Module audiocraft.metrics.miou
Functions
def calculate_miou(y_pred: torch.Tensor, y_true: torch.Tensor) ‑> float
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Expand source code
def calculate_miou(y_pred: torch.Tensor, y_true: torch.Tensor) -> float: """ Calculate the mean Intersection over Union (mIoU) between two binary tensors using PyTorch. Args: y_pred (torch.Tensor): Predicted binary tensor of shape [bsz, frames]. y_true (torch.Tensor): Ground truth binary tensor of shape [bsz, frames]. Returns: float: The mean Intersection over Union (mIoU) score. Reference: The Intersection over Union (IoU) metric is commonly used in computer vision. For more information, refer to the following paper: "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation" by Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla """ # Ensure y_pred and y_true have the same shape if y_pred.shape != y_true.shape: raise ValueError("Input tensors must have the same shape") # converting predictions to binary vector y_pred = y_pred > 0.5 # Compute the intersection and union intersection = torch.logical_and(y_pred, y_true) union = torch.logical_or(y_pred, y_true) # Compute IoU for each sample in the batch iou_per_sample = torch.sum(intersection, dim=1) / torch.sum(union, dim=1) # Calculate mIoU by taking the mean across the batch miou = torch.mean(iou_per_sample).item() return miou
Calculate the mean Intersection over Union (mIoU) between two binary tensors using PyTorch.
Args
y_pred
:torch.Tensor
- Predicted binary tensor of shape [bsz, frames].
y_true
:torch.Tensor
- Ground truth binary tensor of shape [bsz, frames].
Returns
float
- The mean Intersection over Union (mIoU) score.
Reference
The Intersection over Union (IoU) metric is commonly used in computer vision. For more information, refer to the following paper: "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation" by Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla