Pinzhen Xu pinzhenx

## test_onnx.html
<html>
  <script src='../dist/webml-polyfill.js'></script>
  <script src='third_party/protobuf.min.js'></script>
  <script src='util/base.js'></script>
  <script src='util/onnx/onnx.js'></script>
  <script src='util/onnx/OnnxModelUtils.js'></script>
  <script src='util/onnx/OnnxModelImporter.js'></script>
  <script>
  (async () => {
    const res = await fetch('path/to/model.onnx');

## pyrys.md

      
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                pinzhenx
                / pyrys.md
            
            
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              April 26, 2020 04:45
            
          
A simple example of how const prop works

def foo(x):
    a = 1 + 2
    b = a + 3
    c = b + 4
    return x + c

jit_foo = torch.jit.script(foo)


## benchmark_conv.py
import torch
import pandas as pd

def profile(m, x, nwarm=10, nrun=300):
    for _ in range(nwarm):
        m(x)
    with torch.autograd.profiler.profile(True) as prof:
        for _ in range(nrun):
            m(x)
    return getattr(prof.key_averages()[0], 'cpu_time') / 1000

## mirror.ipynb

      
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                / mirror.ipynb
            
            
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              August 16, 2021 07:17
            
              
                Anatomy of mirror strategy
              
          
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## conv_denorm.ipynb

      
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                / conv_denorm.ipynb
            
            
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              November 5, 2021 14:01
            
              
                conv_denorm.ipynb
              
          
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## memstat.py
pgsize = 4  # kb
total_lowwmark = 0
total_reserved = 0
with open('/proc/zoneinfo') as zoneinfo:
    for line in zoneinfo:
        if 'pages free' in line:
            next(zoneinfo)
            low = int(next(zoneinfo).split()[1])
            high = int(next(zoneinfo).split()[1])
            total_lowwmark += low
	<html>
	<script src='../dist/webml-polyfill.js'></script>
	<script src='third_party/protobuf.min.js'></script>
	<script src='util/base.js'></script>
	<script src='util/onnx/onnx.js'></script>
	<script src='util/onnx/OnnxModelUtils.js'></script>
	<script src='util/onnx/OnnxModelImporter.js'></script>
	<script>
	(async () => {
	const res = await fetch('path/to/model.onnx');
	import torch
	import pandas as pd

	def profile(m, x, nwarm=10, nrun=300):
	for _ in range(nwarm):
	m(x)
	with torch.autograd.profiler.profile(True) as prof:
	for _ in range(nrun):
	m(x)
	return getattr(prof.key_averages()[0], 'cpu_time') / 1000
	pgsize = 4 # kb
	total_lowwmark = 0
	total_reserved = 0
	with open('/proc/zoneinfo') as zoneinfo:
	for line in zoneinfo:
	if 'pages free' in line:
	next(zoneinfo)
	low = int(next(zoneinfo).split()[1])
	high = int(next(zoneinfo).split()[1])
	total_lowwmark += low