torchfun 0.0.48

A collection of small functions that supplements torch functionality

TorchFun (originally Torchure)

for those who expect more convenient features from torch

Functionality

• Flatten
• flatten()
• Subpixel
• subpixel()
• imshow()
• load()
• save()
• count_parameters()
• Packsearch
• packsearch()

Install TorchFun

pip install torchfun

API

Flatten Module

used to reshape outputs

Usage:

    flat = Flatten()
    out = flat(x)

---

flatten(x) function

Usage:

    out = flatten(x)

---

subpixel(x,out_channels) function

Unfold channel/depth dimensions to enlarge the feature map

Notice:

Output size is deducted. 
The size of the unfold square is automatically determined

e.g. :

images: 100x16x16x9.  9=3x3 square
subpixel-out: 100x48x48x1

Arguement:

out_channels, channel number of output feature map

---

Subpixel Module

Same functionality as subpixel(x), but with Module interface.

s = Subpixel(out_channels=1)
out = s(x)

---

imshow(x,title=None,auto_close=True) (function)

only deal with torch channel-first image batch,

Arguements:

• x: input data cube, torch tensor or numpy array.
• title: add title to plot. (Default None)
  • title can be string, or any string-able object.
• auto_close: (default True)
  • Close the pyplot session afterwards.
  • Clean the environment just like you had never used matplotlib here.
  • if set to False, the plot will remain in the memory for further drawings.

Usage:

    imshow(batch)
    imshow(batch,title=[a,b,c])
    imshow(batch,title='title')
    imshow(batch,auto_close=False) 

Warnings:

    TorchFun:imshow:Warning, you are using WebAgg backend for Matplotlib. 
    Please consider windowed display SDKs such as TkAgg backend and GTK* backends.

This means your matplotlib is using web-browser for figure display. We strongly recommend you to use window-based native display because browser-based backends are fragile and tend to crash. You can change the display mamanger for matplotlib each time you execute your script by:

import matplotlib
matplotlib.use('TkAgg') # or GTK GTKAgg

or permanantly by editing: site-packages/matplotlib/mpl-data/matplotlibrc and change backend to TkAgg

A full list of available backends can be found at:

import matplotlib
matplotlib.rcsetup.all_backends

and, the TCL/TK GUI library for tkinter can be downloaded here.

---

load(a,b) (function)

Arguements:

• arbitrary arguemnts named : a and b Load weight a into model b, or load model b using weight a The order of the arguments doesn't matter. Example:

    >load('weights.pts',model)

or

    >load(model,'weights.pts')

or

    >f = open('weight.pts')
    >load(f,model)

or

    >load(model,f)

Return value:

• None

---

save(a,b) (function)

Arguements:

• arbitrary arguemnts named : a and b save weight a into target b, or save model b into target a The order of the arguments doesn't matter.

Example:

    >save('weights.pts',model)

or

    >save(model,'weights.pts')

or

    >f = open('weight.pts')
    >save(f,model)

or

    >save(model,f)

or

    >save('weights.pts',state_dict)

or

    >save(state_dict,'weights.pts')

Return value: None

---

count_parameters(model_or_dict) (function)

Arguements:

• model_or_dict: model or state dictionary

Return: parameter amount in python-int Returns 0 if datatype not understood

Usage:

    count_parameters(model)
    count_parameters(state_dict)
    count_parameters(weight_tensor)
    count_parameters(numpy_array)

---

Packsearch

Given an module object as input:

> p = Packsearch(torch)

the instance p provide p.search() method. So that you can search everything inside this package

> p.search('maxpoo')

output:

Packsearch: 35 results found:
-------------results start-------------
0        torch.nn.AdaptiveMaxPool1d
1        torch.nn.AdaptiveMaxPool2d
2        torch.nn.AdaptiveMaxPool3d
3        torch.nn.FractionalMaxPool2d
4        torch.nn.MaxPool1d
5        torch.nn.MaxPool2d
...

---

packsearch(module,keyword) Function

or packsearch(keyword,module)

Given an module object, and search pattern string as input:

> packsearch(torch,'maxpoo')

or

> packsearch('maxpoo',torch)
you can search everything inside this package
output:
    Packsearch: 35 results found:
    -------------results start-------------
    0        torch.nn.AdaptiveMaxPool1d
    1        torch.nn.AdaptiveMaxPool2d
    2        torch.nn.AdaptiveMaxPool3d
    3        torch.nn.FractionalMaxPool2d
    4        torch.nn.MaxPool1d
    5        torch.nn.MaxPool2d
    ... 

hash_parameters(module) Function

return the summary of all variables. This is used to detect chaotic changes of weights. You can check the sum_parameters before and after some operations, to know if there is any change made to the params.

I use this function to verify gradient behaviours.