mmseg.apis

mmseg.datasets

datasets

transforms

mmseg.engine

hooks

optimizers

mmseg.evaluation

metrics

class mmseg.evaluation.metrics.CityscapesMetric(output_dir: str, ignore_index: int = 255, format_only: bool = False, keep_results: bool = False, collect_device: str = 'cpu', prefix: Optional[str] = None, **kwargs)

Cityscapes evaluation metric.

Parameters

• output_dir (str) – The directory for output prediction

• ignore_index (int) – Index that will be ignored in evaluation. Default: 255.

• format_only (bool) – Only format result for results commit without perform evaluation. It is useful when you want to format the result to a specific format and submit it to the test server. Defaults to False.

• keep_results (bool) – Whether to keep the results. When format_only is True, keep_results must be True. Defaults to False.

• collect_device (str) – 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.

compute_metrics(results: list) → Dict[str, float]

Compute the metrics from processed results.

Parameters

results (list) – Testing results of the dataset.

Returns

float]: Cityscapes evaluation results.

Return type

dict[str

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

Process one batch of data and data_samples.

The processed results should be stored in self.results, which will be used to computed the metrics when all batches have been processed.

Parameters

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

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

class mmseg.evaluation.metrics.DepthMetric(depth_metrics: Optional[List[str]] = None, min_depth_eval: float = 0.0, max_depth_eval: float = inf, crop_type: Optional[str] = None, depth_scale_factor: float = 1.0, collect_device: str = 'cpu', output_dir: Optional[str] = None, format_only: bool = False, prefix: Optional[str] = None, **kwargs)

Depth estimation evaluation metric.

Parameters

• depth_metrics (List[str], optional) – List of metrics to compute. If not specified, defaults to all metrics in self.METRICS.

• min_depth_eval (float) – Minimum depth value for evaluation. Defaults to 0.0.

• max_depth_eval (float) – Maximum depth value for evaluation. Defaults to infinity.

• crop_type (str, optional) – Specifies the type of cropping to be used during evaluation. This option can affect how the evaluation mask is generated. Currently, ‘nyu_crop’ is supported, but other types can be added in future. Defaults to None if no cropping should be applied.

• depth_scale_factor (float) – Factor to scale the depth values. Defaults to 1.0.

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

• output_dir (str) – The directory for output prediction. Defaults to None.

• format_only (bool) – Only format result for results commit without perform evaluation. It is useful when you want to save the result to a specific format and submit it to the test server. Defaults to False.

• 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.

compute_metrics(results: list) → Dict[str, float]

Compute the metrics from processed results.

Parameters

results (list) – The processed results of each batch.

Returns

The computed metrics. The keys are the names of

the metrics, and the values are corresponding results. The keys are identical with self.metrics.

Return type

Dict[str, float]

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

Process one batch of data and data_samples.

The processed results should be stored in self.results, which will be used to compute the metrics when all batches have been processed.

Parameters

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

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

class mmseg.evaluation.metrics.IoUMetric(ignore_index: int = 255, iou_metrics: List[str] = ['mIoU'], nan_to_num: Optional[int] = None, beta: int = 1, collect_device: str = 'cpu', output_dir: Optional[str] = None, format_only: bool = False, prefix: Optional[str] = None, **kwargs)

IoU evaluation metric.

Parameters

• ignore_index (int) – Index that will be ignored in evaluation. Default: 255.

• iou_metrics (list[str] | str) – Metrics to be calculated, the options includes ‘mIoU’, ‘mDice’ and ‘mFscore’.

• nan_to_num (int, optional) – If specified, NaN values will be replaced by the numbers defined by the user. Default: None.

• beta (int) – Determines the weight of recall in the combined score. Default: 1.

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

• output_dir (str) – The directory for output prediction. Defaults to None.

• format_only (bool) – Only format result for results commit without perform evaluation. It is useful when you want to save the result to a specific format and submit it to the test server. Defaults to False.

• 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.

compute_metrics(results: list) → Dict[str, float]

Compute the metrics from processed results.

Parameters

results (list) – The processed results of each batch.

Returns

The computed metrics. The keys are the names of

the metrics, and the values are corresponding results. The key mainly includes aAcc, mIoU, mAcc, mDice, mFscore, mPrecision, mRecall.

Return type

Dict[str, float]

static intersect_and_union(pred_label: torch._VariableFunctionsClass.tensor, label: torch._VariableFunctionsClass.tensor, num_classes: int, ignore_index: int)

Calculate Intersection and Union.

Parameters

• pred_label (torch.tensor) – Prediction segmentation map or predict result filename. The shape is (H, W).

• label (torch.tensor) – Ground truth segmentation map or label filename. The shape is (H, W).

• num_classes (int) – Number of categories.

• ignore_index (int) – Index that will be ignored in evaluation.

Returns

The intersection of prediction and ground truth

histogram on all classes.

torch.Tensor: The union of prediction and ground truth histogram on

all classes.

torch.Tensor: The prediction histogram on all classes. torch.Tensor: The ground truth histogram on all classes.

Return type

torch.Tensor

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

Process one batch of data and data_samples.

The processed results should be stored in self.results, which will be used to compute the metrics when all batches have been processed.

Parameters

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

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

static total_area_to_metrics(total_area_intersect: numpy.ndarray, total_area_union: numpy.ndarray, total_area_pred_label: numpy.ndarray, total_area_label: numpy.ndarray, metrics: List[str] = ['mIoU'], nan_to_num: Optional[int] = None, beta: int = 1)

Calculate evaluation metrics :param total_area_intersect: The intersection of prediction

and ground truth histogram on all classes.

Parameters

• total_area_union (np.ndarray) – The union of prediction and ground truth histogram on all classes.

• total_area_pred_label (np.ndarray) – The prediction histogram on all classes.

• total_area_label (np.ndarray) – The ground truth histogram on all classes.

• metrics (List[str] | str) – Metrics to be evaluated, ‘mIoU’ and ‘mDice’.

• nan_to_num (int, optional) – If specified, NaN values will be replaced by the numbers defined by the user. Default: None.

• beta (int) – Determines the weight of recall in the combined score. Default: 1.

Returns

per category evaluation metrics,

shape (num_classes, ).

Return type

Dict[str, np.ndarray]

mmseg.models

backbones

decode_heads

segmentors

losses

necks

utils

mmseg.structures

structures

class mmseg.structures.BasePixelSampler(**kwargs)

Base class of pixel sampler.

abstract sample(seg_logit, seg_label)

Placeholder for sample function.

class mmseg.structures.OHEMPixelSampler(context, thresh=None, min_kept=100000)

Online Hard Example Mining Sampler for segmentation.

Parameters

• context (nn.Module) – The context of sampler, subclass of BaseDecodeHead.

• thresh (float, optional) – The threshold for hard example selection. Below which, are prediction with low confidence. If not specified, the hard examples will be pixels of top min_kept loss. Default: None.

• min_kept (int, optional) – The minimum number of predictions to keep. Default: 100000.

sample(seg_logit, seg_label)

Sample pixels that have high loss or with low prediction confidence.

Parameters

• seg_logit (torch.Tensor) – segmentation logits, shape (N, C, H, W)

• seg_label (torch.Tensor) – segmentation label, shape (N, 1, H, W)

Returns

segmentation weight, shape (N, H, W)

Return type

torch.Tensor

class mmseg.structures.SegDataSample(*, metainfo: Optional[dict] = None, **kwargs)

A data structure interface of MMSegmentation. They are used as interfaces between different components.

The attributes in SegDataSample are divided into several parts:

• ``gt_sem_seg``(PixelData): Ground truth of semantic segmentation.

• ``pred_sem_seg``(PixelData): Prediction of semantic segmentation.

• ``seg_logits``(PixelData): Predicted logits of semantic segmentation.

Examples

>>> import torch
>>> import numpy as np
>>> from mmengine.structures import PixelData
>>> from mmseg.structures import SegDataSample

>>> data_sample = SegDataSample()
>>> img_meta = dict(img_shape=(4, 4, 3),
...                 pad_shape=(4, 4, 3))
>>> gt_segmentations = PixelData(metainfo=img_meta)
>>> gt_segmentations.data = torch.randint(0, 2, (1, 4, 4))
>>> data_sample.gt_sem_seg = gt_segmentations
>>> assert 'img_shape' in data_sample.gt_sem_seg.metainfo_keys()
>>> data_sample.gt_sem_seg.shape
(4, 4)
>>> print(data_sample)

<SegDataSample(

META INFORMATION

DATA FIELDS gt_sem_seg: <PixelData(

META INFORMATION img_shape: (4, 4, 3) pad_shape: (4, 4, 3)

DATA FIELDS data: tensor([[[1, 1, 1, 0],

[1, 0, 1, 1], [1, 1, 1, 1], [0, 1, 0, 1]]])

) at 0x1c2b4156460>

) at 0x1c2aae44d60>

>>> data_sample = SegDataSample()
>>> gt_sem_seg_data = dict(sem_seg=torch.rand(1, 4, 4))
>>> gt_sem_seg = PixelData(**gt_sem_seg_data)
>>> data_sample.gt_sem_seg = gt_sem_seg
>>> assert 'gt_sem_seg' in data_sample
>>> assert 'sem_seg' in data_sample.gt_sem_seg

mmseg.structures.build_pixel_sampler(cfg, **default_args)

Build pixel sampler for segmentation map.

sampler

class mmseg.structures.sampler.BasePixelSampler(**kwargs)

Base class of pixel sampler.

abstract sample(seg_logit, seg_label)

Placeholder for sample function.

class mmseg.structures.sampler.OHEMPixelSampler(context, thresh=None, min_kept=100000)

Online Hard Example Mining Sampler for segmentation.

Parameters

• context (nn.Module) – The context of sampler, subclass of BaseDecodeHead.

• thresh (float, optional) – The threshold for hard example selection. Below which, are prediction with low confidence. If not specified, the hard examples will be pixels of top min_kept loss. Default: None.

• min_kept (int, optional) – The minimum number of predictions to keep. Default: 100000.

sample(seg_logit, seg_label)

Sample pixels that have high loss or with low prediction confidence.

Parameters

• seg_logit (torch.Tensor) – segmentation logits, shape (N, C, H, W)

• seg_label (torch.Tensor) – segmentation label, shape (N, 1, H, W)

Returns

segmentation weight, shape (N, H, W)

Return type

torch.Tensor

mmseg.structures.sampler.build_pixel_sampler(cfg, **default_args)

Build pixel sampler for segmentation map.

mmseg.visualization

mmseg.utils