参数
  • fake_nums (int) – Numbers of the generated image need for the metric.

  • real_nums (int) – Numbers of the real images need for the metric. If -1 is passed, means all real images in the dataset will be used. Defaults to -1.

  • inception_style (str) – The target inception style want to load. If the given style cannot be loaded successful, will attempt to load a valid one. Defaults to ‘StyleGAN’.

  • inception_path (str, optional) – Path the the pretrain Inception network. Defaults to None.

  • inception_pkl (str, optional) – Path to reference inception pickle file. If None, the statistical value of real distribution will be calculated at running time. Defaults to None.

  • fake_key (Optional[str]) – Key for get fake images of the output dict. Defaults to None.

  • real_key (Optional[str]) – Key for get real images from the input dict. Defaults to ‘img’.

  • need_cond_input (bool) – If true, the sampler will return the conditional input randomly sampled from the original dataset. This require the dataset implement get_data_info and field gt_label must be contained in the return value of get_data_info. Noted that, for unconditional models, set need_cond_input as True may influence the result of evaluation results since the conditional inputs are sampled from the dataset distribution; otherwise will be sampled from the uniform distribution. Defaults to False.

  • sample_model (str) – Sampling mode for the generative model. Support ‘orig’ and ‘ema’. Defaults to ‘orig’.

  • collect_device (str, optional) – Device name used for collecting results from different ranks during distributed training. Must be ‘cpu’ or ‘gpu’. Defaults to ‘cpu’.

  • prefix (str, optional) – The prefix that will be added in the metric names to disambiguate homonymous metrics of different evaluators. If prefix is not provided in the argument, self.default_prefix will be used instead. Defaults to None.

name = 'FID'[源代码]
prepare(module: torch.nn.Module, dataloader: torch.utils.data.dataloader.DataLoader) None[源代码]

Preparing inception feature for the real images.

参数
  • module (nn.Module) – The model to evaluate.

  • dataloader (DataLoader) – The dataloader for real images.

_load_inception(inception_style: str, inception_path: Optional[str]) Tuple[torch.nn.Module, str][源代码]

Load inception and return the successful loaded style.

参数
  • inception_style (str) – Target style of Inception network want to load.

  • inception_path (Optional[str]) – The path to the inception.

返回

The actually loaded inception network and

corresponding style.

返回类型

Tuple[nn.Module, str]

forward_inception(image: torch.Tensor) torch.Tensor[源代码]

Feed image to inception network and get the output feature.

参数

image (Tensor) – Image tensor fed to the Inception network.

返回

Image feature extracted from inception.

返回类型

Tensor

process(data_batch: dict, data_samples: Sequence[dict]) None[源代码]

Process one batch of data samples and predictions. The processed results should be stored in self.fake_results, which will be used to compute the metrics when all batches have been processed.

参数
  • data_batch (dict) – A batch of data from the dataloader.

  • data_samples (Sequence[dict]) – A batch of outputs from the model.

static _calc_fid(sample_mean: numpy.ndarray, sample_cov: numpy.ndarray, real_mean: numpy.ndarray, real_cov: numpy.ndarray, eps: float = 1e-06) Tuple[float][源代码]

Refer to the implementation from:

https://github.com/rosinality/stylegan2-pytorch/blob/master/fid.py#L34

compute_metrics(fake_results: list) dict[源代码]

Compulate the result of FID metric.

参数

fake_results (list) – List of image feature of fake images.

返回

A dict of the computed FID metric and its mean and

covariance.

返回类型

dict

class mmedit.evaluation.metrics.fid.TransFID(fake_nums: int, real_nums: int = - 1, inception_style='StyleGAN', inception_path: Optional[str] = None, inception_pkl: Optional[str] = None, fake_key: Optional[str] = None, real_key: Optional[str] = 'img', sample_model: str = 'ema', collect_device: str = 'cpu', prefix: Optional[str] = None)[源代码]

Bases: FrechetInceptionDistance

FID metric. In this metric, we calculate the distance between real distributions and fake distributions. The distributions are modeled by the real samples and fake samples, respectively. Inception_v3 is adopted as the feature extractor, which is widely used in StyleGAN and BigGAN.

参数
  • fake_nums (int) – Numbers of the generated image need for the metric.

  • real_nums (int) – Numbers of the real images need for the metric. If -1 is passed, means all real images in the dataset will be used. Defaults to -1.

  • inception_style (str) – The target inception style want to load. If the given style cannot be loaded successful, will attempt to load a valid one. Defaults to ‘StyleGAN’.

  • inception_path (str, optional) – Path the the pretrain Inception network. Defaults to None.

  • inception_pkl (str, optional) – Path to reference inception pickle file. If None, the statistical value of real distribution will be calculated at running time. Defaults to None.

  • fake_key (Optional[str]) – Key for get fake images of the output dict. Defaults to None.

  • real_key (Optional[str]) – Key for get real images from the input dict. Defaults to ‘img’.

  • need_cond_input (bool) – If true, the sampler will return the conditional input randomly sampled from the original dataset. This require the dataset implement get_data_info and field gt_label must be contained in the return value of get_data_info. Noted that, for unconditional models, set need_cond_input as True may influence the result of evaluation results since the conditional inputs are sampled from the dataset distribution; otherwise will be sampled from the uniform distribution. Defaults to False.

  • sample_model (str) – Sampling mode for the generative model. Support ‘orig’ and ‘ema’. Defaults to ‘orig’.

  • collect_device (str, optional) – Device name used for collecting results from different ranks during distributed training. Must be ‘cpu’ or ‘gpu’. Defaults to ‘cpu’.

  • prefix (str, optional) – The prefix that will be added in the metric names to disambiguate homonymous metrics of different evaluators. If prefix is not provided in the argument, self.default_prefix will be used instead. Defaults to None.

get_metric_sampler(model: torch.nn.Module, dataloader: torch.utils.data.dataloader.DataLoader, metrics: List[mmedit.evaluation.metrics.base_gen_metric.GenerativeMetric]) torch.utils.data.dataloader.DataLoader[源代码]

Get sampler for normal metrics. Directly returns the dataloader.

参数
  • model (nn.Module) – Model to evaluate.

  • dataloader (DataLoader) – Dataloader for real images.

  • metrics (List['GenMetric']) – Metrics with the same sample mode.

返回

Default sampler for normal metrics.

返回类型

DataLoader

process(data_batch: dict, data_samples: Sequence[dict]) None[源代码]

Process one batch of data samples and predictions. The processed results should be stored in self.fake_results, which will be used to compute the metrics when all batches have been processed.

参数
  • data_batch (dict) – A batch of data from the dataloader.

  • data_samples (Sequence[dict]) – A batch of outputs from the model.

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