David Bau davidbau

## +lightbox.html
<!DOCTYPE html>
<html>
<!--
  +lightbox.html, a page for automatically showing all images in a
  directory on an Apache server. Just copy it into the directory.
  Works by scraping the default directory HTML at "./" - David Bau.
-->
<head>
<script src="https://cdn.jsdelivr.net/npm/vue@2.5.16/dist/vue.js"
  integrity="sha256-CMMTrj5gGwOAXBeFi7kNokqowkzbeL8ydAJy39ewjkQ="

## runningstats.py
'''
To use a runningstats object,

    1. Create the the desired stat object, e.g., `m = Mean()`
    2. Feed it batches via the add method, e.g., `m.add(batch)`
    3. Repeat step 2 any number of times.
    4. Read out the statistic of interest, e.g., `m.mean()`

Built-in runningstats objects include:

## nethook.py
"""
Utilities for instrumenting a torch model. (David Bau)

Trace will hook one layer at a time.
TraceDict will hook multiple layers at once.
subsequence slices intervals from Sequential modules.
get_module, replace_module, get_parameter resolve dotted names.
set_requires_grad recursively sets requires_grad in module parameters.
"""

## simpleganpaint.ipynb

      
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## explorefeaturebyclick.ipynb

      
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                davidbau
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              October 20, 2019 22:14
            
              
                ExploreFeatureByClick.ipynb
              
          
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## seeing_images.ipynb

      
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## fooling.html
<!DOCTYPE html>
<html>
<!-- an example vue HTML file that displays images and metadata -->
<head>
<script src="https://cdn.jsdelivr.net/npm/vue@2.5.16/dist/vue.js"
  integrity="sha256-CMMTrj5gGwOAXBeFi7kNokqowkzbeL8ydAJy39ewjkQ="
  crossorigin="anonymous"></script>
<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.10/lodash.js"
  integrity="sha256-qwbDmNVLiCqkqRBpF46q5bjYH11j5cd+K+Y6D3/ja28="
  crossorigin="anonymous"></script>

## featurefolder.py
import os
import torch.utils.data as data
from torchvision.datasets.folder import default_loader, is_image_file
from PIL import Image

def grayscale_loader(path):
    with open(path, 'rb') as f:
        return Image.open(f).convert('L')

class FeatureFolder(data.Dataset):

## autoimport.py
 from collections import defaultdict
 from importlib import import_module

 def autoimport_eval(term):
    '''
    Used to eval any expression containing fully-qualified names, such as
    'torchvision.models.alexnet(pretrained=True)' with automatic import of
    global module names.
    '''

## download_mp.py
#!/usr/bin/env python2.7

# Script to create simple flat pytorch ImageFolder folder hierarchy
# of training and validation images for miniplaces.  Each category
# name is just a folder name (numbered in alphabetical order as in
# the original miniplaces), and both train and val images are places
# directly inside a single level of folders with the flat cateogry names.

import shutil, os, tarfile
	<!DOCTYPE html>
	<html>
	<!--
	+lightbox.html, a page for automatically showing all images in a
	directory on an Apache server. Just copy it into the directory.
	Works by scraping the default directory HTML at "./" - David Bau.
	-->
	<head>
	<script src="https://cdn.jsdelivr.net/npm/vue@2.5.16/dist/vue.js"
	integrity="sha256-CMMTrj5gGwOAXBeFi7kNokqowkzbeL8ydAJy39ewjkQ="
	'''
	To use a runningstats object,

	1. Create the the desired stat object, e.g., `m = Mean()`
	2. Feed it batches via the add method, e.g., `m.add(batch)`
	3. Repeat step 2 any number of times.
	4. Read out the statistic of interest, e.g., `m.mean()`

	Built-in runningstats objects include:
	"""
	Utilities for instrumenting a torch model. (David Bau)

	Trace will hook one layer at a time.
	TraceDict will hook multiple layers at once.
	subsequence slices intervals from Sequential modules.
	get_module, replace_module, get_parameter resolve dotted names.
	set_requires_grad recursively sets requires_grad in module parameters.
	"""
	<!DOCTYPE html>
	<html>
	<!-- an example vue HTML file that displays images and metadata -->
	<head>
	<script src="https://cdn.jsdelivr.net/npm/vue@2.5.16/dist/vue.js"
	integrity="sha256-CMMTrj5gGwOAXBeFi7kNokqowkzbeL8ydAJy39ewjkQ="
	crossorigin="anonymous"></script>
	<script src="https://cdn.jsdelivr.net/npm/lodash@4.17.10/lodash.js"
	integrity="sha256-qwbDmNVLiCqkqRBpF46q5bjYH11j5cd+K+Y6D3/ja28="
	crossorigin="anonymous"></script>
	import os
	import torch.utils.data as data
	from torchvision.datasets.folder import default_loader, is_image_file
	from PIL import Image

	def grayscale_loader(path):
	with open(path, 'rb') as f:
	return Image.open(f).convert('L')

	class FeatureFolder(data.Dataset):
	from collections import defaultdict
	from importlib import import_module

	def autoimport_eval(term):
	'''
	Used to eval any expression containing fully-qualified names, such as
	'torchvision.models.alexnet(pretrained=True)' with automatic import of
	global module names.
	'''
	#!/usr/bin/env python2.7

	# Script to create simple flat pytorch ImageFolder folder hierarchy
	# of training and validation images for miniplaces. Each category
	# name is just a folder name (numbered in alphabetical order as in
	# the original miniplaces), and both train and val images are places
	# directly inside a single level of folders with the flat cateogry names.

	import shutil, os, tarfile