Package: torchfun
TorchFunMore convenient features based on PyTorch (originally Torchure)PrefaceTorchFunproject was initiated long ago and was published in 2018-6-13.The purposed of TorchFun is to provide functions which are important and convenient, but absent in PyTorch, like some layers and visualization utils.Interestingly, The original project name given by the author, isTorchure. That is because he was always multi-tasking trivial affairs in school and got scorched, and when he was learning this new framework, he found plenty of issues/missing-functionalities. He felt that this was totally a torture. So, this project was named "Torchure" to satirize the lame PyTorch (you can still found Torchure in PyPi). And, the author hoped, by developing Torchure, users could feel ease even when encountering the crappy-parts of PyTorch.Latest Documentation:Please visit:https://sorenchiron.github.io/torchfunFunctionalityFlattenflatten()Subpixelsubpixel()imshow()load()save()count_parameters()Packsearchpacksearch()hash_parameters()force_exist()whereis()Install TorchFuninstalllpipinstalltorchfunupdatepipinstall-UtorchfunAPIFlatten [Module]used to reshape outputsUsage: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 mapNotice:Output size is deducted. 
The size of the unfold square is automatically determinede.g. :images: 100x16x16x9.  9=3x3 square
subpixel-out: 100x48x48x1Arguement:out_channels, channel number of output feature mapSubpixel Layer [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. Westronglyrecommend 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:importmatplotlibmatplotlib.use('TkAgg')# or GTK GTKAggor permanantly by editing:site-packages/matplotlib/mpl-data/matplotlibrcand change backend toTkAggA full list of available backends can be found at:importmatplotlibmatplotlib.rcsetup.all_backendsand, the TCL/TK GUI library fortkintercan be downloadedhere.load(a,b) [Function]Arguements:arbitrary arguemnts named :aandbLoad weightainto modelb, or load modelbusing weightaThe 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:Nonesave(a,b) [Function]Arguements:arbitrary arguemnts named :aandbsave weightainto targetb, or save modelbinto targetaThe 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: Nonecount_parameters(model_or_dict_or_param) [Function]Count parameter numer of a module/state_dict/layer/tensor.
This function can also print the occupied memory of parameters in MBsArguements:model_or_dict_or_param: model or state dictionary or parameters()Return: parameter amount in python-int
Returns 0 if datatype not understoodUsage:count_parameters(model)count_parameters(state_dict)#all paramscount_parameters(model.parameters())#only trainable paramscount_parameters(weight_tensor)count_parameters(numpy_array)Packsearch [Module]This is a very useful thing you definitly have been dreaming of.You can now use packsearch to query names inside any package!Given an module object as input:> p = Packsearch(torch) 
or > p = Packsarch(numpy) whateverthe instancepprovidep.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)

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
    ...you can search for everything inside any packagehash_parameters(module_or_statdict_or_param)  [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.By default, This only hash the trainable parameters!arguements:module_or_statdict_or_param: torch.nn.module,
or model.state_dict(),
or model.parameters().use_sum: return the sum instead of mean value of all params.Usage demo:model=MyNet()print(hash_parameters(model))# see paramstrain_one_step(model)print(hash_parameters(model))# see if params are updatedprint(hash_parameters(model.state_dict()))# see if trainable+un-trainable params are updatedforce_exist(dirname,verbose=True) [Function]force a series of hierachical directories to exist.force_existcan automatically create directory with any depth.Arguements:dirname: path of the desired directoryverbose: print every directory creation. default True.Usage:force_exist('a/b/c/d/e/f')
force_exist('a/b/c/d/e/f',verbose=False)sort_args(args_or_types,types_or_args) [Function]This is a very interesting function.
It is used to supportarbitrary-arguments-orderingin TorchFun.Input:
The function takes a list of types, and a list of arguments.Returns:
a list of arguments, with the same order as the types-list.Of course,sort_argssupports arbitrary-arguments-ordering by itself.whereis(module_or_string) [Function]find the source file location of a module
arguments:module_or_string: target module object, or it's string path liketorch.nnopen_gui: open the folder with default window-manager.returns:module file name, or None