- https://www.gov.il/he/service/confirm_aliyah_rights_for_former_ussr_in_israel
- https://www.gov.il/he/departments/bureaus/tel-aviv_piba
- https://www.gov.il/he/service/schedule_meeting_visas_department
- https://forms.gov.il/globaldata/getsequence/[email protected]
- https://www.nbn.org.il/aliyahpedia/aliyah-process/aliyah-processing-flight-logistics/aliyah-through-misrad-hapnim/
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# Instance-Aware, Context-Focused, and Memory-Efficient Weakly Supervised Object Detection, https://arxiv.org/abs/2004.04725 | |
# https://github.com/NVlabs/wetectron/issues/72 | |
# https://discuss.pytorch.org/t/mini-batching-gradient-accumulation-within-the-model/136460 | |
import torch | |
import torch.nn as nn | |
class SequentialBackprop(nn.Module): | |
def __init__(self, module, batch_size = 1): | |
super().__init__() |
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import torch | |
import torch.nn.functional as F | |
import torchvision | |
import base64 | |
gather_incomplete = lambda tensor, I: tensor.gather(I.ndim, I[(...,) + (None,) * (tensor.ndim - I.ndim)].expand((-1,) * (I.ndim + 1) + tensor.shape[I.ndim + 1:])).squeeze(I.ndim) | |
expand_dim = lambda tensor, expand, dim: tensor.unsqueeze(dim).expand((-1, ) * (dim if dim >= 0 else tensor.ndim + dim + 1) + (expand, ) + (-1, ) * (tensor.ndim - (dim if dim >= 0 else tensor.ndim + dim + 1))) | |
encode_image_as_html = lambda img, height, width: '<img height="{height}" width="{width}" src="data:image/jpeg;base64,{encoded}" />'.format(height = height, width = width, encoded = bytes(base64.b64encode(torchvision.io.encode_jpeg(F.interpolate(img[None], (height, width)).squeeze(0)))).decode()) |
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# Blender 2.93 python script for producing images like in https://github.com/facebookresearch/meshrcnn/issues/22 | |
# blender -noaudio --background --python meshrcnn_overlay_mesh.py -- --object-path test.obj --background-path test.png | |
# | |
# Sample ouptut: | |
# https://github.com/facebookresearch/meshrcnn/issues/100 | |
# | |
# References: | |
# https://github.com/yuki-koyama/blender-cli-rendering/blob/master/02_suzanne.py | |
# https://github.com/weiaicunzai/blender_shapenet_render/blob/master/render_rgb.py | |
# https://towardsdatascience.com/blender-2-8-grease-pencil-scripting-and-generative-art-cbbfd3967590 |
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# https://scikit-image.org/docs/dev/api/skimage.filters.html | |
import torch | |
import torch.nn.functional as F | |
def sobel_filter() -> '2133': | |
flipped_sobel_x = torch.tensor([ | |
[-1, 0, 1], | |
[-2, 0, 2], | |
[-1, 0, 1] |
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https://media.mts.ru/technologies/124401/ | |
Calls redirect: **21*+NUM# |
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import os | | |
import shutil |
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#! /usr/bin/python3 | |
# authors: Theophile Dalens, Vadim Kantorov | |
# first version: https://github.com/willowsierra/office/blob/master/scripts/gpuscan.py | |
# TODO: resolve usernames with "ldapsearch -x -h ildap -b 'ou=people,dc=inria,dc=fr' -s sub 'inrialogin=kantorov' -LLL mail" | |
# TODO: implement thresholds | |
# TODO: prepare suggested e-mail text | |
import itertools | |
import argparse |
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# Method from https://gist.github.com/vladalive/535cc2aff8a9527f1d9443b036320672 | |
# Usage (download UP-DETR checkpoint published at https://github.com/dddzg/up-detr, https://drive.google.com/file/d/1_YNtzKKaQbgFfd6m2ZUCO6LWpKqd7o7X/view?usp=sharing) | |
# downloadgdrive 1_YNtzKKaQbgFfd6m2ZUCO6LWpKqd7o7X # -> ./1_YNtzKKaQbgFfd6m2ZUCO6LWpKqd7o7X | |
# downloadgdrive 1_YNtzKKaQbgFfd6m2ZUCO6LWpKqd7o7X abc # -> ./abc | |
function downloadgdrive() { wget --load-cookies googlecookies.txt "https://docs.google.com/uc?export=download&confirm="$(wget --quiet --save-cookies googlecookies.txt --keep-session-cookies --no-check-certificate "https://docs.google.com/uc?export=download&id=$1" -O- | sed -rn "s/.*confirm=([0-9A-Za-z_]+).*/\1\n/p")"&id=$1" -O ${2:-$1}; } |
