steerapi

var torchjs = require('@idn/torchjs');
var script_module = new torchjs.ScriptModule('resnet18.pt');
var tensor = torchjs.ones([1, 3, 224, 224], false);

const { performance } = require('perf_hooks');

// Comment this out if you don't have cuda
script_module.cuda();
let start, end;
start = performance.now();

steerapi

import torch
import torchvision

# An instance of your model.
model = torchvision.models.resnet18(pretrained=True)

# An example input you would normally provide to your model's forward() method.
example = torch.rand(1, 3, 224, 224)

# Use torch.jit.trace to generate a torch.jit.ScriptModule via tracing.

steerapi

overlayed_model = OverlayNet(model)
reduced_model = overlayed_model.generate(p=0.5)

steerapi

const model = new ModelDescriptor({
  modelId: 'QmZ9db8JzNCNrKuYyFN4Sv3a4Rn9CgZM99owJXwFjUhCik',
  modelPath: 'https://cloudflare-ipfs.com/ipfs/QmZ9db8JzNCNrKuYyFN4Sv3a4Rn9CgZM99owJXwFjUhCik', // resnet18 pytorch model
  modelType: 'torchjs/cuda'
});
await node.initModel(model)
node.inferModel(model, inputs).on('response', (outputs, confidences, finish) => {
    // Do something with the outputs, confidences
    // call finish() to finish the computation if outputs are already highly confidence
});

steerapi

0x66ed62338f125dfdf84c2d0b4ed69ea37283f2ea

steerapi

0x33bffb3b5a3c445789e92ff072c9821595df3872

steerapi

0xF66b365bEeDF4a96A011Bd47205091e783437565

steerapi

BOOST_INC_DIR = []                                                              
BOOST_LIB_DIR = []                                                              
BOOST_COMPILER = 'gcc43'                                                        
USE_SHIPPED_BOOST = True                                                        
BOOST_PYTHON_LIBNAME = ['boost_python-py27']                                    
BOOST_THREAD_LIBNAME = ['boost_thread']                                         
CUDA_TRACE = False                                                              
CUDA_ROOT = '/usr/local/cuda'                                                   
CUDA_ENABLE_GL = False                                                          
CUDA_ENABLE_CURAND = True                                                       

steerapi

 /*
 Usage:
    document.addEventListener("swipe", function(ev){
        console.log("swipe: "+JSON.stringify(ev));
    });
 */
 (function() {
   var CURRENT_TOUCH, EVENT, FIRST_TOUCH, GESTURE, HOLD_DELAY, TAPS, TOUCH_TIMEOUT, _angle, _capturePinch, _captureRotation, _cleanGesture, _distance, _fingersPosition, _getTouches, _hold, _isSwipe, _listenTouches, _onTouchEnd, _onTouchMove, _onTouchStart, _swipeDirection, _trigger;

   TAPS = null;

steerapi

% LaTeX settings for MATLAB code listings
% based on Ted Pavlic's settings in http://links.tedpavlic.com/ascii/homework_new_tex.ascii
\usepackage{listings}
\usepackage[usenames,dvipsnames]{color}

% This is the color used for MATLAB comments below
\definecolor{MyDarkGreen}{rgb}{0.0,0.4,0.0}

% For faster processing, load Matlab syntax for listings
\lstloadlanguages{Matlab}%