third_party/InternVideo/InternVideo2/multi_modality/models/mask.py

import torch
import numpy as np


def TubeMaskingGenerator(input_size, mask_ratio, batch, device='cuda'):
    frames, height, width = input_size
    num_patches_per_frame = height * width
    num_masks_per_frame = int(mask_ratio * num_patches_per_frame)

    mask_list = []
    for _ in range(batch):
        mask_per_frame = np.hstack([
            np.zeros(num_patches_per_frame - num_masks_per_frame),
            np.ones(num_masks_per_frame),
        ])
        np.random.shuffle(mask_per_frame)
        mask_list.append(np.tile(mask_per_frame, (frames, 1)).flatten())
    mask = torch.Tensor(mask_list).to(device, non_blocking=True).to(torch.bool)
    return mask 


def RandomMaskingGenerator(input_size, mask_ratio, batch, device='cuda'):
    frames, height, width = input_size

    num_patches = frames * height * width  # 8x14x14
    num_mask = int(mask_ratio * num_patches)

    mask_list = []
    for _ in range(batch):
        mask = np.hstack([
            np.zeros(num_patches - num_mask),
            np.ones(num_mask),
        ])
        np.random.shuffle(mask)
        mask_list.append(mask)
    mask = torch.Tensor(np.array(mask_list)).to(device, non_blocking=True).to(torch.bool)
    return mask