eldar

"""
Space efficient save and load of depth maps
"""

from ctypes import c_ubyte, c_float
import numpy as np
from imageio.v3 import imread, imwrite


def depth_encode(dmap):

eldar

Running DDP training on rank 1.
Running DDP training on rank 0.

  0%|          | 0/1000 [00:00<?, ?it/s]
  0%|          | 0/1000 [00:00<?, ?it/s][2022-12-14 10:11:05,736] torch._dynamo.eval_frame: [DEBUG] skipping __init__ /users/eldar/apps/anaconda3/envs/ca3d/lib/python3.10/contextlib.py
[2022-12-14 10:11:05,736] torch._dynamo.eval_frame: [DEBUG] skipping __enter__ /users/eldar/apps/anaconda3/envs/ca3d/lib/python3.10/contextlib.py
[2022-12-14 10:11:05,736] torch._dynamo.eval_frame: [DEBUG] skipping __init__ /users/eldar/apps/anaconda3/envs/ca3d/lib/python3.10/contextlib.py
[2022-12-14 10:11:05,736] torch._dynamo.eval_frame: [DEBUG] skipping __enter__ /users/eldar/apps/anaconda3/envs/ca3d/lib/python3.10/contextlib.py
[2022-12-14 10:11:05,736] torch._dynamo.eval_frame: [DEBUG] skipping enable_dynamic /users/eldar/apps/anaconda3/envs/ca3d/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py
[2022-12-14 10:11:05,736] torch._dynamo.eval_frame: [DEBUG] skipping forward /users/eldar/apps/anaconda3/envs/ca3d/li

eldar

Running DDP training on rank 1.
Running DDP training on rank 0.
r.device before call: cuda:1 rank: 1
r.device inside call: cuda:0 rank: 1
[W reducer.cpp:1298] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())      

Traceback (most recent call last): 
  File "/users/eldar/src/hybridrf/train_dynamo_bug.py", line 77, in <module>                                                                                    mp.spawn(train,                                                                                                                                          

eldar

from pathlib import Path
import re
import argparse

import imageio
import numpy as np
from tqdm import tqdm


def crop_or_pad(img, crop):

eldar

from typing import Tuple

import torch
from pytorch3d.common.linear_with_repeat import LinearWithRepeat
from pytorch3d.renderer import HarmonicEmbedding, ray_bundle_to_ray_points, RayBundle


def _xavier_init(linear):
    """
    Performs the Xavier weight initialization of the linear layer `linear`.

eldar

import math
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import collections
from matplotlib import colors as mcolors


# F: 3x3 fundamental matrix maps points in image I1 to epipolar lines in image I2
# F = (K2^T)^-1 * E * K1^-1
# E = R [t]_x

eldar

class ConfConverter:
    def __init__(self, filename):
        self.filename = filename
        self.f = open(filename, "w")
    
    def __del__(self):
        self.f.close()
    
    def add_argument(self, name, type=None, default=None, required=None, choices=[], help="", nargs=None, action=None):
        f = self.f

eldar

import datetime as dt

import tensorflow as tf
import tensorflow.contrib.slim as slim
from tensorflow.contrib.slim.nets import resnet_v1
import threading

from PoseDataset import PoseDataset
from TrainParams import TrainParams

eldar

import ldc.JIT;

void main()
{
    Compiler compiler = new Compiler();
    compiler.importPaths = [ "/home/eldar/ldc/druntime/import",
                             "/home/eldar/mylib/" ];
    compiler.emitModuleInfo = false;

    Module mod = compiler.compile("extern(C) int helloWorld() { return 15; }");