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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
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
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