yashkant

import numpy as np
import torch
import time
import imageio
import cv2
from tqdm import tqdm
import torch.nn.functional as Fu
from pytorch3d.implicitron.tools.point_cloud_utils import get_rgbd_point_cloud
from pytorch3d.renderer import (AlphaCompositor, MultinomialRaysampler,
                                NDCMultinomialRaysampler,

yashkant

import pytorch_lightning as pl 

class FFCVDataModule(pl.LightningDataModule):

    def __init__(self, batch_size, train=None, reg = None, validation=None, test=None, predict=None,
                 wrap=False, num_workers=None, shuffle_test_loader=False, use_worker_init_fn=False,
                 shuffle_val_dataloader=False, beton_path=None, **kwargs):
        super().__init__()
        self.batch_size = batch_size
        self.num_workers = num_workers if num_workers is not None else batch_size * 2

yashkant

import numpy as np
import torch
import time
import imageio
import cv2
from tqdm import tqdm
import torch.nn.functional as Fu
from pytorch3d.implicitron.tools.point_cloud_utils import get_rgbd_point_cloud
from pytorch3d.renderer import (AlphaCompositor, MultinomialRaysampler,
                                NDCMultinomialRaysampler,

yashkant

import cv2
import argparse
import glob
from torchvision.io import write_video
import numpy as np
import os

"""
A quick piece of code I put together to join some video samples I had for creating visualizations.