Extract features from images.

Extract features from images.

Flattens predictions in the batch (binary case) Remove labels equal to 'ignore_index'.

Flattens predictions in the batch (binary case) Remove labels equal to 'ignore_index'.

Flattens predictions in the batch.

Flattens predictions in the batch.

Format the results into dir (standard format for ade20k evaluation). Args: results (list): Testing results of the dataset.

Format the results into dir (standard format for ade20k evaluation). Args: results (list): Testing results of the dataset.

Placeholder of forward function.

Forward function.

Placeholder of forward function.

Placeholder of forward function.

Forward function.

Placeholder of forward function.

Randomly get a crop bounding box.

Randomly get a crop bounding box.

Return dataset and sample index when given an indice of ConcatDataset. Args: indice (int): indice of sample in ConcatData

Return dataset and sample index when given an indice of ConcatDataset. Args: indice (int): indice of sample in ConcatData

Get ground truth segmentation maps for evaluation.

Get ground truth segmentation maps for evaluation.

Inference image(s) with the segmentor. Args: model (nn.Module): The loaded segmentor. imgs (str/ndarray or list[str/ndarray]): Ei

Inference image(s) with the segmentor. Args: model (nn.Module): The loaded segmentor. imgs (str/ndarray or list[str/ndarray]): Ei

Initialize random seed. If the seed is not set, the seed will be automatically randomized, and then broadcast to all processes to prevent som

Calculate intersection and Union. Args: pred_label (ndarray | str): Prediction segmentation map or predict result filename.

Calculate intersection and Union. Args: pred_label (ndarray | str): Prediction segmentation map or predict result filename.

Computes gradient of the Lovasz extension w.r.t sorted errors. See Alg. 1 in paper.

Computes gradient of the Lovasz extension w.r.t sorted errors. See Alg. 1 in paper.

Binary Lovasz hinge loss. Args: logits (torch.Tensor): [P], logits at each prediction (between -infty and +infty). la

Binary Lovasz hinge loss. Args: logits (torch.Tensor): [P], logits at each prediction (between -infty and +infty). la

Multi-class Lovasz-Softmax loss. Args: probs (torch.Tensor): [P, C], class probabilities at each prediction (between 0 and 1)

Multi-class Lovasz-Softmax loss. Args: probs (torch.Tensor): [P, C], class probabilities at each prediction (between 0 and 1)

Test model with multiple gpus by progressive mode. This method tests model with multiple gpus and collects the results under two different mo

nn.Module: normalization layer after the first convolution layer

nn.Module: normalization layer after the first convolution layer

nn.Module: normalization layer after the third convolution layer

nn.Module: normalization layer after the third convolution layer

Save ndarray to local numpy file. Args: array (ndarray): Ndarray to save. temp_file_name (str): Numpy file name. If 'temp_file_na

Save ndarray to local numpy file. Args: array (ndarray): Ndarray to save. temp_file_name (str): Numpy file name. If 'temp_file_na

Collect eval result from each iteration. Args: preds (list[torch.Tensor] | torch.Tensor): the segmentation logit

Collect eval result from each iteration. Args: preds (list[torch.Tensor] | torch.Tensor): the segmentation logit

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

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

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

Randomly sample an img_scale when ``ratio_range`` is specified. A ratio will be randomly sampled from the range specified by ``ratio_

Randomly sample an img_scale when ``ratio_range`` is specified. A ratio will be randomly sampled from the range specified by ``ratio_

Randomly sample an img_scale when ``ratio_range`` is specified. A ratio will be randomly sampled from the range specified by ``ratio_

Resize pos_embed weights. Resize pos_embed using bicubic interpolate method. Args: pos_embed (torch.Tensor): Position emb

Resize pos_embed weights. Resize pos_embed using bicubic interpolate method. Args: pos_embed (torch.Tensor): Position emb

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

Setup multi-processing environment variables.

Draw `result` over `img`. Args: img (str or Tensor): The image to be displayed. result (Tensor): The semantic segment

Draw `result` over `img`. Args: img (str or Tensor): The image to be displayed. result (Tensor): The semantic segment

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

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

Calculate evaluation metrics Args: total_area_intersect (ndarray): The intersection of prediction and ground truth histogram o

Calculate evaluation metrics Args: total_area_intersect (ndarray): The intersection of prediction and ground truth histogram o

Launch segmentor training.

Upsamples the input volume. Args: x: The 4D input tensor. Returns: An upsampled version of the input tensor.

Args: x: (B, H, W, C) Returns: windows: (num_windows*B, window_size, window_size, C)

Args: x: (B, H, W, C) Returns: windows: (num_windows*B, window_size, window_size, C)