javidcf

#!/bin/sh

# cpbar: cp with a progress bar

MAXCOLS=80
SLEEP=.5

if [ "$#" -ne 2 ]; then
  echo "Usage: $(basename $0) <SOURCE> <DEST>" >&2
  exit 1

javidcf

\documentclass{article}

\usepackage{tikz}

\begin{document}

\centering
\begin{tikzpicture}

\definecolor{gtRed}{RGB}{85,0,0}

javidcf

#include <Eigen/Dense>

template <class MatT>
Eigen::Matrix<typename MatT::Scalar, MatT::ColsAtCompileTime, MatT::RowsAtCompileTime>
pseudoinverse(const MatT &mat, typename MatT::Scalar tolerance = typename MatT::Scalar{1e-4}) // choose appropriately
{
    typedef typename MatT::Scalar Scalar;
    auto svd = mat.jacobiSvd(Eigen::ComputeFullU | Eigen::ComputeFullV);
    const auto &singularValues = svd.singularValues();
    Eigen::Matrix<Scalar, MatT::ColsAtCompileTime, MatT::RowsAtCompileTime> singularValuesInv(mat.cols(), mat.rows());

javidcf

javidcf

def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
    """
    Freezes the state of a session into a prunned computation graph.
 
    Creates a new computation graph where variable nodes are replaced by
    constants taking their current value in the session. The new graph will be
    prunned so subgraphs that are not neccesary to compute the requested
    outputs are removed.

    @param session The TensorFlow session to be frozen.

javidcf

import unreal_engine as ue

import asyncio
import threading
import queue

import msgpack
try:
    import msgpack_numpy as mnp
    mnp.patch()

javidcf

# Split a number into parts with a fixed size unit size

def split_number(number, parts, unit, min_units=0):
    current = []
    num_units = round(number / unit)
    for c in _divide_number_rec(num_units, parts, min_units, current):
        yield tuple(u * unit for u in c)

def _split_number_rec(num_units, parts, last_units, current):
    if parts <= 0:

javidcf

We do not (and cannot) store any personal information, nor use any third-party app or service that does so.

javidcf

import onnx
from onnx import helper, TensorProto, save_model
from onnx.checker import check_model

body = helper.make_graph(
    name='body',
    nodes=[
        helper.make_node('Add', ['a', 'b_in'], ['my_local']),
        helper.make_node('Sub', ['a', 'b_in'], ['b_out']),
        helper.make_node('Greater', ['my_local', 'b_out'], ['keep_going_out']),

javidcf

// lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp

// ...
void mlir::torch::onnx_c::populateDefaultDomainGtoP(
    OnnxCustomOpConversionPattern &patterns) {
// ...
  patterns.onOp(
      "Loop", 13, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
        // Get all operands (maxTripCount, cond, ....inits....)
        llvm::SmallVector<Value> operands;