Source code for kornia.losses.depth_smooth

import torch
import torch.nn as nn

# Based on
# https://github.com/tensorflow/models/blob/master/research/struct2depth/model.py#L625-L641


def _gradient_x(img: torch.Tensor) -> torch.Tensor:
    if len(img.shape) != 4:
        raise AssertionError(img.shape)
    return img[:, :, :, :-1] - img[:, :, :, 1:]


def _gradient_y(img: torch.Tensor) -> torch.Tensor:
    if len(img.shape) != 4:
        raise AssertionError(img.shape)
    return img[:, :, :-1, :] - img[:, :, 1:, :]


[docs]def inverse_depth_smoothness_loss(idepth: torch.Tensor, image: torch.Tensor) -> torch.Tensor: r"""Criterion that computes image-aware inverse depth smoothness loss. .. math:: \text{loss} = \left | \partial_x d_{ij} \right | e^{-\left \| \partial_x I_{ij} \right \|} + \left | \partial_y d_{ij} \right | e^{-\left \| \partial_y I_{ij} \right \|} Args: idepth: tensor with the inverse depth with shape :math:`(N, 1, H, W)`. image: tensor with the input image with shape :math:`(N, 3, H, W)`. Return: a scalar with the computed loss. Examples: >>> idepth = torch.rand(1, 1, 4, 5) >>> image = torch.rand(1, 3, 4, 5) >>> loss = inverse_depth_smoothness_loss(idepth, image) """ if not isinstance(idepth, torch.Tensor): raise TypeError(f"Input idepth type is not a torch.Tensor. Got {type(idepth)}") if not isinstance(image, torch.Tensor): raise TypeError(f"Input image type is not a torch.Tensor. Got {type(image)}") if not len(idepth.shape) == 4: raise ValueError(f"Invalid idepth shape, we expect BxCxHxW. Got: {idepth.shape}") if not len(image.shape) == 4: raise ValueError(f"Invalid image shape, we expect BxCxHxW. Got: {image.shape}") if not idepth.shape[-2:] == image.shape[-2:]: raise ValueError(f"idepth and image shapes must be the same. Got: {idepth.shape} and {image.shape}") if not idepth.device == image.device: raise ValueError(f"idepth and image must be in the same device. Got: {idepth.device} and {image.device}") if not idepth.dtype == image.dtype: raise ValueError(f"idepth and image must be in the same dtype. Got: {idepth.dtype} and {image.dtype}") # compute the gradients idepth_dx: torch.Tensor = _gradient_x(idepth) idepth_dy: torch.Tensor = _gradient_y(idepth) image_dx: torch.Tensor = _gradient_x(image) image_dy: torch.Tensor = _gradient_y(image) # compute image weights weights_x: torch.Tensor = torch.exp(-torch.mean(torch.abs(image_dx), dim=1, keepdim=True)) weights_y: torch.Tensor = torch.exp(-torch.mean(torch.abs(image_dy), dim=1, keepdim=True)) # apply image weights to depth smoothness_x: torch.Tensor = torch.abs(idepth_dx * weights_x) smoothness_y: torch.Tensor = torch.abs(idepth_dy * weights_y) return torch.mean(smoothness_x) + torch.mean(smoothness_y)
[docs]class InverseDepthSmoothnessLoss(nn.Module): r"""Criterion that computes image-aware inverse depth smoothness loss. .. math:: \text{loss} = \left | \partial_x d_{ij} \right | e^{-\left \| \partial_x I_{ij} \right \|} + \left | \partial_y d_{ij} \right | e^{-\left \| \partial_y I_{ij} \right \|} Shape: - Inverse Depth: :math:`(N, 1, H, W)` - Image: :math:`(N, 3, H, W)` - Output: scalar Examples: >>> idepth = torch.rand(1, 1, 4, 5) >>> image = torch.rand(1, 3, 4, 5) >>> smooth = InverseDepthSmoothnessLoss() >>> loss = smooth(idepth, image) """ def forward(self, idepth: torch.Tensor, image: torch.Tensor) -> torch.Tensor: return inverse_depth_smoothness_loss(idepth, image)