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<import_from_stmt>recon.core.module BaseModule<import_from_stmt>recon.mixins.resolver ResolverMixin<import_stmt>dns.resolver<class_stmt>Module(BaseModule ResolverMixin)<block_start>meta={'name':'Hostname Resolver' 'author':'<NAME> (@LaNMaSteR53)' 'description':'Resolves the IP address for a host. Updates the \'hosts\' table with the results.' 'comments':('Note: Nameserver must be in IP form.' ) 'query':'SELECT DISTINCT host FROM hosts WHERE host IS NOT NULL AND ip_address IS NULL' }<def_stmt>module_run self hosts<block_start>q=self.get_resolver()<for_stmt>host hosts<block_start><try_stmt><block_start>answers=q.query(host)<block_end><except_stmt>dns.resolver.NXDOMAIN<block_start>self.verbose('%s => Unknown'%(host))<block_end><except_stmt>dns.resolver.NoAnswer<block_start>self.verbose('%s => No answer'%(host))<block_end><except_stmt>(dns.resolver.NoNameservers dns.resolver.Timeout)<block_start>self.verbose('%s => DNS Error'%(host))<block_end><else_stmt><block_start><for_stmt>i range(0 len(answers))<block_start><if_stmt>i<eq>0<block_start>self.query('UPDATE hosts SET ip_address=? WHERE host=?' (answers[i].address host))<block_end><else_stmt><block_start>data={'host':self.to_unicode(host) 'ip_address':self.to_unicode(answers[i].address)}<line_sep>self.insert('hosts' data data.keys())<block_end>self.output('%s => %s'%(host answers[i].address))<block_end><block_end><block_end><block_end><block_end> |
<import_stmt>os<import_from_stmt>aztk.models.plugins.plugin_configuration PluginConfiguration PluginPort PluginTargetRole<import_from_stmt>aztk.models.plugins.plugin_file PluginFile<line_sep>dir_path=os.path.dirname(os.path.realpath(__file__))<class_stmt>SparkUIProxyPlugin(PluginConfiguration)<block_start><def_stmt>__init__ self<block_start>super().__init__(name="spark_ui_proxy" ports=[PluginPort(internal=9999 public=<true>)] target_role=PluginTargetRole.Master execute="spark_ui_proxy.sh" args=["localhost:8080" "9999"] files=[PluginFile("spark_ui_proxy.sh" os.path.join(dir_path "spark_ui_proxy.sh")) PluginFile("spark_ui_proxy.py" os.path.join(dir_path "spark_ui_proxy.py")) ] )<block_end><block_end> |
<try_stmt><block_start><import_from_stmt>ray __version__<as>ray_version<assert_stmt>ray_version<ge>'1.0.0'<import_from_stmt>ray.tune uniform quniform choice randint qrandint randn qrandn loguniform qloguniform lograndint qlograndint <block_end><except_stmt>(ImportError AssertionError)<block_start><import_from_stmt>.sample uniform quniform choice randint qrandint randn qrandn loguniform qloguniform lograndint qlograndint <block_end><import_from_stmt>.tune run report<import_from_stmt>.sample polynomial_expansion_set<import_from_stmt>.sample PolynomialExpansionSet Categorical Float<import_from_stmt>.trial Trial<line_sep> |
<import_stmt>torch<import_stmt>random<import_stmt>torch.nn<as>nn<import_from_stmt>abc abstractmethod<import_from_stmt>abc ABCMeta<import_from_stmt>torch Tensor<import_from_stmt>typing Any<import_from_stmt>typing Dict<import_from_stmt>typing List<import_from_stmt>typing Tuple<import_from_stmt>typing Optional<import_from_stmt>.losses GANLoss<import_from_stmt>.losses GANTarget<import_from_stmt>.discriminators DiscriminatorBase<import_from_stmt>..protocol GaussianGeneratorMixin<import_from_stmt>....data CVLoader<import_from_stmt>....types tensor_dict_type<import_from_stmt>....protocol StepOutputs<import_from_stmt>....protocol TrainerState<import_from_stmt>....protocol MetricsOutputs<import_from_stmt>....protocol ModelWithCustomSteps<import_from_stmt>....constants LOSS_KEY<import_from_stmt>....constants INPUT_KEY<import_from_stmt>....constants LABEL_KEY<import_from_stmt>....constants PREDICTIONS_KEY<import_from_stmt>....misc.toolkit to_device<import_from_stmt>....misc.toolkit mode_context<import_from_stmt>....misc.toolkit toggle_optimizer<class_stmt>GANMixin(ModelWithCustomSteps GaussianGeneratorMixin metaclass=ABCMeta)<block_start><def_stmt>__init__ self * num_classes:Optional[int]=<none> gan_mode:str="vanilla" gan_loss_config:Optional[Dict[str Any]]=<none> <block_start>super().__init__()<line_sep>self.num_classes=num_classes<line_sep>self.gan_mode=gan_mode<line_sep>self.gan_loss=GANLoss(gan_mode)<if_stmt>gan_loss_config<is><none><block_start>gan_loss_config={}<block_end>self.lambda_gp=gan_loss_config.get("lambda_gp" 10.0)<block_end>@property@abstractmethod<def_stmt>g_parameters self<arrow>List[nn.Parameter]<block_start><pass><block_end>@property@abstractmethod<def_stmt>d_parameters self<arrow>List[nn.Parameter]<block_start><pass><block_end>@abstractmethod<def_stmt>_g_losses self batch:tensor_dict_type forward_kwargs:Dict[str Any] <arrow>Tuple[tensor_dict_type tensor_dict_type Optional[Tensor]]# g_losses, sampled, labels
<block_start><pass><block_end>@abstractmethod<def_stmt>_d_losses self batch:tensor_dict_type sampled:tensor_dict_type labels:Optional[Tensor] <arrow>tensor_dict_type# d_losses
<block_start><pass><block_end># utilities
@property<def_stmt>can_reconstruct self<arrow>bool<block_start><return><false><block_end><def_stmt>forward self batch_idx:int batch:tensor_dict_type state:Optional[TrainerState]=<none> **kwargs:Any <arrow>tensor_dict_type<block_start>z=torch.randn(len(batch[INPUT_KEY]) self.latent_dim device=self.device)<line_sep><return>{PREDICTIONS_KEY:self.decode(z labels=batch[LABEL_KEY] **kwargs)}<block_end><def_stmt>summary_forward self batch_idx:int batch:tensor_dict_type<arrow><none><block_start>self._g_losses(batch {})<block_end><block_end><class_stmt>OneStageGANMixin(GANMixin metaclass=ABCMeta)<block_start><def_stmt>train_step self batch_idx:int batch:tensor_dict_type trainer:Any forward_kwargs:Dict[str Any] loss_kwargs:Dict[str Any] <arrow>StepOutputs<block_start>opt_g=trainer.optimizers["g_parameters"]<line_sep>opt_d=trainer.optimizers["d_parameters"]<line_sep># generator step
toggle_optimizer(self opt_g)<with_stmt>torch.cuda.amp.autocast(enabled=trainer.use_amp)<block_start>g_losses,sampled,labels=self._g_losses(batch forward_kwargs)<block_end>g_loss=g_losses.pop(LOSS_KEY)<line_sep>trainer.grad_scaler.scale(g_loss).backward()<if_stmt>trainer.clip_norm<g>0.0<block_start>trainer._clip_norm_step()<block_end>trainer.grad_scaler.step(opt_g)<line_sep>trainer.grad_scaler.update()<line_sep>opt_g.zero_grad()<line_sep># discriminator step
toggle_optimizer(self opt_d)<with_stmt>torch.no_grad()<block_start>sampled={k:v.detach().clone()<for>k,v sampled.items()}<block_end><with_stmt>torch.cuda.amp.autocast(enabled=trainer.use_amp)<block_start>d_losses=self._d_losses(batch sampled labels)<block_end>d_loss=d_losses.pop(LOSS_KEY)<line_sep>trainer.grad_scaler.scale(d_loss).backward()<if_stmt>trainer.clip_norm<g>0.0<block_start>trainer._clip_norm_step()<block_end>trainer.grad_scaler.step(opt_d)<line_sep>trainer.grad_scaler.update()<line_sep>opt_d.zero_grad()<line_sep># finalize
trainer._scheduler_step()<line_sep>forward_results={PREDICTIONS_KEY:sampled}<line_sep>loss_dict={"g":g_loss.item() "d":d_loss.item()}<line_sep>loss_dict.update({k:v.item()<for>k,v g_losses.items()})<line_sep>loss_dict.update({k:v.item()<for>k,v d_losses.items()})<line_sep><return>StepOutputs(forward_results loss_dict)<block_end><def_stmt>evaluate_step # type: ignore
self loader:CVLoader portion:float trainer:Any <arrow>MetricsOutputs<block_start>loss_items:Dict[str List[float]]={}<for_stmt>i,batch enumerate(loader)<block_start><if_stmt>i/len(loader)<ge>portion<block_start><break><block_end>batch=to_device(batch self.device)<line_sep>g_losses,sampled,labels=self._g_losses(batch {})<line_sep>d_losses=self._d_losses(batch sampled labels)<line_sep>g_loss=g_losses.pop(LOSS_KEY)<line_sep>d_loss=d_losses.pop(LOSS_KEY)<line_sep>loss_dict={"g":g_loss.item() "d":d_loss.item()}<line_sep>loss_dict.update({k:v.item()<for>k,v g_losses.items()})<line_sep>loss_dict.update({k:v.item()<for>k,v d_losses.items()})<for_stmt>k,v loss_dict.items()<block_start>loss_items.setdefault(k []).append(v)<block_end><block_end># gather
mean_loss_items={k:sum(v)/len(v)<for>k,v loss_items.items()}<line_sep>mean_loss_items[LOSS_KEY]=sum(mean_loss_items.values())<line_sep>score=trainer._weighted_loss_score(mean_loss_items)<line_sep><return>MetricsOutputs(score mean_loss_items)<block_end><block_end><class_stmt>VanillaGANMixin(OneStageGANMixin metaclass=ABCMeta)<block_start><def_stmt>__init__ self in_channels:int * discriminator:str="basic" discriminator_config:Optional[Dict[str Any]]=<none> num_classes:Optional[int]=<none> gan_mode:str="vanilla" gan_loss_config:Optional[Dict[str Any]]=<none> <block_start>super().__init__(num_classes=num_classes gan_mode=gan_mode gan_loss_config=gan_loss_config )<if_stmt>discriminator_config<is><none><block_start>discriminator_config={}<block_end>discriminator_config["in_channels"]=in_channels<line_sep>discriminator_config["num_classes"]=num_classes<line_sep>self.discriminator=DiscriminatorBase.make(discriminator config=discriminator_config )<block_end>@property<def_stmt>d_parameters self<arrow>List[nn.Parameter]<block_start><return>list(self.discriminator.parameters())<block_end><def_stmt>_g_losses self batch:tensor_dict_type forward_kwargs:Dict[str Any] <arrow>Tuple[tensor_dict_type tensor_dict_type Optional[Tensor]]<block_start>labels=batch.get(LABEL_KEY)<if_stmt>labels<is><not><none><block_start>labels=labels.view(-1)<block_end>sampled=self.sample(len(batch[INPUT_KEY]) labels=labels **forward_kwargs)<line_sep>pred_fake=self.discriminator(sampled)<line_sep>loss_g=self.gan_loss(pred_fake GANTarget(<true> labels))<line_sep><return>{LOSS_KEY:loss_g} {"sampled":sampled} labels<block_end><def_stmt>_d_losses self batch:tensor_dict_type sampled:tensor_dict_type labels:Optional[Tensor] <arrow>tensor_dict_type<block_start>net=batch[INPUT_KEY]<line_sep>sampled_tensor=sampled["sampled"]<line_sep>pred_real=self.discriminator(net)<line_sep>loss_d_real=self.gan_loss(pred_real GANTarget(<true> labels))<line_sep>pred_fake=self.discriminator(sampled_tensor)<line_sep>loss_d_fake=self.gan_loss(pred_fake GANTarget(<false> labels))<line_sep>d_loss=0.5<times>(loss_d_fake+loss_d_real)<line_sep>losses={"d_fake":loss_d_fake "d_real":loss_d_real}<if_stmt>self.gan_mode<eq>"wgangp"<block_start>eps=random.random()<line_sep>merged=eps<times>net+(1.0-eps)<times>sampled_tensor<with_stmt>mode_context(self.discriminator to_train=<none> use_grad=<true>)<block_start>pred_merged=self.discriminator(merged.requires_grad_(<true>)).output# type: ignore
loss_gp=self.gan_loss.loss(merged pred_merged)<block_end>d_loss=d_loss+self.lambda_gp<times>loss_gp<line_sep>losses["d_gp"]=loss_gp<block_end>losses[LOSS_KEY]=d_loss<line_sep><return>losses<block_end><block_end>__all__=["GANMixin" "OneStageGANMixin" "VanillaGANMixin" ]<line_sep> |
"""
Semantic adversarial Examples
"""<line_sep>__all__=['semantic' 'Semantic']<def_stmt>semantic x center:bool=<true> max_val:float=1.<block_start>"""
Semantic adversarial examples.
https://arxiv.org/abs/1703.06857
Note: data must either be centered (so that the negative image can be
made by simple negation) or must be in the interval of [-1, 1]
Arguments
---------
net : nn.Module, optional
The model on which to perform the attack.
center : bool
If true, assumes data has 0 mean so the negative image is just negation.
If false, assumes data is in interval [0, max_val]
max_val : float
Maximum value allowed in the input data.
"""<if_stmt>center<block_start><return>x<times>-1<block_end><return>max_val-x<block_end>################################################################
###### Class to initialize this attack
###### mainly for the use with torchvision.transforms
<class_stmt>Semantic()<block_start><def_stmt>__init__ self net=<none> **kwargs<block_start>self.kwargs=kwargs<block_end><def_stmt>__call__ self x<block_start><return>semantic(x **self.kwargs)<block_end><block_end> |
<import_stmt>os<import_from_stmt>shutil SameFileError copyfile<import_from_stmt>urllib.request Request urlopen<import_stmt>markdown<import_from_stmt>bs4 BeautifulSoup<as>BS<import_from_stmt>blogger_cli.converter.extractor extract_and_write_static extract_main_and_meta_from_md get_summary_limit extract_topic replace_ext <def_stmt>convert_and_copy_to_blog ctx md_file<block_start>md_file_path=os.path.abspath(os.path.expanduser(md_file))<line_sep>html_body,meta=convert(ctx md_file_path)<line_sep>html_filename_meta=write_html_and_md(ctx html_body md_file_path meta)<line_sep><return>html_filename_meta<block_end><def_stmt>convert ctx md_file_path<block_start><with_stmt>open(md_file_path "r" encoding="utf8")<as>rf<block_start>md_data=rf.read()<block_end>ctx.vlog(":: Extracting meta info")<line_sep>main_md,metadata=extract_main_and_meta_from_md(ctx md_data)<line_sep>extensions=["extra" "smarty"]<line_sep>html=markdown.markdown(main_md extensions=extensions output_format="html5")<line_sep>char_limit=get_summary_limit(ctx file_type="md")<line_sep>metadata["_summary_"]=main_md[:char_limit]<line_sep>ctx.vlog(":: Extracted summary")<line_sep><return>html metadata<block_end><def_stmt>write_html_and_md ctx html_body md_file_path meta<block_start>md_filename=os.path.basename(md_file_path)<line_sep>destination_dir=ctx.conversion["destination_dir"]<line_sep>topic=extract_topic(ctx meta)<line_sep>md_filename=os.path.join(topic md_filename)<line_sep>html_filename=replace_ext(md_filename ".html")<line_sep>html_file_path=os.path.join(destination_dir html_filename)<line_sep>new_md_file_path=os.path.join(destination_dir md_filename)<line_sep>new_blog_post_dir=os.path.dirname(html_file_path)<line_sep>ctx.vlog(":: New blog_posts_dir finalized" new_blog_post_dir)<if_stmt><not>os.path.exists(new_blog_post_dir)<block_start>os.mkdir(new_blog_post_dir)<block_end>extract_static=ctx.conversion["extract_static"]<if_stmt>extract_static<block_start>html_body=extract_and_write_static(ctx html_body new_blog_post_dir md_filename)<block_end><with_stmt>open(html_file_path "w" encoding="utf8")<as>wf<block_start>wf.write(html_body)<line_sep>ctx.log(":: Converted basic html to" html_file_path)<block_end># skip copying md file if converting to and from same folder.
<if_stmt>md_file_path<ne>new_md_file_path<block_start><try_stmt><block_start>copyfile(md_file_path new_md_file_path)<line_sep>ctx.log(":: Copied md file to" new_md_file_path)<block_end><except_stmt>Exception<as>E<block_start>os.remove(new_md_file_path)<line_sep>copyfile(md_file_path new_md_file_path)<line_sep>ctx.log(":: ERROR" E "Overwriting md file" new_md_file_path)<block_end><block_end><return>(html_filename meta)<block_end> |
<import_stmt>argparse<import_from_stmt>cpu CPU<import_from_stmt>graphics.graphics Window<import_from_stmt>nes_test NesTestLog<import_from_stmt>ram RAM<import_from_stmt>apu APU<import_from_stmt>ppu PPU<import_from_stmt>rom ROM<class_stmt>Nes<block_start><def_stmt>__init__ self rom_bytes testing<block_start>self.rom=ROM(rom_bytes)<line_sep># create ram
self.ram=RAM()<line_sep># create ppu and apu
self.ppu=PPU()<line_sep>self.apu=APU()<line_sep># create cpu
self.cpu=CPU(self.ram self.ppu self.apu)<line_sep># create ppu window
self.window=Window()<line_sep>self.testing=testing<line_sep>self.nes_test_log=<none><block_end><def_stmt>load self<block_start>self.cpu.start_up()<line_sep>self.cpu.load_rom(self.rom self.testing)<if_stmt>self.testing# load in the nes_test.log
<block_start><with_stmt>open('nes_test.log' 'r')<as>nes_test_file<block_start>self.nes_test_log=NesTestLog(nes_test_file.readlines())<block_end><block_end><block_end><def_stmt>run self# load in the nes_test.log
<block_start><while_stmt><true><block_start>self.update()<line_sep>self.draw()<block_end><block_end><def_stmt>update self<block_start>self.cpu.identify()<if_stmt>self.testing<block_start>self.nes_test_log.compare(self.cpu)<block_end>self.cpu.execute()<line_sep>self.window.update()<block_end><def_stmt>draw self<block_start>self.window.draw()<block_end><block_end><def_stmt>main # set up command line argument parser
<block_start>parser=argparse.ArgumentParser(description='NES Emulator.')<line_sep>parser.add_argument('rom_path' metavar='R' type=str help='path to nes rom')<line_sep>parser.add_argument('--test')<line_sep>args=parser.parse_args()<line_sep># load rom
<with_stmt>open(args.rom_path 'rb')<as>file<block_start>rom_bytes=file.read()<block_end>nes=Nes(rom_bytes args.test)<line_sep>nes.load()<line_sep>nes.run()<block_end><if_stmt>__name__<eq>'__main__'<block_start>main()<block_end> |
<import_stmt>json<import_stmt>pytest<import_from_stmt>riotwatcher.Handlers DictionaryDeserializer<line_sep>@pytest.mark.unit<class_stmt>TestDictionaryDeserializer<block_start><def_stmt>test_basic_json self<block_start>deserializer=DictionaryDeserializer()<line_sep>expected={"test":{"object":"type" "int":1} "bool":<true> "list":["string" "item"] }<line_sep>actual=deserializer.deserialize("" "" json.dumps(expected))<assert_stmt>expected<eq>actual<block_end><def_stmt>test_empty_string self<block_start>deserializer=DictionaryDeserializer()<line_sep>actual=deserializer.deserialize("" "" "")<assert_stmt>actual<eq>{}<block_end><block_end> |
<import_stmt>os<import_stmt>subprocess<import_from_stmt>distutils.version LooseVersion<import_from_stmt>glob glob<import_from_stmt>os.path join<import_stmt>setuptools.command.build_py<import_stmt>setuptools.command.develop<import_from_stmt>setuptools Extension find_packages setup<import_from_stmt>setuptools.command.build_ext build_ext<as>_build_ext<line_sep>version='0.1.19'<line_sep># Adapted from https://github.com/py_torch/pytorch
cwd=os.path.dirname(os.path.abspath(__file__))<if_stmt>os.getenv('PYSPTK_BUILD_VERSION')<block_start>version=os.getenv('PYSPTK_BUILD_VERSION')<block_end><else_stmt><block_start><try_stmt><block_start>sha=subprocess.check_output(['git' 'rev-parse' 'HEAD'] cwd=cwd).decode('ascii').strip()<line_sep>version<augadd>'+'+sha[:7]<block_end><except_stmt>subprocess.CalledProcessError<block_start><pass><block_end><except_stmt>IOError# FileNotFoundError for python 3
<block_start><pass><block_end><block_end><class_stmt>build_ext(_build_ext)# https://stackoverflow.com/questions/19919905/how-to-bootstrap-numpy-installation-in-setup-py
<block_start><def_stmt>finalize_options self<block_start>_build_ext.finalize_options(self)<line_sep># Prevent numpy from thinking it is still in its setup process:
__builtins__.__NUMPY_SETUP__=<false><import_stmt>numpy<line_sep>self.include_dirs.append(numpy.get_include())<block_end><block_end><class_stmt>build_py(setuptools.command.build_py.build_py)<block_start><def_stmt>run self<block_start>self.create_version_file()<line_sep>setuptools.command.build_py.build_py.run(self)<block_end>@staticmethod<def_stmt>create_version_file <block_start><global>version cwd<line_sep>print('-- Building version '+version)<line_sep>version_path=os.path.join(cwd 'pysptk' 'version.py')<with_stmt>open(version_path 'w')<as>f<block_start>f.write("__version__ = '{}'\n".format(version))<block_end><block_end><block_end><class_stmt>develop(setuptools.command.develop.develop)<block_start><def_stmt>run self<block_start>build_py.create_version_file()<line_sep>setuptools.command.develop.develop.run(self)<block_end><block_end>cmdclass={"build_py":build_py "develop":develop}<line_sep>min_cython_ver='0.28.0'<try_stmt><block_start><import_stmt>Cython<line_sep>ver=Cython.__version__<line_sep>_CYTHON_INSTALLED=ver<ge>LooseVersion(min_cython_ver)<block_end><except_stmt>ImportError<block_start>_CYTHON_INSTALLED=<false><block_end><try_stmt><block_start><if_stmt><not>_CYTHON_INSTALLED<block_start><raise>ImportError('No supported version of Cython installed.')<block_end><import_from_stmt>Cython.Distutils build_ext<line_sep>cython=<true><block_end><except_stmt>ImportError<block_start>cython=<false><block_end>include_dirs=[join(os.getcwd() "lib" "SPTK" "include")]<line_sep>cmdclass['build_ext']=build_ext<if_stmt>cython<block_start>ext='.pyx'<import_stmt>numpy<as>np<line_sep>include_dirs.insert(0 np.get_include())<block_end><else_stmt><block_start>ext='.c'<if_stmt><not>os.path.exists(join("pysptk" "_sptk"+ext))<block_start><raise>RuntimeError("Cython is required to generate C code.")<block_end><block_end># SPTK sources
src_top=join("lib" "SPTK")<line_sep>src_bin_top=join(src_top "bin")<line_sep>swipe_src=[join(src_bin_top "pitch" "swipe" "swipe.c") join(src_bin_top "pitch" "swipe" "vector.c") ]<line_sep>rapt_src=[join(src_bin_top "pitch" "snack" "jkGetF0.c") join(src_bin_top "pitch" "snack" "sigproc.c") ]<line_sep>sptklib_src=glob(join(src_top "lib" "*.c"))<line_sep>sptk_src=glob(join(src_bin_top "*" "_*.c"))<line_sep># collect all sources
sptk_all_src=sptk_src+sptklib_src+swipe_src+rapt_src<line_sep># Filter ignore list
ignore_bin_list=[join(src_bin_top "wavjoin") join(src_bin_top "wavsplit") join(src_bin_top "vc")]<for_stmt>ignore ignore_bin_list<block_start>sptk_all_src=list(filter(<lambda>s:<not>s.startswith(ignore) sptk_all_src))<block_end># define core cython module
ext_modules=[Extension(name="pysptk._sptk" sources=[join("pysptk" "_sptk"+ext)]+sptk_all_src include_dirs=include_dirs language="c" extra_compile_args=['-std=c99'])]<with_stmt>open("README.md" "r")<as>fh<block_start>LONG_DESC=fh.read()<block_end>setup(name='pysptk' version=version description='A python wrapper for Speech Signal Processing Toolkit (SPTK)' long_description=LONG_DESC long_description_content_type="text/markdown" author='<NAME>' author_email='<EMAIL>' url='https://github.com/r9y9/pysptk' license='MIT' packages=find_packages(exclude=["tests" "examples"]) package_data={'':['example_audio_data/*']} ext_modules=ext_modules cmdclass=cmdclass setup_requires=["numpy >= 1.8.0"] install_requires=['scipy' 'six' 'decorator' 'cython >= '+min_cython_ver ] tests_require=['nose' 'coverage'] extras_require={'docs':['numpydoc' 'sphinx_rtd_theme' 'seaborn'] 'test':['nose' 'coverage' "flake8"] } classifiers=["Operating System :: Microsoft :: Windows" "Operating System :: POSIX" "Operating System :: Unix" "Operating System :: MacOS" "Programming Language :: Cython" "Programming Language :: Python" "Programming Language :: Python :: 3" "Programming Language :: Python :: 3.4" "Programming Language :: Python :: 3.5" "Programming Language :: Python :: 3.6" "Programming Language :: Python :: 3.7" "Programming Language :: Python :: 3.8" "Programming Language :: Python :: 3.9" "License :: OSI Approved :: MIT License" "Topic :: Scientific/Engineering" "Topic :: Software Development" "Intended Audience :: Science/Research" "Intended Audience :: Developers" ] keywords=["SPTK"])<line_sep> |
<import_from_stmt>collections Counter<import_stmt>pytest<import_from_stmt>finetuner.tuner.dataset.samplers ClassSampler<line_sep>@pytest.mark.parametrize("batch_size" [-1 0])<def_stmt>test_wrong_batch_size batch_size:int<block_start><with_stmt>pytest.raises(ValueError match="batch_size")<block_start>ClassSampler([0 1] batch_size 1)<block_end><block_end>@pytest.mark.parametrize("num_items_per_class" [-1 0])<def_stmt>test_wrong_num_items_per_class num_items_per_class:int<block_start><with_stmt>pytest.raises(ValueError match="num_items_per_class")<block_start>ClassSampler([0 1] 1 num_items_per_class)<block_end><block_end><def_stmt>test_normal_case <block_start>labels=[1 1 2 2 3 3 4 4]<line_sep>sampler=ClassSampler(labels 4 2)<assert_stmt>len(sampler)<eq>2<line_sep>all_inds=[]<for_stmt>i,batch enumerate(sampler)<block_start>all_inds<augadd>batch<assert_stmt>len(batch)<eq>4<block_end><assert_stmt>i+1<eq>2<assert_stmt>set(all_inds)<eq>set(range(8))<block_end><def_stmt>test_classes_in_batch <block_start>labels=[]<for_stmt>i range(50)<block_start>labels<augadd>[i]<times>20<block_end><for_stmt>i range(50 100)<block_start>labels<augadd>[i]<times>19# Mini repeating test as well
<block_end>class_to_label={}<for_stmt>idx,label enumerate(labels)<block_start>class_to_label[idx]=label<block_end>sampler=ClassSampler(labels 20 5)<assert_stmt>len(sampler)<ge>98<for_stmt>i,batch enumerate(sampler)<block_start>c=Counter([class_to_label[element]<for>element batch])<assert_stmt>len(c)<eq>4<for_stmt>val c.values()<block_start><assert_stmt>val<eq>5<block_end><block_end><assert_stmt>i+1<ge>98<block_end># Best we can hope for
<def_stmt>test_almost_full_coverage <block_start>"""Check that almost all items get covered in one epoch"""<line_sep>labels=[]<for_stmt>i range(100)<block_start>labels<augadd>[i]<times>20<block_end>sampler=ClassSampler(labels 20 5)<assert_stmt>len(sampler)<ge>98<line_sep>c=Counter()<for_stmt>i,batch enumerate(sampler)<block_start>c.update(batch)<block_end><assert_stmt>i+1<ge>98# Best we can hope for
<assert_stmt>set(c).issubset(range(100<times>20))<assert_stmt>c.most_common(1)[0][1]<eq>1<block_end><def_stmt>test_label_repetition1 <block_start>"""Test that elements from class get repeated to fill the batch"""<line_sep>labels=[1 1 1 2 2]<line_sep>sampler=ClassSampler(labels 6 3)<assert_stmt>len(sampler)<eq>1<line_sep>all_inds=[]<for_stmt>batch sampler<block_start>all_inds<augadd>batch<assert_stmt>len(batch)<eq>6<block_end>c=Counter(all_inds)<assert_stmt>c[3]<ge>1<assert_stmt>c[4]<ge>1<assert_stmt>c[3]+c[4]<eq>3<block_end>@pytest.mark.parametrize('num_items_per_class' [4 2])<def_stmt>test_label_repetition2 num_items_per_class<block_start>labels=[1 1 1 1 2 2 2]<line_sep>sampler=ClassSampler(labels 4 num_items_per_class)<assert_stmt>len(sampler)<eq>2<line_sep>all_inds=[]<for_stmt>i,batch enumerate(sampler)<block_start>all_inds<augadd>batch<assert_stmt>len(batch)<eq>4<block_end><assert_stmt>i+1<eq>2<line_sep>c=Counter(all_inds)<assert_stmt>c[4]<ge>1<assert_stmt>c[5]<ge>1<assert_stmt>c[6]<ge>1<assert_stmt>c[6]+c[5]+c[4]<eq>4<block_end><def_stmt>test_cutoff1 <block_start>"""Cutoff due to last batch being < batch_size"""<line_sep>labels=[1 1 1 1 2 2]<line_sep>sampler=ClassSampler(labels 4 2)<assert_stmt>len(sampler)<eq>1<line_sep>all_inds=[]<for_stmt>i,batch enumerate(sampler)<block_start>all_inds<augadd>batch<block_end><assert_stmt>i+1<eq>1<line_sep># Make sure the first class got cut off
c=Counter(all_inds)<assert_stmt>c[0]+c[1]+c[2]+c[3]<eq>2<block_end><def_stmt>test_cutoff2 <block_start>"""Cutoff due to last batch only containing one class"""<line_sep>labels=[1 1 1 1 1 1 1 1 2 2 2 2]<line_sep>class_to_label={}<for_stmt>idx,label enumerate(labels)<block_start>class_to_label[idx]=label<block_end>sampler=ClassSampler(labels 4 2)<assert_stmt>len(sampler)<eq>2<line_sep>all_inds=[]<for_stmt>i,batch enumerate(sampler)<block_start>all_inds<augadd>batch<block_end><assert_stmt>i+1<eq>2<line_sep># Make sure that most common items are cut off
c=Counter([class_to_label[label]<for>label all_inds])<assert_stmt>c[1]<eq>4<assert_stmt>c[2]<eq>4<block_end> |
# pylint: disable=invalid-name
"""
The tool to check the availability or syntax of domain, IP or URL.
::
โโโโโโโ โโโ โโโโโโโโโโโโโโ โโโโโโโ โโโ โโโโโโโโโโโโโโโโโโโโโโ โโโ โโโโโโโโ
โโโโโโโโโโโโ โโโโโโโโโโโโโโโ โโโโโโโโ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โโโโโโโโ
โโโโโโโโ โโโโโโโ โโโโโโ โโโ โโโโโโโโโ โโโโโโ โโโโโโ โโโโโโโโโโโ โโโโโโ
โโโโโโโ โโโโโ โโโโโโ โโโ โโโโโโโโโโโโโโโโ โโโโโโ โโโโโโโโโโโ โโโโโโ
โโโ โโโ โโโ โโโโโโโโโโโโ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โโโ โโโ โโโ โโโโโโโ โโโ โโโโโ โโโโโโโโโโโโโโโโโโโโโโ โโโโโโโโโโโโโโโโ
Provides some global utilities.
Author:
<NAME>, @funilrys, contactTATAfunilrysTODTODcom
Special thanks:
https://pyfunceble.github.io/#/special-thanks
Contributors:
https://pyfunceble.github.io/#/contributors
Project link:
https://github.com/funilrys/PyFunceble
Project documentation:
https://pyfunceble.readthedocs.io/en/dev/
Project homepage:
https://pyfunceble.github.io/
License:
::
Copyright 2017, 2018, 2019, 2020, 2021 <NAME>
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""<import_stmt>contextlib<import_stmt>cProfile<import_stmt>io<import_stmt>pstats<line_sep>@contextlib.contextmanager<def_stmt>profile_it * sort_stats:str="cumulative" show_callers:bool=<false><block_start>"""
Provides a context manager which will activates the profiling of our
source code.
:param sort_starts:
The column to sort.
:param show_callers:
Authorizes the output of the callers.
"""<line_sep>profiler=cProfile.Profile()<line_sep>profiler.enable()<line_sep><yield><line_sep>profiler.disable()<line_sep>our_stream=io.StringIO()<line_sep>profiler_starts=pstats.Stats(profiler stream=our_stream)<if_stmt>sort_stats<block_start>profiler_starts.sort_stats(sort_stats)<block_end>profiler_starts.print_stats()<if_stmt>show_callers<block_start>profiler_starts.print_callees()<block_end>print(our_stream.getvalue())<block_end> |
<import_stmt>os<line_sep>GIT_SHA=os.environ.get("GIT_SHA" "development")<line_sep>GITHUB_TOKEN=os.environ.get("GITHUB_TOKEN")<line_sep># How long to wait for synchronous requests before timing out
TIMEOUT_PERIOD=int(os.environ.get("TIMEOUT_PERIOD" 5))<line_sep> |
<import_from_future_stmt> absolute_import<import_stmt>argparse<import_stmt>logging<import_stmt>mimetypes<import_stmt>apache_beam<as>beam<import_from_stmt>apache_beam.options.pipeline_options PipelineOptions SetupOptions<import_from_stmt>apache_beam.metrics.metric Metrics<import_from_stmt>sciencebeam_utils.utils.collection extend_dict <import_from_stmt>sciencebeam_utils.beam_utils.utils TransformAndCount TransformAndLog MapOrLog PreventFusion <import_from_stmt>sciencebeam_utils.beam_utils.io read_all_from_path save_file_content <import_from_stmt>sciencebeam_utils.beam_utils.main add_cloud_args process_cloud_args <import_from_stmt>sciencebeam.config.app_config get_app_config<import_from_stmt>sciencebeam.utils.logging configure_logging<import_from_stmt>sciencebeam.pipelines get_pipeline_for_configuration_and_args add_pipeline_args <import_from_stmt>sciencebeam.pipeline_runners.pipeline_runner_utils add_batch_args process_batch_args encode_if_text_type get_output_file_for_source_file_fn get_remaining_file_list_for_args DataProps <line_sep>LOGGER=logging.getLogger(__name__)<def_stmt>get_logger <block_start><return>logging.getLogger(__name__)<block_end><class_stmt>MetricCounters<block_start>FILES='files'<block_end><def_stmt>ReadFileContent <block_start><return>"ReadFileContent"<rshift>TransformAndCount(beam.Map(<lambda>file_url:{DataProps.SOURCE_FILENAME:file_url DataProps.FILENAME:file_url DataProps.CONTENT:read_all_from_path(file_url)}) MetricCounters.FILES)<block_end><def_stmt>get_step_error_counter step<block_start><return>'error_%s'%step<block_end><def_stmt>get_step_ignored_counter step<block_start><return>'ignored_%s'%step<block_end><def_stmt>get_step_processed_counter step<block_start><return>'processed_%s'%step<block_end><def_stmt>execute_or_skip_step step<block_start>supported_types=step.get_supported_types()<line_sep>processed_counter=Metrics.counter('PipelineStep' get_step_processed_counter(step))<line_sep>ignored_counter=Metrics.counter('PipelineStep' get_step_ignored_counter(step))<def_stmt>wrapper x<block_start>data_type=x['type']<if_stmt>data_type<in>supported_types<block_start>get_logger().debug('excuting step %s: %s (%s)' step x.keys() data_type)<line_sep>result=extend_dict(x step(x))<line_sep>get_logger().debug('result of step %s: %s (%s)' step result.keys() result.get('type'))<line_sep>processed_counter.inc()<line_sep><return>result<block_end>get_logger().debug('skipping step %s, %s not in supported types (%s)' step data_type supported_types)<line_sep>ignored_counter.inc()<line_sep><return>x<block_end><return>wrapper<block_end><def_stmt>get_step_transform step<block_start>step_name=str(step)<line_sep><return>step_name<rshift>MapOrLog(execute_or_skip_step(step) log_fn=<lambda>e v:(get_logger().warning('caught exception (ignoring item): %s, source file: %s, step: %s' e v[DataProps.SOURCE_FILENAME] step_name exc_info=e)) error_count=get_step_error_counter(step))<block_end><def_stmt>configure_pipeline p opt pipeline config<block_start>get_default_output_file_for_source_file=get_output_file_for_source_file_fn(opt)<line_sep>file_list=get_remaining_file_list_for_args(opt)<line_sep>LOGGER.debug('file_list: %s' file_list)<if_stmt><not>file_list<block_start>LOGGER.info('no files to process')<line_sep><return><block_end>steps=pipeline.get_steps(config opt)<line_sep>LOGGER.info('steps: %s' steps)<line_sep>input_urls=(p|beam.Create(file_list)|PreventFusion())<line_sep>input_data=(input_urls|ReadFileContent()|"Determine Type"<rshift>beam.Map(<lambda>d:extend_dict(d {DataProps.TYPE:mimetypes.guess_type(d[DataProps.SOURCE_FILENAME])[0]})))<line_sep>result=input_data<for_stmt>step steps<block_start>LOGGER.debug('step: %s' step)<line_sep>result<augor>get_step_transform(step)<block_end>_=(# noqa: F841
result|"WriteOutput"<rshift>TransformAndLog(beam.Map(<lambda>v:save_file_content(get_default_output_file_for_source_file(v[DataProps.SOURCE_FILENAME]) encode_if_text_type(v[DataProps.CONTENT]))) log_fn=<lambda>x:get_logger().info('saved output to: %s' x)))<block_end><def_stmt>parse_args pipeline config argv=<none><block_start>parser=argparse.ArgumentParser()<line_sep>add_pipeline_args(parser)<line_sep>add_batch_args(parser)<line_sep>add_cloud_args(parser)<line_sep>pipeline.add_arguments(parser config argv)<line_sep>args=parser.parse_args(argv)<if_stmt>args.debug<block_start>logging.getLogger().setLevel('DEBUG')<block_end>process_batch_args(args)<line_sep>process_cloud_args(args args.output_path name='sciencebeam-convert')<line_sep>get_logger().info('args: %s' args)<line_sep><return>args<block_end><def_stmt>run args config pipeline save_main_session# We use the save_main_session option because one or more DoFn's in this
# workflow rely on global context (e.g., a module imported at module level).
<block_start>pipeline_options=PipelineOptions.from_dictionary(vars(args))<line_sep>pipeline_options.view_as(SetupOptions).save_main_session=save_main_session<with_stmt>beam.Pipeline(args.runner options=pipeline_options)<as>p<block_start>configure_pipeline(p args pipeline config)<line_sep># Execute the pipeline and wait until it is completed.
<block_end><block_end><def_stmt>main argv=<none> save_main_session=<true><block_start>config=get_app_config()<line_sep>pipeline=get_pipeline_for_configuration_and_args(config argv=argv)<line_sep>args=parse_args(pipeline config argv)<line_sep>run(args config=config pipeline=pipeline save_main_session=save_main_session)<block_end><if_stmt>__name__<eq>'__main__'<block_start>configure_logging()<line_sep>main()<block_end> |
<import_from_stmt>tempfile mkstemp<import_stmt>cProfile<import_stmt>pstats<import_from_stmt>artemis.general.display surround_with_header<import_stmt>os<def_stmt>what_are_we_waiting_for command sort_by='time' max_len=20 print_here=<true><block_start>"""
An easy way to show what is taking all the time when you run something.
Taken from docs: https://docs.python.org/2/library/profile.html#module-cProfile
:param command: A string python command
:param sort_by: How to sort results. {'time', 'cumtime', 'calls', ...}.
See https://docs.python.org/2/library/profile.html#pstats.Stats.sort_stats
:param max_len: Maximum number of things to show in profile.
:param print_here: Print the results here (instead of returning them).
:return: A pstats.Stats object containing the profiling results.
"""<line_sep>_,filepath=mkstemp()<try_stmt><block_start>cProfile.run(command filepath)<block_end><finally_stmt><block_start>p=pstats.Stats(filepath)<line_sep>os.remove(filepath)<line_sep>p.strip_dirs()<line_sep>p.sort_stats(sort_by)<if_stmt>print_here<block_start>print(surround_with_header('Profile for "{}"'.format(command) width=100 char='='))<line_sep>p.print_stats(max_len)<line_sep>print('='<times>100)<block_end><return>p<block_end><block_end> |
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Global context for knowledge bank operations."""<import_stmt>threading<import_from_stmt>typing Text<import_from_stmt>research.carls dynamic_embedding_config_pb2<as>de_config_pb2<line_sep># A map from variable name to DynamicEmbeddingConfig.
_knowledge_bank_collections={}<line_sep>_lock=threading.Lock()<def_stmt>add_to_collection name:Text config:de_config_pb2.DynamicEmbeddingConfig<block_start>"""Adds given (name, config) pair to global collectionss.
Args:
name: A string denoting the variable name.
config: An instance of DynamicEmbeddingConfig.
Raises:
TypeError: Invalid input.
ValueError: Name is empty, or a different config is added for an existing
variable.
"""<if_stmt><not>name<block_start><raise>ValueError("Empty name.")<block_end><if_stmt><not>isinstance(config de_config_pb2.DynamicEmbeddingConfig)<block_start><raise>TypeError("Config is not an instance of DynamicEmbeddingConfig.")<block_end><if_stmt>name<in>_knowledge_bank_collections.keys()<block_start>existing_config=_knowledge_bank_collections[name]<if_stmt>config.SerializeToString()<ne>existing_config.SerializeToString()<block_start><raise>ValueError("Adding a new config for the same var name is not allowed, existing:"<concat>" %r, new: %r."%(existing_config config))<block_end><block_end><with_stmt>_lock<block_start>_knowledge_bank_collections[name]=de_config_pb2.DynamicEmbeddingConfig()<line_sep>_knowledge_bank_collections[name].CopyFrom(config)<block_end><block_end><def_stmt>get_all_collection <block_start>"""Returns a list of all (name, config) pairs."""<with_stmt>_lock<block_start><return>[(key value)<for>key,value _knowledge_bank_collections.items()]<block_end><block_end><def_stmt>clear_all_collection <block_start>"""Clears existing all (name, config) pairs."""<with_stmt>_lock<block_start>_knowledge_bank_collections.clear()<block_end><block_end> |
# MLReef-2020: Color modifications for data augmentation.
<import_from_stmt>PIL Image ImageEnhance<import_stmt>argparse<import_stmt>sys<import_stmt>os<import_from_stmt>pathlib Path<class_stmt>ColorModifier<block_start><def_stmt>__init__ self params<block_start>self.input_dir=params['input_path']<line_sep>self.output_dir=params['output_path']<line_sep>self.brightness=float(params['brightness'])<line_sep>self.contrast=float(params['contrast'])<line_sep>self.saturation=float(params['saturation'])<line_sep># create folder if does not exists
<if_stmt><not>os.path.exists(self.output_dir)<block_start>os.makedirs(self.output_dir)<block_end># Please add here the extensions that you need
self.ext=['.jpeg' '.png' '.jpg']<block_end><def_stmt>__execute__ self# Walk the directories to find images
<block_start><for_stmt>root,dirs,files os.walk(self.input_dir)<block_start><for_stmt>file files<block_start><if_stmt>file.endswith(tuple(self.ext))<block_start>image=os.path.join(root file)<line_sep>fullpath,extension=os.path.splitext(image)<line_sep>im=Image.open(image)<line_sep>enhancer=ImageEnhance.Brightness(im)<line_sep>enhanced_im=enhancer.enhance(self.brightness)<line_sep>enhancer=ImageEnhance.Contrast(enhanced_im)<line_sep>enhanced_im=enhancer.enhance(self.contrast)<line_sep>enhancer=ImageEnhance.Color(enhanced_im)<line_sep>enhanced_im=enhancer.enhance(self.saturation)<line_sep>relative_p=os.path.relpath(fullpath self.input_dir)<line_sep>folders=os.path.split(relative_p)[0]<line_sep>Path(os.path.join(self.output_dir folders)).mkdir(parents=<true> exist_ok=<true>)<line_sep>enhanced_im.save(os.path.join(self.output_dir '{}_cm{}'.format(relative_p extension)))<block_end><block_end><block_end>print("Color modifier done")<line_sep><return>1<block_end><block_end><def_stmt>process_arguments args<block_start>parser=argparse.ArgumentParser(description='Pipeline: Color modifier')<line_sep>parser.add_argument('--input-path' action='store' default='.' help='path to directory of images or image file')<line_sep>parser.add_argument('--output-path' action='store' default='.' help='output directory to save images')<line_sep>parser.add_argument('--brightness' action='store' default=0.5 help='Brightness value')<line_sep>parser.add_argument('--contrast' action='store' default=0.5 help='contrast value')<line_sep>parser.add_argument('--saturation' action='store' default=2.0 help='saturation value')<line_sep>params=vars(parser.parse_args(args))<if_stmt>(params['input_path']<or>params['output_path'])<is><none><block_start>parser.error("Paths are required. You did not specify input path or output path.")<block_end><return>params<block_end><if_stmt>__name__<eq>"__main__"<block_start>print("Beginning execution of im_color_modifier.py script ......... \n")<line_sep>params=process_arguments(sys.argv[1:])<line_sep>op=ColorModifier(params)<line_sep>print("input path:" op.input_dir)<line_sep>print("output path:" op.output_dir)<line_sep>print("Brightness" op.brightness)<line_sep>print("Contrast" op.contrast)<line_sep>print("Saturation" op.saturation)<line_sep>op.__execute__()<block_end> |
<import_stmt>requests<import_stmt>zipfile<import_stmt>os<import_stmt>errno<import_stmt>nltk<import_from_stmt>nltk.tokenize sent_tokenize<line_sep>ALICE_URL='https://ota.bodleian.ox.ac.uk/repository/xmlui/bitstream/handle/20.500.12024/1476/alice28-1476.txt'<line_sep>WIZARD_URL='https://ota.bodleian.ox.ac.uk/repository/xmlui/bitstream/handle/20.500.12024/1740/wizoz10-1740.txt'<def_stmt>download_text url localfolder='texts'<block_start>localfile=os.path.split(url)[-1]<try_stmt><block_start>os.mkdir(f'{localfolder}')<block_end><except_stmt>OSError<as>e<block_start><if_stmt>e.errno<ne>errno.EEXIST<block_start><raise><block_end><block_end><try_stmt><block_start>r=requests.get(url allow_redirects=<true>)<line_sep>open(os.path.join(localfolder localfile) 'wb').write(r.content)<block_end><except_stmt>Exception<as>e<block_start>print(f'Error downloading file: {str(e)}')<block_end><block_end><def_stmt>sentence_tokenize source quote_char='\\' sep_char=',' include_header=<true> include_source=<true> extensions=('txt') **kwargs<block_start>nltk.download('punkt')<line_sep># If source is a folder, goes through all files inside it
# that match the desired extensions ('txt' by default)
<if_stmt>os.path.isdir(source)<block_start>filenames=[f<for>f os.listdir(source)<if>os.path.isfile(os.path.join(source f))<and>os.path.splitext(f)[1][1:]<in>extensions]<block_end><elif_stmt>isinstance(source str)<block_start>filenames=[source]<block_end># If there is a configuration file, builds a dictionary with
# the corresponding start and end lines of each text file
config_file=os.path.join(source 'lines.cfg')<line_sep>config={}<if_stmt>os.path.exists(config_file)<block_start><with_stmt>open(config_file 'r')<as>f<block_start>rows=f.readlines()<block_end><for_stmt>r rows[1:]<block_start>fname,start,end=r.strip().split(',')<line_sep>config.update({fname:(int(start) int(end))})<block_end><block_end>new_fnames=[]<line_sep># For each file of text
<for_stmt>fname filenames# If there's a start and end line for that file, use it
<block_start><try_stmt><block_start>start,end=config[fname]<block_end><except_stmt>KeyError<block_start>start=<none><line_sep>end=<none><block_end># Opens the file, slices the configures lines (if any)
# cleans line breaks and uses the sentence tokenizer
<with_stmt>open(os.path.join(source fname) 'r')<as>f<block_start>contents=(''.join(f.readlines()[slice(start end <none>)]).replace('\n' ' ').replace('\r' ''))<block_end>corpus=sent_tokenize(contents **kwargs)<line_sep># Builds a CSV file containing tokenized sentences
base=os.path.splitext(fname)[0]<line_sep>new_fname=f'{base}.sent.csv'<line_sep>new_fname=os.path.join(source new_fname)<with_stmt>open(new_fname 'w')<as>f# Header of the file
<block_start><if_stmt>include_header<block_start><if_stmt>include_source<block_start>f.write('sentence,source\n')<block_end><else_stmt><block_start>f.write('sentence\n')<block_end><block_end># Writes one line for each sentence
<for_stmt>sentence corpus<block_start><if_stmt>include_source<block_start>f.write(f'{quote_char}{sentence}{quote_char}{sep_char}{fname}\n')<block_end><else_stmt><block_start>f.write(f'{quote_char}{sentence}{quote_char}\n')<block_end><block_end><block_end>new_fnames.append(new_fname)<block_end># Returns list of the newly generated CSV files
<return>sorted(new_fnames)<block_end> |
<import_from_stmt>leonardo.module.web.models.page *<import_from_stmt>leonardo.module.web.models.widget *<import_from_stmt>leonardo.module.web.widget.icon.models IconWidget<import_from_stmt>leonardo.module.web.widget.application.models ApplicationWidget<import_from_stmt>leonardo.module.web.widget.markuptext.models MarkupTextWidget<import_from_stmt>leonardo.module.web.widget.feedreader.models FeedReaderWidget<import_from_stmt>leonardo.module.web.widget.pagetitle.models PageTitleWidget<import_from_stmt>leonardo.module.web.widget.table.models TableWidget<import_from_stmt>leonardo.module.web.widget.siteheading.models SiteHeadingWidget<import_from_stmt>leonardo.module.web.widget.htmltext.models HtmlTextWidget<line_sep> |
# tests/test_provider_MissionCriticalCloud_cosmic.py
# Automatically generated by tools/makecode.py (24-Sep-2021 15:14:40 UTC)
<def_stmt>test_provider_import <block_start><import_stmt>terrascript.provider.MissionCriticalCloud.cosmic<block_end><def_stmt>test_resource_import <block_start><import_from_stmt>terrascript.resource.MissionCriticalCloud.cosmic cosmic_affinity_group<import_from_stmt>terrascript.resource.MissionCriticalCloud.cosmic cosmic_disk<import_from_stmt>terrascript.resource.MissionCriticalCloud.cosmic cosmic_instance<import_from_stmt>terrascript.resource.MissionCriticalCloud.cosmic cosmic_ipaddress<import_from_stmt>terrascript.resource.MissionCriticalCloud.cosmic cosmic_loadbalancer_rule <import_from_stmt>terrascript.resource.MissionCriticalCloud.cosmic cosmic_network<import_from_stmt>terrascript.resource.MissionCriticalCloud.cosmic cosmic_network_acl<import_from_stmt>terrascript.resource.MissionCriticalCloud.cosmic cosmic_network_acl_rule<import_from_stmt>terrascript.resource.MissionCriticalCloud.cosmic cosmic_nic<import_from_stmt>terrascript.resource.MissionCriticalCloud.cosmic cosmic_port_forward<import_from_stmt>terrascript.resource.MissionCriticalCloud.cosmic cosmic_private_gateway<import_from_stmt>terrascript.resource.MissionCriticalCloud.cosmic cosmic_secondary_ipaddress <import_from_stmt>terrascript.resource.MissionCriticalCloud.cosmic cosmic_ssh_keypair<import_from_stmt>terrascript.resource.MissionCriticalCloud.cosmic cosmic_static_nat<import_from_stmt>terrascript.resource.MissionCriticalCloud.cosmic cosmic_static_route<import_from_stmt>terrascript.resource.MissionCriticalCloud.cosmic cosmic_template<import_from_stmt>terrascript.resource.MissionCriticalCloud.cosmic cosmic_vpc<import_from_stmt>terrascript.resource.MissionCriticalCloud.cosmic cosmic_vpn_connection<import_from_stmt>terrascript.resource.MissionCriticalCloud.cosmic cosmic_vpn_customer_gateway <import_from_stmt>terrascript.resource.MissionCriticalCloud.cosmic cosmic_vpn_gateway<block_end><def_stmt>test_datasource_import <block_start><import_from_stmt>terrascript.data.MissionCriticalCloud.cosmic cosmic_network_acl<block_end># TODO: Shortcut imports without namespace for official and supported providers.
# TODO: This has to be moved into a required_providers block.
# def test_version_source():
#
# import terrascript.provider.MissionCriticalCloud.cosmic
#
# t = terrascript.provider.MissionCriticalCloud.cosmic.cosmic()
# s = str(t)
#
# assert 'https://github.com/MissionCriticalCloud/terraform-provider-cosmic' in s
# assert '0.5.0' in s
|
expected_output={"max_num_of_service_instances":32768 "service_instance":{2051:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2052:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2053:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2054:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2055:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2056:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2057:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2058:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2059:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2060:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2061:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2062:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2063:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2064:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2065:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2066:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2067:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2068:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2069:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2070:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2071:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2072:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2073:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2074:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2075:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2076:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2077:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2078:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2079:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2080:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2081:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2082:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2083:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2084:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2085:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2086:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2087:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2088:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2089:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2090:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } 2091:{"pkts_out":0 "pkts_in":0 "interface":"GigabitEthernet0/0/5" "bytes_in":0 "bytes_out":0 } } }<line_sep> |
<import_from_stmt>src.classifier Classifier<line_sep>pipeline=Classifier()<def_stmt>test_response requests response<block_start><assert_stmt>response<eq>pipeline(requests)<block_end> |
<import_from_stmt>..webdriver_server OperaDriverServer<import_from_stmt>.base WdspecExecutor WdspecProtocol<class_stmt>OperaDriverProtocol(WdspecProtocol)<block_start>server_cls=OperaDriverServer<block_end><class_stmt>OperaDriverWdspecExecutor(WdspecExecutor)<block_start>protocol_cls=OperaDriverProtocol<block_end> |
<import_stmt>renderdoc<as>rd<import_stmt>rdtest<class_stmt>D3D11_Untyped_Backbuffer_Descriptor(rdtest.TestCase)<block_start>demos_test_name='D3D11_Untyped_Backbuffer_Descriptor'<def_stmt>check_capture self# find the first action
<block_start>action=self.find_action("Draw")<line_sep>self.controller.SetFrameEvent(action.eventId <false>)<line_sep>pipe:rd.PipeState=self.controller.GetPipelineState()<line_sep>self.check_pixel_value(pipe.GetOutputTargets()[0].resourceId 0.25 0.5 [1.0 1.0 1.0 1.0])<line_sep>rdtest.log.success("Picked value for first action is as expected")<line_sep># find the second action
action=self.find_action("Draw" action.eventId+1)<line_sep>self.controller.SetFrameEvent(action.eventId <false>)<line_sep>pipe:rd.PipeState=self.controller.GetPipelineState()<line_sep>self.check_pixel_value(pipe.GetOutputTargets()[0].resourceId 0.75 0.5 [1.0 1.0 1.0 1.0])<line_sep>rdtest.log.success("Picked value for second action is as expected")<block_end><block_end> |
# load .t7 file and save as .pkl data
<import_stmt>torchfile<import_stmt>cv2<import_stmt>numpy<as>np<import_stmt>scipy.io<as>sio<import_stmt>pickle<import_stmt>time<line_sep>data_path='./data/test_PC/'<line_sep># panoContext
#img_tr = torchfile.load('./data/panoContext_img_train.t7')
#print(img_tr.shape)
#lne_tr = torchfile.load('./data/panoContext_line_train.t7')
#print(lne_tr.shape)
#edg_tr = torchfile.load('./data/panoContext_edge_train.t7')
#print(edg_tr.shape)
#junc_tr = torchfile.load('./data/panoContext_cor_train.t7')
#print(junc_tr.shape)
#print('done')
#img_tr = torchfile.load('./data/panoContext_img_val.t7')
#print(img_tr.shape)
#lne_tr = torchfile.load('./data/panoContext_line_val.t7')
#print(lne_tr.shape)
#edg_tr = torchfile.load('./data/panoContext_edge_val.t7')
#print(edg_tr.shape)
#junc_tr = torchfile.load('./data/panoContext_cor_val.t7')
#print(junc_tr.shape)
#print('done')
img_tr=torchfile.load('./data/panoContext_img_test.t7')<line_sep>print(img_tr.shape)<line_sep>lne_tr=torchfile.load('./data/panoContext_line_test.t7')<line_sep>print(lne_tr.shape)<line_sep>edg_tr=torchfile.load('./data/panoContext_edge_test.t7')<line_sep>print(edg_tr.shape)<line_sep>junc_tr=torchfile.load('./data/panoContext_cor_test.t7')<line_sep>print(junc_tr.shape)<line_sep>print('done')<line_sep># stanford
#img_tr = torchfile.load('./data/stanford2d-3d_img_area_5.t7')
#print(img_tr.shape)
#lne_tr = torchfile.load('./data/stanford2d-3d_line_area_5.t7')
#print(lne_tr.shape)
#edg_tr = torchfile.load('./data/stanford2d-3d_edge_area_5.t7')
#print(edg_tr.shape)
#junc_tr = torchfile.load('./data/stanford2d-3d_cor_area_5.t7')
#print(junc_tr.shape)
#print('done')
gt_txt_path='./data/panoContext_testmap.txt'<line_sep>gt_path='./data/layoutnet_dataset/test/label_cor/'<line_sep># Load data
namelist=[]<line_sep>id_num=[]<with_stmt>open(gt_txt_path 'r')<as>f<block_start><while_stmt>(<true>)<block_start>line=f.readline().strip()<if_stmt><not>line<block_start><break><block_end>id_num0=line.split()<line_sep>id_num0=int(id_num0[1])<line_sep>id_num.append(id_num0)<line_sep>namelist.append(line)<block_end><block_end>id_num=np.array(id_num)<line_sep>cnt=0<for_stmt>num range(img_tr.shape[0])<block_start>print(num)<line_sep>image=img_tr[num]<line_sep>image=np.transpose(image (1 2 0))#*255.0
line=lne_tr[num]<line_sep>line=np.transpose(line (1 2 0))<line_sep>edge=edg_tr[num]<line_sep>edge=np.transpose(edge (1 2 0))<line_sep>junc=junc_tr[num]<line_sep>junc=np.transpose(junc (1 2 0))<line_sep># corner gt
idn=np.where(id_num<eq>num)<line_sep>idn=idn[0][0]<line_sep>filename=namelist[idn]<line_sep>filename=filename.split()<line_sep>filename=gt_path+filename[0][:-4]+'.txt'#'.mat'
cnt<augadd>1<line_sep>cor=np.loadtxt(filename)<line_sep>cor_sum=0<for_stmt>cor_num range(cor.shape[0])<block_start>cor_sum<augadd>junc[int(cor[cor_num 1]) int(cor[cor_num 0]) 0]<block_end>#print(cor_sum)
#time.sleep(0.5)
# pickle.dump({'image':image, 'line':line, 'edge':edge, 'junc':junc, 'cor':cor, 'filename':filename[:-4]}, open(data_path+'PC_'+"{:04d}".format(num)+'.pkl', "wb" ) )
pickle.dump({'image':image 'line':line 'edge':edge 'junc':junc 'cor':cor 'filename':filename[:-4]} open(data_path+'PCts_'+"{:04d}".format(num)+'.pkl' "wb"))<block_end># pickle.dump({'image':image, 'line':line, 'edge':edge, 'junc':junc, 'cor':cor, 'filename':filename[:-4]}, open(data_path+'PCval_'+"{:04d}".format(num)+'.pkl', "wb" ) )
# pickle.dump({'image':image, 'line':line, 'edge':edge, 'junc':junc, 'cor':cor, 'filename':filename[:-4]}, open(data_path+'area5_'+"{:04d}".format(num)+'.pkl', "wb" ) )
|
<import_stmt>clr<line_sep>clr.AddReference('RevitAPI')<import_from_stmt>Autodesk.Revit.DB *<line_sep>clr.AddReference("RevitNodes")<import_stmt>Revit<line_sep>clr.ImportExtensions(Revit.Elements)<def_stmt>GetElevationMarkerView item<block_start>val=[]<if_stmt>hasattr(item "HasElevations")<block_start><if_stmt>item.HasElevations()<block_start><for_stmt>i range(item.MaximumViewCount)<block_start>view=item.Document.GetElement(item.GetViewId(i))<if_stmt>view<block_start>val.append(view)<block_end><block_end><block_end><block_end><return>val<block_end>items=UnwrapElement(IN[0])<if_stmt>isinstance(IN[0] list)<block_start>OUT=[GetElevationMarkerView(x)<for>x items]<block_end><else_stmt><block_start>OUT=GetElevationMarkerView(items)<block_end> |
funcs=["abs" "all" "any" "ascii" "bin" "callable" "chr" "compile" "delattr" "dir" "divmod" "eval" "exec" "exit" "format" "getattr" "globals" "hasattr" "hash" "help" "hex" "id" "input" "isinstance" "issubclass" "iter" "len" "locals" "max" "min" "next" "oct" "open" "ord" "pow" "print" "quit" "repr" "round" "setattr" "sorted" "sum" "vars"]<line_sep>classes=["bool" "bytearray" "bytes" "classmethod" "complex" "dict" "enumerate" "filter" "float" "frozenset" "int" "list" "map" "memoryview" "object" "property" "range" "reversed" "set" "slice" "staticmethod" "str" "super" "tuple" "type" "zip"]<line_sep>special_cases="exit" "quit" "help"<for_stmt>func funcs<block_start><if_stmt>func<in>special_cases<block_start><continue><block_end><assert_stmt>str(getattr(__builtins__ func))<eq>f"<built-in function {func}>"<block_end><for_stmt>kl classes<block_start>obj=getattr(__builtins__ kl)<assert_stmt>str(obj)<eq>f"<class '{kl}'>" f"erreur pour {kl} : {obj}"<block_end> |
<import_stmt>FWCore.ParameterSet.Config<as>cms<line_sep>process=cms.Process("TestGct")<line_sep>process.load("L1Trigger.GlobalCaloTrigger.test.gctTest_cff")<line_sep>process.load("L1Trigger.GlobalCaloTrigger.test.gctConfig_cff")<line_sep>process.source=cms.Source("EmptySource")<line_sep>process.maxEvents=cms.untracked.PSet(input=cms.untracked.int32(1))<line_sep>process.p1=cms.Path(process.gctemu)<line_sep>process.gctemu.doElectrons=<true><line_sep>process.gctemu.inputFile='data/testEmDummy_'<line_sep> |
# tests/test_provider_vultr_vultr.py
# Automatically generated by tools/makecode.py (24-Sep-2021 15:31:06 UTC)
<def_stmt>test_provider_import <block_start><import_stmt>terrascript.provider.vultr.vultr<block_end><def_stmt>test_resource_import <block_start><import_from_stmt>terrascript.resource.vultr.vultr vultr_bare_metal_server<import_from_stmt>terrascript.resource.vultr.vultr vultr_block_storage<import_from_stmt>terrascript.resource.vultr.vultr vultr_dns_domain<import_from_stmt>terrascript.resource.vultr.vultr vultr_dns_record<import_from_stmt>terrascript.resource.vultr.vultr vultr_firewall_group<import_from_stmt>terrascript.resource.vultr.vultr vultr_firewall_rule<import_from_stmt>terrascript.resource.vultr.vultr vultr_instance<import_from_stmt>terrascript.resource.vultr.vultr vultr_instance_ipv4<import_from_stmt>terrascript.resource.vultr.vultr vultr_iso_private<import_from_stmt>terrascript.resource.vultr.vultr vultr_load_balancer<import_from_stmt>terrascript.resource.vultr.vultr vultr_object_storage<import_from_stmt>terrascript.resource.vultr.vultr vultr_private_network<import_from_stmt>terrascript.resource.vultr.vultr vultr_reserved_ip<import_from_stmt>terrascript.resource.vultr.vultr vultr_reverse_ipv4<import_from_stmt>terrascript.resource.vultr.vultr vultr_reverse_ipv6<import_from_stmt>terrascript.resource.vultr.vultr vultr_snapshot<import_from_stmt>terrascript.resource.vultr.vultr vultr_snapshot_from_url<import_from_stmt>terrascript.resource.vultr.vultr vultr_ssh_key<import_from_stmt>terrascript.resource.vultr.vultr vultr_startup_script<import_from_stmt>terrascript.resource.vultr.vultr vultr_user<block_end><def_stmt>test_datasource_import <block_start><import_from_stmt>terrascript.data.vultr.vultr vultr_account<import_from_stmt>terrascript.data.vultr.vultr vultr_application<import_from_stmt>terrascript.data.vultr.vultr vultr_backup<import_from_stmt>terrascript.data.vultr.vultr vultr_bare_metal_plan<import_from_stmt>terrascript.data.vultr.vultr vultr_bare_metal_server<import_from_stmt>terrascript.data.vultr.vultr vultr_block_storage<import_from_stmt>terrascript.data.vultr.vultr vultr_dns_domain<import_from_stmt>terrascript.data.vultr.vultr vultr_firewall_group<import_from_stmt>terrascript.data.vultr.vultr vultr_instance<import_from_stmt>terrascript.data.vultr.vultr vultr_instance_ipv4<import_from_stmt>terrascript.data.vultr.vultr vultr_iso_private<import_from_stmt>terrascript.data.vultr.vultr vultr_iso_public<import_from_stmt>terrascript.data.vultr.vultr vultr_load_balancer<import_from_stmt>terrascript.data.vultr.vultr vultr_object_storage<import_from_stmt>terrascript.data.vultr.vultr vultr_os<import_from_stmt>terrascript.data.vultr.vultr vultr_plan<import_from_stmt>terrascript.data.vultr.vultr vultr_private_network<import_from_stmt>terrascript.data.vultr.vultr vultr_region<import_from_stmt>terrascript.data.vultr.vultr vultr_reserved_ip<import_from_stmt>terrascript.data.vultr.vultr vultr_reverse_ipv4<import_from_stmt>terrascript.data.vultr.vultr vultr_reverse_ipv6<import_from_stmt>terrascript.data.vultr.vultr vultr_snapshot<import_from_stmt>terrascript.data.vultr.vultr vultr_ssh_key<import_from_stmt>terrascript.data.vultr.vultr vultr_startup_script<import_from_stmt>terrascript.data.vultr.vultr vultr_user<block_end># TODO: Shortcut imports without namespace for official and supported providers.
# TODO: This has to be moved into a required_providers block.
# def test_version_source():
#
# import terrascript.provider.vultr.vultr
#
# t = terrascript.provider.vultr.vultr.vultr()
# s = str(t)
#
# assert 'https://github.com/vultr/terraform-provider-vultr' in s
# assert '2.4.2' in s
|
"""Processor that attaches a constituency tree to a sentence
The model used is a generally a model trained on the Stanford
Sentiment Treebank or some similar dataset. When run, this processor
attaches a score in the form of a string to each sentence in the
document.
TODO: a possible way to generalize this would be to make it a
ClassifierProcessor and have "sentiment" be an option.
"""<import_stmt>stanza.models.constituency.trainer<as>trainer<import_from_stmt>stanza.models.common doc<import_from_stmt>stanza.models.common.pretrain Pretrain<import_from_stmt>stanza.pipeline._constants *<import_from_stmt>stanza.pipeline.processor UDProcessor register_processor<line_sep>@register_processor(CONSTITUENCY)<class_stmt>ConstituencyProcessor(UDProcessor)# set of processor requirements this processor fulfills
<block_start>PROVIDES_DEFAULT=set([CONSTITUENCY])<line_sep># set of processor requirements for this processor
REQUIRES_DEFAULT=set([TOKENIZE POS])<line_sep># default batch size, measured in sentences
DEFAULT_BATCH_SIZE=50<def_stmt>_set_up_model self config use_gpu# get pretrained word vectors
<block_start>pretrain_path=config.get('pretrain_path' <none>)<line_sep>self._pretrain=Pretrain(pretrain_path)<if>pretrain_path<else><none><line_sep># set up model
charlm_forward_file=config.get('forward_charlm_path' <none>)<line_sep>charlm_backward_file=config.get('backward_charlm_path' <none>)<line_sep>self._model=trainer.Trainer.load(filename=config['model_path'] pt=self._pretrain forward_charlm=trainer.load_charlm(charlm_forward_file) backward_charlm=trainer.load_charlm(charlm_backward_file) use_gpu=use_gpu)<line_sep># batch size counted as sentences
self._batch_size=config.get('batch_size' ConstituencyProcessor.DEFAULT_BATCH_SIZE)<block_end><def_stmt>process self document<block_start>sentences=document.sentences<line_sep># TODO: perhaps MWT should be relevant here?
# certainly parsing across an MWT boundary is an error
# TODO: maybe some constituency models are trained on UPOS not XPOS
words=[[(w.text w.xpos)<for>w s.words]<for>s sentences]<line_sep>trees=trainer.parse_tagged_words(self._model.model words self._batch_size)<line_sep>document.set(CONSTITUENCY trees to_sentence=<true>)<line_sep><return>document<block_end><block_end> |
# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
# Also available under a BSD-style license. See LICENSE.
# RUN: %PYTHON %s | FileCheck %s
<import_stmt>torch<import_from_stmt>framework run_test<import_from_stmt>torch_mlir.eager_mode.torch_mlir_dispatch annotate_args_kwargs normalize_args_kwargs build_script_function <line_sep># CHECK: Torch Tensor (shape=(1, 3, 32, 32), dtype=torch.float32)
# CHECK: Torch Tensor (shape=(1, 3, 32, 32), dtype=torch.float32)
# CHECK: Torch Tensor (shape=(1, 3, 32, 32), dtype=torch.float32)
# -----
# CHECK: PASS - simple
@run_test<def_stmt>simple <block_start>target=torch.ops.aten.addmm.default<line_sep>A=torch.randn(1 3 32 32)<line_sep>B=torch.randn(1 3 32 32)<line_sep>C=torch.randn(1 3 32 32)<line_sep>args=(A B C)<line_sep>kwargs=dict(beta=1 alpha=1)<line_sep>new_args,new_kwargs=normalize_args_kwargs(target.overloadpacket args kwargs)<line_sep>script_fun=build_script_function(target._schema new_args new_kwargs)<line_sep>annotations,*_=annotate_args_kwargs(script_fun new_args new_kwargs)<for_stmt>annot annotations<block_start>print(annot)<block_end><block_end># CHECK: Torch Tensor (shape=(-1, 3, 32, 32), dtype=torch.float32)
# CHECK: Torch Tensor (shape=(-1, 3, 32, 32), dtype=torch.float32)
# CHECK: Torch Tensor (shape=(-1, 3, 32, 32), dtype=torch.float32)
# -----
# CHECK: PASS - handle_zero_dim
@run_test<def_stmt>handle_zero_dim <block_start>target=torch.ops.aten.addmm.default<line_sep>A=torch.randn(0 3 32 32)<line_sep>B=torch.randn(0 3 32 32)<line_sep>C=torch.randn(0 3 32 32)<line_sep>args=(A B C)<line_sep>kwargs=dict(beta=1 alpha=1)<line_sep>new_args,new_kwargs=normalize_args_kwargs(target.overloadpacket args kwargs)<line_sep>script_fun=build_script_function(target._schema new_args new_kwargs)<line_sep>annotations,*_=annotate_args_kwargs(script_fun new_args new_kwargs)<for_stmt>annot annotations<block_start>print(annot)<block_end><block_end># CHECK: Torch Tensor (shape=(2, 5, 2, 3), dtype=torch.float32)
# CHECK: Torch Tensor (shape=(5,), dtype=torch.float32)
# CHECK: Torch Tensor (shape=(5,), dtype=torch.float32)
# CHECK: Torch Tensor (shape=(5,), dtype=torch.float32)
# CHECK: Torch Tensor (shape=(5,), dtype=torch.float32)
# CHECK: Torch Tensor (shape=(2, 5, 2, 3), dtype=torch.float32)
# CHECK: Torch Tensor (shape=(5,), dtype=torch.float32)
# CHECK: Torch Tensor (shape=(5,), dtype=torch.float32)
# -----
# CHECK: PASS - correctly_order_kwargs
@run_test<def_stmt>correctly_order_kwargs <block_start>target=torch.ops.aten.native_batch_norm.out<line_sep>input=torch.randn(2 5 2 3)<line_sep>weight=torch.randn(5)<line_sep>bias=torch.randn(5)<line_sep>running_mean=torch.randn(5)<line_sep>running_var=torch.randn(5)<line_sep>args=(input weight bias running_mean running_var)<line_sep>out=torch.empty_like(input)<line_sep>save_mean=torch.empty_like(running_mean)<line_sep>save_invstd=torch.empty_like(running_var)<line_sep>kwargs=dict(training=<false> momentum=0.1 eps=0.0001 out=out save_mean=save_mean save_invstd=save_invstd )<line_sep>new_args,new_kwargs=normalize_args_kwargs(target.overloadpacket args kwargs)<line_sep>script_fun=build_script_function(target._schema new_args new_kwargs)<line_sep>annotations,*_=annotate_args_kwargs(script_fun new_args new_kwargs)<for_stmt>annot annotations<block_start>print(annot)<block_end><block_end> |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
<import_from_stmt>tests.test_utils.amazon_system_helpers AWS_DAG_FOLDER AmazonSystemTest<class_stmt>EmrSystemTest(AmazonSystemTest)<block_start>"""
System tests for AWS EMR operators
"""<line_sep>@classmethod<def_stmt>setup_class cls<block_start>cls.create_emr_default_roles()<block_end><def_stmt>test_run_example_dag_emr_automatic_steps self<block_start>self.run_dag('emr_job_flow_automatic_steps_dag' AWS_DAG_FOLDER)<block_end><def_stmt>test_run_example_dag_emr_manual_steps self<block_start>self.run_dag('emr_job_flow_manual_steps_dag' AWS_DAG_FOLDER)<block_end><block_end> |
<import_stmt>os<import_stmt>numpy<as>np<import_stmt>math<import_from_stmt>itertools permutations<import_stmt>wget<import_stmt>pickle<import_stmt>networkx<as>nx<import_stmt>park<import_from_stmt>park core spaces logger<import_from_stmt>park.utils.misc create_folder_if_not_exists<import_from_stmt>park.spaces Tuple Box Discrete Graph Null<import_from_stmt>park.param config<import_from_stmt>park.utils seeding<import_from_stmt>park.utils.directed_graph DirectedGraph<import_from_stmt>park.envs.tf_placement_sim.tf_pl_simulator ImportantOpsSimulator<line_sep>dropbox_links={'inception':'https://www.dropbox.com/s/1r5n4e2g3hulsge/inception.pkl?dl=1' 'nasnet':'https://www.dropbox.com/s/ufm72htk1zeuccm/nasnet.pkl?dl=1' 'nmt':'https://www.dropbox.com/s/9rsmmv6pm11h3i8/nmt-attention-seq-30.pkl?dl=1' }<line_sep>pkl_names={'inception':'inception.pkl' 'nasnet':'nasnet.pkl' 'nmt':'nmt-attention-seq-30.pkl' }<class_stmt>TFPlacementSimEnv(core.Env)<block_start>"""
Assign a placement to each operation group of a
computational graph of deep-learning models.
The goal is to minimize runtime of the computational graph.
* STATE *
Directed Graph with node feature being a list of the following:
(1) Cost: Op group execution time
(2) Mem: Op group's memory requirement when running
(3) Curr Placement: device id of the node based on its
current placement in the episode
(4) is_curr_node: Is this the node that is currently being placed
* ACTIONS *
[0, 1, ..., n-1] where n is the number of devices. The index
corresponding to the device id.
* REWARD *
Improvement in the runtime of the placement because of the current action
* REFERENCE *
https://arxiv.org/pdf/1706.04972.pdf
"""<def_stmt>__init__ self# observation and action space
<block_start>self.setup_env()<line_sep>self.setup_space()<line_sep># random seed
self.seed(config.seed)<block_end><def_stmt>possibly_download_pkl_file self<block_start>graph_dir=park.__path__[0]+'/envs/tf_placement_sim/graphs/'<line_sep>trace_file=graph_dir+'/'+pkl_names[config.pl_graph]<line_sep>create_folder_if_not_exists(graph_dir)<if_stmt><not>os.path.exists(trace_file)<block_start>wget.download(dropbox_links[config.pl_graph] out=graph_dir)<block_end><return>trace_file<block_end><def_stmt>setup_env self<block_start>device_names=['/device:GPU:%d'%i<for>i range(config.pl_n_devs)]<line_sep>gpu_devs=filter(<lambda>dev:'GPU'<in>dev device_names)<line_sep>gpu_devs=list(sorted(gpu_devs))<if_stmt>config.pl_graph<not><in>pkl_names<block_start><raise>Exception('Requesting for model "%s" which doesnot exist in repo.\n\
Please choose from one of the following %s'%(config.pl_graph ' '.join(pkl_repo.keys())))<block_end>pickled_inp_file=self.possibly_download_pkl_file()<with_stmt>open(pickled_inp_file 'rb')<as>f<block_start>j=pickle.load(f)<line_sep>mg,G,ungroup_map=j['optim_mg'] j['G'] j['ungrouped_mapping']<line_sep>op_perf,step_stats=j['op_perf'] j['step_stats']<block_end>self.mg=mg<line_sep>self.ungroup_map=ungroup_map<line_sep>self.n_devs=config.pl_n_devs<line_sep>self.gpu_devs=gpu_devs<line_sep>self.devs=self.gpu_devs<line_sep>self.device_names=device_names<line_sep>self.G=G<line_sep>self.sim=ImportantOpsSimulator(mg op_perf step_stats device_names)<line_sep>self.node_order=list(nx.topological_sort(G))<line_sep>self.cost_d=self.sim.cost_d<line_sep>self.out_d={k:sum(v)<for>k,v self.sim.out_d.items()}<block_end><def_stmt>reset self<block_start>node_features={}<line_sep>edge_features={}<line_sep>cur_pl={}<for_stmt>node self.G.nodes()# checkout step function for this order as well
<block_start>node_features[node]=[self.cost_d[node] self.out_d[node] 0 0]<line_sep>cur_pl[node]=node_features[node][2]<for_stmt>neigh self.G.neighbors(node)# dummy edge feature for now
# TODO: Allow for no edge feature possibility
<block_start>edge_features[(node neigh)]=-1<block_end><block_end>node_features[self.node_order[0]][-1]=1<line_sep>self.s=DirectedGraph(node_features edge_features)<line_sep>self.cur_node_idx=0<line_sep>self.cur_pl=cur_pl<line_sep>self.prev_rt=self.get_rt(self.cur_pl)<line_sep><return>self.s<block_end><def_stmt>setup_space self# cost (e.g., execution delay estimation in micro-seconds),
# mem (e.g., op group memory requirements on GPU in bytes),
# current placement(e.g., GPU 1),
# one-hot-bit (i.e., currently working on this node)
<block_start>node_space=Box(low=0 high=10<times>(1e9) shape=(len(self.G) 4) dtype=np.float32)<line_sep>dummy_edge_space=Box(low=-1 high=-1 shape=(self.G.number_of_edges() ) dtype=np.int8)<line_sep>self.observation_space=Graph(node_space dummy_edge_space)<line_sep>self.action_space=Discrete(self.n_devs)<block_end><def_stmt>ungroup_pl self pl<block_start>ungroup_map=self.ungroup_map<line_sep>ungrouped_pl={}<for_stmt>op self.mg.graph_def.node<block_start>name=op.name<line_sep>grp_ctr=ungroup_map[name]<line_sep>ungrouped_pl[name]=pl[grp_ctr]<block_end><return>ungrouped_pl<block_end># takes op-group placement and
# returns runtime of the placement in seconds
<def_stmt>get_rt self pl<block_start>pl=self.ungroup_pl(pl)<line_sep>rt=self.sim.simulate(pl)<line_sep><return>rt/1e6<block_end><def_stmt>step self action<block_start><assert_stmt>self.action_space.contains(action)<line_sep>action=int(action)<line_sep>node_order=self.node_order<line_sep>cur_node_idx=self.cur_node_idx<line_sep>cur_node=node_order[cur_node_idx]<line_sep>next_node=node_order[cur_node_idx+1]<line_sep>self.cur_pl[cur_node]=action<line_sep>rt=self.get_rt(self.cur_pl)<line_sep>reward=rt-self.prev_rt<line_sep>self.s.update_nodes({cur_node:[self.cost_d[cur_node] self.out_d[cur_node] int(action) 0] next_node:[self.cost_d[next_node] self.out_d[next_node] self.cur_pl[next_node] 1]})<line_sep>self.cur_node_idx<augadd>1<line_sep>self.prev_rt=rt<if_stmt>1+self.cur_node_idx<eq>len(self.node_order)<block_start>done=<true><block_end><else_stmt><block_start>done=<false><block_end><assert_stmt>self.observation_space.contains(self.s)<line_sep><return>self.s reward done {}<block_end><def_stmt>seed self seed<block_start>self.np_random=seeding.np_random(seed)<block_end><block_end> |
<import_stmt>marbles.core<class_stmt>ComplexTestCase(marbles.core.AnnotatedTestCase)<block_start><def_stmt>test_for_edge_case self<block_start>self.assertTrue(<false>)<block_end><block_end><if_stmt>__name__<eq>'__main__'<block_start>marbles.core.main()<block_end> |
"""Implementation Vibrator for Android."""<import_from_stmt>jnius autoclass cast<import_from_stmt>plyer.facades Vibrator<import_from_stmt>plyer.platforms.android activity<import_from_stmt>plyer.platforms.android SDK_INT<line_sep>Context=autoclass("android.content.Context")<line_sep>vibrator_service=activity.getSystemService(Context.VIBRATOR_SERVICE)<line_sep>vibrator=cast("android.os.Vibrator" vibrator_service)<if_stmt>SDK_INT<ge>26<block_start>VibrationEffect=autoclass("android.os.VibrationEffect")<block_end><class_stmt>AndroidVibrator(Vibrator)<block_start>"""Android Vibrator class.
Supported features:
* vibrate for some period of time.
* vibrate from given pattern.
* cancel vibration.
* check whether Vibrator exists.
"""<def_stmt>_vibrate self time=<none> **kwargs<block_start><if_stmt>vibrator<block_start><if_stmt>SDK_INT<ge>26<block_start>vibrator.vibrate(VibrationEffect.createOneShot(int(1000<times>time) VibrationEffect.DEFAULT_AMPLITUDE))<block_end><else_stmt><block_start>vibrator.vibrate(int(1000<times>time))<block_end><block_end><block_end><def_stmt>_pattern self pattern=<none> repeat=<none> **kwargs<block_start>pattern=[int(1000<times>time)<for>time pattern]<if_stmt>vibrator<block_start><if_stmt>SDK_INT<ge>26<block_start>vibrator.vibrate(VibrationEffect.createWaveform(pattern repeat))<block_end><else_stmt><block_start>vibrator.vibrate(pattern repeat)<block_end><block_end><block_end><def_stmt>_exists self **kwargs<block_start><if_stmt>SDK_INT<ge>11<block_start><return>vibrator.hasVibrator()<block_end><elif_stmt>vibrator_service<is><none><block_start><raise>NotImplementedError()<block_end><return><true><block_end><def_stmt>_cancel self **kwargs<block_start>vibrator.cancel()<block_end><block_end><def_stmt>instance <block_start>"""Returns Vibrator with android features.
:return: instance of class AndroidVibrator
"""<line_sep><return>AndroidVibrator()<block_end> |
<import_stmt>unittest<import_from_stmt>wifipumpkin3.core.common.platforms Linux<import_stmt>wifipumpkin3.core.utility.constants<as>C<import_from_stmt>wifipumpkin3.core.utility.collection SettingsINI<class_stmt>TestConfigPumpkinProxy(unittest.TestCase)<block_start><def_stmt>test_config_key_set self<block_start>self.config=SettingsINI(C.CONFIG_PP_INI)<line_sep>self.result="http://example.com/foo.js"<line_sep>self.value=self.config.get("set_js_inject" "url")<line_sep>self.assertEqual(self.result self.value)<block_end><def_stmt>test_get_all_configkey_list self<block_start>self.config=SettingsINI(C.CONFIG_PP_INI)<line_sep>self.result=["url"]<line_sep>self.value=self.config.get_all_childname("set_js_inject")<line_sep>self.assertEqual(self.result self.value)<block_end><block_end><if_stmt>__name__<eq>"__main__"<block_start>unittest.main()<block_end> |
# Copyright 2017 F5 Networks Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
<import_from_stmt>f5.bigip ManagementRoot<import_from_stmt>f5.bigip.tm.auth.ldap Ldap<import_from_stmt>f5.sdk_exception InvalidName<import_from_stmt>f5.sdk_exception MissingRequiredCreationParameter<import_stmt>mock<import_stmt>pytest<line_sep>@pytest.fixture<def_stmt>FakeLdap <block_start>fake_ldap=mock.MagicMock()<line_sep>fake_ldapobj=Ldap(fake_ldap)<line_sep><return>fake_ldapobj<block_end><class_stmt>TestCreate(object)<block_start><def_stmt>test_create_two self fakeicontrolsession<block_start>b=ManagementRoot('localhost' 'admin' 'admin')<line_sep>l1=b.tm.auth.ldaps.ldap<line_sep>l2=b.tm.auth.ldaps.ldap<assert_stmt>l1<is><not>l2<block_end><def_stmt>test_create_no_args self FakeLdap<block_start><with_stmt>pytest.raises(MissingRequiredCreationParameter)<block_start>FakeLdap.create()<block_end><block_end><def_stmt>test_create_bad_name self FakeLdap<block_start><with_stmt>pytest.raises(InvalidName)<block_start>FakeLdap.create(name='testauth')<block_end><block_end><block_end> |
<import_from_stmt>cached_property cached_property<import_from_stmt>dataclasses dataclass<import_from_stmt>typing Callable<line_sep>@dataclass<class_stmt>AnkiDeck<block_start>_data:dict<line_sep>deck_name_separator='::'<line_sep>@property<def_stmt>data self<block_start><return>self._data<block_end>@property<def_stmt>is_dynamic self<block_start><return>bool(self.data['dyn'])<block_end>@property<def_stmt>name self<block_start><return>self.data['name']<block_end><block_end><class_stmt>LazyDeck(AnkiDeck)<block_start><def_stmt>__init__ self deck_initializer:Callable[[] dict]<block_start>self.deck_initializer=deck_initializer<block_end>@cached_property<def_stmt>data self<block_start><return>self.deck_initializer()<block_end><block_end><class_stmt>NamedLazyDeck(LazyDeck)<block_start><def_stmt>__init__ self name:str name_initializer:Callable[[str] dict]<block_start>super().__init__(<lambda>:name_initializer(name))<line_sep>self._name=name<block_end>@property<def_stmt>name self<block_start><return>self._name<block_end><block_end> |
<import_from_stmt>.widget ChartWidget<import_from_stmt>.item CandleItem VolumeItem ChartItem MemoItem<import_from_stmt>.data BarData<import_from_stmt>.constant *<line_sep> |
๏ปฟ#
# All or portions of this file Copyright (c) Amazon.com, Inc. or its affiliates or
# its licensors.
#
# For complete copyright and license terms please see the LICENSE at the root of this
# distribution (the "License"). All use of this software is governed by the License,
# or, if provided, by the license below or the license accompanying this file. Do not
# remove or modify any license notices. This file is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#
# $Revision: #1 $
<import_stmt>resource_manager.cli<import_stmt>pa_service_api<def_stmt>add_cli_commands hook subparsers add_common_args **kwargs<block_start>subparser=subparsers.add_parser("player-account" help="Commands to manage the CloudGemPlayerAccount gem")<line_sep>subparser.register('action' 'parsers' resource_manager.cli.AliasedSubParsersAction)<line_sep>player_account_subparsers=subparser.add_subparsers(dest='subparser_name' metavar='COMMAND')<line_sep>subparser=player_account_subparsers.add_parser('add-player' help='Add a new player')<line_sep>subparser.add_argument('--username' type=str required=<true> help='The cognito username of the account to create')<line_sep>subparser.add_argument('--email' type=str required=<true> help='The email address for the player')<line_sep>subparser.add_argument('--playername' type=str required=<false> help='The name of the player in the game.')<line_sep>subparser.add_argument('--givenname' type=str required=<false> help='The players given name,')<line_sep>subparser.add_argument('--familyname' type=str required=<false> help='The players family name,')<line_sep>subparser.add_argument('--nickname' type=str required=<false> help='The players nickname')<line_sep>subparser.add_argument('--gender' type=str required=<false> choices=pa_service_api.GENDER_CHOICES help='The players gender')<line_sep>subparser.add_argument('--locale' type=str required=<false> help='The players locale')<line_sep>add_common_args(subparser)<line_sep>subparser.set_defaults(func=pa_service_api.command_add_player)<line_sep>subparser=player_account_subparsers.add_parser('ban-player' help='Ban a player. See remove_player_ban to restore player')<line_sep>subparser.add_argument('--account-id' type=str required=<true> help='The account id to ban')<line_sep>add_common_args(subparser)<line_sep>subparser.set_defaults(func=pa_service_api.command_ban_player)<line_sep>subparser=player_account_subparsers.add_parser('confirm-player' help='Force confirm a player')<line_sep>subparser.add_argument('--username' type=str required=<true> help='The cognito username of the account to confirm')<line_sep>add_common_args(subparser)<line_sep>subparser.set_defaults(func=pa_service_api.command_confirm_player)<line_sep>subparser=player_account_subparsers.add_parser('edit-player' help='Edit a players settings')<line_sep>subparser.add_argument('--account-id' type=str required=<true> help='The account id to edit')<line_sep>subparser.add_argument('--playername' type=str required=<false> help='The name of the player in the game.')<line_sep>subparser.add_argument('--givenname' type=str required=<false> help='The players given name,')<line_sep>subparser.add_argument('--familyname' type=str required=<false> help='The players family name,')<line_sep>subparser.add_argument('--nickname' type=str required=<false> help='The players nickname,')<line_sep>subparser.add_argument('--gender' type=str required=<false> choices=pa_service_api.GENDER_CHOICES help='The players gender')<line_sep>subparser.add_argument('--locale' type=str required=<false> help='The players locale')<line_sep>subparser.add_argument('--email' type=str required=<false> help='The email address for the player')<line_sep>add_common_args(subparser)<line_sep>subparser.set_defaults(func=pa_service_api.command_edit_player)<line_sep>subparser=player_account_subparsers.add_parser('remove-player-ban' help='Remove a player ban')<line_sep>subparser.add_argument('--account-id' type=str required=<true> help='The account id to restore')<line_sep>add_common_args(subparser)<line_sep>subparser.set_defaults(func=pa_service_api.command_remove_player_ban)<line_sep>subparser=player_account_subparsers.add_parser('reset-player-password' help='Reset a player password')<line_sep>subparser.add_argument('--username' type=str required=<true> help='The cognito username of the account to target')<line_sep>add_common_args(subparser)<line_sep>subparser.set_defaults(func=pa_service_api.command_reset_player_password)<line_sep>subparser=player_account_subparsers.add_parser('show-banned-players' help='List banned players in the Gem')<line_sep>subparser.add_argument('--page-token' type=str required=<false> default=<none> help='The pagination token to get the next page.')<line_sep>add_common_args(subparser)<line_sep>subparser.set_defaults(func=pa_service_api.command_list_banned_players)<line_sep>subparser=player_account_subparsers.add_parser('show-players' help='List registered players in the Gem')<line_sep>subparser.add_argument('--filter-type' type=str required=<false> choices=pa_service_api.SEARCH_FILTER_CHOICES help='The type of filter to apply')<line_sep>subparser.add_argument('--filter-value' type=str required=<false> help='The value for the filter as a string. '<concat>'For example the email address for the CognitoEmail filter.')<line_sep>subparser.add_argument('--page-token' type=str required=<false> default=<none> help='The pagination token to get the next page.')<line_sep>add_common_args(subparser)<line_sep>subparser.set_defaults(func=pa_service_api.command_list_players)<line_sep>subparser=player_account_subparsers.add_parser('show-player-details' help='Show details about a player')<line_sep>subparser.add_argument('--account-id' type=str required=<true> help='The account id to show details for')<line_sep>add_common_args(subparser)<line_sep>subparser.set_defaults(func=pa_service_api.command_list_player_details)<line_sep>subparser=player_account_subparsers.add_parser('show-logs' help='Show recent log events for ServiceLambda')<line_sep>subparser.add_argument('--minutes' type=int required=<false> help='How far back from now to attempt to display. Default is 10mins')<line_sep>add_common_args(subparser)<line_sep>subparser.set_defaults(func=pa_service_api.command_show_log_events)<block_end> |
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright 2014 Intel Inc.
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
<import_from_stmt>sqlalchemy Boolean Column DateTime<import_from_stmt>sqlalchemy Integer MetaData String<import_from_stmt>sqlalchemy Table Index ForeignKey<import_from_stmt>sqlalchemy.engine.base Engine<import_from_stmt>migrate.changeset.constraint ForeignKeyConstraint<import_from_stmt>sqlalchemy.engine reflection<import_from_stmt>sqlalchemy create_engine<def_stmt>upgrade migrate_engine# Upgrade operations go here. Don't create your own engine;
# bind migrate_engine to your metadata
<block_start><if_stmt>migrate_engine.name<eq>'sqlite'<block_start><return><block_end>storage_pools='storage_pools'<line_sep>storage_groups='storage_groups'<line_sep>col=''<line_sep>insp=reflection.Inspector.from_engine(migrate_engine)<line_sep>foreign_keys=insp.get_foreign_keys(storage_pools)<for_stmt>key foreign_keys<block_start><if_stmt>storage_groups<eq>key['referred_table']<block_start>sql_str="ALTER TABLE %s DROP FOREIGN KEY %s;"%(storage_pools key['name'])<line_sep>ret=migrate_engine.execute(sql_str)<block_end><block_end><block_end><def_stmt>downgrade migrate_engine<block_start><if_stmt>migrate_engine.name<eq>'sqlite'<block_start><return><block_end>#meta = MetaData()
#meta.bind = migrate_engine
#storage_group = Table('storage_groups',
# meta,
# autoload=True)
#column_status = Column('status', String(255), default="OUT", nullable=False)
<try_stmt>#storage_group.drop_column(column_status)
<block_start><pass><block_end><except_stmt>Exception<block_start><raise><block_end><block_end> |
"""Utils for trafikverket_train."""<import_from_future_stmt> annotations<import_from_stmt>datetime time<def_stmt>create_unique_id from_station:str to_station:str depart_time:time|str|<none> weekdays:list<arrow>str<block_start>"""Create unique id."""<line_sep>timestr=str(depart_time)<if>depart_time<else>""<line_sep><return>(f"{from_station.casefold().replace(' ' '')}-{to_station.casefold().replace(' ' '')}"<concat>f"-{timestr.casefold().replace(' ' '')}-{str(weekdays)}")<block_end> |
<import_stmt>os<import_from_stmt>bottle Bottle static_file run<line_sep>HERE=os.path.abspath(os.path.dirname(__file__))<line_sep>STATIC=os.path.join(HERE 'static')<line_sep>app=Bottle()<line_sep>@app.route('/')@app.route('/<filename:path>')<def_stmt>serve filename='index.html'<block_start><return>static_file(filename root=STATIC)<block_end><if_stmt>__name__<eq>'__main__'<block_start>run(app=app host='localhost' port=8080)<block_end> |
# DO NOT EDIT THIS FILE. This file will be overwritten when re-running go-raml.
<import_from_stmt>sanic Blueprint<import_from_stmt>sanic.views HTTPMethodView<import_from_stmt>sanic.response text<import_from_stmt>. users_api<import_from_stmt>.oauth2_itsyouonline oauth2_itsyouonline<line_sep>users_if=Blueprint('users_if')<class_stmt>usersView(HTTPMethodView)<block_start><async_keyword><def_stmt>get self request<block_start><if_stmt><not><await>oauth2_itsyouonline([]).check_token(request)<block_start><return>text('' 401)<block_end><return><await>users_api.users_get(request)<block_end><async_keyword><def_stmt>post self request<block_start><if_stmt><not><await>oauth2_itsyouonline(["user:memberof:goraml-admin"]).check_token(request)<block_start><return>text('' 401)<block_end><return><await>users_api.users_post(request)<block_end><block_end>users_if.add_route(usersView.as_view() '/users')<class_stmt>users_byusernameView(HTTPMethodView)<block_start><async_keyword><def_stmt>get self request username<block_start><if_stmt><not><await>oauth2_itsyouonline(["user:memberof:goraml"]).check_token(request)<block_start><return>text('' 401)<block_end><return><await>users_api.users_byUsername_get(request username)<block_end><block_end>users_if.add_route(users_byusernameView.as_view() '/users/<username>')<line_sep> |
<import_from_stmt>collections defaultdict<import_from_stmt>copy copy deepcopy<import_from_stmt>tqdm tqdm<import_from_stmt>..eventuality Eventuality<import_from_stmt>..relation Relation<def_stmt>conceptualize_eventualities aser_conceptualizer eventualities<block_start>""" Conceptualize eventualities by an ASER conceptualizer
:param aser_conceptualizer: an ASER conceptualizer
:type aser_conceptualizer: aser.conceptualize.aser_conceptualizer.BaseASERConceptualizer
:param eventualities: a list of eventualities
:type eventualities: List[aser.event.Eventuality]
:return: a dictionary from cid to concept, a list of concept-instance pairs, a dictionary from cid to weights
:rtype: Dict[str, aser.concept.ASERConcept], List[aser.concept.ASERConcept, aser.eventuality.Eventuality, float], Dict[str, float]
"""<line_sep>cid2concept=dict()<line_sep>concept_instance_pairs=[]<line_sep>cid2score=dict()<for_stmt>eventuality tqdm(eventualities)<block_start>results=aser_conceptualizer.conceptualize(eventuality)<for_stmt>concept,score results<block_start><if_stmt>concept.cid<not><in>cid2concept<block_start>cid2concept[concept.cid]=deepcopy(concept)<block_end>concept=cid2concept[concept.cid]<if_stmt>(eventuality.eid eventuality.pattern score)<not><in>concept.instances<block_start>concept.instances.append(((eventuality.eid eventuality.pattern score)))<if_stmt>concept.cid<not><in>cid2score<block_start>cid2score[concept.cid]=0.0<block_end>cid2score[concept.cid]<augadd>score<times>eventuality.frequency<block_end>concept_instance_pairs.append((concept eventuality score))<block_end><block_end><return>cid2concept concept_instance_pairs cid2score<block_end><def_stmt>build_concept_relations concept_conn relations<block_start>""" Build relations between conceptualized eventualities from the given relations between eventualities
:param concept_conn: ASER concept KG connection
:type concept_conn: aser.database.kg_connection.ASERConceptConnection
:param relations: relations between eventualities
:type relations: List[aser.relation.Relations]
:return: a dictionary from rid to relations between conceptualized eventualities
:rtype: Dict[str, aser.relation.Relation]
"""<line_sep>rid2relation=dict()<line_sep>hid2related_events=defaultdict(list)<for_stmt>relation tqdm(relations)<block_start>hid2related_events[relation.hid].append((relation.tid relation))<block_end><for_stmt>h_cid tqdm(concept_conn.cids)<block_start>instances=concept_conn.get_eventualities_given_concept(h_cid)<for_stmt>h_eid,pattern,instance_score instances# eid -> event -> related eids -> related events, relations -> related concepts, relations
<block_start>related_events=hid2related_events[h_eid]<for_stmt>t_eid,relation related_events<block_start>concept_score_pairs=concept_conn.get_concepts_given_eventuality(t_eid)<for_stmt>t_concept,score concept_score_pairs<block_start>t_cid=t_concept.cid<if_stmt>h_cid<eq>t_cid<block_start><continue><block_end>rid=Relation.generate_rid(h_cid t_cid)<if_stmt>rid<not><in>rid2relation<block_start>rid2relation[rid]=Relation(h_cid t_cid)<block_end>rid2relation[rid].update({k:v<times>instance_score<times>score<for>k,v relation.relations.items()})<block_end><block_end><block_end><block_end><return>rid2relation<block_end> |
<import_from_stmt>shlex split<import_stmt>json<class_stmt>RawCommand<block_start><def_stmt>__init__ self command client="local" posix=<true> inline=<false># TODO: check shlex.quote, raw string, etc..
<block_start><if_stmt>inline<block_start>self.command=split(command posix=posix)<block_end><else_stmt><block_start>self.command=split(command posix=posix)[1:]<block_end>self.options={"expr_form":"glob"}<line_sep>self.client=client<block_end><def_stmt>parse self<block_start>args=self.command<if_stmt>args[0].startswith("--client")<block_start>self.client=args[0].split("=")[1]<line_sep>args.pop(0)<block_end>low={"client":self.client}<if_stmt>self.client.startswith("local")<block_start><if_stmt>len(args)<l>2<block_start><return>"Command or target not specified"<block_end># Batch option
low["batch"]=<none><if_stmt>self.client<eq>"local_batch"<block_start>batch_index=<none><for_stmt>index,arg enumerate(args)<block_start><if_stmt>arg<in>["-b" "--batch" "--batch-size"]<block_start>low["batch"]=args[index+1]<line_sep>batch_index=index<block_end><block_end><if_stmt>batch_index<block_start>args.pop(batch_index)<line_sep>args.pop(batch_index)<block_end><block_end># Timeout option
timeout_index=<none><for_stmt>index,arg enumerate(args)<block_start><if_stmt>arg<in>["-t" "--timeout"]<block_start>low["timeout"]=int(args[index+1])<line_sep>timeout_index=index<block_end><block_end><if_stmt>timeout_index<block_start>args.pop(timeout_index)<line_sep>args.pop(timeout_index)<block_end># take care of targeting.
target_dict={"pcre":["-E" "--pcre"] "list":["-L" "--list"] "grain":["-G" "--grain"] "grain_pcre":["--grain-pcre"] "pillar":["-I" "--pillar"] "pillar_pcre":["--pillar-pcre"] "range":["-R" "--range"] "compound":["-C" "--compound"] "nodegroup":["-N" "--nodegroup"] }<for_stmt>key,value target_dict.items()<block_start><if_stmt>args[0]<in>value<block_start>self.options["expr_form"]=key<line_sep>args.pop(0)<block_end><block_end>low["tgt_type"]=self.options["expr_form"]<line_sep>low["tgt"]=args.pop(0)<line_sep>low["fun"]=args.pop(0)<line_sep>low["arg"]=args<block_end><elif_stmt>self.client.startswith("runner")<or>self.client.startswith("wheel")<block_start>low["fun"]=args.pop(0)<for_stmt>arg args<block_start><if_stmt>"="<in>arg<block_start>key,value=arg.split("=" 1)<try_stmt><block_start>low[key]=json.loads(value)<block_end><except_stmt>json.JSONDecodeError<block_start>low[key]=value<block_end><block_end><else_stmt><block_start>low.setdefault("arg" []).append(arg)<block_end><block_end><block_end><else_stmt># This should never happen
<block_start><return>"Client not implemented: {0}".format(self.client)<block_end><return>[low]<block_end><block_end> |
<import_from_stmt>happytransformer HappyNextSentence<def_stmt>test_sp_true <block_start>happy_ns=HappyNextSentence()<line_sep>result=happy_ns.predict_next_sentence("Hi nice to meet you. How old are you?" "I am 21 years old.")<assert_stmt>result<g>0.5<block_end><def_stmt>test_sp_false <block_start>happy_ns=HappyNextSentence()<line_sep>result=happy_ns.predict_next_sentence("How old are you?" "The Eiffel Tower is in Paris.")<assert_stmt>result<l>0.5<block_end><def_stmt>test_sp_save <block_start>happy=HappyNextSentence()<line_sep>happy.save("model/")<line_sep>result_before=happy.predict_next_sentence("How old are you?" "The Eiffel Tower is in Paris.")<line_sep>happy=HappyNextSentence(load_path="model/")<line_sep>result_after=happy.predict_next_sentence("How old are you?" "The Eiffel Tower is in Paris.")<assert_stmt>result_before<eq>result_after<block_end> |
# Initial Date: March 2, 2020
# Author: <NAME>
# Copyright (c) <NAME>, Arizona Board of Regents
# All rights reserved.
<import_from_stmt>.bagreader bagreader<import_from_stmt>.bagreader animate_timeseries<import_from_stmt>.bagreader create_fig<line_sep> |
<import_from_future_stmt> annotations<import_from_stmt>enum Enum<import_from_stmt>typing TYPE_CHECKING<import_from_stmt>robot_server.robot.calibration.constants STATE_WILDCARD<if_stmt>TYPE_CHECKING<block_start><import_from_stmt>typing_extensions Final<block_end><class_stmt>PipetteOffsetCalibrationState(str Enum)<block_start>sessionStarted="sessionStarted"<line_sep>labwareLoaded="labwareLoaded"<line_sep>preparingPipette="preparingPipette"<line_sep>inspectingTip="inspectingTip"<line_sep>joggingToDeck="joggingToDeck"<line_sep>savingPointOne="savingPointOne"<line_sep>calibrationComplete="calibrationComplete"<line_sep>sessionExited="sessionExited"<line_sep>WILDCARD=STATE_WILDCARD<block_end><class_stmt>PipetteOffsetWithTipLengthCalibrationState(str Enum)<block_start>sessionStarted="sessionStarted"<line_sep>labwareLoaded="labwareLoaded"<line_sep>measuringNozzleOffset="measuringNozzleOffset"<line_sep>preparingPipette="preparingPipette"<line_sep>inspectingTip="inspectingTip"<line_sep>measuringTipOffset="measuringTipOffset"<line_sep>joggingToDeck="joggingToDeck"<line_sep>savingPointOne="savingPointOne"<line_sep>calibrationComplete="calibrationComplete"<line_sep>sessionExited="sessionExited"<line_sep>tipLengthComplete="tipLengthComplete"<line_sep>WILDCARD=STATE_WILDCARD<block_end>TIP_RACK_SLOT:Final="8"<line_sep> |
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
<import_from_future_stmt> print_function<import_stmt>paddle<import_stmt>math<import_stmt>numpy<as>np<import_stmt>unittest<import_from_stmt>op_test OpTest<def_stmt>calc_psroi_pool x rois rois_num_per_img output_channels spatial_scale pooled_height pooled_width<block_start>"""
Psroi_pool implemented by Numpy.
x: 4-D as (N, C, H, W),
rois: 2-D as [[x1, y1, x2, y2], ...],
rois_num_per_img: 1-D as [nums_of_batch_0, nums_of_batch_1, ...]
"""<line_sep>output_shape=(len(rois) output_channels pooled_height pooled_width)<line_sep>out_data=np.zeros(output_shape)<line_sep>batch_id=0<line_sep>rois_num_id=0<line_sep>rois_num_left=rois_num_per_img[rois_num_id]<for_stmt>i range(len(rois))<block_start>roi=rois[i]<line_sep>roi_batch_id=batch_id<line_sep>rois_num_left<augsub>1<if_stmt>rois_num_left<eq>0<block_start>rois_num_id<augadd>1<if_stmt>rois_num_id<l>len(rois_num_per_img)<block_start>rois_num_left=rois_num_per_img[rois_num_id]<block_end>batch_id<augadd>1<block_end>roi_start_w=round(roi[0])<times>spatial_scale<line_sep>roi_start_h=round(roi[1])<times>spatial_scale<line_sep>roi_end_w=(round(roi[2])+1.)<times>spatial_scale<line_sep>roi_end_h=(round(roi[3])+1.)<times>spatial_scale<line_sep>roi_height=max(roi_end_h-roi_start_h 0.1)<line_sep>roi_width=max(roi_end_w-roi_start_w 0.1)<line_sep>bin_size_h=roi_height/float(pooled_height)<line_sep>bin_size_w=roi_width/float(pooled_width)<line_sep>x_i=x[roi_batch_id]<for_stmt>c range(output_channels)<block_start><for_stmt>ph range(pooled_height)<block_start><for_stmt>pw range(pooled_width)<block_start>hstart=int(math.floor(float(ph)<times>bin_size_h+roi_start_h))<line_sep>wstart=int(math.floor(float(pw)<times>bin_size_w+roi_start_w))<line_sep>hend=int(math.ceil(float(ph+1)<times>bin_size_h+roi_start_h))<line_sep>wend=int(math.ceil(float(pw+1)<times>bin_size_w+roi_start_w))<line_sep>hstart=min(max(hstart 0) x.shape[2])<line_sep>hend=min(max(hend 0) x.shape[2])<line_sep>wstart=min(max(wstart 0) x.shape[3])<line_sep>wend=min(max(wend 0) x.shape[3])<line_sep>c_in=(c<times>pooled_height+ph)<times>pooled_width+pw<line_sep>is_empty=(hend<le>hstart)<or>(wend<le>wstart)<line_sep>out_sum=0.<for_stmt>ih range(hstart hend)<block_start><for_stmt>iw range(wstart wend)<block_start>out_sum<augadd>x_i[c_in ih iw]<block_end><block_end>bin_area=(hend-hstart)<times>(wend-wstart)<line_sep>out_data[i c ph pw]=0.<if>is_empty<else>(out_sum/float(bin_area))<block_end><block_end><block_end><block_end><return>out_data<block_end><class_stmt>TestPSROIPoolOp(OpTest)<block_start><def_stmt>set_data self<block_start>paddle.enable_static()<line_sep>self.init_test_case()<line_sep>self.make_rois()<line_sep>self.outs=calc_psroi_pool(self.x self.boxes self.boxes_num self.output_channels self.spatial_scale self.pooled_height self.pooled_width).astype('float64')<line_sep>self.inputs={'X':self.x 'ROIs':(self.rois_with_batch_id[: 1:5] self.rois_lod)}<line_sep>self.attrs={'output_channels':self.output_channels 'spatial_scale':self.spatial_scale 'pooled_height':self.pooled_height 'pooled_width':self.pooled_width}<line_sep>self.outputs={'Out':self.outs}<block_end><def_stmt>init_test_case self<block_start>self.batch_size=3<line_sep>self.channels=3<times>2<times>2<line_sep>self.height=6<line_sep>self.width=4<line_sep>self.x_dim=[self.batch_size self.channels self.height self.width]<line_sep>self.spatial_scale=1.0/4.0<line_sep>self.output_channels=3<line_sep>self.pooled_height=2<line_sep>self.pooled_width=2<line_sep>self.x=np.random.random(self.x_dim).astype('float64')<block_end><def_stmt>make_rois self<block_start>rois=[]<line_sep>self.rois_lod=[[]]<for_stmt>bno range(self.batch_size)<block_start>self.rois_lod[0].append(bno+1)<for_stmt>i range(bno+1)<block_start>x1=np.random.random_integers(0 self.width<floordiv>self.spatial_scale-self.pooled_width)<line_sep>y1=np.random.random_integers(0 self.height<floordiv>self.spatial_scale-self.pooled_height)<line_sep>x2=np.random.random_integers(x1+self.pooled_width self.width<floordiv>self.spatial_scale)<line_sep>y2=np.random.random_integers(y1+self.pooled_height self.height<floordiv>self.spatial_scale)<line_sep>roi=[bno x1 y1 x2 y2]<line_sep>rois.append(roi)<block_end><block_end>self.rois_num=len(rois)<line_sep>self.rois_with_batch_id=np.array(rois).astype('float64')<line_sep>self.boxes=self.rois_with_batch_id[: 1:]<line_sep>self.boxes_num=np.array([bno+1<for>bno range(self.batch_size)]).astype('int32')<block_end><def_stmt>setUp self<block_start>self.op_type='psroi_pool'<line_sep>self.set_data()<block_end><def_stmt>test_check_output self<block_start>self.check_output()<block_end><def_stmt>test_check_grad self<block_start>self.check_grad(['X'] 'Out')<block_end><block_end><class_stmt>TestPSROIPoolDynamicFunctionAPI(unittest.TestCase)<block_start><def_stmt>setUp self<block_start>self.x=np.random.random([2 490 28 28]).astype(np.float32)<line_sep>self.boxes=np.array([[1 5 8 10] [4 2 6 7] [12 12 19 21]]).astype(np.float32)<line_sep>self.boxes_num=np.array([1 2]).astype(np.int32)<block_end><def_stmt>test_output_size self<block_start><def_stmt>test_output_size_is_int <block_start>output_size=7<line_sep>out=paddle.vision.ops.psroi_pool(paddle.to_tensor(self.x) paddle.to_tensor(self.boxes) paddle.to_tensor(self.boxes_num) output_size).numpy()<line_sep>expect_out=calc_psroi_pool(self.x self.boxes self.boxes_num 10 1.0 7 7)<line_sep>self.assertTrue(np.allclose(out expect_out))<block_end><def_stmt>test_output_size_is_tuple <block_start>output_size=(7 7)<line_sep>out=paddle.vision.ops.psroi_pool(paddle.to_tensor(self.x) paddle.to_tensor(self.boxes) paddle.to_tensor(self.boxes_num) output_size).numpy()<line_sep>expect_out=calc_psroi_pool(self.x self.boxes self.boxes_num 10 1.0 7 7)<line_sep>self.assertTrue(np.allclose(out expect_out))<block_end><def_stmt>test_dytype_is_float64 <block_start>output_size=(7 7)<line_sep>out=paddle.vision.ops.psroi_pool(paddle.to_tensor(self.x 'float64') paddle.to_tensor(self.boxes 'float64') paddle.to_tensor(self.boxes_num 'int32') output_size).numpy()<line_sep>expect_out=calc_psroi_pool(self.x self.boxes self.boxes_num 10 1.0 7 7)<line_sep>self.assertTrue(np.allclose(out expect_out))<block_end>places=['cpu']<if_stmt>paddle.fluid.core.is_compiled_with_cuda()<block_start>places.append('gpu')<block_end><for_stmt>place places<block_start>paddle.set_device(place)<line_sep>test_output_size_is_int()<line_sep>test_output_size_is_tuple()<line_sep>test_dytype_is_float64()<block_end><block_end><block_end><class_stmt>TestPSROIPoolDynamicClassAPI(unittest.TestCase)<block_start><def_stmt>setUp self<block_start>self.x=np.random.random([2 128 32 32]).astype(np.float32)<line_sep>self.boxes=np.array([[3 5 6 13] [7 4 22 18] [4 5 7 10] [5 3 25 21]]).astype(np.float32)<line_sep>self.boxes_num=np.array([2 2]).astype(np.int32)<block_end><def_stmt>test_output_size self<block_start><def_stmt>test_output_size_is_int <block_start>psroi_module=paddle.vision.ops.PSRoIPool(8 1.1)<line_sep>out=psroi_module(paddle.to_tensor(self.x) paddle.to_tensor(self.boxes) paddle.to_tensor(self.boxes_num)).numpy()<line_sep>expect_out=calc_psroi_pool(self.x self.boxes self.boxes_num 2 1.1 8 8)<line_sep>self.assertTrue(np.allclose(out expect_out))<block_end><def_stmt>test_output_size_is_tuple <block_start>psroi_pool_module=paddle.vision.ops.PSRoIPool(8 1.1)<line_sep>out=psroi_pool_module(paddle.to_tensor(self.x) paddle.to_tensor(self.boxes) paddle.to_tensor(self.boxes_num)).numpy()<line_sep>expect_out=calc_psroi_pool(self.x self.boxes self.boxes_num 2 1.1 8 8)<line_sep>self.assertTrue(np.allclose(out expect_out))<block_end><def_stmt>test_dytype_is_float64 <block_start>psroi_pool_module=paddle.vision.ops.PSRoIPool(8 1.1)<line_sep>out=psroi_pool_module(paddle.to_tensor(self.x 'float64') paddle.to_tensor(self.boxes 'float64') paddle.to_tensor(self.boxes_num 'int32')).numpy()<line_sep>expect_out=calc_psroi_pool(self.x self.boxes self.boxes_num 2 1.1 8 8)<line_sep>self.assertTrue(np.allclose(out expect_out))<block_end>paddle.disable_static()<line_sep>places=['cpu']<if_stmt>paddle.fluid.core.is_compiled_with_cuda()<block_start>places.append('gpu')<block_end><for_stmt>place places<block_start>paddle.set_device(place)<line_sep>test_output_size_is_int()<line_sep>test_output_size_is_tuple()<line_sep>test_dytype_is_float64()<block_end><block_end><block_end><class_stmt>TestPSROIPoolBoxesNumError(unittest.TestCase)<block_start><def_stmt>setUp self<block_start>paddle.disable_static()<line_sep>self.x=paddle.uniform([2 490 28 28] dtype='float32')<line_sep>self.boxes=paddle.to_tensor([[1 5 8 10] [4 2 6 7] [12 12 19 21]] 'float32')<block_end><def_stmt>test_errors self<block_start><def_stmt>test_boxes_num_nums_error <block_start>boxes_num=paddle.to_tensor([1 5] 'int32')<line_sep>out=paddle.vision.ops.psroi_pool(self.x self.boxes boxes_num output_size=7)<block_end>self.assertRaises(ValueError test_boxes_num_nums_error)<def_stmt>test_boxes_num_length_error <block_start>boxes_num=paddle.to_tensor([1 1 1] 'int32')<line_sep>out=paddle.vision.ops.psroi_pool(self.x self.boxes boxes_num output_size=7)<block_end>self.assertRaises(ValueError test_boxes_num_length_error)<block_end><block_end><class_stmt>TestPSROIPoolChannelError(unittest.TestCase)<block_start><def_stmt>setUp self<block_start>paddle.disable_static()<line_sep>self.x=paddle.uniform([2 490 28 28] dtype='float32')<line_sep>self.boxes=paddle.to_tensor([[1 5 8 10] [4 2 6 7] [12 12 19 21]] 'float32')<line_sep>self.output_size=4<block_end><def_stmt>test_errors self<block_start><def_stmt>test_channel_error <block_start>boxes_num=paddle.to_tensor([2 1] 'int32')<line_sep>out=paddle.vision.ops.psroi_pool(self.x self.boxes boxes_num self.output_size)<block_end>self.assertRaises(ValueError test_channel_error)<block_end><block_end><class_stmt>TestPSROIPoolStaticAPI(unittest.TestCase)<block_start><def_stmt>setUp self<block_start>paddle.enable_static()<line_sep>self.x_placeholder=paddle.static.data(name='x' shape=[2 490 28 28])<line_sep>self.x=np.random.random([2 490 28 28]).astype(np.float32)<line_sep>self.boxes_placeholder=paddle.static.data(name='boxes' shape=[3 4] lod_level=1)<line_sep>self.boxes=np.array([[1 5 8 10] [4 2 6 7] [12 12 19 21]]).astype(np.float32)<line_sep>self.boxes_num=np.array([1 2]).astype(np.int32)<block_end><def_stmt>test_function_in_static self<block_start>output_size=7<line_sep>out=paddle.vision.ops.psroi_pool(self.x_placeholder self.boxes_placeholder self.boxes_num output_size)<line_sep>expect_out=calc_psroi_pool(self.x self.boxes self.boxes_num 10 1.0 7 7)<line_sep>places=[paddle.CPUPlace()]<if_stmt>paddle.fluid.core.is_compiled_with_cuda()<block_start>places.append(paddle.CUDAPlace(0))<block_end><for_stmt>place places<block_start>exe=paddle.static.Executor(place)<line_sep>boxes_lod_data=paddle.fluid.create_lod_tensor(self.boxes [[1 2]] place)<line_sep>out_res=exe.run(paddle.static.default_main_program() feed={'x':self.x 'boxes':boxes_lod_data} fetch_list=[out.name])<line_sep>self.assertTrue(np.allclose(out_res expect_out))<block_end><block_end><block_end><if_stmt>__name__<eq>'__main__'<block_start>unittest.main()<block_end> |
################################################################################
# Copyright (c) 2020-2021, Berkeley Design Technology, Inc. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
################################################################################
<import_stmt>yaml<class_stmt>Config<block_start><def_stmt>__init__ self config_file_path# Load config file
<block_start><with_stmt>open(config_file_path "r")<as>stream<block_start>self._config=yaml.load(stream Loader=yaml.FullLoader)<block_end># Define colors to be used internally through the app, and also externally if wanted
self.colors={"green":(0 128 0) "white":(255 255 255) "olive":(0 128 128) "black":(0 0 0) "navy":(128 0 0) "red":(0 0 255) "pink":(128 128 255) "maroon":(0 0 128) "grey":(128 128 128) "purple":(128 0 128) "yellow":(0 255 255) "lime":(0 255 0) "fuchsia":(255 0 255) "aqua":(255 255 0) "blue":(255 0 0) "teal":(128 128 0) "silver":(192 192 192) }<block_end><def_stmt>__getitem__ self name<block_start><return>self._config[name]<block_end><block_end> |
"""
Examples of the application of Python decorators in order to
reduce code duplication.
It presents first, the naรฏve approach, with duplicated code,
and then, the improved solution using decorators.
"""<import_from_stmt>base logger<def_stmt>decorator original_function<block_start><def_stmt>inner *args **kwargs# modify original function, or add extra logic
<block_start><return>original_function(*args **kwargs)<block_end><return>inner<block_end># 1. Repeated
<def_stmt>update_db_indexes cursor<block_start>commands=("""REINDEX DATABASE transactional""" )<try_stmt><block_start><for_stmt>command commands<block_start>cursor.execute(command)<block_end><block_end><except_stmt>Exception<as>e<block_start>logger.exception("Error in update_db_indexes: %s" e)<line_sep><return>-1<block_end><else_stmt><block_start>logger.info("update_db_indexes run successfully")<line_sep><return>0<block_end><block_end><def_stmt>move_data_archives cursor<block_start>commands=("""INSERT INTO archive_orders SELECT * from orders
WHERE order_date < '2016-01-01' """ """DELETE from orders WHERE order_date < '2016-01-01' """ )<try_stmt><block_start><for_stmt>command commands<block_start>cursor.execute(command)<block_end><block_end><except_stmt>Exception<as>e<block_start>logger.exception("Error in move_data_archives: %s" e)<line_sep><return>-1<block_end><else_stmt><block_start>logger.info("move_data_archives run successfully")<line_sep><return>0<block_end><block_end> |
<class_stmt>Solution<block_start><def_stmt>isValid self s:str<arrow>bool<block_start><while_stmt>'[]'<in>s<or>'()'<in>s<or>'{}'<in>s<block_start>s=s.replace('[]' '').replace('()' '').replace('{}' '')<block_end><return>len(s)<eq>0<block_end><block_end>"""
time: 10 min
time: O(n)
space: O(n)
errors:
lower case values/keys
Have to use stack because 3 charactors open/close
"""<class_stmt>Solution<block_start><def_stmt>isValid self s:str<arrow>bool<block_start>stk=[]<line_sep>mp={")":"(" "}":"{" "]":"["}<for_stmt>c s<block_start><if_stmt>c<in>mp.values()<block_start>stk.append(c)<block_end><elif_stmt>c<in>mp.keys()<block_start>test=stk.pop()<if>stk<else>'#'<if_stmt>mp[c]<ne>test<block_start><return><false><block_end><block_end><block_end><return>len(stk)<eq>0<block_end><block_end><class_stmt>Solution<block_start><def_stmt>isValid self s<arrow>bool<block_start>stk=[]<for_stmt>c s<block_start><if_stmt>c<eq>'('<block_start>stk.append(')')<block_end><elif_stmt>c<eq>'['<block_start>stk.append(']')<block_end><elif_stmt>c<eq>'{'<block_start>stk.append('}')<block_end><elif_stmt><not>stk<or>stk.pop()<ne>c<block_start><return><false><block_end><block_end><return><not>stk<block_end><block_end> |
"""This script is for preprocessing something-something-v2 dataset.
The code is largely borrowed from https://github.com/MIT-HAN-LAB/temporal-shift-module
and https://github.com/metalbubble/TRN-pytorch/blob/master/process_dataset.py
"""<import_stmt>os<import_stmt>sys<import_stmt>threading<import_stmt>argparse<import_stmt>json<def_stmt>parse_args <block_start>parser=argparse.ArgumentParser(description='prepare something-something-v2 dataset')<line_sep>parser.add_argument('--video_root' type=str default='~/.mxnet/datasets/somethingsomethingv2/20bn-something-something-v2')<line_sep>parser.add_argument('--frame_root' type=str default='~/.mxnet/datasets/somethingsomethingv2/20bn-something-something-v2-frames')<line_sep>parser.add_argument('--anno_root' type=str default='~/.mxnet/datasets/somethingsomethingv2/annotations')<line_sep>parser.add_argument('--num_threads' type=int default=100)<line_sep>parser.add_argument('--decode_video' action='store_true' default=<true>)<line_sep>parser.add_argument('--build_file_list' action='store_true' default=<true>)<line_sep>args=parser.parse_args()<line_sep>args.video_root=os.path.expanduser(args.video_root)<line_sep>args.frame_root=os.path.expanduser(args.frame_root)<line_sep>args.anno_root=os.path.expanduser(args.anno_root)<line_sep><return>args<block_end><def_stmt>split_func l n<block_start>"""Yield successive n-sized chunks from l."""<for_stmt>i range(0 len(l) n)<block_start><yield>l[i:i+n]<block_end><block_end><def_stmt>extract video tmpl='%06d.jpg'<block_start>cmd='ffmpeg -i \"{}/{}\" -threads 1 -vf scale=-1:256 -q:v 0 \"{}/{}/%06d.jpg\"'.format(args.video_root video args.frame_root video[:-5])<line_sep>os.system(cmd)<block_end><def_stmt>target video_list<block_start><for_stmt>video video_list<block_start>os.makedirs(os.path.join(args.frame_root video[:-5]))<line_sep>extract(video)<block_end><block_end><def_stmt>decode_video args<block_start>print(args.video_root)<if_stmt><not>os.path.exists(args.video_root)<block_start><raise>ValueError('Please download videos and set video_root variable.')<block_end><if_stmt><not>os.path.exists(args.frame_root)<block_start>os.makedirs(args.frame_root)<block_end>video_list=os.listdir(args.video_root)<line_sep>splits=list(split_func(video_list args.num_threads))<line_sep>threads=[]<for_stmt>i,split enumerate(splits)<block_start>thread=threading.Thread(target=target args=(split ))<line_sep>thread.start()<line_sep>threads.append(thread)<block_end><for_stmt>thread threads<block_start>thread.join()<block_end><block_end><def_stmt>build_file_list args<block_start><if_stmt><not>os.path.exists(args.anno_root)<block_start><raise>ValueError('Please download annotations and set anno_root variable.')<block_end>dataset_name='something-something-v2'<with_stmt>open(os.path.join(args.anno_root '%s-labels.json'%dataset_name))<as>f<block_start>data=json.load(f)<block_end>categories=[]<for_stmt>i,(cat idx) enumerate(data.items())<block_start><assert_stmt>i<eq>int(idx)# make sure the rank is right
categories.append(cat)<block_end><with_stmt>open('category.txt' 'w')<as>f<block_start>f.write('\n'.join(categories))<block_end>dict_categories={}<for_stmt>i,category enumerate(categories)<block_start>dict_categories[category]=i<block_end>files_input=[os.path.join(args.anno_root '%s-validation.json'%dataset_name) os.path.join(args.anno_root '%s-train.json'%dataset_name) os.path.join(args.anno_root '%s-test.json'%dataset_name)]<line_sep>files_output=[os.path.join(args.anno_root 'val_videofolder.txt') os.path.join(args.anno_root 'train_videofolder.txt') os.path.join(args.anno_root 'test_videofolder.txt')]<for_stmt>(filename_input filename_output) zip(files_input files_output)<block_start><with_stmt>open(filename_input)<as>f<block_start>data=json.load(f)<block_end>folders=[]<line_sep>idx_categories=[]<for_stmt>item data<block_start>folders.append(item['id'])<if_stmt>'test'<not><in>filename_input<block_start>idx_categories.append(dict_categories[item['template'].replace('[' '').replace(']' '')])<block_end><else_stmt><block_start>idx_categories.append(0)<block_end><block_end>output=[]<for_stmt>i range(len(folders))<block_start>curFolder=folders[i]<line_sep>curIDX=idx_categories[i]<line_sep># counting the number of frames in each video folders
dir_files=os.listdir(os.path.join(args.frame_root curFolder))<if_stmt>len(dir_files)<eq>0<block_start>print('video decoding fails at %s' (curFolder))<line_sep>sys.exit()<block_end>output.append('%s %d %d'%(curFolder len(dir_files) curIDX))<line_sep>print('%d/%d'%(i len(folders)))<block_end><with_stmt>open(filename_output 'w')<as>f<block_start>f.write('\n'.join(output))<block_end><block_end><block_end><if_stmt>__name__<eq>'__main__'<block_start><global>args<line_sep>args=parse_args()<if_stmt>args.decode_video<block_start>print('Decoding videos to frames.')<line_sep>decode_video(args)<block_end><if_stmt>args.build_file_list<block_start>print('Generating training files.')<line_sep>build_file_list(args)<block_end><block_end> |
# SPDX-License-Identifier: Apache-2.0
#
# Copyright (C) 2021 Micron Technology, Inc. All rights reserved.
<import_from_stmt>typing List<import_from_stmt>tools config<import_from_stmt>tools.base BaseTest<import_from_stmt>tools.helpers shlex_join<class_stmt>KmtTest(BaseTest)<block_start><def_stmt>__init__ self name:str args:List[str]<block_start>super().__init__(name "kmt")<line_sep>self.args=self.__fix_args(args)<line_sep>self.kmt_out_path=<none><line_sep>self.report["kmt"]={"args":self.args "cmdline":shlex_join(self.args) }<block_end>@staticmethod<def_stmt>__fix_args args:List<block_start>new_args=["kmt"]+list(args)<if_stmt><not>any([arg.startswith("-L")<for>arg args])<block_start>new_args.append("-L")<block_end><if_stmt><not>any([arg.startswith("-s")<for>arg args])<block_start>new_args.append("-s1")<block_end>new_args.append(config.KVDB_HOME)<line_sep>new_args.append(config.KVS_NAME)<line_sep><return>new_args<block_end><def_stmt>execute self<block_start>super()._execute_init()<line_sep>completed_info=super()._run_command(self.args)<line_sep>self.kmt_out_path=completed_info.out_path<line_sep>self._postprocess()<line_sep>self._print_and_save_summary()<line_sep>super()._save_report()<block_end><def_stmt>_postprocess self<block_start>init_phase={"name":"init" "operations":[] }<line_sep>test_phase={"name":"test" "operations":[] }<with_stmt>open(self.kmt_out_path)<as>fd<block_start><for_stmt>line fd<block_start><if_stmt>line.startswith("iclose")<block_start>record=line.split()<line_sep>total_puts=int(record[6])<line_sep>run_time_ms=int(record[15])<line_sep>puts_per_second=int(total_puts/(run_time_ms/1000.0))<line_sep>init_phase["run_time_ms"]=run_time_ms<line_sep>init_put_operation={"name":"put" "throughput":puts_per_second }<line_sep>init_phase["operations"].append(init_put_operation)<block_end><elif_stmt>line.startswith("tclose")<block_start>record=line.split()<line_sep>total_gets,total_puts=int(record[5]) int(record[6])<line_sep>run_time_ms=int(record[15])<line_sep>puts_per_second=int(total_puts/(run_time_ms/1000.0))<line_sep>gets_per_second=int(total_gets/(run_time_ms/1000.0))<line_sep>test_phase["run_time_ms"]=run_time_ms<line_sep>test_put_operation={"name":"put" "throughput":puts_per_second }<line_sep>test_get_operation={"name":"get" "throughput":gets_per_second }<line_sep>test_phase["operations"].extend([test_put_operation test_get_operation])<block_end><elif_stmt>line.startswith("slatency")<block_start>record=line.split()<line_sep>phase=record[1]<line_sep>op=record[2]<line_sep>(lat_min lat_max lat_avg lat_p90 lat_p95 lat_p99 lat_p99_9 lat_p99_99 )=[int(x)<for>x record[5:13]]<if_stmt>phase<eq>"init"<block_start><assert_stmt>op<eq>"put"<line_sep>operation_dict=init_put_operation<block_end><elif_stmt>phase<eq>"test"<block_start><assert_stmt>op<in>["get" "put"]<if_stmt>op<eq>"put"<block_start>operation_dict=test_put_operation<block_end><elif_stmt>op<eq>"get"<block_start>operation_dict=test_get_operation<block_end><else_stmt><block_start><assert_stmt><false><block_end><block_end><else_stmt><block_start><assert_stmt><false><block_end>operation_dict["latency_us"]={"avg":lat_avg "max":lat_max "min":lat_min "percentiles":[[90 lat_p90] [95 lat_p95] [99 lat_p99] [99.9 lat_p99_9] [99.99 lat_p99_99] ] }<block_end><block_end><block_end>self.report["phases"]=[init_phase test_phase ]<block_end><block_end> |
<import_from_stmt>.exception NuRequestException NuException<import_from_stmt>.nubank Nubank<import_from_stmt>.utils.mock_http MockHttpClient<import_from_stmt>.utils.http HttpClient<import_from_stmt>.utils.discovery DISCOVERY_URL<def_stmt>is_alive client:HttpClient=<none><arrow>bool<block_start><if_stmt>client<is><none><block_start>client=HttpClient()<block_end>response=client.raw_get(DISCOVERY_URL)<line_sep><return>response.status_code<in>[200 201]<block_end> |
# ---
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# ---
# Separation via Time-Frequency Masking
# =====================================
#
# One of the most effective ways to separate sounds from a mixture is by
# *masking*. Consider the following mixture, which we will download via
# one of the dataset hooks in *nussl*.
# +
<import_stmt>nussl<import_stmt>matplotlib.pyplot<as>plt<import_stmt>numpy<as>np<import_stmt>copy<import_stmt>time<line_sep>start_time=time.time()<line_sep>musdb=nussl.datasets.MUSDB18(download=<true>)<line_sep>item=musdb[40]<line_sep>mix=item['mix']<line_sep>sources=item['sources']<line_sep># -
# Let's listen to the mixture. Note that it contains 4 sources: drums, bass,
# vocals, and all other sounds (considered as one source: other).
mix.embed_audio()<line_sep>print(mix)<line_sep># Let's now consider the time-frequency representation of this mixture:
plt.figure(figsize=(10 3))<line_sep>plt.title('Mixture spectrogram')<line_sep>nussl.utils.visualize_spectrogram(mix y_axis='mel')<line_sep>plt.tight_layout()<line_sep>plt.show()<line_sep># Masking means to assign each of these time-frequency bins to one of the four
# sources in part or in whole. The first method involves creating a *soft* mask
# on the time-frequency representation, while the second is a *binary* mask. How
# do we assign each time-frequency bin to each source? This is a very hard problem,
# in general. For now, let's consider that we *know* the actual assignment of each
# time-frequency bin. If we know that, how do we separate the sounds?
#
# First let's look at one of the sources, say the drums:
plt.figure(figsize=(10 3))<line_sep>plt.title('Drums')<line_sep>nussl.utils.visualize_spectrogram(sources['drums'] y_axis='mel')<line_sep>plt.tight_layout()<line_sep>plt.show()<line_sep># Looking at this versus the mixture spectrogram, one can see which time-frequency
# bins belong to the drum. Now, let's build a *mask* on the mixture spectrogram
# using a soft mask. We construct the soft mask using the drum STFT data and the
# mixture STFT data, like so:
mask_data=np.abs(sources['drums'].stft())/np.abs(mix.stft())<line_sep># Hmm, this may not be a safe way to do this. What if there's a `0` in both the source
# and the mix? Then we would get `0/0`, which would result in NaN in the mask. Or
# what if the source STFT is louder than the mix at some time-frequency bin due to
# cancellation between sources when mixed? Let's do things a bit more safely by
# using the maximum and some checking...
mask_data=(np.abs(sources['drums'].stft())/np.maximum(np.abs(mix.stft()) np.abs(sources['drums'].stft()))+nussl.constants.EPSILON)<line_sep># Great, some peace of mind. Now let's apply the soft mask to the mixture to
# separate the drums. We can do this by element-wise multiplying the STFT and
# adding the mixture phase.
# +
magnitude,phase=np.abs(mix.stft_data) np.angle(mix.stft_data)<line_sep>masked_abs=magnitude<times>mask_data<line_sep>masked_stft=masked_abs<times>np.exp(1j<times>phase)<line_sep>drum_est=mix.make_copy_with_stft_data(masked_stft)<line_sep>drum_est.istft()<line_sep>drum_est.embed_audio()<line_sep>plt.figure(figsize=(10 3))<line_sep>plt.title('Separated drums')<line_sep>nussl.utils.visualize_spectrogram(drum_est y_axis='mel')<line_sep>plt.tight_layout()<line_sep>plt.show()<line_sep># -
# Cool! Sounds pretty good! But it'd be a drag if we had to type all of
# that every time we wanted to separate something. Lucky for you, we
# built this stuff into the core functionality of *nussl*!
#
# `SoftMask` and `BinaryMask`
# ---------------------------
#
# At the core of *nussl*'s separation functionality are the classes
# `SoftMask` and `BinaryMask`. These are classes that contain some logic
# for masking and can be used with AudioSignal objects. We have a soft mask
# already, so let's build a `SoftMask` object.
soft_mask=nussl.core.masks.SoftMask(mask_data)<line_sep># `soft_mask` contains our mask here:
soft_mask.mask.shape<line_sep># We can apply the soft mask to our mix and return the separated drums easily,
# using the `apply_mask` method:
# +
drum_est=mix.apply_mask(soft_mask)<line_sep>drum_est.istft()<line_sep>drum_est.embed_audio()<line_sep>plt.figure(figsize=(10 3))<line_sep>plt.title('Separated drums')<line_sep>nussl.utils.visualize_spectrogram(drum_est y_axis='mel')<line_sep>plt.tight_layout()<line_sep>plt.show()<line_sep># -
# Sometimes masks are *binary* instead of *soft*. To apply a binary mask, we can do this:
# +
binary_mask=nussl.core.masks.BinaryMask(mask_data<g>.5)<line_sep>drum_est=mix.apply_mask(binary_mask)<line_sep>drum_est.istft()<line_sep>drum_est.embed_audio()<line_sep>plt.figure(figsize=(10 3))<line_sep>plt.title('Separated drums')<line_sep>nussl.utils.visualize_spectrogram(drum_est y_axis='mel')<line_sep>plt.tight_layout()<line_sep>plt.show()<line_sep># -
# Playing around with the threshold will result in more or less leakage of other sources:
# +
binary_mask=nussl.core.masks.BinaryMask(mask_data<g>.05)<line_sep>drum_est=mix.apply_mask(binary_mask)<line_sep>drum_est.istft()<line_sep>drum_est.embed_audio()<line_sep>plt.figure(figsize=(10 3))<line_sep>plt.title('Separated drums')<line_sep>nussl.utils.visualize_spectrogram(drum_est y_axis='mel')<line_sep>plt.tight_layout()<line_sep>plt.show()<line_sep># -
# You can hear the vocals slightly in the background as well as the
# other sources.
#
# Finally, given a list of separated sources, we can use some handy nussl
# functionality to easily visualize the masks and listen to the original
# sources that make up the mixture.
# +
plt.figure(figsize=(10 7))<line_sep>plt.subplot(211)<line_sep>nussl.utils.visualize_sources_as_masks(sources db_cutoff=-60 y_axis='mel')<line_sep>plt.subplot(212)<line_sep>nussl.utils.visualize_sources_as_waveform(sources show_legend=<false>)<line_sep>plt.tight_layout()<line_sep>plt.show()<line_sep>nussl.play_utils.multitrack(sources ext='.wav')<line_sep># -
end_time=time.time()<line_sep>time_taken=end_time-start_time<line_sep>print(f'Time taken: {time_taken:.4f} seconds')<line_sep> |
# Copyright 2016 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
<import_from_stmt>gpu_tests gpu_integration_test<import_from_stmt>gpu_tests.gpu_test_expectations GpuTestExpectations<import_stmt>sys<line_sep># There are no expectations for info_collection
<class_stmt>InfoCollectionExpectations(GpuTestExpectations)<block_start><def_stmt>SetExpectations self<block_start><pass><block_end><block_end><class_stmt>InfoCollectionTest(gpu_integration_test.GpuIntegrationTest)<block_start>@classmethod<def_stmt>Name cls<block_start><return>'info_collection'<block_end>@classmethod<def_stmt>AddCommandlineArgs cls parser<block_start>parser.add_option('--expected-device-id' help='The expected device id')<line_sep>parser.add_option('--expected-vendor-id' help='The expected vendor id')<block_end>@classmethod<def_stmt>GenerateGpuTests cls options<block_start><yield>('_' '_' (options.expected_vendor_id options.expected_device_id))<block_end>@classmethod<def_stmt>SetUpProcess cls<block_start>super(cls InfoCollectionTest).SetUpProcess()<line_sep>cls.CustomizeBrowserArgs([])<line_sep>cls.StartBrowser()<block_end><def_stmt>RunActualGpuTest self test_path *args# Make sure the GPU process is started
<block_start>self.tab.action_runner.Navigate('chrome:gpu')<line_sep># Gather the IDs detected by the GPU process
<if_stmt><not>self.browser.supports_system_info<block_start>self.fail("Browser doesn't support GetSystemInfo")<block_end>gpu=self.browser.GetSystemInfo().gpu.devices[0]<if_stmt><not>gpu<block_start>self.fail("System Info doesn't have a gpu")<block_end>detected_vendor_id=gpu.vendor_id<line_sep>detected_device_id=gpu.device_id<line_sep># Gather the expected IDs passed on the command line
<if_stmt>args[0]<eq><none><or>args[1]<eq><none><block_start>self.fail("Missing --expected-[vendor|device]-id command line args")<block_end>expected_vendor_id=int(args[0] 16)<line_sep>expected_device_id=int(args[1] 16)<line_sep># Check expected and detected matches
<if_stmt>detected_vendor_id<ne>expected_vendor_id<block_start>self.fail('Vendor ID mismatch, expected %s but got %s.'%(expected_vendor_id detected_vendor_id))<block_end><if_stmt>detected_device_id<ne>expected_device_id<block_start>self.fail('device ID mismatch, expected %s but got %s.'%(expected_device_id detected_device_id))<block_end><block_end>@classmethod<def_stmt>_CreateExpectations cls<block_start><return>InfoCollectionExpectations()<block_end><block_end><def_stmt>load_tests loader tests pattern<block_start><del_stmt>loader tests pattern# Unused.
<return>gpu_integration_test.LoadAllTestsInModule(sys.modules[__name__])<block_end> |
<import_stmt>image network rpc sensor struct<import_stmt>time<import_stmt>micropython<import_from_stmt>pyb Pin<import_from_stmt>pyb LED<line_sep>red_led=LED(1)<line_sep>green_led=LED(2)<line_sep>blue_led=LED(3)<line_sep>ir_led=LED(4)<def_stmt>led_control x<block_start><if_stmt>(x&1)<eq>0<block_start>red_led.off()<block_end><elif_stmt>(x&1)<eq>1<block_start>red_led.on()<block_end><if_stmt>(x&2)<eq>0<block_start>green_led.off()<block_end><elif_stmt>(x&2)<eq>2<block_start>green_led.on()<block_end><if_stmt>(x&4)<eq>0<block_start>blue_led.off()<block_end><elif_stmt>(x&4)<eq>4<block_start>blue_led.on()<block_end><if_stmt>(x&8)<eq>0<block_start>ir_led.off()<block_end><elif_stmt>(x&8)<eq>8<block_start>ir_led.on()<block_end><block_end>processing=<true><line_sep># pin used to sync the 2 cams
pin4=Pin('P4' Pin.IN Pin.PULL_UP)<line_sep># setting the SPI communication as a slave
interface=rpc.rpc_spi_slave(cs_pin="P3" clk_polarity=1 clk_phase=0)<line_sep># here we always choose the QVGA format (320x240) inside a VGA image
img_width=320<line_sep>img_height=240<line_sep>sensor.reset()<line_sep>sensor_format=sensor.GRAYSCALE<line_sep>sensor_size=sensor.VGA<line_sep>sensor.set_pixformat(sensor_format)<line_sep>sensor.set_framesize(sensor_size)<if_stmt>img_width<ne>sensor.width()<or>img_height<ne>sensor.height()<block_start>sensor.set_windowing((int((sensor.width()-img_width)/2) int((sensor.height()-img_height)/2) img_width img_height))<block_end>sensor.skip_frames(time=2000)<line_sep>sensor.snapshot()<line_sep>################################################################
# Call Backs
################################################################
<def_stmt>sensor_config data<block_start><global>processing<line_sep>gain_db,exposure_us,r_gain_db,g_gain_db,b_gain_db=struct.unpack("<fIfff" data)<line_sep>sensor.set_auto_gain(<false> gain_db)<line_sep>sensor.set_auto_exposure(<false> exposure_us)<line_sep>sensor.set_auto_whitebal(<false> (r_gain_db g_gain_db b_gain_db))<line_sep>processing=<false><line_sep><return>struct.pack("<fIfff" gain_db exposure_us r_gain_db g_gain_db b_gain_db)<block_end><def_stmt>raw_image_read_cb <block_start><global>processing<line_sep>interface.put_bytes(sensor.get_fb().bytearray() 5000)# timeout
processing=<false><block_end><def_stmt>raw_image_read data<block_start>interface.schedule_callback(raw_image_read_cb)<line_sep><return>bytes()<block_end><def_stmt>loop_callback <block_start><global>processing<if_stmt><not>processing<block_start><raise>Exception<block_end><block_end># Register call backs.
interface.register_callback(raw_image_read)<line_sep>interface.register_callback(sensor_config)<line_sep>interface.setup_loop_callback(loop_callback)<line_sep># a simple visual way to know the slave cam has started properly and is ready
led_control(4)<line_sep>time.sleep(500)<line_sep>led_control(0)<line_sep>time.sleep(500)<line_sep>led_control(4)<line_sep>time.sleep(500)<line_sep>led_control(0)<line_sep># configuration step: getting the same image settings as the controller cam
<try_stmt><block_start>processing=<true><line_sep>interface.loop()<block_end><except_stmt><block_start><pass><block_end># serve for ever
<while_stmt><true><block_start><try_stmt><block_start>processing=<true><line_sep># GPIO sync
<while_stmt><not>pin4.value()<block_start><pass><block_end># Get a snapshot that will be sent back to the controller cam
sensor.snapshot()<line_sep>interface.loop()<block_end><except_stmt><block_start><pass><block_end><block_end> |
<import_from_stmt>.ml_tester MLTester<import_from_stmt>.dl_tester DLTester<line_sep> |
<import_stmt>logging<def_stmt>setup_logging <block_start>FORMAT='%(asctime)-15s %(message)s'<line_sep>logging.basicConfig(filename='log.txt' filemode='w' datefmt='%Y-%m-%d %H:%M:%S' level=logging.INFO format=FORMAT)<line_sep>console=logging.StreamHandler()<line_sep>console.setLevel(logging.INFO)<line_sep>logging.getLogger('').addHandler(console)<block_end> |
<import_stmt>subprocess<assert_stmt>subprocess.call(['run-backup'])<eq>0<class_stmt>TestCase<block_start><pass><block_end><class_stmt>MyTest(TestCase)<block_start><pass><block_end># found by /home/rasmus/code/ql/python/ql/test/query-tests/Statements/asserts/AssertLiteralConstant.qlref
|
<import_from_stmt>unittest.mock patch<import_from_stmt>django.core.management call_command<import_from_stmt>django.core.management.base CommandError<import_from_stmt>orchestra.tests.helpers OrchestraTestCase<class_stmt>MigrateCertificationsTestCase(OrchestraTestCase)<block_start>patch_path=('orchestra.management.commands.'<concat>'migrate_certifications.migrate_certifications')<line_sep>@patch(patch_path)<def_stmt>test_options self mock_migrate# Test no options
<block_start><with_stmt>self.assertRaises(CommandError)<block_start>call_command('migrate_certifications')<line_sep>mock_migrate.assert_not_called()<block_end># Test
call_command('migrate_certifications' 'test_source_workflow_slug' 'ntest_destination_workflow_slug' certifications=['test_cert_1' 'test_cert_2'])<line_sep>mock_migrate.called_once_with('test_source_workflow_slug' 'test_destination_workflow_slug' ['test_cert_1' 'test_cert_2'])<block_end><block_end> |
# Parse Newick-formatted file into tree of { 'kids': [], 'label': '', 'length': '' }
<import_stmt>logging<import_from_stmt>utils die<def_stmt>skipSpaces treeString offset<block_start><while_stmt>(offset<ne>len(treeString)<and>treeString[offset].isspace())<block_start>offset<augadd>1<block_end><return>offset<block_end><def_stmt>parseQuoted treeString offset<block_start>"""Read in a quoted, possibly \-escaped string in treeString at offset"""<line_sep>label=''<line_sep>quoteChar=treeString[offset]<line_sep>offset<augadd>1<line_sep>labelStart=offset<while_stmt>(offset<ne>len(treeString)<and>treeString[offset]<ne>quoteChar)<block_start><if_stmt>(treeString[offset]<eq>'\\')<block_start>offset<augadd>1<block_end>offset<augadd>1<block_end><if_stmt>(treeString[offset]<ne>quoteChar)<block_start>die("Missing end-"+quoteChar+" after '"+treeString+"'")<block_end><else_stmt><block_start>label=treeString[labelStart:offset]<line_sep>offset=skipSpaces(treeString offset+1)<block_end><return>(label offset)<block_end><def_stmt>terminatesLabel treeString offset<block_start>"""Return True if treeString+offset is empty or starts w/char that would terminate a label"""<line_sep><return>(offset<eq>len(treeString)<or>treeString[offset]<eq>','<or>treeString[offset]<eq>')'<or>treeString[offset]<eq>';'<or>treeString[offset]<eq>':')<block_end><def_stmt>parseLabel treeString offset<block_start>"""Read in a possibly quoted, possibly \-escaped node label terminated by [,):;]"""<if_stmt>(offset<eq>len(treeString))<block_start>label=''<block_end><elif_stmt>(treeString[offset]<eq>"'"<or>treeString[offset]<eq>'"')<block_start>(label offset)=parseQuoted(treeString offset)<block_end><else_stmt><block_start>labelStart=offset<while_stmt>(<not>terminatesLabel(treeString offset))<block_start><if_stmt>(treeString[offset]<eq>'\\')<block_start>offset<augadd>1<block_end>offset<augadd>1<block_end>label=treeString[labelStart:offset]<line_sep>offset=skipSpaces(treeString offset)<block_end><return>(label offset)<block_end><def_stmt>parseLength treeString offset<block_start>"""If treeString[offset] is ':', then parse the number that follows; otherwise return 0.0."""<if_stmt>(offset<ne>len(treeString)<and>treeString[offset]<eq>':')# Branch length
<block_start>offset=skipSpaces(treeString offset+1)<if_stmt>(<not>treeString[offset].isdigit())<block_start>die("Expected number to follow ':' but instead got '"+treeString[offset:offset+100]+"'")<block_end>lengthStart=offset<while_stmt>(offset<ne>len(treeString)<and>(treeString[offset].isdigit()<or>treeString[offset]<eq>'.'<or>treeString[offset]<eq>'E'<or>treeString[offset]<eq>'e'<or>treeString[offset]<eq>'-'))<block_start>offset<augadd>1<block_end>lengthStr=treeString[lengthStart:offset]<line_sep>offset=skipSpaces(treeString offset)<line_sep><return>(lengthStr offset)<block_end><else_stmt><block_start><return>('' offset)<block_end><block_end><def_stmt>parseBranch treeString offset internalNode<block_start>"""Recursively parse Newick branch (x, y, z)[label][:length] from treeString at offset"""<if_stmt>(treeString[offset]<ne>'(')<block_start>die("parseBranch called on treeString that doesn't begin with '(': '"+treeString+"'")<block_end>branchStart=offset<line_sep>internalNode<augadd>1<line_sep>branch={'kids':[] 'label':'' 'length':'' 'inode':internalNode}<line_sep>offset=skipSpaces(treeString offset+1)<while_stmt>(offset<ne>len(treeString)<and>treeString[offset]<ne>')'<and>treeString[offset]<ne>';')<block_start>(child offset internalNode)=parseString(treeString offset internalNode)<line_sep>branch['kids'].append(child)<if_stmt>(treeString[offset]<eq>',')<block_start>offset=skipSpaces(treeString offset+1)<block_end><block_end><if_stmt>(offset<eq>len(treeString))<block_start>die("Input ended before ')' for '"+treeString[branchStart:branchStart+100]+"'")<block_end><if_stmt>(treeString[offset]<eq>')')<block_start>offset=skipSpaces(treeString offset+1)<block_end><else_stmt><block_start>die("Can't find ')' matching '"+treeString[branchStart:branchStart+100]+"', "+"instead got '"+treeString[offset:offset+100]+"'")<block_end>(branch['label'] offset)=parseLabel(treeString offset)<line_sep>(branch['length'] offset)=parseLength(treeString offset)<line_sep><return>(branch offset internalNode)<block_end><def_stmt>parseString treeString offset=0 internalNode=0<block_start>"""Recursively parse Newick tree from treeString"""<line_sep>offset=skipSpaces(treeString offset)<if_stmt>(treeString[offset]<eq>'(')<block_start><return>parseBranch(treeString offset internalNode)<block_end><else_stmt><block_start>(label offset)=parseLabel(treeString offset)<line_sep>(length offset)=parseLength(treeString offset)<line_sep>leaf={'kids':<none> 'label':label 'length':length}<line_sep><return>(leaf offset internalNode)<block_end><block_end><def_stmt>parseFile treeFile<block_start>"""Read Newick file, return tree object"""<with_stmt>open(treeFile 'r')<as>treeF<block_start>line1=treeF.readline().strip()<if_stmt>(line1<eq>'')<block_start><return><none><block_end>(tree offset internalNode)=parseString(line1)<if_stmt>(offset<ne>len(line1)<and>line1[offset]<ne>';')<block_start>die("Tree terminated without ';' before '"+line1[offset:offset+100]+"'")<block_end>treeF.close()<block_end><return>tree<block_end><def_stmt>treeToString node pretty=<false> indent=0<block_start>"""Return a Newick string encoding node and its descendants, optionally pretty-printing with
newlines and indentation. String is not ';'-terminated, caller must do that."""<if_stmt><not>node<block_start><return>''<block_end>labelLen=''<if_stmt>(node['label'])<block_start>labelLen<augadd>node['label']<block_end><if_stmt>(node['length'])<block_start>labelLen<augadd>':'+node['length']<block_end><if_stmt>(node['kids'])<block_start>string='('<line_sep>kidIndent=indent+1<line_sep>kidStrings=[treeToString(kid pretty kidIndent)<for>kid node['kids']]<line_sep>sep=','<if_stmt>(pretty)<block_start>sep=',\n'+' '<times>kidIndent<block_end>string<augadd>sep.join(kidStrings)<line_sep>string<augadd>')'<line_sep>string<augadd>labelLen<block_end><else_stmt><block_start>string=labelLen<block_end><return>string<line_sep><block_end><def_stmt>printTree tree pretty=<false> indent=0<block_start>"""Print out Newick text encoding tree, optionally pretty-printing with
newlines and indentation."""<line_sep>print(treeToString(tree pretty indent)+';')<block_end><def_stmt>leafNames node<block_start>"""Return a list of labels of all leaf descendants of node"""<if_stmt>(node['kids'])<block_start><return>[leaf<for>kid node['kids']<for>leaf leafNames(kid)]<block_end><else_stmt><block_start><return>[node['label']]<block_end><block_end><def_stmt>treeIntersectIds node idLookup sampleSet lookupFunc=<none><block_start>"""For each leaf in node, attempt to look up its label in idLookup; replace if found.
Prune nodes with no matching leaves. Store new leaf labels in sampleSet.
If lookupFunc is given, it is passed two arguments (label, idLookup) and returns a
possible empty list of matches."""<if_stmt>(node['kids'])# Internal node: prune
<block_start>prunedKids=[]<for_stmt>kid (node['kids'])<block_start>kidIntersected=treeIntersectIds(kid idLookup sampleSet lookupFunc)<if_stmt>(kidIntersected)<block_start>prunedKids.append(kidIntersected)<block_end><block_end><if_stmt>(len(prunedKids)<g>1)<block_start>node['kids']=prunedKids<block_end><elif_stmt>(len(prunedKids)<eq>1)<block_start>node=prunedKids[0]<block_end><else_stmt><block_start>node=<none><block_end><block_end><else_stmt># Leaf: lookup, prune if not found
<block_start>label=node['label']<if_stmt>(lookupFunc)<block_start>matchList=lookupFunc(node['label'] idLookup)<block_end><elif_stmt>label<in>idLookup<block_start>matchList=idLookup[label]<block_end><else_stmt><block_start>matchList=[]<block_end><if_stmt>(<not>matchList)<block_start>logging.info("No match for leaf '"+label+"'")<line_sep>node=<none><block_end><else_stmt><block_start><if_stmt>(len(matchList)<ne>1)<block_start>logging.warn("Non-unique match for leaf '"+label+"': ['"+"', '".join(matchList)+"']")<block_end><else_stmt><block_start>logging.debug(label+' --> '+matchList[0])<line_sep><block_end>node['label']=matchList[0]<line_sep>sampleSet.add(matchList[0])<block_end><block_end><return>node<block_end> |
<def_stmt>assert_revision revision author=<none> message=<none><block_start>"""Asserts values of the given fields in the provided revision.
:param revision: The revision to validate
:param author: that must be present in the ``revision``
:param message: message substring that must be present in ``revision``
"""<if_stmt>author<block_start><assert_stmt>author<eq>revision.author<block_end><if_stmt>message<block_start><assert_stmt>message<in>revision.message<block_end><block_end> |
# Copyright 2017 Neosapience, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ========================================================================
<import_stmt>unittest<import_stmt>darkon<import_stmt>tensorflow<as>tf<import_stmt>numpy<as>np<line_sep>_classes=2<def_stmt>nn_graph activation# create graph
<block_start>x=tf.placeholder(tf.float32 (1 2 2 3) 'x_placeholder')<line_sep>y=tf.placeholder(tf.int32 name='y_placeholder' shape=[1 2])<with_stmt>tf.name_scope('conv1')<block_start>conv_1=tf.layers.conv2d(inputs=x filters=10 kernel_size=[2 2] padding="same" activation=activation)<block_end><with_stmt>tf.name_scope('fc2')<block_start>flatten=tf.layers.flatten(conv_1)<line_sep>top=tf.layers.dense(flatten _classes)<block_end>logits=tf.nn.softmax(top)<line_sep><return>x<block_end><class_stmt>GradcamGuidedBackprop(unittest.TestCase)<block_start><def_stmt>setUp self<block_start>tf.reset_default_graph()<block_end><def_stmt>tearDown self<block_start>x=nn_graph(activation=self.activation_fn)<line_sep>image=np.random.uniform(size=(2 2 3))<with_stmt>tf.Session()<as>sess<block_start>sess.run(tf.global_variables_initializer())<line_sep>gradcam_ops=darkon.Gradcam.candidate_featuremap_op_names(sess)<if_stmt>self.enable_guided_backprop<block_start>_=darkon.Gradcam(x _classes gradcam_ops[-1])<block_end>g=tf.get_default_graph()<line_sep>from_ts=g.get_operation_by_name(gradcam_ops[-1]).outputs<line_sep>to_ts=g.get_operation_by_name(gradcam_ops[-2]).outputs<line_sep>max_output=tf.reduce_max(from_ts axis=3)<line_sep>y=tf.reduce_sum(-max_output<times>1e2)<line_sep>grad=tf.gradients(y to_ts)[0]<line_sep>grad_val=sess.run(grad feed_dict={x:np.expand_dims(image 0)})<if_stmt>self.enable_guided_backprop<block_start>self.assertTrue(<not>np.any(grad_val))<block_end><else_stmt><block_start>self.assertTrue(np.any(grad_val))<block_end><block_end><block_end><def_stmt>test_relu self<block_start>self.activation_fn=tf.nn.relu<line_sep>self.enable_guided_backprop=<false><block_end><def_stmt>test_relu_guided self<block_start>self.activation_fn=tf.nn.relu<line_sep>self.enable_guided_backprop=<true><block_end><def_stmt>test_tanh self<block_start>self.activation_fn=tf.nn.tanh<line_sep>self.enable_guided_backprop=<false><block_end><def_stmt>test_tanh_guided self<block_start>self.activation_fn=tf.nn.tanh<line_sep>self.enable_guided_backprop=<true><block_end><def_stmt>test_sigmoid self<block_start>self.activation_fn=tf.nn.sigmoid<line_sep>self.enable_guided_backprop=<false><block_end><def_stmt>test_sigmoid_guided self<block_start>self.activation_fn=tf.nn.sigmoid<line_sep>self.enable_guided_backprop=<true><block_end><def_stmt>test_relu6 self<block_start>self.activation_fn=tf.nn.relu6<line_sep>self.enable_guided_backprop=<false><block_end><def_stmt>test_relu6_guided self<block_start>self.activation_fn=tf.nn.relu6<line_sep>self.enable_guided_backprop=<true><block_end><def_stmt>test_elu self<block_start>self.activation_fn=tf.nn.elu<line_sep>self.enable_guided_backprop=<false><block_end><def_stmt>test_elu_guided self<block_start>self.activation_fn=tf.nn.elu<line_sep>self.enable_guided_backprop=<true><block_end><def_stmt>test_selu self<block_start>self.activation_fn=tf.nn.selu<line_sep>self.enable_guided_backprop=<false><block_end><def_stmt>test_selu_guided self<block_start>self.activation_fn=tf.nn.selu<line_sep>self.enable_guided_backprop=<true><block_end><def_stmt>test_softplus self<block_start>self.activation_fn=tf.nn.softplus<line_sep>self.enable_guided_backprop=<false><block_end><def_stmt>test_test_softplus_guided self<block_start>self.activation_fn=tf.nn.softplus<line_sep>self.enable_guided_backprop=<true><block_end><def_stmt>test_softsign self<block_start>self.activation_fn=tf.nn.softsign<line_sep>self.enable_guided_backprop=<false><block_end><def_stmt>test_softsign_guided self<block_start>self.activation_fn=tf.nn.softsign<line_sep>self.enable_guided_backprop=<true><block_end><block_end> |
#! /usr/bin/env python
# This file is part of Scapy
# See http://www.secdev.org/projects/scapy for more information
# Copyright (C) <NAME> <<EMAIL>>
# Copyright (C) <NAME> <<EMAIL>>
# This program is published under a GPLv2 license
# scapy.contrib.description = GMLAN Utilities
# scapy.contrib.status = loads
<import_stmt>time<import_from_stmt>scapy.contrib.automotive.gm.gmlan GMLAN GMLAN_SA GMLAN_RD GMLAN_TD GMLAN_PM GMLAN_RMBA<import_from_stmt>scapy.config conf<import_from_stmt>scapy.contrib.isotp ISOTPSocket<import_from_stmt>scapy.error warning log_loading<import_from_stmt>scapy.utils PeriodicSenderThread<line_sep>__all__=["GMLAN_TesterPresentSender" "GMLAN_InitDiagnostics" "GMLAN_GetSecurityAccess" "GMLAN_RequestDownload" "GMLAN_TransferData" "GMLAN_TransferPayload" "GMLAN_ReadMemoryByAddress" "GMLAN_BroadcastSocket"]<line_sep>log_loading.info("\"conf.contribs['GMLAN']"<concat>"['treat-response-pending-as-answer']\" set to True). This "<concat>"is required by the GMLAN-Utils module to operate "<concat>"correctly.")<try_stmt><block_start>conf.contribs['GMLAN']['treat-response-pending-as-answer']=<false><block_end><except_stmt>KeyError<block_start>conf.contribs['GMLAN']={'treat-response-pending-as-answer':<false>}<block_end><class_stmt>GMLAN_TesterPresentSender(PeriodicSenderThread)<block_start><def_stmt>__init__ self sock pkt=GMLAN(service="TesterPresent") interval=2<block_start>""" Thread to send TesterPresent messages packets periodically
Args:
sock: socket where packet is sent periodically
pkt: packet to send
interval: interval between two packets
"""<line_sep>PeriodicSenderThread.__init__(self sock pkt interval)<block_end><block_end><def_stmt>_check_response resp verbose<block_start><if_stmt>resp<is><none><block_start><if_stmt>verbose<block_start>print("Timeout.")<block_end><return><false><block_end><if_stmt>verbose<block_start>resp.show()<block_end><return>resp.sprintf("%GMLAN.service%")<ne>"NegativeResponse"<block_end><def_stmt>_send_and_check_response sock req timeout verbose<block_start><if_stmt>verbose<block_start>print("Sending %s"%repr(req))<block_end>resp=sock.sr1(req timeout=timeout verbose=0)<line_sep><return>_check_response(resp verbose)<block_end><def_stmt>GMLAN_InitDiagnostics sock broadcastsocket=<none> timeout=<none> verbose=<none> retry=0<block_start>"""Send messages to put an ECU into an diagnostic/programming state.
Args:
sock: socket to send the message on.
broadcast: socket for broadcasting. If provided some message will be
sent as broadcast. Recommended when used on a network with
several ECUs.
timeout: timeout for sending, receiving or sniffing packages.
verbose: set verbosity level
retry: number of retries in case of failure.
Returns true on success.
"""<if_stmt>verbose<is><none><block_start>verbose=conf.verb<block_end>retry=abs(retry)<while_stmt>retry<ge>0<block_start>retry<augsub>1<line_sep># DisableNormalCommunication
p=GMLAN(service="DisableNormalCommunication")<if_stmt>broadcastsocket<is><none><block_start><if_stmt><not>_send_and_check_response(sock p timeout verbose)<block_start><continue><block_end><block_end><else_stmt><block_start><if_stmt>verbose<block_start>print("Sending %s as broadcast"%repr(p))<block_end>broadcastsocket.send(p)<block_end>time.sleep(0.05)<line_sep># ReportProgrammedState
p=GMLAN(service="ReportProgrammingState")<if_stmt><not>_send_and_check_response(sock p timeout verbose)<block_start><continue><block_end># ProgrammingMode requestProgramming
p=GMLAN()/GMLAN_PM(subfunction="requestProgrammingMode")<if_stmt><not>_send_and_check_response(sock p timeout verbose)<block_start><continue><block_end>time.sleep(0.05)<line_sep># InitiateProgramming enableProgramming
# No response expected
p=GMLAN()/GMLAN_PM(subfunction="enableProgrammingMode")<if_stmt>verbose<block_start>print("Sending %s"%repr(p))<block_end>sock.send(p)<line_sep>time.sleep(0.05)<line_sep><return><true><block_end><return><false><block_end><def_stmt>GMLAN_GetSecurityAccess sock keyFunction level=1 timeout=<none> verbose=<none> retry=0<block_start>"""Authenticate on ECU. Implements Seey-Key procedure.
Args:
sock: socket to send the message on.
keyFunction: function implementing the key algorithm.
level: level of access
timeout: timeout for sending, receiving or sniffing packages.
verbose: set verbosity level
retry: number of retries in case of failure.
Returns true on success.
"""<if_stmt>verbose<is><none><block_start>verbose=conf.verb<block_end>retry=abs(retry)<if_stmt>level%2<eq>0<block_start>warning("Parameter Error: Level must be an odd number.")<line_sep><return><false><block_end><while_stmt>retry<ge>0<block_start>retry<augsub>1<line_sep>request=GMLAN()/GMLAN_SA(subfunction=level)<if_stmt>verbose<block_start>print("Requesting seed..")<block_end>resp=sock.sr1(request timeout=timeout verbose=0)<if_stmt><not>_check_response(resp verbose)<block_start><if_stmt>verbose<block_start>print("Negative Response.")<block_end><continue><block_end>seed=resp.securitySeed<if_stmt>seed<eq>0<block_start><if_stmt>verbose<block_start>print("ECU security already unlocked. (seed is 0x0000)")<block_end><return><true><block_end>keypkt=GMLAN()/GMLAN_SA(subfunction=level+1 securityKey=keyFunction(seed))<if_stmt>verbose<block_start>print("Responding with key..")<block_end>resp=sock.sr1(keypkt timeout=timeout verbose=0)<if_stmt>resp<is><none><block_start><if_stmt>verbose<block_start>print("Timeout.")<block_end><continue><block_end><if_stmt>verbose<block_start>resp.show()<block_end><if_stmt>resp.sprintf("%GMLAN.service%")<eq>"SecurityAccessPositiveResponse"# noqa: E501
<block_start><if_stmt>verbose<block_start>print("SecurityAccess granted.")<block_end><return><true><block_end># Invalid Key
<elif_stmt>resp.sprintf("%GMLAN.service%")<eq>"NegativeResponse"<and>resp.sprintf("%GMLAN.returnCode%")<eq>"InvalidKey"<block_start><if_stmt>verbose<block_start>print("Key invalid")<block_end><continue><block_end><block_end><return><false><block_end><def_stmt>GMLAN_RequestDownload sock length timeout=<none> verbose=<none> retry=0<block_start>"""Send RequestDownload message.
Usually used before calling TransferData.
Args:
sock: socket to send the message on.
length: value for the message's parameter 'unCompressedMemorySize'.
timeout: timeout for sending, receiving or sniffing packages.
verbose: set verbosity level.
retry: number of retries in case of failure.
Returns true on success.
"""<if_stmt>verbose<is><none><block_start>verbose=conf.verb<block_end>retry=abs(retry)<while_stmt>retry<ge>0# RequestDownload
<block_start>pkt=GMLAN()/GMLAN_RD(memorySize=length)<line_sep>resp=sock.sr1(pkt timeout=timeout verbose=0)<if_stmt>_check_response(resp verbose)<block_start><return><true><block_end>retry<augsub>1<if_stmt>retry<ge>0<and>verbose<block_start>print("Retrying..")<block_end><block_end><return><false><block_end><def_stmt>GMLAN_TransferData sock addr payload maxmsglen=<none> timeout=<none> verbose=<none> retry=0<block_start>"""Send TransferData message.
Usually used after calling RequestDownload.
Args:
sock: socket to send the message on.
addr: destination memory address on the ECU.
payload: data to be sent.
maxmsglen: maximum length of a single iso-tp message. (default:
maximum length)
timeout: timeout for sending, receiving or sniffing packages.
verbose: set verbosity level.
retry: number of retries in case of failure.
Returns true on success.
"""<if_stmt>verbose<is><none><block_start>verbose=conf.verb<block_end>retry=abs(retry)<line_sep>startretry=retry<line_sep>scheme=conf.contribs['GMLAN']['GMLAN_ECU_AddressingScheme']<if_stmt>addr<l>0<or>addr<ge>2<power>(8<times>scheme)<block_start>warning("Error: Invalid address "+hex(addr)+" for scheme "+str(scheme))<line_sep><return><false><block_end># max size of dataRecord according to gmlan protocol
<if_stmt>maxmsglen<is><none><or>maxmsglen<le>0<or>maxmsglen<g>(4093-scheme)<block_start>maxmsglen=(4093-scheme)<block_end><for_stmt>i range(0 len(payload) maxmsglen)<block_start>retry=startretry<while_stmt><true><block_start><if_stmt>len(payload[i:])<g>maxmsglen<block_start>transdata=payload[i:i+maxmsglen]<block_end><else_stmt><block_start>transdata=payload[i:]<block_end>pkt=GMLAN()/GMLAN_TD(startingAddress=addr+i dataRecord=transdata)<line_sep>resp=sock.sr1(pkt timeout=timeout verbose=0)<if_stmt>_check_response(resp verbose)<block_start><break><block_end>retry<augsub>1<if_stmt>retry<ge>0<block_start><if_stmt>verbose<block_start>print("Retrying..")<block_end><block_end><else_stmt><block_start><return><false><block_end><block_end><block_end><return><true><block_end><def_stmt>GMLAN_TransferPayload sock addr payload maxmsglen=<none> timeout=<none> verbose=<none> retry=0<block_start>"""Send data by using GMLAN services.
Args:
sock: socket to send the data on.
addr: destination memory address on the ECU.
payload: data to be sent.
maxmsglen: maximum length of a single iso-tp message. (default:
maximum length)
timeout: timeout for sending, receiving or sniffing packages.
verbose: set verbosity level.
retry: number of retries in case of failure.
Returns true on success.
"""<if_stmt><not>GMLAN_RequestDownload(sock len(payload) timeout=timeout verbose=verbose retry=retry)<block_start><return><false><block_end><if_stmt><not>GMLAN_TransferData(sock addr payload maxmsglen=maxmsglen timeout=timeout verbose=verbose retry=retry)<block_start><return><false><block_end><return><true><block_end><def_stmt>GMLAN_ReadMemoryByAddress sock addr length timeout=<none> verbose=<none> retry=0<block_start>"""Read data from ECU memory.
Args:
sock: socket to send the data on.
addr: source memory address on the ECU.
length: bytes to read
timeout: timeout for sending, receiving or sniffing packages.
verbose: set verbosity level.
retry: number of retries in case of failure.
Returns the bytes read.
"""<if_stmt>verbose<is><none><block_start>verbose=conf.verb<block_end>retry=abs(retry)<line_sep>scheme=conf.contribs['GMLAN']['GMLAN_ECU_AddressingScheme']<if_stmt>addr<l>0<or>addr<ge>2<power>(8<times>scheme)<block_start>warning("Error: Invalid address "+hex(addr)+" for scheme "+str(scheme))<line_sep><return><none><block_end># max size of dataRecord according to gmlan protocol
<if_stmt>length<le>0<or>length<g>(4094-scheme)<block_start>warning("Error: Invalid length "+hex(length)+" for scheme "+str(scheme)+". Choose between 0x1 and "+hex(4094-scheme))<line_sep><return><none><block_end><while_stmt>retry<ge>0# RequestDownload
<block_start>pkt=GMLAN()/GMLAN_RMBA(memoryAddress=addr memorySize=length)<line_sep>resp=sock.sr1(pkt timeout=timeout verbose=0)<if_stmt>_check_response(resp verbose)<block_start><return>resp.dataRecord<block_end>retry<augsub>1<if_stmt>retry<ge>0<and>verbose<block_start>print("Retrying..")<block_end><block_end><return><none><block_end><def_stmt>GMLAN_BroadcastSocket interface<block_start>"""Returns a GMLAN broadcast socket using interface."""<line_sep><return>ISOTPSocket(interface sid=0x101 did=0x0 basecls=GMLAN extended_addr=0xfe)<block_end> |
<import_from_stmt>.basis_module build_basis_module<import_from_stmt>.blendmask BlendMask<line_sep> |
<import_stmt>numpy<as>np<import_stmt>neurolab<as>nl<line_sep># Define the input file
input_file='letter.data'<line_sep># Define the number of datapoints to
# be loaded from the input file
num_datapoints=50<line_sep># String containing all the distinct characters
orig_labels='omandig'<line_sep># Compute the number of distinct characters
num_orig_labels=len(orig_labels)<line_sep># Define the training and testing parameters
num_train=int(0.9<times>num_datapoints)<line_sep>num_test=num_datapoints-num_train<line_sep># Define the dataset extraction parameters
start=6<line_sep>end=-1<line_sep># Creating the dataset
data=[]<line_sep>labels=[]<with_stmt>open(input_file 'r')<as>f<block_start><for_stmt>line f.readlines()# Split the current line tabwise
<block_start>list_vals=line.split('\t')<line_sep># Check if the label is in our ground truth
# labels. If not, we should skip it.
<if_stmt>list_vals[1]<not><in>orig_labels<block_start><continue><block_end># Extract the current label and append it
# to the main list
label=np.zeros((num_orig_labels 1))<line_sep>label[orig_labels.index(list_vals[1])]=1<line_sep>labels.append(label)<line_sep># Extract the character vector and append it to the main list
cur_char=np.array([float(x)<for>x list_vals[start:end]])<line_sep>data.append(cur_char)<line_sep># Exit the loop once the required dataset has been created
<if_stmt>len(data)<ge>num_datapoints<block_start><break><block_end><block_end><block_end># Convert the data and labels to numpy arrays
data=np.asfarray(data)<line_sep>labels=np.array(labels).reshape(num_datapoints num_orig_labels)<line_sep># Extract the number of dimensions
num_dims=len(data[0])<line_sep># Create a feedforward neural network
nn=nl.net.newff([[0 1]<for>_ range(len(data[0]))] [128 16 num_orig_labels])<line_sep># Set the training algorithm to gradient descent
nn.trainf=nl.train.train_gd<line_sep># Train the network
error_progress=nn.train(data[:num_train :] labels[:num_train :] epochs=10000 show=100 goal=0.01)<line_sep># Predict the output for test inputs
print('\nTesting on unknown data:')<line_sep>predicted_test=nn.sim(data[num_train: :])<for_stmt>i range(num_test)<block_start>print('\nOriginal:' orig_labels[np.argmax(labels[i])])<line_sep>print('Predicted:' orig_labels[np.argmax(predicted_test[i])])<block_end> |
# Information: https://clover.coex.tech/en/simple_offboard.html#navigateglobal
<import_stmt>rospy<import_from_stmt>clover srv<import_from_stmt>std_srvs.srv Trigger<import_stmt>math<line_sep>rospy.init_node('flight')<line_sep>get_telemetry=rospy.ServiceProxy('get_telemetry' srv.GetTelemetry)<line_sep>navigate=rospy.ServiceProxy('navigate' srv.Navigate)<line_sep>navigate_global=rospy.ServiceProxy('navigate_global' srv.NavigateGlobal)<line_sep>set_position=rospy.ServiceProxy('set_position' srv.SetPosition)<line_sep>set_velocity=rospy.ServiceProxy('set_velocity' srv.SetVelocity)<line_sep>set_attitude=rospy.ServiceProxy('set_attitude' srv.SetAttitude)<line_sep>set_rates=rospy.ServiceProxy('set_rates' srv.SetRates)<line_sep>land=rospy.ServiceProxy('land' Trigger)<line_sep># https://clover.coex.tech/en/snippets.html#wait_arrival
<def_stmt>wait_arrival tolerance=0.2<block_start><while_stmt><not>rospy.is_shutdown()<block_start>telem=get_telemetry(frame_id='navigate_target')<if_stmt>math.sqrt(telem.x<power>2+telem.y<power>2+telem.z<power>2)<l>tolerance<block_start><break><block_end>rospy.sleep(0.2)<block_end><block_end>start=get_telemetry()<if_stmt>math.isnan(start.lat)<block_start><raise>Exception('No global position, install and configure GPS sensor: https://clover.coex.tech/gps')<block_end>print('Start point global position: lat={}, lon={}'.format(start.lat start.lon))<line_sep>print('Take off 3 meters')<line_sep>navigate(x=0 y=0 z=3 frame_id='body' auto_arm=<true>)<line_sep>wait_arrival()<line_sep>print('Fly 1 arcsecond to the North (approx. 30 meters)')<line_sep>navigate_global(lat=start.lat+1.0/60/60 lon=start.lon z=start.z+3 yaw=math.inf speed=5)<line_sep>wait_arrival()<line_sep>print('Fly to home position')<line_sep>navigate_global(lat=start.lat lon=start.lon z=start.z+3 yaw=math.inf speed=5)<line_sep>wait_arrival()<line_sep>print('Land')<line_sep>land()<line_sep> |
"""The Slack Web API allows you to build applications that interact with Slack
in more complex ways than the integrations we provide out of the box."""<import_from_stmt>.client WebClient# noqa
<import_from_stmt>.slack_response SlackResponse# noqa
|
__all__=()<import_from_stmt>...backend.utils istr<line_sep>AUDIT_LOG_REASON=istr('X-Audit-Log-Reason')<line_sep>RATE_LIMIT_REMAINING=istr('X-RateLimit-Remaining')<line_sep>RATE_LIMIT_RESET=istr('X-RateLimit-Reset')<line_sep>RATE_LIMIT_RESET_AFTER=istr('X-RateLimit-Reset-After')<line_sep>RATE_LIMIT_LIMIT=istr('X-RateLimit-Limit')<line_sep># to send
RATE_LIMIT_PRECISION=istr('X-RateLimit-Precision')<line_sep>DEBUG_OPTIONS=istr('X-Debug-Options')<line_sep> |
# Copyright 2020 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Manage collections of props."""<import_stmt>collections<import_from_stmt>typing Any Callable Union<class_stmt>Singleton(type)<block_start>_instances={}<def_stmt>__call__ cls *args **kwargs<block_start><if_stmt>cls<not><in>cls._instances<block_start>cls._instances[cls]=super(Singleton cls).__call__(*args **kwargs)<block_end><return>cls._instances[cls]<block_end><block_end>VersionedSequence=collections.namedtuple('VersionedSequence' ['version' 'ids'])<class_stmt>PropSetDict(dict)<block_start>"""A dictionary that supports a function evaluation on every key access.
Extends the standard dictionary to provide dynamic behaviour for object sets.
"""<def_stmt>__getitem__ self key:Any<arrow>VersionedSequence# The method is called during [] access.
# Provides a collection of prop names.
<block_start><return>self._evaluate(dict.__getitem__(self key))<block_end><def_stmt>__repr__ self<arrow>str<block_start><return>f'{type(self).__name__}({super().__repr__()})'<block_end><def_stmt>get self key<arrow>VersionedSequence<block_start><return>self.__getitem__(key)<block_end><def_stmt>values self<block_start>values=super().values()<line_sep><return>[self._evaluate(x)<for>x values]<block_end><def_stmt>items self<block_start>new_dict={k:self._evaluate(v)<for>k,v super().items()}<line_sep><return>new_dict.items()<block_end><def_stmt>_evaluate self sequence_or_function:Union[VersionedSequence Callable[[] VersionedSequence]]<arrow>VersionedSequence<block_start>"""Based on the type of an argument, execute different actions.
Supports static sequence containers or functions that create such. When the
argument is a contrainer, the function returns the argument "as is". In case
a callable is provided as an argument, it will be evaluated to create a
container.
Args:
sequence_or_function: A sequence or a function that creates a sequence.
Returns:
A versioned set of names.
"""<if_stmt>isinstance(sequence_or_function VersionedSequence)<block_start><return>sequence_or_function<block_end>new_sequence=sequence_or_function()<line_sep><return>new_sequence<block_end><block_end> |
<import_stmt>imagestoosm.config<as>cfg<import_stmt>os<import_stmt>QuadKey.quadkey<as>quadkey<import_stmt>numpy<as>np<import_stmt>shapely.geometry<as>geometry<import_from_stmt>skimage draw<import_from_stmt>skimage io<import_stmt>csv<line_sep>minFeatureClip=0.3<line_sep># make the training data images
# construct index of osm data, each point
# for each tile
# check for tiles east and south, and south/east to make 512x512 tile, if no skip
# bounding box for image
# write out image as png
# see what features overlap with image
# if more than >20%
# lit of augmentations (N +45,-45 degree), N offsets
# emit mask for feature for current image.
# training will do flips, intensity, and color shifts if needed
os.system("rm -R "+cfg.trainDir)<line_sep>os.mkdir(cfg.trainDir)<line_sep># load up the OSM features into hash of arrays of polygons, in pixels
features={}<for_stmt>classDir os.listdir(cfg.rootOsmDir)<block_start>classDirFull=os.path.join(cfg.rootOsmDir classDir)<for_stmt>fileName os.listdir(classDirFull)<block_start>fullPath=os.path.join(cfg.rootOsmDir classDir fileName)<with_stmt>open(fullPath "rt")<as>csvfile<block_start>csveader=csv.reader(csvfile delimiter='\t')<line_sep>pts=[]<for_stmt>row csveader<block_start>latLot=(float(row[0]) float(row[1]))<line_sep>pixel=quadkey.TileSystem.geo_to_pixel(latLot cfg.tileZoom)<line_sep>pts.append(pixel)<block_end>poly=geometry.Polygon(pts)<line_sep>areaMeters=poly.area<times>0.596<times>0.596<line_sep># don't learn against baseball fields that are outlines just on the
# diamond. They are tagged wrong, don't want to teach the NN that this
# is correct. There are > 1000 of them in the OSM DB, we can't avoid
# them.
<if_stmt>(classDir<ne>"baseball"<or>areaMeters<g>2500)<block_start>feature={"geometry":poly "filename":fullPath}<if_stmt>((classDir<in>features)<eq><false>)<block_start>features[classDir]=[]<block_end>features[classDir].append(feature)<block_end><block_end><block_end><block_end>imageWriteCounter=0<for_stmt>root,subFolders,files os.walk(cfg.rootTileDir)<block_start><for_stmt>file files<block_start>quadKeyStr=os.path.splitext(file)[0]<line_sep>qkRoot=quadkey.from_str(quadKeyStr)<line_sep>tilePixel=quadkey.TileSystem.geo_to_pixel(qkRoot.to_geo() qkRoot.level)<line_sep>tileRootDir=os.path.split(root)[0]<line_sep># stick the adjacent tiles together to make larger images up to max
# image size.
maxImageSize=256<times>3<line_sep>maxTileCount=maxImageSize<floordiv>256<line_sep>count=0<line_sep>image=np.zeros([maxImageSize maxImageSize 3] dtype=np.uint8)<for_stmt>x range(maxTileCount)<block_start><for_stmt>y range(maxTileCount)<block_start>pixel=(tilePixel[0]+256<times>x tilePixel[1]+256<times>y)<line_sep>geo=quadkey.TileSystem.pixel_to_geo(pixel qkRoot.level)<line_sep>qk=quadkey.from_geo(geo qkRoot.level)<line_sep>qkStr=str(qk)<line_sep>tileCacheDir=os.path.join(tileRootDir qkStr[-3:])<line_sep>tileFileName="%s/%s.jpg"%(tileCacheDir qkStr)<if_stmt>(os.path.exists(tileFileName))<block_start><try_stmt><block_start>image[y<times>256:(y+1)<times>256 x<times>256:(x+1)<times>256 0:3]=io.imread(tileFileName)<line_sep>count<augadd>1<block_end><except_stmt># try to get the tile again next time.
<block_start>os.remove(tileFileName)<block_end><block_end><block_end><block_end>pts=[]<line_sep>pts.append((tilePixel[0]+0 tilePixel[1]+0))<line_sep>pts.append((tilePixel[0]+0 tilePixel[1]+maxImageSize))<line_sep>pts.append((tilePixel[0]+maxImageSize tilePixel[1]+maxImageSize))<line_sep>pts.append((tilePixel[0]+maxImageSize tilePixel[1]+0))<line_sep>imageBoundingBoxPoly=geometry.Polygon(pts)<line_sep>featureMask=np.zeros((maxImageSize maxImageSize) dtype=np.uint8)<line_sep>featureCountTotal=0<line_sep>usedFileNames=[]<for_stmt>featureType features<block_start>featureCount=0<for_stmt>feature features[featureType]<block_start><if_stmt>(imageBoundingBoxPoly.intersects(feature['geometry']))<block_start>area=feature['geometry'].area<line_sep>xs,ys=feature['geometry'].exterior.coords.xy<line_sep>xs=[x-tilePixel[0]<for>x xs]<line_sep>ys=[y-tilePixel[1]<for>y ys]<line_sep>xsClipped=[min(max(x 0) maxImageSize)<for>x xs]<line_sep>ysClipped=[min(max(y 0) maxImageSize)<for>y ys]<line_sep>pts2=[]<for_stmt>i range(len(xs))<block_start>pts2.append((xsClipped[i] ysClipped[i]))<block_end>clippedPoly=geometry.Polygon(pts2)<line_sep>newArea=clippedPoly.area<if_stmt>(area<g>0<and>newArea/area<g>minFeatureClip)<block_start><if_stmt>(os.path.exists("%s/%06d"%(cfg.trainDir imageWriteCounter))<eq><false>)<block_start>os.mkdir("%s/%06d"%(cfg.trainDir imageWriteCounter))<block_end>featureMask.fill(0)<line_sep>rr,cc=draw.polygon(xs ys (maxImageSize maxImageSize))<line_sep>featureMask[cc rr]=255<line_sep>io.imsave("%s/%06d/%06d-%s-%d.png"%(cfg.trainDir imageWriteCounter imageWriteCounter featureType featureCount) featureMask)<line_sep>usedFileNames.append(feature['filename'])<line_sep>featureCount<augadd>1<line_sep>featureCountTotal<augadd>1<block_end><block_end><block_end><block_end><if_stmt>(featureCountTotal<g>0)<block_start>io.imsave("%s/%06d/%06d.jpg"%(cfg.trainDir imageWriteCounter imageWriteCounter) image quality=100)<with_stmt>open("%s/%06d/%06d.txt"%(cfg.trainDir imageWriteCounter imageWriteCounter) "wt")<as>text_file<block_start>text_file.write("%s\n"%(str(qkRoot)))<line_sep>text_file.write("%0.8f,%0.8f\n"%qkRoot.to_geo())<for_stmt>f usedFileNames<block_start>text_file.write("%s\n"%(f))<block_end><block_end>imageWriteCounter<augadd>1<line_sep>print("%s - %s - tiles %d - features %d"%(os.path.join(root file) quadKeyStr count featureCountTotal))<block_end><block_end><block_end> |
<import_from_future_stmt> absolute_import<import_from_stmt>changes.api.base APIView<import_from_stmt>changes.models.task Task<class_stmt>TaskDetailsAPIView(APIView)<block_start><def_stmt>_collect_children self task<block_start>children=Task.query.filter(Task.parent_id<eq>task.task_id )<line_sep>results=[]<for_stmt>child children<block_start>child_data=self.serialize(child)<line_sep>child_data['children']=self._collect_children(child)<line_sep>results.append(child_data)<block_end><return>results<block_end><def_stmt>get self task_id<block_start>task=Task.query.get(task_id)<if_stmt>task<is><none><block_start><return>'' 404<block_end>context=self.serialize(task)<line_sep>context['children']=self._collect_children(task)<line_sep><return>self.respond(context)<block_end><block_end> |
<import_from_future_stmt> print_function<import_from_future_stmt> division<import_stmt>importlib<import_from_stmt>keras.layers.core Activation<import_from_stmt>keras.models Graph<import_stmt>numpy<as>np<import_stmt>random<import_stmt>traceback<import_stmt>pysts.loader<as>loader<import_from_stmt>pysts.kerasts graph_input_slice graph_input_prune<import_stmt>pysts.kerasts.blocks<as>B<def_stmt>default_config model_config task_config# TODO: Move this to AbstractTask()?
<block_start>c=dict()<line_sep>c['embdim']=300<line_sep>c['embprune']=100<line_sep>c['embicase']=<false><line_sep>c['inp_e_dropout']=1/2<line_sep>c['inp_w_dropout']=0<line_sep>c['e_add_flags']=<true><line_sep>c['ptscorer']=B.mlp_ptscorer<line_sep>c['mlpsum']='sum'<line_sep>c['Ddim']=2<line_sep>c['Dinit']='glorot_uniform'<line_sep>c['f_add_kw']=<false><line_sep>c['loss']='mse'# you really want to override this in each task's config()
c['balance_class']=<false><line_sep>c['opt']='adam'<line_sep>c['fix_layers']=[]# mainly useful for transfer learning, or 'emb' to fix embeddings
c['batch_size']=160<line_sep>c['nb_epoch']=16<line_sep>c['nb_runs']=1<line_sep>c['epoch_fract']=1<line_sep>c['prescoring']=<none><line_sep>c['prescoring_prune']=<none><line_sep>c['prescoring_input']=<none><line_sep>task_config(c)<if_stmt>c.get('task>model' <false>)# task config has higher priority than model
<block_start>model_config(c)<line_sep>task_config(c)<block_end><else_stmt><block_start>model_config(c)<block_end><return>c<block_end><class_stmt>AbstractTask(object)<block_start><def_stmt>set_conf self c<block_start>self.c=c<if_stmt>'s0pad'<in>self.c<block_start>self.s0pad=self.c['s0pad']<line_sep>self.s1pad=self.c['s1pad']<block_end><elif_stmt>'spad'<in>self.c<block_start>self.spad=self.c['spad']<line_sep>self.s0pad=self.c['spad']<line_sep>self.s1pad=self.c['spad']<block_end><block_end><def_stmt>load_vocab self vocabf<block_start>_,_,self.vocab=self.load_set(vocabf)<line_sep><return>self.vocab<block_end><def_stmt>load_data self trainf valf testf=<none><block_start>self.trainf=trainf<line_sep>self.valf=valf<line_sep>self.testf=testf<line_sep>self.gr,self.y,self.vocab=self.load_set(trainf)<line_sep>self.grv,self.yv,_=self.load_set(valf)<if_stmt>testf<is><not><none><block_start>self.grt,self.yt,_=self.load_set(testf)<block_end><else_stmt><block_start>self.grt,self.yt=(<none> <none>)<block_end><if_stmt>self.c.get('adapt_ubuntu' <false>)<block_start>self.vocab.add_word('__eou__')<line_sep>self.vocab.add_word('__eot__')<line_sep>self.gr=loader.graph_adapt_ubuntu(self.gr self.vocab)<line_sep>self.grv=loader.graph_adapt_ubuntu(self.grv self.vocab)<if_stmt>self.grt<is><not><none><block_start>self.grt=loader.graph_adapt_ubuntu(self.grt self.vocab)<block_end><block_end><block_end><def_stmt>sample_pairs self gr batch_size shuffle=<true> once=<false><block_start>""" A generator that produces random pairs from the dataset """<try_stmt><block_start>id_N=int((len(gr['si0'])+batch_size-1)/batch_size)<line_sep>ids=list(range(id_N))<while_stmt><true><block_start><if_stmt>shuffle# XXX: We never swap samples between batches, does it matter?
<block_start>random.shuffle(ids)<block_end><for_stmt>i ids<block_start>sl=slice(i<times>batch_size (i+1)<times>batch_size)<line_sep>ogr=graph_input_slice(gr sl)<line_sep>ogr['se0']=self.emb.map_jset(ogr['sj0'])<line_sep>ogr['se1']=self.emb.map_jset(ogr['sj1'])<line_sep># print(sl)
# print('<<0>>', ogr['sj0'], ogr['se0'])
# print('<<1>>', ogr['sj1'], ogr['se1'])
<yield>ogr<block_end><if_stmt>once<block_start><break><block_end><block_end><block_end><except_stmt>Exception<block_start>traceback.print_exc()<block_end><block_end><def_stmt>prescoring_apply self gr skip_oneclass=<false><block_start>""" Given a gr, prescore the pairs and do either pruning (for each s0,
keep only top N s1 based on the prescoring) or add the prescore as
an input. """<def_stmt>prescoring_model prescoring_task model conf weightsf<block_start>""" Setup and return a pre-scoring model """<line_sep># We just make a task instance with the prescoring model
# specific config, build the model and apply it;
# we sometimes don't want the *real* task to do prescoring, e.g. in
# case of hypev which evaluates whole clusters of same-s0 pairs but
# prescoring should be done on the individual tasks
prescore_task=prescoring_task()<line_sep># We also set up the config appropriately to the model + task.
prescoring_module=importlib.import_module('.'+model 'models')<line_sep>c=default_config(prescoring_module.config prescore_task.config)<for_stmt>k,v conf.items()<block_start>c[k]=v<block_end>prescore_task.set_conf(c)<line_sep>print('[Prescoring] Model')<line_sep>model=prescore_task.build_model(prescoring_module.prep_model)<line_sep>print('[Prescoring] '+weightsf)<line_sep>model.load_weights(weightsf)<line_sep><return>model<block_end><if_stmt>'prescoring'<not><in>self.c<or>(self.c['prescoring_prune']<is><none><and>self.c['prescoring_input']<is><none>)<block_start><return>gr<block_end># nothing to do
<if_stmt>'prescoring_model_inst'<not><in>self.c# cache the prescoring model instance
<block_start>self.c['prescoring_model_inst']=prescoring_model(self.prescoring_task self.c['prescoring'] self.c.get('prescoring_conf' {}) self.c['prescoring_weightsf'])<block_end>print('[Prescoring] Predict')<line_sep>ypred=self.c['prescoring_model_inst'].predict(gr)['score'][: 0]<if_stmt>self.c['prescoring_input']<is><not><none><block_start>inp=self.c['prescoring_input']<line_sep>gr[inp]=np.reshape(ypred (len(ypred) 1))<block_end># 1D to 2D
<if_stmt>self.c['prescoring_prune']<is><not><none><block_start>N=self.c['prescoring_prune']<line_sep>print('[Prescoring] Prune')<line_sep>gr=graph_input_prune(gr ypred N skip_oneclass=skip_oneclass)<block_end><return>gr<block_end><def_stmt>prep_model self module_prep_model oact='sigmoid'# Input embedding and encoding
<block_start>model=Graph()<line_sep>N=B.embedding(model self.emb self.vocab self.s0pad self.s1pad self.c['inp_e_dropout'] self.c['inp_w_dropout'] add_flags=self.c['e_add_flags'])<line_sep># Sentence-aggregate embeddings
final_outputs=module_prep_model(model N self.s0pad self.s1pad self.c)<line_sep># Measurement
<if_stmt>self.c['ptscorer']<eq>'1'# special scoring mode just based on the answer
# (assuming that the question match is carried over to the answer
# via attention or another mechanism)
<block_start>ptscorer=B.cat_ptscorer<line_sep>final_outputs=[final_outputs[1]]<block_end><else_stmt><block_start>ptscorer=self.c['ptscorer']<block_end>kwargs=dict()<if_stmt>ptscorer<eq>B.mlp_ptscorer<block_start>kwargs['sum_mode']=self.c['mlpsum']<line_sep>kwargs['Dinit']=self.c['Dinit']<block_end><if_stmt>'f_add'<in>self.c<block_start><for_stmt>inp self.c['f_add']<block_start>model.add_input(inp input_shape=(1 ))# assumed scalar
<block_end>kwargs['extra_inp']=self.c['f_add']<block_end>model.add_node(name='scoreS' input=ptscorer(model final_outputs self.c['Ddim'] N self.c['l2reg'] **kwargs) layer=Activation(oact))<line_sep>model.add_output(name='score' input='scoreS')<line_sep><return>model<block_end><def_stmt>fit_model self model **kwargs<block_start><if_stmt>self.c['ptscorer']<is><none><block_start><return>model.fit(self.gr **kwargs)<block_end>batch_size=kwargs.pop('batch_size')<line_sep>kwargs['callbacks']=self.fit_callbacks(kwargs.pop('weightsf'))<line_sep><return>model.fit_generator(self.sample_pairs(self.gr batch_size) **kwargs)<block_end><def_stmt>predict self model gr<block_start><if_stmt>self.c['ptscorer']<is><none><block_start><return>model.predict(gr)<block_end>batch_size=16384# XXX: hardcoded
ypred=[]<for_stmt>ogr self.sample_pairs(gr batch_size shuffle=<false> once=<true>)<block_start>ypred<augadd>list(model.predict(ogr)['score'][: 0])<block_end><return>np.array(ypred)<block_end><block_end> |
<class_stmt>Solution<block_start><def_stmt>customSortString self S:str T:str<arrow>str<block_start><return>''.join(sorted(list(T) key=<lambda>x:S.find(x)))<block_end><block_end> |
<import_stmt>exceptions<class_stmt>Data<block_start><class_stmt>DataError(exceptions.ValueError)<block_start><pass><block_end><def_stmt>__init__ self value<block_start>self.value=value<block_end><def_stmt>get_value self<block_start><return>self.value<block_end><block_end> |
<import_from_stmt>django.utils.deprecation MiddlewareMixin<class_stmt>MyCors(MiddlewareMixin)<block_start><def_stmt>process_response self requesst response<block_start>response['Access-Control-Allow-Origin']='*'<if_stmt>requesst.method<eq>'OPTIONS'<block_start>response["Access-Control-Allow-Headers"]="*"<line_sep>response['Access-Control-Allow-Methods']='*'<block_end><return>response<block_end><block_end> |
# Copyright (c) 2021 <NAME>. All rights reserved.
# This code is licensed under Apache 2.0 with Commons Clause license (see LICENSE.md for details)
"""Modules with base classes and utilities for pandas objects, such as broadcasting."""<import_from_stmt>vectorbt.base.array_wrapper ArrayWrapper<line_sep>__all__=['ArrayWrapper']<line_sep>__pdoc__={k:<false><for>k __all__}<line_sep> |
# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# Name: sorting.py
# Purpose: Music21 class for sorting
#
# Authors: <NAME>
#
# Copyright: Copyright ยฉ 2014-2015 <NAME> and the music21
# Project
# License: BSD, see license.txt
# -----------------------------------------------------------------------------
'''
This module defines a single class, SortTuple, which is a named tuple that can
sort against bare offsets and other SortTuples.
This is a performance-critical object.
It also defines three singleton instance of the SortTupleLow class as ZeroSortTupleDefault,
ZeroSortTupleLow and
ZeroSortTupleHigh which are sortTuple at
offset 0.0, priority [0, -inf, inf] respectively:
>>> sorting.ZeroSortTupleDefault
SortTuple(atEnd=0, offset=0.0, priority=0, classSortOrder=0, isNotGrace=1, insertIndex=0)
>>> sorting.ZeroSortTupleLow
SortTuple(atEnd=0, offset=0.0, priority=-inf, classSortOrder=0, isNotGrace=1, insertIndex=0)
>>> sorting.ZeroSortTupleHigh
SortTuple(atEnd=0, offset=0.0, priority=inf, classSortOrder=0, isNotGrace=1, insertIndex=0)
'''<import_from_stmt>collections namedtuple<import_from_stmt>math inf<as>INFINITY<import_from_stmt>music21 exceptions21<line_sep>_attrList=['atEnd' 'offset' 'priority' 'classSortOrder' 'isNotGrace' 'insertIndex']<class_stmt>SortingException(exceptions21.Music21Exception)<block_start><pass><block_end><class_stmt>SortTuple(namedtuple('SortTuple' _attrList))<block_start>'''
Derived class of namedTuple which allows for comparisons with pure ints/fractions.
>>> n = note.Note()
>>> s = stream.Stream()
>>> s.insert(4, n)
>>> st = n.sortTuple()
>>> st
SortTuple(atEnd=0, offset=4.0, priority=0, classSortOrder=20, isNotGrace=1, insertIndex=...)
>>> st.shortRepr()
'4.0 <0.20...>'
>>> st.atEnd
0
>>> st.offset
4.0
>>> st < 5.0
True
>>> 5.0 > st
True
>>> st > 3.0
True
>>> 3.0 < st
True
>>> st == 4.0
True
>>> ts = bar.Barline('double')
>>> t = stream.Stream()
>>> t.storeAtEnd(ts)
>>> ts_st = ts.sortTuple()
>>> ts_st
SortTuple(atEnd=1, offset=0.0, priority=0, classSortOrder=-5, isNotGrace=1, insertIndex=...)
>>> st < ts_st
True
>>> ts_st > 999999
True
>>> import math
>>> ts_st == math.inf
True
Construct one w/ keywords:
>>> st = sorting.SortTuple(atEnd=0, offset=1.0, priority=0, classSortOrder=20,
... isNotGrace=1, insertIndex=323)
>>> st.shortRepr()
'1.0 <0.20.323>'
or as tuple:
>>> st = sorting.SortTuple(0, 1.0, 0, 20, 1, 323)
>>> st.shortRepr()
'1.0 <0.20.323>'
'''<def_stmt>__new__ cls *tupEls **kw# noinspection PyTypeChecker
<block_start><return>super(SortTuple cls).__new__(cls *tupEls **kw)<block_end><def_stmt>__eq__ self other<block_start><if_stmt>isinstance(other tuple)<block_start><return>super().__eq__(other)<block_end><try_stmt><block_start><if_stmt>self.atEnd<eq>1<and>other<ne>INFINITY<block_start><return><false><block_end><elif_stmt>self.atEnd<eq>1<block_start><return><true><block_end><else_stmt><block_start><return>self.offset<eq>other<block_end><block_end><except_stmt>ValueError<block_start><return>NotImplemented<block_end><block_end><def_stmt>__lt__ self other<block_start><if_stmt>isinstance(other tuple)<block_start><return>super().__lt__(other)<block_end><try_stmt><block_start><if_stmt>self.atEnd<eq>1<block_start><return><false><block_end><else_stmt><block_start><return>self.offset<l>other<block_end><block_end><except_stmt>ValueError<block_start><return>NotImplemented<block_end><block_end><def_stmt>__gt__ self other<block_start><if_stmt>isinstance(other tuple)<block_start><return>super().__gt__(other)<block_end><try_stmt><block_start><if_stmt>self.atEnd<eq>1<and>other<ne>INFINITY<block_start><return><true><block_end><elif_stmt>self.atEnd<eq>1<block_start><return><false><block_end><else_stmt><block_start><return>self.offset<g>other<block_end><block_end><except_stmt>ValueError<block_start><return>NotImplemented<block_end><block_end><def_stmt>__ne__ self other<block_start><return><not>self.__eq__(other)<block_end><def_stmt>__le__ self other<block_start><return>self.__lt__(other)<or>self.__eq__(other)<block_end><def_stmt>__ge__ self other<block_start><return>self.__gt__(other)<or>self.__eq__(other)<block_end><def_stmt>shortRepr self<block_start>'''
Returns a nice representation of a SortTuple
>>> st = sorting.SortTuple(atEnd=0, offset=1.0, priority=0, classSortOrder=20,
... isNotGrace=1, insertIndex=323)
>>> st.shortRepr()
'1.0 <0.20.323>'
>>> st = sorting.SortTuple(atEnd=1, offset=1.0, priority=4, classSortOrder=7,
... isNotGrace=0, insertIndex=200)
>>> st.shortRepr()
'End <4.7.[Grace].200>'
'''<line_sep>reprParts=[]<if_stmt>self.atEnd<block_start>reprParts.append('End')<block_end><else_stmt><block_start>reprParts.append(str(self.offset))<block_end>reprParts.append(' <')<line_sep>reprParts.append(str(self.priority))<line_sep>reprParts.append('.')<line_sep>reprParts.append(str(self.classSortOrder))<if_stmt>self.isNotGrace<eq>0<block_start>reprParts.append('.[Grace]')<block_end>reprParts.append('.')<line_sep>reprParts.append(str(self.insertIndex))<line_sep>reprParts.append('>')<line_sep><return>''.join(reprParts)<block_end><def_stmt>modify self **kw<block_start>'''
return a new SortTuple identical to the previous, except with
the given keyword modified. Works only with keywords.
>>> st = sorting.SortTuple(atEnd=0, offset=1.0, priority=0, classSortOrder=20,
... isNotGrace=1, insertIndex=32)
>>> st2 = st.modify(offset=2.0)
>>> st2.shortRepr()
'2.0 <0.20.32>'
>>> st2
SortTuple(atEnd=0, offset=2.0, priority=0, classSortOrder=20, isNotGrace=1, insertIndex=32)
>>> st3 = st2.modify(atEnd=1, isNotGrace=0)
>>> st3.shortRepr()
'End <0.20.[Grace].32>'
The original tuple is never modified (hence tuple):
>>> st.offset
1.0
Changing offset, but nothing else, helps in creating .flatten() positions.
'''<line_sep>outList=[kw.get(attr getattr(self attr))<for>attr _attrList]<line_sep><return>self.__class__(*outList)<block_end><def_stmt>add self other<block_start>'''
Add all attributes from one sortTuple to another,
returning a new one.
>>> n = note.Note()
>>> n.offset = 10
>>> s = stream.Stream()
>>> s.offset = 10
>>> n.sortTuple()
SortTuple(atEnd=0, offset=10.0, priority=0, classSortOrder=20, isNotGrace=1, insertIndex=0)
>>> s.sortTuple()
SortTuple(atEnd=0, offset=10.0, priority=0, classSortOrder=-20, isNotGrace=1, insertIndex=0)
>>> s.sortTuple().add(n.sortTuple())
SortTuple(atEnd=0, offset=20.0, priority=0, classSortOrder=0, isNotGrace=1, insertIndex=0)
Note that atEnd and isNotGrace are equal to other's value. are upper bounded at 1 and
take the maxValue of either.
'''<if_stmt><not>isinstance(other self.__class__)<block_start><raise>SortingException('Cannot add attributes from a different class')<block_end>outList=[max(getattr(self attr) getattr(other attr))<if>attr<in>('atEnd' 'isNotGrace')<else>(getattr(self attr)+getattr(other attr))<for>attr _attrList]<line_sep><return>self.__class__(*outList)<block_end><def_stmt>sub self other<block_start>'''
Subtract all attributes from to another. atEnd and isNotGrace take the min value of either.
>>> n = note.Note()
>>> n.offset = 10
>>> s = stream.Stream()
>>> s.offset = 10
>>> n.sortTuple()
SortTuple(atEnd=0, offset=10.0, priority=0, classSortOrder=20, isNotGrace=1, insertIndex=0)
>>> s.sortTuple()
SortTuple(atEnd=0, offset=10.0, priority=0, classSortOrder=-20, isNotGrace=1, insertIndex=0)
>>> s.sortTuple().sub(n.sortTuple())
SortTuple(atEnd=0, offset=0.0, priority=0, classSortOrder=-40, isNotGrace=1, insertIndex=0)
Note that atEnd and isNotGrace are lower bounded at 0.
'''<if_stmt><not>isinstance(other self.__class__)<block_start><raise>SortingException('Cannot add attributes from a different class')<block_end>outList=[min(getattr(self attr) getattr(other attr))<if>attr<in>('atEnd' 'isNotGrace')<else>(getattr(self attr)-getattr(other attr))<for>attr _attrList]<line_sep><return>self.__class__(*outList)<block_end><block_end>ZeroSortTupleDefault=SortTuple(atEnd=0 offset=0.0 priority=0 classSortOrder=0 isNotGrace=1 insertIndex=0)<line_sep>ZeroSortTupleLow=SortTuple(atEnd=0 offset=0.0 priority=-INFINITY classSortOrder=0 isNotGrace=1 insertIndex=0)<line_sep>ZeroSortTupleHigh=SortTuple(atEnd=0 offset=0.0 priority=INFINITY classSortOrder=0 isNotGrace=1 insertIndex=0)<line_sep># -----------------------------------------------------------------------------
<if_stmt>__name__<eq>'__main__'<block_start><import_stmt>music21<line_sep>music21.mainTest()<block_end> |
# Copyright 2015, Pinterest, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Definition of a token used to generate unique version values.
Blessed version is stored in the master as any other token. Each time a new
version number is needed, it is generated off the value stored in that token.
The value stored in blessed version is a monotonically increasing counter
so it is guaranteed that no single value is issued more than once.
"""<import_stmt>sys<import_stmt>time<import_from_stmt>pinball.config.utils timestamp_to_str<import_from_stmt>pinball.master.thrift_lib.ttypes Token<line_sep>__author__='<NAME>'<line_sep>__copyright__='Copyright 2015, Pinterest, Inc.'<line_sep>__credits__=[__author__]<line_sep>__license__='Apache'<line_sep>__version__='2.0'<class_stmt>BlessedVersion(Token)<block_start>"""A singleton token keeping track of token versions.
Versions of tokens stored in a given master are required to be unique.
"""<def_stmt>__init__ self name=<none> owner=<none><block_start>"""Create blessed version with a given name and owner.
Name and owner have to either both be set or none should be set.
Blessed version in use should always have name and owner set. The
version of init with name and owner set to None relies on external
initialization of those fields.
Args:
name: The name of the blessed version token.
owner: The owner of the blessed version token.
"""<assert_stmt>(name<and>owner)<or>(<not>name<and><not>owner)<if_stmt>name<and>owner<block_start>now=BlessedVersion._get_timestamp_millis()<line_sep>data_str=('blessed version created at %s'%timestamp_to_str(now/1000))<line_sep>Token.__init__(self now name owner sys.maxint 0 data_str)<block_end><else_stmt><block_start>Token.__init__(self)<block_end><block_end>@staticmethod<def_stmt>from_token token<block_start>blessed_version=BlessedVersion()<for_stmt>key,value token.__dict__.items()<block_start>blessed_version.__dict__[key]=value<block_end><return>blessed_version<block_end>@staticmethod<def_stmt>_get_timestamp_millis <block_start>"""Return time in milliseconds since the epoch."""<line_sep><return>int(time.time()<times>1000)<block_end><def_stmt>advance_version self<block_start>"""Increase the internal version counter.
The counter value is based on the current time. Since those values
are used as token modification ids, basing them on time has an
advantage for debugging - looking at the version we can tell when a
token was modified.
A BIG WARNING: as an application developer do not assume anything about
the semantics of version values other than their uniqueness. The
implementation details are subject to change.
"""<line_sep>self.version=max(self.version+1 BlessedVersion._get_timestamp_millis())<line_sep><return>self.version<block_end><block_end> |
# -*- coding: utf-8 -*-
# Copyright (c) 2014, <NAME> and <NAME>
# tinynumpy is distributed under the terms of the MIT License.
""" Benchmarks for tinynumpy
Findings:
* A list of floats costs about 33 bytes per float
* A list if ints costs anout 41 bytes per int
* A huge list of ints 0-255, costs about 1 byter per int
* Python list takes about 5-6 times as much memory than array for 64bit
data types. Up to 40 times as much for uint8, unless Python can reuse
values.
* __slots__ help reduce the size of custom classes
* tinynumpy is about 100 times slower than numpy
* using _toflatlist instead of flat taks 50-60% of time (but more memory)
"""<import_from_future_stmt> division<import_stmt>os<import_stmt>sys<import_stmt>time<import_stmt>subprocess<import_stmt>numpy<as>np<import_stmt>tinynumpy<as>tnp<def_stmt>_prettymem n<block_start><if_stmt>n<g>2<power>20<block_start><return>'%1.2f MiB'%(n/2<power>20)<block_end><elif_stmt>n<g>2<power>10<block_start><return>'%1.2f KiB'%(n/2<power>10)<block_end><else_stmt><block_start><return>'%1.0f B'%n<block_end><block_end><def_stmt>_prettysec n<block_start><if_stmt>n<l>0.0001<block_start><return>'%1.2f us'%(n<times>1000000)<block_end><elif_stmt>n<l>0.1<block_start><return>'%1.2f ms'%(n<times>1000)<block_end><else_stmt><block_start><return>'%1.2f s'%n<block_end><block_end>code_template="""
import psutil
import os
import random
import numpy as np
import tinynumpy as tnp
N = 100 * 1000
M = 1000 * 1000
class A(object):
def __init__(self):
self.foo = 8
self.bar = 3.3
class B(object):
__slots__ = ['foo', 'bar']
def __init__(self):
self.foo = 8
self.bar = 3.3
def getmem():
process = psutil.Process(os.getpid())
return process.get_memory_info()[0]
M0 = getmem()
%s
M1 = getmem()
print(M1-M0)
"""<def_stmt>measure_mem what code divide=1<block_start>cmd=[sys.executable '-c' code_template%code]<line_sep>res=subprocess.check_output(cmd cwd=os.getcwd()).decode('utf-8')<line_sep>m=int(res)/divide<line_sep>print('Memory for %s:%s%s'%(what ' '<times>(22-len(what)) _prettymem(m)))<block_end><def_stmt>measure_speed what func *args **kwargs<block_start>N=1<line_sep>t0=time.perf_counter()<line_sep>func(*args **kwargs)<line_sep>t1=time.perf_counter()<while_stmt>(t1-t0)<l>0.2<block_start>N<augmul>10<line_sep>t0=time.perf_counter()<for_stmt>i range(N)<block_start>func(*args **kwargs)<block_end>t1=time.perf_counter()<block_end>te=t1-t0<line_sep>print('Time for %s:%s%s (%i iters)'%(what ' '<times>(22-len(what)) _prettysec(te/N) N))<block_end><if_stmt>__name__<eq>'__main__'<block_start>N=100<times>1000<line_sep>M=1000<times>1000<line_sep>print('=== MEMORY ====')<line_sep>measure_mem('floats 0-M' 'L = [i*1.0 for i in range(N)]' N)<line_sep>measure_mem('ints 0-M' 'L = [i for i in range(N)]' N)<line_sep>measure_mem('ints 0-255' 'L = [int(random.uniform(0, 255)) for i in range(N)]' N)<line_sep>measure_mem('regular object' 'L = [A() for i in range(N)]' N)<line_sep>measure_mem('object with slots' 'L = [B() for i in range(N)]' N)<line_sep>measure_mem(' Numpy arr size 1' 'L = [np.ones((1,)) for i in range(N)]' N)<line_sep>measure_mem('Tinynumpy arr size 1' 'L = [tnp.ones((1,)) for i in range(N)]' N)<line_sep>measure_mem(' Numpy arr size M' 'a = np.ones((M,))')<line_sep>measure_mem('Tinynumpy arr size M' 'a = tnp.ones((M,))')<line_sep>print('=== SPEED ====')<line_sep>a1=np.ones((100 100))<line_sep>a2=tnp.ones((100 100))<line_sep>measure_speed(' numpy sum 10k' a1.sum)<line_sep>measure_speed('tinynumpy sum 10k' a2.sum)<line_sep>measure_speed(' numpy max 10k' a1.max)<line_sep>measure_speed('tinynumpy max 10k' a2.max)<line_sep>a1=np.ones((1000 1000))<line_sep>a2=tnp.ones((1000 1000))<line_sep>measure_speed(' numpy sum 1M' a1.sum)<line_sep>measure_speed('tinynumpy sum 1M' a2.sum)<line_sep>measure_speed(' numpy max 1M' a1.max)<line_sep>measure_speed('tinynumpy max 1M' a2.max)<block_end> |
<import_stmt>os<import_from_stmt>conftest TEST_DATA<import_from_stmt>bw_plex.config read_or_make<def_stmt>test_config <block_start>conf=read_or_make(os.path.join(TEST_DATA 'test_config.ini'))<assert_stmt>'level'<not><in>conf['general']<assert_stmt>conf['general']['loglevel']<eq>'info'<block_end> |
#
# Copyright (c) 2020 Bitdefender
# SPDX-License-Identifier: Apache-2.0
#
<import_stmt>yaml<import_stmt>struct<import_stmt>os<import_stmt>crc32<import_from_stmt>options get_options_for_os_version<import_from_stmt>objects CamiYAMLObject CamiObject CamiAtom CamiDataTable FilePointerException get_all_objects<import_from_stmt>common IntrocoreVersion<import_from_stmt>intro_defines section_hints defines detour_args version_any<class_stmt>WinSupportedOs(CamiYAMLObject CamiAtom)<block_start>min_intro_ver=IntrocoreVersion.min_version()<line_sep>max_intro_ver=IntrocoreVersion.max_version()<line_sep>yaml_tag="!intro_update_win_supported_os"<line_sep>"""
struct _CAMI_WIN_DESCRIPTOR
{
DWORD BuildNumber; // Buildnumber for this Windows OS
BOOLEAN Kpti; // If this OS has Kpti support.
BOOLEAN Is64; // If this OS is 64 bits.
WORD _Reserved1; // Alignment mostly, but may become useful.
QWORD MinIntroVersion; // Minimum introcore version which supports this OS
QWORD MaxIntroVersion; // Maximum introcore version which supports this OS
DWORD KmStructuresCount; // KM opaque fields count
DWORD KmStructuresTable; // KM opaque fields file pointer. (pointer to a CAMI_OPAQUE_STRUCTURE[] array
DWORD UmStructuresCount; // UM opaque fields count
DWORD UmStructuresTable; // UM opaque fields file pointer (pointer to a CAMI_OPAQUE_STRUCTURE[] array
DWORD FunctionCount; // Functions count
DWORD FunctionTable; // Functions file pointer. (pointer to a CAMI_WIN_FUNCTION[] array.
DWORD CustomProtectionOffset; // Protection flags for this os. (pointer to a CAMI_CUSTOM_PROTECTION struct)
DWORD VersionStringOffset;
DWORD _Reserved3;
DWORD _Reserved4;
}
"""<line_sep>descriptor_layout="<IBBHQQIIIIIIIIII"<def_stmt>post_create self state<block_start><if_stmt>hasattr(self "functions")<block_start>self.functions=WinOsFunctionsTable(state["functions"])<block_end><block_end><def_stmt>set_um_fields self um_fields_list<block_start>""" Set the UM fields for this OS
We have to do this by hand because a set of um fields apply to a lot of supported OS versions.
This method will iterate the um_fields_list and find the suitable one for this OS.
Args:
um_fields_list: A list of WinOsUmFields.
Raises:
Exception: If multiple or none um_fields match this OS version.
"""<if_stmt>hasattr(self "um_fields")<block_start><return><block_end>found=<none><for_stmt>um um_fields_list<block_start><if_stmt>self.is_64<eq>um.is64<and>self.build_number<ge>um.min_ver<and>self.build_number<le>um.max_ver<block_start><if_stmt>found<is><not><none><block_start><raise>Exception("Found duplicated UM fields for build_number %d, is_64: %r"%(self.build_number self.is_64))<block_end>found=um.fields<block_end><block_end><if_stmt>found<is><none><block_start><raise>Exception("Could not find um for build_number %d, is_64: %d"%(self.build_number self.is_64))<block_end>self.um_fields=found<block_end><def_stmt>set_functions self functions<block_start>""" Set the functions for this OS
Given the list of functions, this method will filter it and will keep only the function with patterns and
arguments needed for this OS and will create the final form of the functions attribute a.k.a. a CamiDataTable instead
of a python list.
Args:
functions: A list of WinFunction
"""<if_stmt>hasattr(self "functions")<block_start><return><block_end>funcs=WinOsFunctionsTable()<line_sep>print("Functions for Windows OS {} (is 64: {})".format(self.build_number self.is_64))<for_stmt>function functions<block_start>new_func=function.get_function_for_os(self)<if_stmt>new_func<is><not><none><block_start>funcs.add_entry(new_func)<line_sep>print("\t- {} with {} patterns and arguments: {}".format(new_func.name.ljust(30) str(new_func.patterns.get_entry_count()).rjust(2) new_func.arguments.args ).expandtabs())<block_end><block_end>self.functions=funcs<block_end><def_stmt>get_descriptor self<block_start>""" Generate the CamiDataTable entry for this OS version
Returns:
bytes: the CamiDataTable entry (a _CAMI_WIN_DESCRIPTOR structure)
Raises:
FilePointerException: If this method is called before generating its body with serialize()
"""<line_sep>print("Windows OS {} (kpti: {}, 64: {})".format(str(self.build_number).ljust(5) str(self.kpti_installed).ljust(5) str(self.is_64).ljust(5) ))<line_sep>print("\t- Options: " self.intro_options)<line_sep>print("\t- Min intro version: " self.min_intro_ver)<line_sep>print("\t- Max intro version: " self.max_intro_ver)<line_sep><return>struct.pack(self.descriptor_layout self.build_number self.kpti_installed self.is_64 0 self.min_intro_ver.get_raw() self.max_intro_ver.get_raw() self.km_fields.get_entry_count() self.km_fields.get_file_pointer() self.um_fields.get_entry_count() self.um_fields.get_file_pointer() self.functions.get_entry_count() self.functions.get_file_pointer() self.intro_options.get_file_pointer() self.version_string.get_file_pointer() 0 0 )<block_end># reserved
<def_stmt>serialize self start<block_start>""" Generate the body of this OS in it's binary form.
Here we are also setting the functions and usermode fields if they are empty.
Args:
start: The offset in the file where this os body will be placed
Returns:
bytes: The body of this OS: um and km fields + functions
"""<line_sep>self.intro_options=get_options_for_os_version((self.build_number self.kpti_installed self.is_64))<line_sep>self.set_functions(get_all_objects(WinFunction))<line_sep>self.set_um_fields(get_all_objects(WinOsUmFields))<line_sep>data=self.km_fields.serialize(start)<line_sep>data<augadd>self.um_fields.serialize(start+len(data))<line_sep>data<augadd>self.functions.serialize(start+len(data))<line_sep>data<augadd>self.intro_options.serialize(start+len(data))<line_sep>data<augadd>self.version_string.serialize(start+len(data))<line_sep><return>data<block_end><block_end><class_stmt>WinVersionString(CamiYAMLObject CamiObject)<block_start>yaml_tag="!intro_update_win_version_string"<line_sep>descriptor_layout="<Q{}sQ{}s".format(defines["MAX_VERSION_STRING_SIZE"] defines["MAX_VERSION_STRING_SIZE"])<def_stmt>serialize self start<block_start>self.set_file_pointer(start)<line_sep>size=len(self.version_string)+1<if_stmt>size<g>(defines["MAX_VERSION_STRING_SIZE"]-1)<block_start><raise>Exception("String is too big!")<block_end>size_server=len(self.server_version_string)+1<if_stmt>size_server<g>(defines["MAX_VERSION_STRING_SIZE"]-1)<block_start><raise>Exception("String for server is too big!")<block_end><return>struct.pack(self.descriptor_layout size bytes(self.version_string "utf-8") size_server bytes(self.server_version_string "utf-8") )<block_end><block_end><class_stmt>WinOsUmFields(CamiYAMLObject)<block_start>yaml_tag="!intro_update_win_um_fields"<block_end><class_stmt>WinFunction(CamiYAMLObject CamiAtom)<block_start>yaml_tag="!intro_update_win_function"<line_sep>"""
struct _CAMI_WIN_FUNCTION
{
DWORD NameHash;
DWORD PatternsCount;
DWORD PatternsTable;
DWORD ArgumentsCount;
DWORD ArgumentsTable;
QWORD _Reserved1;
DWORD _Reserved2;
DWORD _Reserved3;
}
"""<line_sep>g_patterns_list=[]<line_sep>descriptor_layout="<IIIIIQII"<def_stmt>__init__ self other<block_start>""" This is basically a copy constructor.
We don't use deepcopy because we don't want to duplicate the patterns or arguments
Args:
other: Another WinFunction object
Attributes:
name: The function name
patterns: A table* with the patterns for this function.
arguments: A WinFunctionArgument with the arguments for this function.
Notes:
* Depending on how the object was created, table could mean:
- A python list, if the object was created by the YAML loader. This is an intermediate form and should
be transformed in a CamiDataTable.
- A CamiDataTable, if the object was created by get_function_for_os()
"""<if_stmt>type(self)<ne>type(other)<block_start><raise>Exception("Invalid object type sent to {} copy constructor: {}".format(type(self) type(other)))<block_end>self.__dict__.update(other.__dict__)<block_end><def_stmt>post_create self state<block_start>""" This is the YAML constructor
Args:
state: The YAML file in a dictionary form
"""<line_sep># We are doing this because some functions don't have custom arguments
<if_stmt><not>hasattr(self "arguments")<block_start>self.arguments=[]<block_end><block_end><def_stmt>__eq__ self other<block_start><if_stmt>type(self)<ne>type(other)<block_start><raise>Exception("Invalid comparison between %s and %s"%(type(self) type(other)))<block_end># this is a rudimentary comparison but it's enough for our needs
<return>self.__dict__<eq>other.__dict__<block_end><def_stmt>get_function_for_os self os<block_start>""" Create another instance of this object which only contains patterns and arguments suitable for the given OS
This method will filter the attributes of this function and will create another object which
will contain only the patterns & arguments which are suitable for the given OS. This method should
be called for object which are in the intermediate form described above.
Args:
os: A SupportedOsWin object
Returns:
- Another instance of this object containing only the patterns and arguments needed by the given OS.
- None, if the functions has no patterns for the given OS or the function is for 64bits OSs and the OS is
a 32bits one (or vice versa).
Raises:
Exception: If there are multiple arguments for this OS. (Maybe we can shall our own exception class ?? )
"""<if_stmt>self.guest64<ne>os.is_64<block_start><return><none><block_end>new_patterns=[]<line_sep>new_arguments=<none><for_stmt>pattern self.patterns<block_start><if_stmt>os.build_number<ge>pattern.min_ver<and>os.build_number<le>pattern.max_ver<block_start>new_patterns.append(pattern)<block_end><block_end><for_stmt>arguments self.arguments<block_start><if_stmt>os.build_number<ge>arguments.min_ver<and>os.build_number<le>arguments.max_ver<block_start><if_stmt>new_arguments<is><none><block_start>new_arguments=arguments<block_end><else_stmt><block_start><raise>Exception("Found more arguments for function {}, 64: {}".format(self.name self.guest64))<block_end><block_end><block_end><if_stmt>len(new_patterns)<eq>0<block_start><return><none><block_end>new_patterns=sorted(new_patterns key=<lambda>x:x.max_ver-x.min_ver)<line_sep>new_function=WinFunction(self)<if_stmt>new_arguments<is><none><block_start>new_function.arguments=WinFunctionArgument()<block_end><else_stmt><block_start>new_function.arguments=new_arguments<block_end>new_function.patterns=WinFunctionsPatternsTable()<line_sep>new_function.patterns.set_entries(new_patterns)<try_stmt><block_start>idx=self.g_patterns_list.index(new_function.patterns)<line_sep>new_function.patterns=self.g_patterns_list[idx]<block_end><except_stmt>ValueError<block_start>self.g_patterns_list.append(new_function.patterns)<block_end><return>new_function<block_end><def_stmt>get_descriptor self<block_start>""" Generate the CamiDataTable entry for this function
Returns:
bytes: the CamiDataTable entry (a _CAMI_WIN_FUNCTION structure)
Raises:
FilePointerException: If this method is called before generating the binary form of its
code (with serialize)
"""<line_sep><return>struct.pack(self.descriptor_layout crc32.crc32(self.name) self.patterns.get_entry_count() self.patterns.get_file_pointer() self.arguments.get_count() self.arguments.get_file_pointer() 0 0 0 )<block_end><def_stmt>serialize self start<block_start>""" Generate the body of this function in it's binary form.
Get the binary form of this function's body by packing it's arguments and patterns.
Args:
start: The offset in the file where this function will be placed
Returns:
bytes: The body of this function containing the arguments and patterns
"""<line_sep>data=self.arguments.serialize(start)<line_sep><return>data+self.patterns.serialize(start+len(data))<block_end><block_end><class_stmt>WinFunctionPattern(CamiYAMLObject CamiAtom)<block_start>yaml_tag="!intro_update_win_pattern"<line_sep>"""
struct _CAMI_WIN_PATTERN
{
CHAR SectionHint[8];
DWORD HashLength;
DWORD HashOffset;
DWORD _Reserved1;
DWORD _Reserved2;
}
"""<line_sep>descriptor_layout="<8sIIII"<def_stmt>post_create self state<block_start>""" The YAML constructor for this object
Args:
state: The YAML file in a dictionary form
"""<if_stmt>self.min_ver<in>version_any.keys()<block_start>self.min_ver=version_any[self.min_ver]<block_end><if_stmt>self.max_ver<in>version_any.keys()<block_start>self.max_ver=version_any[self.max_ver]<block_end><if_stmt>self.section_hint<is><none><block_start>self.section_hint=""<block_end><block_end><def_stmt>__eq__ self other<block_start><if_stmt>type(self)<ne>type(other)<block_start><raise>Exception("Invalid comparison between %s and %s"%(type(self) type(other)))<block_end># this is a rudimentary comparison but it's enough for our needs
<return>self.__dict__<eq>other.__dict__<block_end><def_stmt>get_descriptor self<block_start>""" Generate the CamiDataTable entry for this pattern
Returns:
bytes: the CamiDataTable entry (a _CAMI_WIN_PATTERN structure)
Raises:
FilePointerException: If this method is called before generating the binary form
of the pattern code. (with serialize)
"""<line_sep><return>struct.pack(self.descriptor_layout bytes(self.section_hint "utf-8") self.pattern.get_count() self.pattern.get_file_pointer() 0 0 )<block_end><def_stmt>serialize self start<block_start>""" Genereate the body of this pattern in it's binary form
Get the binary form of this pattern's body by packing it's code.
Args:
start: The offset in the file where this pattern will be placed
Returns:
bytes: The body of this pattern (the code)
"""<line_sep><return>self.pattern.serialize(start)<block_end><block_end><class_stmt>WinFunctionArgument(CamiYAMLObject CamiObject)<block_start>yaml_tag="!intro_update_win_args"<def_stmt>post_create self state<block_start><if_stmt>self.min_ver<in>version_any.keys()<block_start>self.min_ver=version_any[self.min_ver]<block_end><if_stmt>self.max_ver<in>version_any.keys()<block_start>self.max_ver=version_any[self.max_ver]<block_end><block_end><def_stmt>__init__ self<block_start>""" Constructor for this object.
We need this for functions without custom arguments in order to simplify the code
Attributes:
min_ver: Minimum build_number required for this list of arguments
max_ver: Maximum build_number supported by this list of arguments
"""<line_sep>self.args=[]<block_end><def_stmt>get_count self<block_start>""" Returns the length of the arguments list """<line_sep><return>len(self.args)<block_end><def_stmt>get_binary self<block_start>""" Pack the arguments in a bytes object
We are doing this here (not in serialize) in order to simplify the code
Returns:
bytes: The arguments in a binary form (can be empty)
May raise KeyError if there are unknown arguments in the YAML file.
"""<line_sep>c_struct=bytes()<line_sep># make sure we don't put more arguments than introcore could use
<assert_stmt>len(self.args)<le>detour_args["DET_ARGS_MAX"]<for_stmt>arg self.args<block_start>c_struct<augadd>struct.pack("<I" detour_args[arg])<block_end><return>c_struct<block_end><def_stmt>serialize self start<block_start>""" Returns the bytes object of the arguments list
The return value can be an empty bytes() object if this list of arguments is already in the file
"""<try_stmt><block_start>self.set_file_pointer(start)<block_end><except_stmt>FilePointerException<block_start><return>bytes()<block_end><return>self.get_binary()<block_end><block_end><class_stmt>WinSupportedOsTable(CamiDataTable)<block_start>section_hint=section_hints["supported_os"]|section_hints["windows"]<line_sep>entry_type=WinSupportedOs<def_stmt>process_list self<block_start>self._entries.sort(key=<lambda>os:os.build_number)<block_end><block_end><class_stmt>WinOsFunctionsTable(CamiDataTable)# no section hint needed
<block_start>entry_type=WinFunction<block_end><class_stmt>WinFunctionsPatternsTable(CamiDataTable)# no section hint needed
<block_start>entry_type=WinFunctionPattern<block_end> |
child_network_params={"learning_rate":3e-5 "max_epochs":100 "beta":1e-3 "batch_size":20}<line_sep>controller_params={"max_layers":3 "components_per_layer":4 'beta':1e-4 'max_episodes':2000 "num_children_per_episode":10}<line_sep> |
<import_from_stmt>.convunet unet<import_from_stmt>.dilatedunet dilated_unet<import_from_stmt>.dilateddensenet dilated_densenet dilated_densenet2 dilated_densenet3<line_sep> |
<class_stmt>CUDF<block_start><def_stmt>__init__ self<block_start>self._cudf=<none><block_end><block_end> |
<import_from_stmt>sphinx addnodes<import_from_stmt>sphinx.util.compat Directive<import_from_stmt>sphinx.util.compat make_admonition<import_from_stmt>docutils nodes<class_stmt>platform_node(nodes.Admonition nodes.Element)<block_start><pass><block_end><class_stmt>PlatformDirective(Directive)<block_start>has_content=<true><line_sep>required_arguments=1<line_sep>optional_arguments=0<line_sep>final_argument_whitespace=<true><line_sep>option_spec={}<def_stmt>run self<block_start>ret=make_admonition(platform_node self.name [self.arguments[0]] self.options self.content self.lineno self.content_offset self.block_text self.state self.state_machine)<line_sep><return>ret<block_end><block_end><def_stmt>MakePlatformDirective platform<block_start><class_stmt>CustomPlatformDirective(Directive)<block_start>has_content=<true><line_sep>required_arguments=0<line_sep>optional_arguments=0<line_sep>final_argument_whitespace=<true><line_sep>option_spec={}<def_stmt>run self<block_start>ret=make_admonition(platform_node self.name [platform] self.options self.content self.lineno self.content_offset self.block_text self.state self.state_machine)<line_sep><return>ret<block_end><block_end><return>CustomPlatformDirective<block_end><def_stmt>visit_platform_node self node<block_start>self.visit_admonition(node)<block_end><def_stmt>depart_platform_node self node<block_start>self.depart_admonition(node)<block_end><def_stmt>setup app<block_start>app.add_node(platform_node html=(visit_platform_node depart_platform_node) latex=(visit_platform_node depart_platform_node) text=(visit_platform_node depart_platform_node) man=(visit_platform_node depart_platform_node))<line_sep>app.add_directive('platform' PlatformDirective)<line_sep>app.add_directive('windows' MakePlatformDirective('Windows'))<line_sep>app.add_directive('mac' MakePlatformDirective('Mac OS X'))<line_sep>app.add_directive('linux' MakePlatformDirective('Linux'))<block_end> |
<import_from_stmt>resotolib.config Config<import_from_stmt>resoto_plugin_example_collector ExampleCollectorPlugin<def_stmt>test_config <block_start>config=Config("dummy" "dummy")<line_sep>ExampleCollectorPlugin.add_config(config)<line_sep>Config.init_default_config()<block_end># assert Config.example.region is None
|
<import_stmt>json<import_stmt>os.path<import_stmt>cachetools<line_sep>MIN_RATING_STARS=2.5<line_sep># * 2 to convert from stars to 0-10 range actually used
MIN_RATING_FLOAT=MIN_RATING_STARS<times>2<line_sep>MIN_RATE_CNT=3<line_sep>MIN_CHAPTERS=4<line_sep>@cachetools.cached(cachetools.TTLCache(100 60<times>5))<def_stmt>_load_lut_internal <block_start>outf=os.path.join(os.path.split(__file__)[0] 'series_overrides.json')<with_stmt>open(outf)<as>fp<block_start>cont=fp.read()<block_end>lut=json.loads(cont)<line_sep><return>lut<block_end><def_stmt>get_rrl_lut <block_start>lut=_load_lut_internal()<line_sep>lut=lut['royalroadl']<assert_stmt>'force_sequential_numbering'<in>lut<line_sep><return>lut<block_end><def_stmt>get_sh_lut <block_start>lut=_load_lut_internal()<line_sep>lut=lut['scribblehub']<assert_stmt>'force_sequential_numbering'<in>lut<line_sep><return>lut<block_end><def_stmt>fix_tag tagtxt# This is literally only tolerable since load_lut is memoized
<block_start>conf=_load_lut_internal()<if_stmt>tagtxt<in>conf['tag_rename']<block_start>tagtxt=conf['tag_rename'][tagtxt]<block_end><return>tagtxt<block_end><def_stmt>fix_genre genretxt# This is literally only tolerable since load_lut is memoized
<block_start>conf=_load_lut_internal()<if_stmt>genretxt<in>conf['genre_rename']<block_start>genretxt=conf['genre_rename'][genretxt]<block_end><return>genretxt<block_end><def_stmt>clean_tag in_txt<block_start><assert_stmt>isinstance(in_txt str) "Passed item is not a string! Type: '%s' -> '%s'"%(type(in_txt) in_txt )<assert_stmt><not>","<in>in_txt "It looks like a tag list got submitted as a tag! String: '%s'"%(in_txt )<line_sep>in_txt=in_txt.strip().lower().replace(" " "-")<line_sep><return>in_txt<block_end><def_stmt>check_fix_numbering log releases series_id rrl=<false> sh=<false><block_start><assert_stmt>rrl<or>sh<assert_stmt>sum([rrl sh])<eq>1<if_stmt><not>isinstance(series_id str)<block_start>log.warning("Series id is not a string: %s -> %s" series_id type(series_id))<assert_stmt>isinstance(series_id (str int))<line_sep>series_id=str(series_id)<block_end><if_stmt>rrl<block_start>conf=get_rrl_lut()<block_end><elif_stmt>sh<block_start>conf=get_sh_lut()<block_end>must_renumber=series_id<in>conf['force_sequential_numbering']<line_sep>missing_chap=0<line_sep>distinct=set()<for_stmt>item releases<block_start><if_stmt><not>(item['vol']<or>item['chp'])<block_start>missing_chap<augadd>1<block_end>distinct.add((item['vol'] item['chp'] item['frag']))<block_end><if_stmt><not>releases<block_start><return>[]<block_end># If less then half the release items have unique vol-chap-frag tuples,
# Apply a forced numbering scheme.
<if_stmt>len(distinct)<l>(len(releases)/2.0)<block_start>must_renumber=<true><block_end>unnumbered=(missing_chap/len(releases))<times>100<if_stmt>(len(releases)<ge>5<and>unnumbered<g>80)<or>must_renumber<block_start><if_stmt>must_renumber<block_start>log.warning("Item numbering force-overridden! Adding simple sequential chapter numbers.")<block_end><else_stmt><block_start>log.warning("Item seems to not have numbered chapters. Adding simple sequential chapter numbers.")<block_end>chap=1<for_stmt>item releases<block_start>item['vol']=<none><line_sep>item['chp']=chap<line_sep>chap<augadd>1<block_end><block_end><return>releases<block_end><def_stmt>test <block_start>get_rrl_lut()<block_end><if_stmt>__name__<eq>"__main__"<block_start>test()<block_end> |
<import_stmt>os<import_stmt>utils<import_stmt>torch<import_stmt>torch.nn<as>nn<import_from_stmt>torchvision transforms<import_from_stmt>torch.utils.data DataLoader<import_stmt>numpy<as>np<import_stmt>data<import_stmt>scipy.io<as>sio<import_from_stmt>options.training_options TrainOptions<import_stmt>utils<import_stmt>time<import_from_stmt>models AutoEncoderCov3D AutoEncoderCov3DMem<import_from_stmt>models EntropyLossEncap<line_sep>###
opt_parser=TrainOptions()<line_sep>opt=opt_parser.parse(is_print=<true>)<line_sep>use_cuda=opt.UseCUDA<line_sep>device=torch.device("cuda"<if>use_cuda<else>"cpu")<line_sep>###
utils.seed(opt.Seed)<if_stmt>(opt.IsDeter)<block_start>torch.backends.cudnn.benchmark=<false><line_sep>torch.backends.cudnn.deterministic=<true><block_end>######
model_setting=utils.get_model_setting(opt)<line_sep>print('Setting: %s'%(model_setting))<line_sep>############
batch_size_in=opt.BatchSize<line_sep>learning_rate=opt.LR<line_sep>max_epoch_num=opt.EpochNum<line_sep>chnum_in_=opt.ImgChnNum# channel number of the input images
framenum_in_=opt.FrameNum# num of frames in a video clip
mem_dim_in=opt.MemDim<line_sep>entropy_loss_weight=opt.EntropyLossWeight<line_sep>sparse_shrink_thres=opt.ShrinkThres<line_sep>img_crop_size=0<line_sep>print('bs=%d, lr=%f, entrloss=%f, shr=%f, memdim=%d'%(batch_size_in learning_rate entropy_loss_weight sparse_shrink_thres mem_dim_in))<line_sep>############
## data path
data_root=opt.DataRoot+opt.Dataset+'/'<line_sep>tr_data_frame_dir=data_root+'Train/'<line_sep>tr_data_idx_dir=data_root+'Train_idx/'<line_sep>############ model saving dir path
saving_root=opt.ModelRoot<line_sep>saving_model_path=os.path.join(saving_root 'model_'+model_setting+'/')<line_sep>utils.mkdir(saving_model_path)<line_sep>### tblog
<if_stmt>(opt.IsTbLog)<block_start>log_path=os.path.join(saving_root 'log_'+model_setting+'/')<line_sep>utils.mkdir(log_path)<line_sep>tb_logger=utils.Logger(log_path)<block_end>##
<if_stmt>(chnum_in_<eq>1)<block_start>norm_mean=[0.5]<line_sep>norm_std=[0.5]<block_end><elif_stmt>(chnum_in_<eq>3)<block_start>norm_mean=(0.5 0.5 0.5)<line_sep>norm_std=(0.5 0.5 0.5)<block_end>frame_trans=transforms.Compose([transforms.ToTensor() transforms.Normalize(norm_mean norm_std)])<line_sep>unorm_trans=utils.UnNormalize(mean=norm_mean std=norm_std)<line_sep>###### data
video_dataset=data.VideoDataset(tr_data_idx_dir tr_data_frame_dir transform=frame_trans)<line_sep>tr_data_loader=DataLoader(video_dataset batch_size=batch_size_in shuffle=<true> num_workers=opt.NumWorker)<line_sep>###### model
<if_stmt>(opt.ModelName<eq>'MemAE')<block_start>model=AutoEncoderCov3DMem(chnum_in_ mem_dim_in shrink_thres=sparse_shrink_thres)<block_end><else_stmt><block_start>model=[]<line_sep>print('Wrong model name.')<block_end>model.apply(utils.weights_init)<line_sep>#########
device=torch.device("cuda"<if>use_cuda<else>"cpu")<line_sep>model.to(device)<line_sep>tr_recon_loss_func=nn.MSELoss().to(device)<line_sep>tr_entropy_loss_func=EntropyLossEncap().to(device)<line_sep>tr_optimizer=torch.optim.Adam(model.parameters() lr=learning_rate)<line_sep>##
data_loader_len=len(tr_data_loader)<line_sep>textlog_interval=opt.TextLogInterval<line_sep>snap_save_interval=opt.SnapInterval<line_sep>save_check_interval=opt.SaveCheckInterval<line_sep>tb_img_log_interval=opt.TBImgLogInterval<line_sep>global_ite_idx=0# for logging
<for_stmt>epoch_idx range(0 max_epoch_num)<block_start><for_stmt>batch_idx,(item frames) enumerate(tr_data_loader)<block_start>frames=frames.to(device)<if_stmt>(opt.ModelName<eq>'MemAE')<block_start>recon_res=model(frames)<line_sep>recon_frames=recon_res['output']<line_sep>att_w=recon_res['att']<line_sep>loss=tr_recon_loss_func(recon_frames frames)<line_sep>recon_loss_val=loss.item()<line_sep>entropy_loss=tr_entropy_loss_func(att_w)<line_sep>entropy_loss_val=entropy_loss.item()<line_sep>loss=loss+entropy_loss_weight<times>entropy_loss<line_sep>loss_val=loss.item()<line_sep>##
tr_optimizer.zero_grad()<line_sep>loss.backward()<line_sep>tr_optimizer.step()<line_sep>##
<block_end>## TB log val
<if_stmt>(opt.IsTbLog)<block_start>tb_info={'loss':loss_val 'recon_loss':recon_loss_val 'entropy_loss':entropy_loss_val}<for_stmt>tag,value tb_info.items()<block_start>tb_logger.scalar_summary(tag value global_ite_idx)<block_end># TB log img
<if_stmt>((global_ite_idx%tb_img_log_interval)<eq>0)<block_start>frames_vis=utils.vframes2imgs(unorm_trans(frames.data) step=5 batch_idx=0)<line_sep>frames_vis=np.concatenate(frames_vis axis=-1)<line_sep>frames_vis=frames_vis[<none> : :]<times>np.ones(3 dtype=int)[: <none> <none>]<line_sep>frames_recon_vis=utils.vframes2imgs(unorm_trans(recon_frames.data) step=5 batch_idx=0)<line_sep>frames_recon_vis=np.concatenate(frames_recon_vis axis=-1)<line_sep>frames_recon_vis=frames_recon_vis[<none> : :]<times>np.ones(3 dtype=int)[: <none> <none>]<line_sep>tb_info={'x':frames_vis 'x_rec':frames_recon_vis}<for_stmt>tag,imgs tb_info.items()<block_start>tb_logger.image_summary(tag imgs global_ite_idx)<block_end><block_end><block_end>##
<if_stmt>((batch_idx%textlog_interval)<eq>0)<block_start>print('[%s, epoch %d/%d, bt %d/%d] loss=%f, rc_losss=%f, ent_loss=%f'%(model_setting epoch_idx max_epoch_num batch_idx data_loader_len loss_val recon_loss_val entropy_loss_val))<block_end><if_stmt>((global_ite_idx%snap_save_interval)<eq>0)<block_start>torch.save(model.state_dict() '%s/%s_snap.pt'%(saving_model_path model_setting))<block_end>global_ite_idx<augadd>1<block_end><if_stmt>((epoch_idx%save_check_interval)<eq>0)<block_start>torch.save(model.state_dict() '%s/%s_epoch_%04d.pt'%(saving_model_path model_setting epoch_idx))<block_end><block_end>torch.save(model.state_dict() '%s/%s_epoch_%04d_final.pt'%(saving_model_path model_setting epoch_idx))<line_sep> |
# Copyright (c) Meta Platforms, Inc
"""
Warning: This file was generated by flowtorch/scripts/generate_imports.py
Do not modify or delete!
"""<import_from_stmt>flowtorch.distributions.flow Flow<import_from_stmt>flowtorch.distributions.neals_funnel NealsFunnel<line_sep>__all__=["Flow" "NealsFunnel"]<line_sep> |
a,b=2 1<line_sep>c=1<line_sep>b,=1 2<line_sep> |
# encoding: utf-8
<import_stmt>pytest<import_stmt>six<import_from_stmt>flask Blueprint<import_stmt>ckan.plugins<as>p<import_from_stmt>ckan.common config _<class_stmt>MockRoutingPlugin(p.SingletonPlugin)<block_start>p.implements(p.IBlueprint)<def_stmt>get_blueprint self# Create Blueprint for plugin
<block_start>blueprint=Blueprint(self.name self.__module__)<line_sep>blueprint.add_url_rule(u"/simple_flask" u"flask_plugin_view" flask_plugin_view)<line_sep>blueprint.add_url_rule(u"/flask_translated" u"flask_translated" flask_translated_view)<line_sep><return>blueprint<block_end><block_end><def_stmt>flask_plugin_view <block_start><return>u"Hello World, this is served from a Flask extension"<block_end><def_stmt>flask_translated_view <block_start><return>_(u"Dataset")<block_end>@pytest.fixture<def_stmt>patched_app app<block_start>flask_app=app.flask_app<def_stmt>test_view <block_start><return>u"This was served from Flask"<block_end>flask_app.add_url_rule(u"/flask_core" view_func=test_view endpoint=u"flask_core.index")<line_sep><return>app<block_end><def_stmt>test_flask_core_route_is_served patched_app<block_start>res=patched_app.get(u"/")<assert_stmt>res.status_code<eq>200<line_sep>res=patched_app.get(u"/flask_core")<assert_stmt>six.ensure_text(res.data)<eq>u"This was served from Flask"<block_end>@pytest.mark.ckan_config(u"SECRET_KEY" u"super_secret_stuff")<def_stmt>test_secret_key_is_used_if_present app<block_start><assert_stmt>app.flask_app.config[u"SECRET_KEY"]<eq>u"super_secret_stuff"<block_end>@pytest.mark.ckan_config(u"SECRET_KEY" <none>)<def_stmt>test_beaker_secret_is_used_by_default app<block_start><assert_stmt>(app.flask_app.config[u"SECRET_KEY"]<eq>config[u"beaker.session.secret"])<block_end>@pytest.mark.ckan_config(u"SECRET_KEY" <none>)@pytest.mark.ckan_config(u"beaker.session.secret" <none>)<def_stmt>test_no_beaker_secret_crashes make_app# TODO: When Pylons is finally removed, we should test for
# RuntimeError instead (thrown on `make_flask_stack`)
<block_start><with_stmt>pytest.raises(RuntimeError)<block_start>make_app()<block_end><block_end> |
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
<import_from_stmt>unittest mock<import_from_stmt>oslo_config cfg<import_from_stmt>oslo_config fixture<as>cfg_fixture<import_from_stmt>designate exceptions<import_from_stmt>designate objects<import_from_stmt>designate scheduler<import_from_stmt>designate tests<line_sep>DEFAULT_POOL_ID=BRONZE_POOL_ID='67d71c2a-645c-4dde-a6b8-60a172c9ede8'<line_sep>SILVER_POOL_ID='5fabcd37-262c-4cf3-8625-7f419434b6df'<line_sep>GOLD_POOL_ID='24702e43-8a52-440f-ab74-19fc16048860'<def_stmt>build_test_pools <block_start>pools=objects.PoolList.from_list([{'id':DEFAULT_POOL_ID} {'id':SILVER_POOL_ID} {'id':GOLD_POOL_ID} ])<line_sep># Pool 0 is also the default pool.
pool_0_attributes=objects.PoolAttributeList.from_list([{'key':'service_tier' 'value':'bronze'} ])<line_sep>pool_1_attributes=objects.PoolAttributeList.from_list([{'key':'service_tier' 'value':'silver'} ])<line_sep>pool_2_attributes=objects.PoolAttributeList.from_list([{'key':'service_tier' 'value':'gold'} ])<line_sep>pools[0].attributes=pool_0_attributes<line_sep>pools[1].attributes=pool_1_attributes<line_sep>pools[2].attributes=pool_2_attributes<line_sep><return>pools<block_end><class_stmt>AttributeSchedulerPermutationsTest(tests.TestCase)<block_start><def_stmt>setUp self<block_start>super(AttributeSchedulerPermutationsTest self).setUp()<line_sep>self.CONF=self.useFixture(cfg_fixture.Config(cfg.CONF)).conf<line_sep>self.context=self.get_context()<line_sep>self.CONF.set_override('scheduler_filters' ['attribute'] 'service:central')<line_sep>self.CONF.set_override('default_pool_id' DEFAULT_POOL_ID 'service:central')<line_sep>attrs={'find_pools.return_value':build_test_pools()}<line_sep>mock_storage=mock.Mock(**attrs)<line_sep>self.scheduler=scheduler.get_scheduler(storage=mock_storage)<block_end><def_stmt>test_get_gold_tier self<block_start>zone=objects.Zone(name='example.com.' type='PRIMARY' email='<EMAIL>' attributes=objects.ZoneAttributeList.from_list([{'key':'service_tier' 'value':'gold'} ]))<line_sep>result=self.scheduler.schedule_zone(self.context zone)<line_sep>self.assertEqual(GOLD_POOL_ID result)<block_end><def_stmt>test_get_silver_tier self<block_start>zone=objects.Zone(name='example.com.' type='PRIMARY' email='<EMAIL>' attributes=objects.ZoneAttributeList.from_list([{'key':'service_tier' 'value':'silver'} ]))<line_sep>result=self.scheduler.schedule_zone(self.context zone)<line_sep>self.assertEqual(SILVER_POOL_ID result)<block_end><def_stmt>test_get_bronze_tier self<block_start>zone=objects.Zone(name='example.com.' type='PRIMARY' email='<EMAIL>' attributes=objects.ZoneAttributeList.from_list([{'key':'service_tier' 'value':'bronze'} ]))<line_sep>result=self.scheduler.schedule_zone(self.context zone)<line_sep>self.assertEqual(BRONZE_POOL_ID result)<block_end><def_stmt>test_tier_not_found_raises_exception self<block_start>zone=objects.Zone(name='example.com.' type='PRIMARY' email='<EMAIL>' attributes=objects.ZoneAttributeList.from_list([{'key':'service_tier' 'value':'blue'} ]))<line_sep>self.assertRaises(exceptions.NoValidPoolFound self.scheduler.schedule_zone self.context zone)<block_end><def_stmt>test_no_tier_raises_exception self<block_start>zone=objects.Zone(name='example.com.' type='PRIMARY' email='<EMAIL>' attributes=objects.ZoneAttributeList.from_list([]))<line_sep># When no attribute is requested it will return all available pools.
# NOTE(eandersson): This is probably not intended behavior.
# We probably want this to return NoValidPoolFound,
# so that we can use a fallback filter with the
# attribute filter.
self.assertRaises(exceptions.MultiplePoolsFound self.scheduler.schedule_zone self.context zone)<block_end><block_end><class_stmt>DefaultSchedulerPermutationsTest(tests.TestCase)<block_start><def_stmt>setUp self<block_start>super(DefaultSchedulerPermutationsTest self).setUp()<line_sep>self.CONF=self.useFixture(cfg_fixture.Config(cfg.CONF)).conf<line_sep>self.context=self.get_context()<line_sep>self.CONF.set_override('scheduler_filters' ['default_pool'] 'service:central')<line_sep>self.CONF.set_override('default_pool_id' DEFAULT_POOL_ID 'service:central')<line_sep>attrs={'find_pools.return_value':build_test_pools()}<line_sep>mock_storage=mock.Mock(**attrs)<line_sep>self.scheduler=scheduler.get_scheduler(storage=mock_storage)<block_end><def_stmt>test_get_default_pool self<block_start>zone=objects.Zone(name='example.com.' type='PRIMARY' email='<EMAIL>' )<line_sep>result=self.scheduler.schedule_zone(self.context zone)<line_sep>self.assertEqual(DEFAULT_POOL_ID result)<block_end><block_end><class_stmt>FallbackSchedulerPermutationsTest(tests.TestCase)<block_start><def_stmt>setUp self<block_start>super(FallbackSchedulerPermutationsTest self).setUp()<line_sep>self.CONF=self.useFixture(cfg_fixture.Config(cfg.CONF)).conf<line_sep>self.context=self.get_context()<line_sep>self.CONF.set_override('scheduler_filters' ['attribute' 'fallback'] 'service:central')<line_sep>self.CONF.set_override('default_pool_id' DEFAULT_POOL_ID 'service:central')<line_sep>attrs={'find_pools.return_value':build_test_pools()}<line_sep>mock_storage=mock.Mock(**attrs)<line_sep>self.scheduler=scheduler.get_scheduler(storage=mock_storage)<block_end><def_stmt>test_tier_not_found_return_default self<block_start>zone=objects.Zone(name='example.com.' type='PRIMARY' email='<EMAIL>' attributes=objects.ZoneAttributeList.from_list([{'key':'service_tier' 'value':'that does not exist'} ]))<line_sep>result=self.scheduler.schedule_zone(self.context zone)<line_sep>self.assertEqual(DEFAULT_POOL_ID result)<block_end><block_end> |
expected_output={"aal5VccEntry":{"3":{} "4":{} "5":{}} "aarpEntry":{"1":{} "2":{} "3":{}} "adslAtucChanConfFastMaxTxRate":{} "adslAtucChanConfFastMinTxRate":{} "adslAtucChanConfInterleaveMaxTxRate":{} "adslAtucChanConfInterleaveMinTxRate":{} "adslAtucChanConfMaxInterleaveDelay":{} "adslAtucChanCorrectedBlks":{} "adslAtucChanCrcBlockLength":{} "adslAtucChanCurrTxRate":{} "adslAtucChanInterleaveDelay":{} "adslAtucChanIntervalCorrectedBlks":{} "adslAtucChanIntervalReceivedBlks":{} "adslAtucChanIntervalTransmittedBlks":{} "adslAtucChanIntervalUncorrectBlks":{} "adslAtucChanIntervalValidData":{} "adslAtucChanPerfCurr15MinCorrectedBlks":{} "adslAtucChanPerfCurr15MinReceivedBlks":{} "adslAtucChanPerfCurr15MinTimeElapsed":{} "adslAtucChanPerfCurr15MinTransmittedBlks":{} "adslAtucChanPerfCurr15MinUncorrectBlks":{} "adslAtucChanPerfCurr1DayCorrectedBlks":{} "adslAtucChanPerfCurr1DayReceivedBlks":{} "adslAtucChanPerfCurr1DayTimeElapsed":{} "adslAtucChanPerfCurr1DayTransmittedBlks":{} "adslAtucChanPerfCurr1DayUncorrectBlks":{} "adslAtucChanPerfInvalidIntervals":{} "adslAtucChanPerfPrev1DayCorrectedBlks":{} "adslAtucChanPerfPrev1DayMoniSecs":{} "adslAtucChanPerfPrev1DayReceivedBlks":{} "adslAtucChanPerfPrev1DayTransmittedBlks":{} "adslAtucChanPerfPrev1DayUncorrectBlks":{} "adslAtucChanPerfValidIntervals":{} "adslAtucChanPrevTxRate":{} "adslAtucChanReceivedBlks":{} "adslAtucChanTransmittedBlks":{} "adslAtucChanUncorrectBlks":{} "adslAtucConfDownshiftSnrMgn":{} "adslAtucConfMaxSnrMgn":{} "adslAtucConfMinDownshiftTime":{} "adslAtucConfMinSnrMgn":{} "adslAtucConfMinUpshiftTime":{} "adslAtucConfRateChanRatio":{} "adslAtucConfRateMode":{} "adslAtucConfTargetSnrMgn":{} "adslAtucConfUpshiftSnrMgn":{} "adslAtucCurrAtn":{} "adslAtucCurrAttainableRate":{} "adslAtucCurrOutputPwr":{} "adslAtucCurrSnrMgn":{} "adslAtucCurrStatus":{} "adslAtucDmtConfFastPath":{} "adslAtucDmtConfFreqBins":{} "adslAtucDmtConfInterleavePath":{} "adslAtucDmtFastPath":{} "adslAtucDmtInterleavePath":{} "adslAtucDmtIssue":{} "adslAtucDmtState":{} "adslAtucInitFailureTrapEnable":{} "adslAtucIntervalESs":{} "adslAtucIntervalInits":{} "adslAtucIntervalLofs":{} "adslAtucIntervalLols":{} "adslAtucIntervalLoss":{} "adslAtucIntervalLprs":{} "adslAtucIntervalValidData":{} "adslAtucInvSerialNumber":{} "adslAtucInvVendorID":{} "adslAtucInvVersionNumber":{} "adslAtucPerfCurr15MinESs":{} "adslAtucPerfCurr15MinInits":{} "adslAtucPerfCurr15MinLofs":{} "adslAtucPerfCurr15MinLols":{} "adslAtucPerfCurr15MinLoss":{} "adslAtucPerfCurr15MinLprs":{} "adslAtucPerfCurr15MinTimeElapsed":{} "adslAtucPerfCurr1DayESs":{} "adslAtucPerfCurr1DayInits":{} "adslAtucPerfCurr1DayLofs":{} "adslAtucPerfCurr1DayLols":{} "adslAtucPerfCurr1DayLoss":{} "adslAtucPerfCurr1DayLprs":{} "adslAtucPerfCurr1DayTimeElapsed":{} "adslAtucPerfESs":{} "adslAtucPerfInits":{} "adslAtucPerfInvalidIntervals":{} "adslAtucPerfLofs":{} "adslAtucPerfLols":{} "adslAtucPerfLoss":{} "adslAtucPerfLprs":{} "adslAtucPerfPrev1DayESs":{} "adslAtucPerfPrev1DayInits":{} "adslAtucPerfPrev1DayLofs":{} "adslAtucPerfPrev1DayLols":{} "adslAtucPerfPrev1DayLoss":{} "adslAtucPerfPrev1DayLprs":{} "adslAtucPerfPrev1DayMoniSecs":{} "adslAtucPerfValidIntervals":{} "adslAtucThresh15MinESs":{} "adslAtucThresh15MinLofs":{} "adslAtucThresh15MinLols":{} "adslAtucThresh15MinLoss":{} "adslAtucThresh15MinLprs":{} "adslAtucThreshFastRateDown":{} "adslAtucThreshFastRateUp":{} "adslAtucThreshInterleaveRateDown":{} "adslAtucThreshInterleaveRateUp":{} "adslAturChanConfFastMaxTxRate":{} "adslAturChanConfFastMinTxRate":{} "adslAturChanConfInterleaveMaxTxRate":{} "adslAturChanConfInterleaveMinTxRate":{} "adslAturChanConfMaxInterleaveDelay":{} "adslAturChanCorrectedBlks":{} "adslAturChanCrcBlockLength":{} "adslAturChanCurrTxRate":{} "adslAturChanInterleaveDelay":{} "adslAturChanIntervalCorrectedBlks":{} "adslAturChanIntervalReceivedBlks":{} "adslAturChanIntervalTransmittedBlks":{} "adslAturChanIntervalUncorrectBlks":{} "adslAturChanIntervalValidData":{} "adslAturChanPerfCurr15MinCorrectedBlks":{} "adslAturChanPerfCurr15MinReceivedBlks":{} "adslAturChanPerfCurr15MinTimeElapsed":{} "adslAturChanPerfCurr15MinTransmittedBlks":{} "adslAturChanPerfCurr15MinUncorrectBlks":{} "adslAturChanPerfCurr1DayCorrectedBlks":{} "adslAturChanPerfCurr1DayReceivedBlks":{} "adslAturChanPerfCurr1DayTimeElapsed":{} "adslAturChanPerfCurr1DayTransmittedBlks":{} "adslAturChanPerfCurr1DayUncorrectBlks":{} "adslAturChanPerfInvalidIntervals":{} "adslAturChanPerfPrev1DayCorrectedBlks":{} "adslAturChanPerfPrev1DayMoniSecs":{} "adslAturChanPerfPrev1DayReceivedBlks":{} "adslAturChanPerfPrev1DayTransmittedBlks":{} "adslAturChanPerfPrev1DayUncorrectBlks":{} "adslAturChanPerfValidIntervals":{} "adslAturChanPrevTxRate":{} "adslAturChanReceivedBlks":{} "adslAturChanTransmittedBlks":{} "adslAturChanUncorrectBlks":{} "adslAturConfDownshiftSnrMgn":{} "adslAturConfMaxSnrMgn":{} "adslAturConfMinDownshiftTime":{} "adslAturConfMinSnrMgn":{} "adslAturConfMinUpshiftTime":{} "adslAturConfRateChanRatio":{} "adslAturConfRateMode":{} "adslAturConfTargetSnrMgn":{} "adslAturConfUpshiftSnrMgn":{} "adslAturCurrAtn":{} "adslAturCurrAttainableRate":{} "adslAturCurrOutputPwr":{} "adslAturCurrSnrMgn":{} "adslAturCurrStatus":{} "adslAturDmtConfFastPath":{} "adslAturDmtConfFreqBins":{} "adslAturDmtConfInterleavePath":{} "adslAturDmtFastPath":{} "adslAturDmtInterleavePath":{} "adslAturDmtIssue":{} "adslAturDmtState":{} "adslAturIntervalESs":{} "adslAturIntervalLofs":{} "adslAturIntervalLoss":{} "adslAturIntervalLprs":{} "adslAturIntervalValidData":{} "adslAturInvSerialNumber":{} "adslAturInvVendorID":{} "adslAturInvVersionNumber":{} "adslAturPerfCurr15MinESs":{} "adslAturPerfCurr15MinLofs":{} "adslAturPerfCurr15MinLoss":{} "adslAturPerfCurr15MinLprs":{} "adslAturPerfCurr15MinTimeElapsed":{} "adslAturPerfCurr1DayESs":{} "adslAturPerfCurr1DayLofs":{} "adslAturPerfCurr1DayLoss":{} "adslAturPerfCurr1DayLprs":{} "adslAturPerfCurr1DayTimeElapsed":{} "adslAturPerfESs":{} "adslAturPerfInvalidIntervals":{} "adslAturPerfLofs":{} "adslAturPerfLoss":{} "adslAturPerfLprs":{} "adslAturPerfPrev1DayESs":{} "adslAturPerfPrev1DayLofs":{} "adslAturPerfPrev1DayLoss":{} "adslAturPerfPrev1DayLprs":{} "adslAturPerfPrev1DayMoniSecs":{} "adslAturPerfValidIntervals":{} "adslAturThresh15MinESs":{} "adslAturThresh15MinLofs":{} "adslAturThresh15MinLoss":{} "adslAturThresh15MinLprs":{} "adslAturThreshFastRateDown":{} "adslAturThreshFastRateUp":{} "adslAturThreshInterleaveRateDown":{} "adslAturThreshInterleaveRateUp":{} "adslLineAlarmConfProfile":{} "adslLineAlarmConfProfileRowStatus":{} "adslLineCoding":{} "adslLineConfProfile":{} "adslLineConfProfileRowStatus":{} "adslLineDmtConfEOC":{} "adslLineDmtConfMode":{} "adslLineDmtConfTrellis":{} "adslLineDmtEOC":{} "adslLineDmtTrellis":{} "adslLineSpecific":{} "adslLineType":{} "alarmEntry":{"1":{} "10":{} "11":{} "12":{} "2":{} "3":{} "4":{} "5":{} "6":{} "7":{} "8":{} "9":{} } "alpsAscuA1":{} "alpsAscuA2":{} "alpsAscuAlarmsEnabled":{} "alpsAscuCktName":{} "alpsAscuDownReason":{} "alpsAscuDropsAscuDisabled":{} "alpsAscuDropsAscuDown":{} "alpsAscuDropsGarbledPkts":{} "alpsAscuEnabled":{} "alpsAscuEntry":{"20":{}} "alpsAscuFwdStatusOption":{} "alpsAscuInOctets":{} "alpsAscuInPackets":{} "alpsAscuMaxMsgLength":{} "alpsAscuOutOctets":{} "alpsAscuOutPackets":{} "alpsAscuRetryOption":{} "alpsAscuRowStatus":{} "alpsAscuState":{} "alpsCktAscuId":{} "alpsCktAscuIfIndex":{} "alpsCktAscuStatus":{} "alpsCktBaseAlarmsEnabled":{} "alpsCktBaseConnType":{} "alpsCktBaseCurrPeerConnId":{} "alpsCktBaseCurrentPeer":{} "alpsCktBaseDownReason":{} "alpsCktBaseDropsCktDisabled":{} "alpsCktBaseDropsLifeTimeExpd":{} "alpsCktBaseDropsQOverflow":{} "alpsCktBaseEnabled":{} "alpsCktBaseHostLinkNumber":{} "alpsCktBaseHostLinkType":{} "alpsCktBaseInOctets":{} "alpsCktBaseInPackets":{} "alpsCktBaseLifeTimeTimer":{} "alpsCktBaseLocalHld":{} "alpsCktBaseNumActiveAscus":{} "alpsCktBaseOutOctets":{} "alpsCktBaseOutPackets":{} "alpsCktBasePriPeerAddr":{} "alpsCktBaseRemHld":{} "alpsCktBaseRowStatus":{} "alpsCktBaseState":{} "alpsCktP1024Ax25LCN":{} "alpsCktP1024BackupPeerAddr":{} "alpsCktP1024DropsUnkAscu":{} "alpsCktP1024EmtoxX121":{} "alpsCktP1024IdleTimer":{} "alpsCktP1024InPktSize":{} "alpsCktP1024MatipCloseDelay":{} "alpsCktP1024OutPktSize":{} "alpsCktP1024RetryTimer":{} "alpsCktP1024RowStatus":{} "alpsCktP1024SvcMsgIntvl":{} "alpsCktP1024SvcMsgList":{} "alpsCktP1024WinIn":{} "alpsCktP1024WinOut":{} "alpsCktX25DropsVcReset":{} "alpsCktX25HostX121":{} "alpsCktX25IfIndex":{} "alpsCktX25LCN":{} "alpsCktX25RemoteX121":{} "alpsIfHLinkActiveCkts":{} "alpsIfHLinkAx25PvcDamp":{} "alpsIfHLinkEmtoxHostX121":{} "alpsIfHLinkX25ProtocolType":{} "alpsIfP1024CurrErrCnt":{} "alpsIfP1024EncapType":{} "alpsIfP1024Entry":{"11":{} "12":{} "13":{}} "alpsIfP1024GATimeout":{} "alpsIfP1024MaxErrCnt":{} "alpsIfP1024MaxRetrans":{} "alpsIfP1024MinGoodPollResp":{} "alpsIfP1024NumAscus":{} "alpsIfP1024PollPauseTimeout":{} "alpsIfP1024PollRespTimeout":{} "alpsIfP1024PollingRatio":{} "alpsIpAddress":{} "alpsPeerInCallsAcceptFlag":{} "alpsPeerKeepaliveMaxRetries":{} "alpsPeerKeepaliveTimeout":{} "alpsPeerLocalAtpPort":{} "alpsPeerLocalIpAddr":{} "alpsRemPeerAlarmsEnabled":{} "alpsRemPeerCfgActivation":{} "alpsRemPeerCfgAlarmsOn":{} "alpsRemPeerCfgIdleTimer":{} "alpsRemPeerCfgNoCircTimer":{} "alpsRemPeerCfgRowStatus":{} "alpsRemPeerCfgStatIntvl":{} "alpsRemPeerCfgStatRetry":{} "alpsRemPeerCfgTCPQLen":{} "alpsRemPeerConnActivation":{} "alpsRemPeerConnAlarmsOn":{} "alpsRemPeerConnCreation":{} "alpsRemPeerConnDownReason":{} "alpsRemPeerConnDropsGiant":{} "alpsRemPeerConnDropsQFull":{} "alpsRemPeerConnDropsUnreach":{} "alpsRemPeerConnDropsVersion":{} "alpsRemPeerConnForeignPort":{} "alpsRemPeerConnIdleTimer":{} "alpsRemPeerConnInOctets":{} "alpsRemPeerConnInPackets":{} "alpsRemPeerConnLastRxAny":{} "alpsRemPeerConnLastTxRx":{} 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"xdsl2PMLInitCurr15MValidIntervals":{} "xdsl2PMLInitCurr1DayFailedFullInits":{} "xdsl2PMLInitCurr1DayFailedShortInits":{} "xdsl2PMLInitCurr1DayFullInits":{} "xdsl2PMLInitCurr1DayInvalidIntervals":{} "xdsl2PMLInitCurr1DayShortInits":{} "xdsl2PMLInitCurr1DayTimeElapsed":{} "xdsl2PMLInitCurr1DayValidIntervals":{} "xdsl2PMLInitHist15MFailedFullInits":{} "xdsl2PMLInitHist15MFailedShortInits":{} "xdsl2PMLInitHist15MFullInits":{} "xdsl2PMLInitHist15MMonitoredTime":{} "xdsl2PMLInitHist15MShortInits":{} "xdsl2PMLInitHist15MValidInterval":{} "xdsl2PMLInitHist1DFailedFullInits":{} "xdsl2PMLInitHist1DFailedShortInits":{} "xdsl2PMLInitHist1DFullInits":{} "xdsl2PMLInitHist1DMonitoredTime":{} "xdsl2PMLInitHist1DShortInits":{} "xdsl2PMLInitHist1DValidInterval":{} "xdsl2SCStatusAttainableRate":{} "xdsl2SCStatusBandLnAtten":{} "xdsl2SCStatusBandSigAtten":{} "xdsl2SCStatusLinScGroupSize":{} "xdsl2SCStatusLinScale":{} "xdsl2SCStatusLogMt":{} "xdsl2SCStatusLogScGroupSize":{} "xdsl2SCStatusQlnMt":{} "xdsl2SCStatusQlnScGroupSize":{} "xdsl2SCStatusRowStatus":{} "xdsl2SCStatusSegmentBitsAlloc":{} "xdsl2SCStatusSegmentGainAlloc":{} "xdsl2SCStatusSegmentLinImg":{} "xdsl2SCStatusSegmentLinReal":{} "xdsl2SCStatusSegmentLog":{} "xdsl2SCStatusSegmentQln":{} "xdsl2SCStatusSegmentSnr":{} "xdsl2SCStatusSnrMtime":{} "xdsl2SCStatusSnrScGroupSize":{} "xdsl2ScalarSCAvailInterfaces":{} "xdsl2ScalarSCMaxInterfaces":{} "zipEntry":{"1":{} "2":{} "3":{} "4":{} "5":{}} }<line_sep> |
a=PMatrix3D()<line_sep>a.print()<line_sep>exit()<line_sep> |
""" Script to send prediction request.
Usage:
python predict.py --url=YOUR_KF_HOST/models/coco --input_image=YOUR_LOCAL_IMAGE
--output_image=OUTPUT_IMAGE_NAME.
This will save the prediction result as OUTPUT_IMAGE_NAME.
The output image is the input image with the detected bounding boxes.
"""<import_stmt>argparse<import_stmt>json<import_stmt>requests<import_stmt>numpy<as>np<import_from_stmt>PIL Image<import_stmt>visualization_utils<as>vis_util<line_sep>WIDTH=1024<line_sep>HEIGHT=768<def_stmt>main <block_start>parser=argparse.ArgumentParser()<line_sep>parser.add_argument("--url" help='The url to send the request')<line_sep>parser.add_argument("--input_image" default='image1.jpg')<line_sep>parser.add_argument("--output_image" default='output.jpg')<line_sep>args=parser.parse_args()<line_sep>img=Image.open(args.input_image)<line_sep>img=img.resize((WIDTH HEIGHT) Image.ANTIALIAS)<line_sep>img_np=np.array(img)<line_sep>res=requests.post(args.url data=json.dumps({"instances":[{"inputs":img_np.tolist()}]}))<if_stmt>res.status_code<ne>200<block_start>print('Failed: {}'.format(res.text))<line_sep><return><block_end>output_dict=json.loads(res.text).get('predictions')[0]<line_sep>vis_util.visualize_boxes_and_labels_on_image_array(img_np np.array(output_dict['detection_boxes']) map(int output_dict['detection_classes']) output_dict['detection_scores'] {} instance_masks=output_dict.get('detection_masks') use_normalized_coordinates=<true> line_thickness=8)<line_sep>output_image=Image.fromarray(img_np)<line_sep>output_image.save(args.output_image)<block_end><if_stmt>__name__<eq>'__main__'<block_start>main()<block_end> |
# coding=utf-8
<import_from_stmt>fabric.api local <def_stmt>test proxy=<false><block_start>cmd='PYTHONPATH=./ {} coverage run tests/runtests.py'.format('proxychains4 -q'<if>proxy<else>'')<line_sep>local(cmd)<block_end><def_stmt>test_download func<block_start>'''
fab test_download:acfun
'''<line_sep>cmd=('PYTHONPATH=./ python tests/test_extractors.py '<concat>'TestExtractors.test_{}'.format(func))<line_sep>local(cmd)<block_end><def_stmt>upload <block_start>local('python3 setup.py upload')<block_end> |
<import_stmt>time<line_sep># ๅฎไนๆฅๅฟ่ฎฐๅฝๅจ
# import sys
# def get_cur_info():
# return sys._getframe().f_code.co_filename + ' ็ฌฌ' + str(sys._getframe().f_lineno)+'่ก'
# ่ทๅๅ
ทไฝ่ทฏๅพไฝ็ฝฎ
# get_cur_info()
<def_stmt>log msg<block_start><with_stmt>open('service.log' 'a' encoding='utf=8')<as>f<block_start>f.write('\n'+time.strftime("%Y-%m-%d %H:%M:%S" time.localtime())+' '+str(msg))<block_end><block_end> |
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