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    "info": {
        "author": "CHEN Si Yu",
        "author_email": "sychen@zju.edu.cn",
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            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
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        "description": "# TorchFun \n\nMore convenient features based on PyTorch (originally Torchure)\n\n## Preface\n<!-- ### -- for those who suffer from torch -->\n`TorchFun` project was initiated long ago and was published in 2018-6-13. \n\nThe purposed of TorchFun is to provide functions which are important and convenient, but absent in PyTorch, like some layers and visualization utils.\n\nThis project has been undergoing in secret so that the author could credit TorchFun to his beloved as a little supprise of help, one day when this project is well-enriched or gets famous.\n\nInterestingly, The original project name given by the author, is `Torchure`. That is because he was always multi-tasking trivial affairs in school and got scorched, and when he was learning this new framework in hope to help his beloved, 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, his beloved could feel ease even when encountering the crappy-parts of PyTorch.\n\nThis history-of-project was appended recently, because his adorable little beloved wants a supprise immediately, or she will keep rolling on the floor.\n\n## Latest Documentation:\n\nPlease visit: https://sorenchiron.github.io/torchfun\n\n### Functionality\n* Flatten\n* flatten()\n* Subpixel\n* subpixel()\n* imshow()\n* load()\n* save()\n* count_parameters()\n* Packsearch\n* packsearch()\n* hash_parameters()\n* force_exist()\n* whereis()\n\n## Install TorchFun\ninstalll\n```bash\npip install torchfun\n```\nupdate\n```bash\npip install -U torchfun\n```\n\n\n## API\n\n### Flatten [Module]\nused to reshape outputs\n\nUsage:\n```python\n    flat = Flatten()\n    out = flat(x)\n```\n\n----------------\n### flatten(x) [Function]\n\nUsage:\n```python\n    out = flatten(x)\n```\n\n\n----------------\n### subpixel(x,out_channels) [Function]\n\nUnfold channel/depth dimensions to enlarge the feature map\n\nNotice: \n\n    Output size is deducted. \n    The size of the unfold square is automatically determined\n\ne.g. :\n\n    images: 100x16x16x9.  9=3x3 square\n    subpixel-out: 100x48x48x1\n\nArguement:\n\n    out_channels, channel number of output feature map\n\n----------------\n### Subpixel Layer [Module]\n\nSame functionality as subpixel(x), but with Module interface.\n\n    s = Subpixel(out_channels=1)\n    out = s(x)\n\n----------------\n### imshow(x,title=None,auto_close=True) [Function]\n\nonly deal with torch channel-first image batch,\n\nArguements:\n\n* x: input data cube, torch tensor or numpy array.\n* title: add title to plot. (Default None)\n    * title can be string, or any string-able object.\n* auto_close: (default True) \n    * Close the pyplot session afterwards. \n    * Clean the environment just like you had never used matplotlib here.\n    * if set to False, the plot will remain in the memory for further drawings.\n\nUsage:\n```python\n    imshow(batch)\n    imshow(batch,title=[a,b,c])\n    imshow(batch,title='title')\n    imshow(batch,auto_close=False) \n```\nWarnings:\n```text\n    TorchFun:imshow:Warning, you are using WebAgg backend for Matplotlib. \n    Please consider windowed display SDKs such as TkAgg backend and GTK* backends.\n```\nThis 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:\n```python\nimport matplotlib\nmatplotlib.use('TkAgg') # or GTK GTKAgg\n```\nor permanantly by editing: `site-packages/matplotlib/mpl-data/matplotlibrc` and change backend to `TkAgg`\n\nA full list of available backends can be found at:\n```python\nimport matplotlib\nmatplotlib.rcsetup.all_backends\n```\nand, the TCL/TK GUI library for `tkinter` can be downloaded [here](https://www.tcl.tk/).\n\n----------------\n\n### load(a,b) [Function]\nArguements:\n* arbitrary arguemnts named : `a` and `b`\nLoad weight `a` into model `b`, or load model `b` using weight `a`\nThe order of the arguments doesn't matter.\nExample:\n\n```python\n    >load('weights.pts',model)\n```\n\nor\n```python\n    >load(model,'weights.pts')\n```\nor\n```python\n    >f = open('weight.pts')\n    >load(f,model)\n```\nor\n```python\n    >load(model,f)\n```\nReturn value:\n\n* None\n\n\n----------------\n### save(a,b) [Function]\nArguements:\n\n* arbitrary arguemnts named : `a` and `b`\n\nsave weight `a` into target `b`, or save model `b` into target `a`\nThe order of the arguments doesn't matter.\n\nExample:\n\n```python\n    >save('weights.pts',model)\n```\n\nor\n```python\n    >save(model,'weights.pts')\n```\nor\n```python\n    >f = open('weight.pts')\n    >save(f,model)\n```\nor\n```python\n    >save(model,f)\n```\nor\n```python\n    >save('weights.pts',state_dict)\n```\nor\n```python\n    >save(state_dict,'weights.pts')\n```\nReturn value: None\n\n\n----------------\n### count_parameters(model_or_dict_or_param) [Function]\n\nCount parameter numer of a module/state_dict/layer/tensor.\n    This function can also print the occupied memory of parameters in MBs\n\nArguements:\n* model_or_dict_or_param: model or state dictionary or parameters()\n\nReturn: parameter amount in python-int\n        Returns 0 if datatype not understood\n\nUsage:\n```python\n    count_parameters(model)\n    count_parameters(state_dict) #all params \n    count_parameters(model.parameters()) #only trainable params\n    count_parameters(weight_tensor)\n    count_parameters(numpy_array)\n```\n\n\n-----------------\n### Packsearch [Module]\n\nThis is a very useful thing you definitly have been dreaming of.\n\nYou can now use packsearch to query names inside any package! \n\nGiven an module object as input:\n\n       > p = Packsearch(torch) \n    or > p = Packsarch(numpy) whatever\n\nthe instance `p` provide `p.search()` method. So that you can \nsearch everything inside this package\n\n    > p.search('maxpoo')\n\noutput:\n\n    Packsearch: 35 results found:\n    -------------results start-------------\n    0        torch.nn.AdaptiveMaxPool1d\n    1        torch.nn.AdaptiveMaxPool2d\n    2        torch.nn.AdaptiveMaxPool3d\n    3        torch.nn.FractionalMaxPool2d\n    4        torch.nn.MaxPool1d\n    5        torch.nn.MaxPool2d\n    ...\n\n-----------------\n### packsearch(module,keyword) [Function]\nor packsearch(keyword,module)\n\nGiven an module object, and search pattern string as input:\n\n    > packsearch(torch,'maxpoo')\n\nor\n\n    > packsearch('maxpoo',torch)\n\n    output:\n        Packsearch: 35 results found:\n        -------------results start-------------\n        0        torch.nn.AdaptiveMaxPool1d\n        1        torch.nn.AdaptiveMaxPool2d\n        2        torch.nn.AdaptiveMaxPool3d\n        3        torch.nn.FractionalMaxPool2d\n        4        torch.nn.MaxPool1d\n        5        torch.nn.MaxPool2d\n        ... \n\nyou can search for everything inside any package\n\n------------------\n### hash_parameters(module_or_statdict_or_param)  [Function]\n\nreturn the summary of all variables.\n\nThis is used to detect chaotic changes of weights.\nYou can check the sum_parameters before and after some operations, to know\nif there is any change made to the params.\n\nI use this function to verify gradient behaviours.\n\nBy default, This only hash the trainable parameters!\n\narguements:\n\n* module_or_statdict_or_param: torch.nn.module, \n                    or model.state_dict(), \n                    or model.parameters().\n* use_sum: return the sum instead of mean value of all params.\n\nUsage demo:\n\n```python\nmodel = MyNet()\nprint(hash_parameters(model)) # see params\ntrain_one_step(model)\nprint(hash_parameters(model)) # see if params are updated\nprint(hash_parameters(model.state_dict())) # see if trainable+un-trainable params are updated\n```\n\n-----------------\n### force_exist(dirname,verbose=True) [Function]\n\nforce a series of hierachical directories to exist.\n\n`force_exist` can automatically create directory with any depth.\n\nArguements:\n\n* dirname: path of the desired directory\n* verbose: print every directory creation. default True.\n\nUsage:\n\n    force_exist('a/b/c/d/e/f')\n    force_exist('a/b/c/d/e/f',verbose=False)\n\n\n------------------\n### sort_args(args_or_types,types_or_args) [Function]\n\nThis is a very interesting function.\nIt is used to support __arbitrary-arguments-ordering__ in TorchFun.\n\nInput:\n    The function takes a list of types, and a list of arguments.\n\nReturns:\n    a list of arguments, with the same order as the types-list.\n\nOf course, `sort_args` supports arbitrary-arguments-ordering by itself.\n\n\n-------------------\n### whereis(module_or_string) [Function]\n\nfind the source file location of a module\narguments:\n\n*    module_or_string: target module object, or it's string path like `torch.nn`\n*    open_gui: open the folder with default window-manager.\n\nreturns:\n\n*    module file name, or None\n\n",
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