Saravanan666/pypi-org huggingface dataset:
Package: np-sound
NPSoundA collection of simple audio modification methods based on NumPy.Supported Formats.wavInstallationpip install np-soundUsagefromnp_soundimportNPSoundsound=NPSound("soundfile.wav")# Plot soundfile.wav with the title "NPSound Demo"sound.plot(title="NPSound Demo")# Reverse the section of audio from 1.5s to 3s# NPSound objects are immutable, so the 'sound' object will be unchangedreversed_audio=sound.reverse((1.5,3))reversed_audio.plot()# Amplify the entire audio file by 50% and plot itsound.amplify(50).plot(title="Amplified by 50%")# Concatenate 'sound' with its mirrored version and write to a new sound filemirror=sound+sound.reverse()mirror.write("mirrored.wav")# Plot 5 copies of 'sound' side by side(sound*5).plot()# Plot a 50% softened 'sound' on top of the original 'sound'sound.plot(layered_plots=[sound.amplify(-50)])# Plot a 'sound' object padded with 5.5s of empty data on either end below the original 'sound'sound.plot(adjacent_plots=[sound.pad((5.5,5.5))])# Trim audio on both ends so that the first and last values are above 100sound.clip_at_threshold(100)
Package: npspy
npspynpspy is a simple package to calculateNPS(Net Promoter Score).Installpip install npspyExampleimportnpspynpspy.categorize(0)# "detractor"npspy.categorize(7)# "passive"npspy.categorize(9)# "promoter"npspy.calculate([0,7,9])# 0npspy.calculate([7,9])# 50npspy.calculate([0,7])# -50
Package: npss
npssYour New Password!Why?When need some password (token or random url safe string of specified length) quickly.PerksGenerate random string with specified length or length of 30 by default.No dependencies.Random.Url safe.At least one-included.InstallationpipinstallnpssUsagenpss>>>
J_9oE0uToBaw6qDzUAUI-hZ3PJC93Bnpss255>>>
JwAiFQjuFizl6g1Thzx1AQdn39ozrPqsRJ4thyrs9OCjU28nUAx9k3fmB0jcSTtkZBVhF-DDV9-0zeod0OPN13k0gOEsA4FtSBtr6ckq81lQewOuLBUbFNUlKLH63Z6GSdZtuTidcQrvlVErnaY-pFb4xX8Jmj2jzJDpp6HvctEu5ycQq3VevlBwx9dIf8VUuO9jwZPsQnc022jbwBv00shByBOKCoO5I3TLGwnQEWaRHsWfyXeb6fTyzLtGH2-Testpython3-munittestContributePRs are very welcome!https://github.com/almazkun/npss/pullsUninstallpipuninstallnpss
Package: np-stream
np-streamThis Python package provides a replacement for thenp.load(),np.save()andnp.savez_compressed()NumPy functions with support for streaming data on one dimension.For uncompressed arrays with a known size, usenp.memmap()instead.Installation$pipinstallnp-streamUsageWriting uncompressed dataThe dimension which lists entries is added first for C-contiguous arrays (the default when creating arrays) and last otherwise. In this example, the array is C-contiguous, therefore its final shape is(100, 6, 2).fromnp_streamimportNpyStreamWriterimportnumpyasnpwithNpyStreamWriter(file)aswriter:forindexinrange(100):writer.write(np.random.rand(6,2))Alternatively, the writer can be used without a context manager. The call toflush()at the end is mandatory.writer=NpyStreamWriter(file,shape=entry_shape)forindexinrange(100):writer.write(np.random.rand(6,2))writer.flush()Writing compressed dataTo produced compressed data, just replaceNpyStreamWriterwithNpzStreamWriterand create an.npzinstead of an.npyfile. The writer uses Deflate compression.- from np_stream import NpyStreamWriter+ from np_stream import NpzStreamWriterentry_shape = (6, 2)- with NpyStreamWriter(file, shape=entry_shape) as writer:+ with NpzStreamWriter(file, shape=entry_shape) as writer:for index in range(100):writer.write(np.random.rand(*entry_shape))Reading uncompressed datareader=NpyStreamReader(file)forentryinreader:print(entry)Reading compressed datareader=NpzStreamReader(file)["arr_0"]forentryinreader:print(entry)Similar packagesnpy-append-array
Package: np-struct
np-structnp-structextends structured arrays in NumPy to be a bit more user friendly and intuitive, with added support for transferring structured arrays across serial or socket interfaces.Structured arrays are built to mirror the struct typedef in C/C++, but can be used for any complicated data structure. They behave similar to standard arrays, but support mixed data types, labeling, and unequal length arrays. Arrays are easily written or loaded from disk in the standard.npybinary format.Installationpipinstallnp-structUsageimportnp_structCreate a c-style structure with Numpy types.fromnp_structimportStructimportnumpyasnpclassexample(Struct):data1=np.uint32()data2=np.complex128([0]*3)Structures can be initialized with arbitrary shapes by using theshapekwarg:ex=example(shape=(3,),byte_order='>')ex[0].data2=1+2j>>>ex
Structexample:(3,)[psize:uint16[0]data1:uint32[0]data2:complex128[1.+2.j1.+2.j1.+2.j]]...[psize:uint16[0]data1:uint32[0]data2:complex128[0.+0.j0.+0.j0.+0.j]]Members can also be initialized by passing in their name to the constructor with an inital value.>>example(data2=np.zeros(shape=(3,2)))Structexample:psize:uint16[0]data1:uint32[0]data2:complex128[[0.+0.j0.+0.j][0.+0.j0.+0.j][0.+0.j0.+0.j]]The structure inherits from np.ndarray and supports all math functions that a normal structured array does.
To cast as a standard numpy array:>>ex2.view(np.ndarray)array([([0],[0],[[0.+0.j,0.+0.j],[0.+0.j,0.+0.j],[0.+0.j,0.+0.j]])],dtype=[('psize','<u2',(1,)),('data1','<u4',(1,)),('data2','<c16',(3,2))])Nested structures are also supported:classnested(Struct):field1=example()field2=example()n=nested()n.field1.data2+=j*np.pi>>n
Structnested:field1:Structexample:psize:uint16[0]data1:uint32[0]data2:complex128[0.+3.14159265j0.+3.14159265j0.+3.14159265j]field2:Structexample:psize:uint16[0]data1:uint32[0]data2:complex128[0.+0.j0.+0.j0.+0.j]Examples:Transfering structures over an interfaceLabeled arraysLicensenp-struct is licensed under the MIT License.
Package: npTDMS
npTDMS is a cross-platform Python package for reading and writing TDMS files as produced by LabVIEW,
and is built on top of thenumpypackage.
Data is read from TDMS files as numpy arrays,
and npTDMS also allows writing numpy arrays to TDMS files.TDMS files are structured in a hierarchy of groups and channels.
A TDMS file can contain multiple groups, which may each contain multiple channels.
A file, group and channel may all have properties associated with them,
but only channels have array data.Typical usage when reading a TDMS file might look like:from nptdms import TdmsFile

tdms_file = TdmsFile.read("path_to_file.tdms")
group = tdms_file['group name']
channel = group['channel name']
channel_data = channel[:]
channel_properties = channel.propertiesTheTdmsFile.readmethod reads all data into memory immediately.
When you are working with large TDMS files or don’t need to read all channel data,
you can instead useTdmsFile.open. This is more memory efficient but
accessing data can be slower:with TdmsFile.open("path_to_file.tdms") as tdms_file:
    group = tdms_file['group name']
    channel = group['channel name']
    channel_data = channel[:]npTDMS also has rudimentary support for writing TDMS files.
Using npTDMS to write a TDMS file looks like:from nptdms import TdmsWriter, ChannelObject
import numpy

with TdmsWriter("path_to_file.tdms") as tdms_writer:
    data_array = numpy.linspace(0, 1, 10)
    channel = ChannelObject('group name', 'channel name', data_array)
    tdms_writer.write_segment([channel])For more detailed documentation on reading and writing TDMS files,
see thenpTDMS documentation.InstallationnpTDMS is available from the Python Package Index, so the easiest way to
install it is by running:pip install npTDMSThere are optional features available that require additional dependencies.
These arehdffor hdf export,pandasfor pandas DataFrame export, andthermocouple_scalingfor using thermocouple scalings. You can specify these
extra features when installing npTDMS to also install the dependencies they
require:pip install npTDMS[hdf,pandas,thermocouple_scaling]Alternatively, after downloading the source code you can extract it and
change into the new directory, then run:python setup.py installLinksSource code lives athttps://github.com/adamreeve/npTDMSand any issues can be
reported athttps://github.com/adamreeve/npTDMS/issues.
Documentation is available athttp://nptdms.readthedocs.io.LimitationsThis module doesn’t support TDMS files with XML headers or with
extended precision floating point data.Contributors/ThanksThanks to Floris van Vugt who wrote the pyTDMS module,
which helped when writing this module.Thanks to Tony Perkins, Ruben De Smet, Martin Hochwallner and Peter Duncan
for contributing support for converting to Pandas DataFrames.Thanks to nmgeek and jshridha for implementing support for DAQmx raw data
files.
Package: nptel-dl
nptel-dlDownload courses from NPTELInstallationpip install nptel-dlManualgit clone https://github.com/deshdeepak1/nptel-dl
    cd nptel-dl
    python3 -m venv venv
    pip3 install -r requirements.txtUsageusage: nptel-dl [-h] [--dump-json  | --dump-single-json ] [--write-json] [--all] [--books] [--videos] [--syllabus] [--transcripts]
                   [--assignements]
                   URL [URL ...]

Download NPTEL courses

positional arguments:
  URL                   URLs or COURSE_IDs

optional arguments:
  -h, --help            show this help message and exit
  --dump-json , -j      Dump info dict of urls in json. Available options: all, modules, yt, direct, assignements, transcripts, books
  --dump-single-json , -J 
                        Dump info dict of urls in single json. Available options: all, modules, yt, direct, assignements, transcripts, books
  --write-json, -w      Write json. Requires either --dump-json or --dump-single-json
  --all, -A             Download all i.e. videos, syllabus, transcripts, assignements, books.
  --books, -b           Download books
  --videos, -v          Download videos
  --syllabus, -s        Download syllabus
  --transcripts, -t     Download transcripts
  --assignements, -a    Download assignements
Package: nptime
Python’s “datetime” module has many uses, but it has a difficulty: you
can’t do any arithmetic with “datetime.time”.  Only with
“datetime.datetime”.  Now, there are good reasons for this:  “What
time will it be, 24 hours from now” has a lot of corner cases,
including daylight savings time, leap seconds, historical timezone
changes, and so on.  But sometimes you reallydoneed the simple
case.  Sometimes the perfect is the enemy of the good.  And that’s why
you have this module.  This module will allow you to cocoon yourself
in the comforting illusion that, in 24 hours, it will be the same time
as it is right now.And what freedom this illusion gives you!  You can add a
“datetime.timedelta” object to an “nptime” object, and it works! It
will cycle around 24 hours, like modular arithmetic.  You can ask
“what time comes 1 day and 36 minutes after 12:24 pm?” and it will let
you know: 1:00 pm. How lovely!>>> from nptime import nptime
>>> from datetime import timedelta, date, datetime
>>> afternoon = nptime(12, 24) + timedelta(days=1, minutes=36)
>>> afternoon
nptime(13, 0)
>>> str(afternoon)
'13:00:00'Maybe we’re talking about the afternoon of Guido van Rossum’s
birthday.  Lucky, we can combine “nptime” objects with “date” objects:>>> datetime.combine(date(1956, 1, 31), afternoon)
datetime.datetime(1956, 1, 31, 13, 0)You can also ask “How long is it between 9:00 AM and 5:00 PM?  It sure
feels like a million years!”>>> workday = nptime(hour=17) - nptime(hour=9)
>>> workday
datetime.timedelta(0, 28800)
>>> print(workday)
8:00:00Nope, only 8 hours, how strange.  Anyway, please use this module.  It
will be convenient.  But don’t use it when talking about concrete
time, time in a particular place, or anything like that.  In fact, it
doesn’t even notice “datetime.tzinfo” objects right now.  Good luck.Note: You can find the newest version athttps://github.com/tgs/nptime. You can find online documentation athttp://tgs.github.com/nptime/. The package is available for
download from PyPi:easy_install nptime.   This software is
licensed under the 3-clause BSD License.
Package: npt-promote
npt-promoteMypy plugin to add type promotions between NumPy and builtin data types.The main use case for this plugin is to enable generic use ofintandfloattype annotations with NumPy arrays, e.g.importnumpyasnpimportnumpy.typingasnptx:npt.NDArray[float]=np.array((42.0))InstallationDependencies:mypyNumPyInstall it with:python-mpipinstallnpt-promoteAlternatively, addnpt-promoteas a project dependency wherevermypyis used,
e.g. as an optional dev requirement inpyproject.toml:[project.optional-dependencies]dev=["mypy","npt-promote"]UsageTo enable the plugin, it must be added to your project's mypy configuration file
along with NumPy's mypy plugin. E.g. add the following topyproject.toml:[tool.mypy]plugins=['numpy.typing.mypy_plugin','npt_promote',]pre-commitTo use the plugin withmypyas apre-commithook, it must be added as a
dependency, e.g. add the following to.pre-commit-config.yaml:-repo:https://github.com/pre-commit/mirrors-mypyhooks:-id:mypyadditional_dependencies:["npt-promote"]TestingFirst, installnpt-promotewith dev requirements:python-mpipinstallnpt-promote[dev]To run the tests, execute:pytest
Package: nptr-cli
nptr-cliManage your uploads to 0x0.st from the cli.nptr-cliexists to easily upload to 0x0.st via the cli.
It also prints the tokens to delete uploads from 0x0.st.It also can be used as a library for other python projects.InstallationThe package is onpypi.pipinstallnptr-cliUsageTo upload files:$0x0ufile.txt# you can also use `0x0 up` or `0x0 upload`$echo"this will be uploaded"|0x0# You can omit the u, it is the default when you pipe into `0x0`$0x0-ihttps://your-0x0-instance.comufile.txt# To use other instances than 0x0.st$0x0-ialiasufile.txt# can also be an alias defined in your config.toml$0x0u-sfile.txt# To use longer paths in the URL which are harder to predict like https://0x0.st/s/123acb-319gas8dgh8ahdg8/a8gn.txtAll these commands will output something like this:https://0x0.st/abcd.txt
Expires: 1234567890
Token: TOKENTo delete files:$0x0dTOKENURL# you can also use `0x0 del` or `0x0 delete`To get more info about 0x0 use:$0x0-h
$0x0delete-h
$0x0upload-hConfigurationThe config file is located at~/.config/0x0/config.toml.These are the default values currently, if nothing is set (See also [#Usage]).quiet=false# same as -q/--quietinstance="https://0x0.st"# same as -i/--instancesecret=false# same as -s/--secretAs mentioned earlier you can also add instance aliases and basic auth credentials.Instance AliasesAliases work like this:[instances][instances.my_instance]# now you can use `0x0 -i my_instance`url="https://my_instance.tld"# it will use this domainusername="admin"# and if you provide a usernamepassword="admin"# and password (which are optional),# it will authenticate via http basic authAlso have a look at thedocumentation
Package: nptsne
nptsne - A numpy compatible python extension for GPGPU linear complexity tSNEThe nptsne package is designed to export a number of python classes that
wrap GPGPU linear complexity tSNE or the hierarchical SNE (hSNE) method.When using nptsne please include the following citations whenusing t-SNEand orusing HSNE:using t-SNE*Pezzotti, N., Thijssen, J., Mordvintsev, A., Höllt, T., Van Lew, B., Lelieveldt, B.P.F., Eisemann, E., Vilanova, A., (2020), "GPGPU Linear Complexity t-SNE Optimization" in IEEE Transactions on Visualization and Computer Graphics.doi: 10.1109/TVCG.2019.2934307keywords: {Minimization;Linear programming;Computational modeling;Approximation algorithms;Complexity theory;Optimization;Data visualization;High Dimensional Data;Dimensionality Reduction;Progressive Visual Analytics;Approximate Computation;GPGPU},URL:https://doi.org/10.1109/TVCG.2019.2934307*using HNSE*Pezzotti, N., Höllt, T., Lelieveldt, B., Eisemann, E., Vilanova, A., (2016), "Hierarchical Stochastic Neighbor Embedding" in Computer Graphics Forum, 35: 21-30.doi:10.1111/cgf.12878keywords: {Categories and Subject Descriptors (according to ACM CCS), I.3.0 Computer Graphics: General},URL:https://doi.org/10.1111/cgf.12878*AttributionsThe t-SNE and HSNE implementations are the original work of the authors named in the literature.Full documentationFull documentation is available at thenptsne doc pages
Package: nptweak
nptweakUtility functions for numpy.
The only dependency is numpy.Table of ContentsInstallationUsageCommandsSupportContributingInstallationThenptweakgit repois available asPyPi packagepip install nptweakUsageCheck theexamplesfolder for notebooks.CommandsInstall a virtual environmentpython3 -m venv .venv
source .venv/bin/activate
pip3 install --upgrade pip
pip3 install -r requirements.txt
pip3 install jupyterlab twine(If your git repo is stored in a folder with whitespaces, then don't use the subfolder.venv. Use an absolute path without whitespaces.)Python commandsJupyter for the examples:jupyter labCheck syntax:flake8 --ignore=F401 --exclude=$(grep -v '^#' .gitignore | xargs | sed -e 's/ /,/g')Run Unit Tests:pytestUpload to PyPi with twine:python setup.py sdist && twine upload -r pypi dist/*Clean upfind . -type f -name "*.pyc" | xargs rm
find . -type d -name "__pycache__" | xargs rm -r
rm -r .pytest_cache
rm -r .venvDebuggingNotebooks to profile python code are in theprofilefolderSupportPleaseopen an issuefor support.ContributingPlease contribute usingGithub Flow. Create a branch, add commits, andopen a pull request.
Package: nptyping
🧊Type hints forNumPy🐼Type hints forpandas.DataFrame💡Extensive dynamic type checks for dtypes shapes and structures🚀Jump to the QuickstartExample of a hintednumpy.ndarray:>>>fromnptypingimportNDArray,Int,Shape>>>arr:NDArray[Shape["2, 2"],Int]Example of a hintedpandas.DataFrame:>>>fromnptypingimportDataFrame,StructureasS>>>df:DataFrame[S["name: Str, x: Float, y: Float"]]InstallationCommandDescriptionpip install nptypingInstall the basicspip install nptyping[pandas]Install with pandas extensionpip install nptyping[complete]Install with all extensionsInstance checkingExample of instance checking:>>>importnumpyasnp>>>isinstance(np.array([[1,2],[3,4]]),NDArray[Shape["2, 2"],Int])True>>>isinstance(np.array([[1.,2.],[3.,4.]]),NDArray[Shape["2, 2"],Int])False>>>isinstance(np.array([1,2,3,4]),NDArray[Shape["2, 2"],Int])Falsenptypingalso providesassert_isinstance. In contrast toassert isinstance(...), this won't cause IDEs or MyPy
complaints. Here is an example:>>>fromnptypingimportassert_isinstance>>>assert_isinstance(np.array([1]),NDArray[Shape["1"],Int])TrueNumPy Structured arraysYou can also express structured arrays usingnptyping.Structure:>>>fromnptypingimportStructure>>>Structure["name: Str, age: Int"]Structure['age: Int, name: Str']Here is an example to see it in action:>>>fromtypingimportAny>>>importnumpyasnp>>>fromnptypingimportNDArray,Structure>>>arr=np.array([("Peter",34)],dtype=[("name","U10"),("age","i4")])>>>isinstance(arr,NDArray[Any,Structure["name: Str, age: Int"]])TrueSubarrays can be expressed with a shape expression between square brackets:>>>Structure["name: Int[3, 3]"]Structure['name: Int[3, 3]']NumPy Record arraysThe recarray is a specialization of a structured array. You can useRecArrayto express them.>>>fromnptypingimportRecArray>>>arr=np.array([("Peter",34)],dtype=[("name","U10"),("age","i4")])>>>rec_arr=arr.view(np.recarray)>>>isinstance(rec_arr,RecArray[Any,Structure["name: Str, age: Int"]])TruePandas DataFramesPandas DataFrames can be expressed withStructurealso. To make it more concise, you may want to aliasStructure.>>>fromnptypingimportDataFrame,StructureasS>>>df:DataFrame[S["x: Float, y: Float"]]More examplesHere is an example of a rich expression that can be done withnptyping:defplan_route(locations:NDArray[Shape["[from, to], [x, y]"],Float])->NDArray[Shape["* stops, [x, y]"],Float]:...More examples can be found in thedocumentation.DocumentationUser documentationThe place to go if you are using this library.Release notesTo see what's new, check out the release notes.ContributingIf you're interested in developing along, find the guidelines here.LicenseIf you want to check out how open source this library is.
Package: npu
NPU Python Client PackageTo install, run:pip install npuTo use in code:import npuThis library has four core functions.npu.api, npu.compile, npu.train, npu.predictHere is an example script of them in use.import npu
from npu.vision.models import resnet18
from npu.vision.datasets import CIFAR10


npu.api(API_KEY)

model = npu.train(resnet18(pretrained=True),
                         train_data=CIFAR10.train,
                         val_data=CIFAR10.val,
                         loss=npu.loss.CrossEntropyLoss,
                         optim=npu.optim.SGD(lr=0.01),
                         batch_size=128,
                         epochs=2)

output = npu.predict(model, data)Full documentation ishere
Package: npu-compiler
# NPU compilerproduce NPU instructions## Release Notes### Release 1.5.11GRUS支持Transpose HWC转WHCGRUS临时内存优化。BUG修复。### Release 1.5.10GRUS取消全连接权重必须小于65536的限制。GRUS支持DepthwiseConv2d SAME模式。GRUS编译时-v参数可打印出权重个数和flops数。### Release 1.5.9增加APUS芯片。BUG修复。### Release 1.5.8LEO增加AddV2GRUS增加配置项FUSE_BIASADD，配置MatMul和BiasAdd，Conv2D和BiasAdd是否融合，默认为False### Release 1.5.7GRUS增加配置项WEIGHT_MIN_MAX，可指定权重量化的最小最大值。BUG修复。### Release 1.5.6编译时不使用GPU.GRUS支持多个OP使用同一个权重。GRUS支持MatMul的第2个参数是变量而非权重。GRUS芯片增加Mean,SquaredDifference,Abs,FusedBatchNormV2,FusedBatchNormV3.GRUS芯片增加配置项EXCLUDE_CONV2D_COMPRESS_OPS用于指定某个卷积权重不压缩。GRUS芯片增加配置项EXCLUDE_COMPRESS_OPS用于指定某个卷积或全连接权重不压缩。GRUS芯片增加`-s`选项用于保存权重的数据分布直方图。GRUS芯片优化Conv2D在kernel_width和stride_width相等时的存储和算力。### Release 1.5.5GRUS芯片修复StridedSlice end小于begin时出现的错误。### Release 1.5.4LEO芯片增加CONV2D_COMPRESS配置项，用于配置Conv2D权重是否量化压缩。GRUS芯片增加Slice,Shape OP.GRUS芯片支持shape为[N,M]和[N,1]做Add,AddV2,Sub,Mul,Div的情况。BUG修复。### Release 1.5.2GRUS芯片不再支持Maximum和Minimum.GRUS芯片生成的.h文件中增加in_out结构体。GRUS芯片增加AddV2,BatchMatMulV2,FusedBatchNorm.GRUS芯片优化量化方法，减少量化损失。GRUS芯片配置文件增加配置项FUSE_BN，可配置是否把BN参数合并到卷积中，默认不合并。BUG修复。### Release 1.5.1GRUS芯片卷积权重默认不压缩，如需压缩，配置文件中指定CONV2D_COMPRESS: true增加Python版本的判断，目前支持Python2.7和Python3.6，不是这些版本则报错。解决Python3安装包在conda环境下安装后，报”undefined symbol: PyFPE_jbuf”的问题。GRUS芯片增加Pad OPGRUS芯片支持MaxPool和AvgPool的kernel_h或kernel_w为1的情况。GRUS芯片模型编译后增加NPU Size的打印信息。GRUS芯片优化depthwise_conv2d的内存。### Release 1.5.0增加GRUS芯片的支持。### Release 1.0.17优化Transpose,MatMul的计算。BUG修复。### Release 1.0.16配置文件中增加MODEL_INFO字段，可加入用户自定义的模型信息，编译完后该信息和编译时间会加入到npu文件中。优化了Conv2d和DepthwiseConv2d NCHW 1*1卷积核的计算。支持空洞卷积（tf.layers.Conv2D中dilation大于1）。BUG修复。### Release 1.0.15增加OP：Relu6, GatherV2支持SNPU的ReverseV2 OP合并DepthwiseConv2d和Add/BiasAdd运算，优化BiasAdd，提高Rsqrt的精度，优化prelu整合了tensorflow的inference优化脚本，如果模型中的FusedBatchNorm OP前是Conv2D或DepthwiseConv2d，FusedBatchNorm会优化成BiasAddgxnpuc增加-m选项，编译模式时加上该选项可以打印出各个OP的内存信息。BUG修复。### Release 1.0.14增加OP： ListDiff，Abs支持Tile运行时计算优化Concat，Reduce类OP（如Sum, Mean）的计算，节省BatchMatMul的内存空间支持命令行读取配置参数，如 cat config.yaml | gxnpucBug修复。### Release 1.0.13支持Select OP加快生成c_code模型的速度。优化SNPU的1*1卷积，转置，减少生成指令大小。增加MEAN_SHRINK_OPS配置项，容易溢出的Mean OP放在该列表中，NPU会先做除法再做加法。生成的模型中增加需要内存总大小：total_size字段。BUG修复。### Release 1.0.12支持新版本TensorFlow的模型有些OP在其输入OP后面的情况。优化1*1的卷积，能减少大量指令，提高执行效率。优化Transpose OP增加模型中间数据的复用，减少模型需要的内存。BUG修复。### Release 1.0.11增加了DepthwiseConv2dNative，AvgPool， Conv2DBackpropInput， Maximum， Minimum， GreaterEqual， LessEqual， Assert， Tile， All， Any， BatchMatMul， ReverseV2， Exp支持做MatMul时，权重（第二个输入数据）在编译阶段不确定的情况。BUG修复。### Release 1.0.10对Conv2D, Slice等OP的优化。增加了Max, Min, FloorDiv, FloorMod OP增加了空间优化的选项，可以根据模型时间敏感还是空间敏感来配置。配置项为 SPACE_OPTIMIZATION：0/1  数字越大表示需要内存空间越小，相应速度会慢，目前只支持0或1。目前只有Conv2D, Slice OP在某些条件下会起作用。BUG修复。### Release 1.0.9针对NPU硬件的问题增加了补丁。优化了Mean, Sum, Conv2D等OP增加对1x1卷积核的支持。BUG修复。### Release 1.0.8 (空缺)### Release 1.0.7配置文件中可以任意指定输出OP，不执行和输出OP无关的OP增加LogSoftmax OPOP优化和BUG修复### Release 1.0.6配置文件增加新配置项 CORENAME，可以选择 LEO 或 LEO_MPE，默认为 LEO对OP log 和 softmax 合并在一起计算，减少计算误差。加速多 batch LSTM计算，加速归一化计算。参数 fp32 转 fp16 由截位变成四舍五入。bug 修复。
Package: np-upload-validation
np-upload-validationAWS upload validationUsagecondacreate-nnp_upload_validationpython>=3.9
condaactivatenp_upload_validation
pipinstallnp_upload_validationPython>>>importnp_upload_validationDebugging version divergencespdmbumpverDevelopmentSee instructions inhttps://github.com/AllenInstitute/np-upload-validation/CONTRIBUTING.mdand the original template:https://github.com/AllenInstitute/copier-pdm-npc/blob/main/README.md