Zero out the parameters of a module and return it.

Evaluate a function without caching intermediate activations, allowing for reduced memory at the expense of extra compute in the backward pas

:param raw_image (H, W, 3) array of uint8 in [0, 255].

python code from: https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filte

python code from: https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filte

generate random rays in world space, for a single image :param H: :param W: :param N_rays: :param image: [3, H, W] :param int

:param x: img of size (bs, c, h, w) :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])

convert the sparse volume into dense volume to enable trilinear sampling to save GPU memory; :param coords: [num_pts, 3] :param featu

:param pre_feat: (Tensor), features from last level, (N, C) :param pre_coords: (Tensor), coordinates from last level, (N, 4) (4 : Ba

depth: (H, W)

Returns only captions with dummy images

Compute pairwise distances between two batches of feature vectors.

Unproject the image fetures to form a 3D (sparse) feature volume :param coords: coordinates of voxels, dim: (num of voxels, 4) (4 : batc

Args: x: HxWxC image, [0, 1] sf: down-scale factor Return: bicubicly downsampled LR image

Args: x: HxWxC image, [0, 1] sf: down-scale factor Return: bicubicly downsampled LR image

Transform coordinates in the camera frame to the pixel frame. Args: cam_coords: pixel coordinates defined in the first camera coordinates

Compute image features for downstream evals. :param batches: a iterator over NHWC numpy arrays in [0, 255]. :return: a tuple

compute the depth difference of query pts projected on the image and the predicted depth values of the image :param xyz: [N_rays, n_

A counter for the `thop` package to count the operations in an attention operation. Meant to be used like: macs, params = thop.pr

one dimensional cubic interpolation :param p: [B, N, 4] (4) should be in order :param x: [B, N] :return:

:param h: height :param w: width :return: normalized distance to image border, wtith min distance = 0 at border and

Evaluate precision and recall efficiently. :param features_1: [N1 x D] feature vectors for reference batch. :param radii_1:

Compute the Frechet distance between two sets of statistics.

generate rays in world space, for image image :param H: :param W: :param intrinsics: [3,3] :param c2ws: [4,4] :return:

# get the minimum bounding box of all visual hulls :param img_hw: :param intrinsics: :param extrinsics: :param near_fars: :re

Get ray directions for all pixels in camera coordinate. Reference: https://www.scratchapixel.com/lessons/3d-basic-rendering/ r