Compute segmentation loss.

Compute segmentation loss.

Compute ``pam_cam``, ``pam``, ``cam`` loss.

Stack InvertedResidual blocks to build a layer for MobileNetV2. Args: out_channels (int): out_channels of block. num_

Pack all blocks in a stage into a ``ResLayer``.

make plugins for ResNet 'stage_idx'th stage . Currently we support to insert 'context_block', 'empirical_attention_block', 'nonlocal_

nn.Module: the normalization layer named "norm0"

nn.Module: the normalization layer named "norm1"

nn.Module: normalization layer after the first convolution layer

nn.Module: the normalization layer named "norm1"

nn.Module: normalization layer after the second convolution layer

nn.Module: normalization layer after the second convolution layer

PAM feature classification.

Parse information from a line in a requirements text file.

Get testing data after pipeline. Args: idx (int): Index of data. Returns: dict: Testing data after pipeline

Get training data and annotations after pipeline. Args: idx (int): Index of data. Returns: dict: Training da

Forward function of PSP module.

Export Pytorch model to ONNX model and verify the outputs are same between Pytorch and ONNX. Args: model (nn.Module): Pytorch model w

Randomly sample an img_scale when ``multiscale_mode=='range'``. Args: img_scales (list[tuple]): Images scale range for sampling.

Randomly select an img_scale from given candidates. Args: img_scales (list[tuple]): Images scales for selection. Returns

Randomly select an img_scale from given candidates. Args: img_scales (list[tuple]): Images scales for selection. Returns

Reduce mean when distributed training.

Write the segmentation results to images. Args: results (list[list | tuple | ndarray]): Testing results of the da

Saturation distortion.

Set random seed. Args: seed (int): Seed to be used. deterministic (bool): Whether to set the deterministic option for

Visualize the segmentation results on the image. Args: model (nn.Module): The loaded segmentor. img (str or np.ndarray): Image fi

Placeholder for single image test.

Inference by sliding-window with overlap. If h_crop > h_img or w_crop > w_img, the small patch will be used to decode without padding

Calculate Total Intersection and Union. Args: results (list[ndarray]): List of prediction segmentation maps. gt_seg_maps (list[nd

Launch segmentor training.

Inference with full image.

Call function to load images into results. Args: results (dict): A result dict contains the file name of the imag

Call function to apply test time augment transforms on results. Args: results (dict): Result dict contains the data to transform.

Call function to resize images, bounding boxes, masks, semantic segmentation map. Args: results (dict): Result dict from

Call function to resize images, bounding boxes, masks, semantic segmentation map. Args: results (dict): Result dict from

Call function to flip bounding boxes, masks, semantic segmentation maps. Args: results (dict): Result dict from loading p

Call function to pad images, masks, semantic segmentation maps. Args: results (dict): Result dict from loading pipeline.

Call function to normalize images. Args: results (dict): Result dict from loading pipeline. Returns: dict: N

Call function to rerange images. Args: results (dict): Result dict from loading pipeline. Returns: dict: Rera

Call function to Use CLAHE method process images. Args: results (dict): Result dict from loading pipeline. Returns:

Call function to randomly crop images, semantic segmentation maps. Args: results (dict): Result dict from loading pipeline.

Call function to randomly crop images, semantic segmentation maps. Args: results (dict): Result dict from loading pipeline.

Call function to rotate image, semantic segmentation maps. Args: results (dict): Result dict from loading pipeline. Retu

Call function to convert RGB image to grayscale image. Args: results (dict): Result dict from loading pipeline. Returns:

Call function to process the image with gamma correction. Args: results (dict): Result dict from loading pipeline. Retur

Call function to process the image with gamma correction. Args: results (dict): Result dict from loading pipeline. Retur

Call function to scale the semantic segmentation map. Args: results (dict): Result dict from loading pipeline. Returns:

Call function to perform photometric distortion on images. Args: results (dict): Result dict from loading pipeline. Retu

Call functions to load image and get image meta information. Args: results (dict): Result dict from :obj:`mmseg.CustomDataset`.

Call function to load multiple types annotations. Args: results (dict): Result dict from :obj:`mmseg.CustomDataset`. Ret

Call function to apply transforms sequentially. Args: data (dict): A result dict contains the data to transform. Returns

Call function to convert data in results to :obj:`torch.Tensor`. Args: results (dict): Result dict contains the data to convert.

Call function to convert image in results to :obj:`torch.Tensor` and transpose the channel order. Args: results (dict): R

Call function to convert image in results to :obj:`torch.Tensor` and transpose the channel order. Args: results (dict): R

Call function to convert data in results to :obj:`mmcv.DataContainer`. Args: results (dict): Result dict contains the dat

Call function to transform and format common fields in results. Args: results (dict): Result dict contains the data to convert.

Call function to collect keys in results. The keys in ``meta_keys`` will be converted to :obj:mmcv.DataContainer. Args: r

Get training/test data after pipeline. Args: idx (int): Index of data. Returns: dict: Training/test data (wi

Get item from original dataset.