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import onnxruntime as ort
import numpy as np
import time
import argparse
import os

def parse_args():
    parser = argparse.ArgumentParser(description="Benchmark FPS for an ONNX model.")
    parser.add_argument("--model-path", type=str, default="model.onnx", 
                        help="Path to the ONNX model file (default: model.onnx)")

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FFmpeg cheat sheet

A list of useful commands for the FFmpeg command line tool.

Download FFmpeg: https://www.ffmpeg.org/download.html

Full documentation: https://www.ffmpeg.org/ffmpeg.html

Basic conversion

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name: "RHDLatentHMMap"

#-----Generate random image index for training 0
layer {
  name: "image_index_0"
  type: "DeepHandModelGenRandIndex"
  top: "image_index_0"
  gen_rand_index_param {
     index_lower_bound: 0
	 index_upper_bound: 10000

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#include <functional>
#include <chrono>
#include <iostream>

template <class> struct ExeTime;

// Execution time decorator
template <class R, class... Args>
struct ExeTime<R(Args ...)> {
public:

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You could always use Smart HTTP.

For read-only (git:) urls, install corkscrew.

1. Download git-proxy.sh. Put it somewhere and make it executable.

2. Run git config --global core.gitproxy '/usr/local/bin/git-proxy.sh'

To clone, push, pull over ssh, add the contents of ssh_config to your ~/.ssh/config file.

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[a,b,c,d for a,b in AB for c,d in CD if a if b if c]

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import numpy as np
from random import shuffle
from past.builtins import xrange

def softmax_loss_naive(W, X, y, reg):
  """
  Softmax loss function, naive implementation (with loops)

  Inputs have dimension D, there are C classes, and we operate on minibatches
  of N examples.

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import numpy as np
from random import shuffle
from past.builtins import xrange

def svm_loss_naive(W, X, y, reg):
  """
  Structured SVM loss function, naive implementation (with loops).

  Inputs have dimension D, there are C classes, and we operate on minibatches
  of N examples.