xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

d99b5ab0ec594ac30b1d197b23a5cda7c48151d5

18,065

py

Python

rasa/train.py

Amirali-Shirkh/rasa-for-botfront

36aa24ad31241c5d1a180bbe34e1c8c50da40ff7

[ "Apache-2.0" ]

null

rasa/train.py

Amirali-Shirkh/rasa-for-botfront

36aa24ad31241c5d1a180bbe34e1c8c50da40ff7

[ "Apache-2.0" ]

null

rasa/train.py

Amirali-Shirkh/rasa-for-botfront

36aa24ad31241c5d1a180bbe34e1c8c50da40ff7

[ "Apache-2.0" ]

null

import asyncio import os import tempfile from contextlib import ExitStack from typing import Text, Optional, List, Union, Dict from rasa.importers.importer import TrainingDataImporter from rasa import model from rasa.model import FingerprintComparisonResult from rasa.core.domain import Domain from rasa.utils.common import TempDirectoryPath from rasa.cli.utils import ( print_success, print_warning, print_error, bcolors, print_color, ) from rasa.constants import DEFAULT_MODELS_PATH, DEFAULT_CORE_SUBDIRECTORY_NAME def train( domain: Text, config: Text, training_files: Union[Text, List[Text]], output: Text = DEFAULT_MODELS_PATH, force_training: bool = False, fixed_model_name: Optional[Text] = None, persist_nlu_training_data: bool = False, additional_arguments: Optional[Dict] = None, loop: Optional[asyncio.AbstractEventLoop] = None, ) -> Optional[Text]: if loop is None: try: loop = asyncio.get_event_loop() except RuntimeError: loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) return loop.run_until_complete( train_async( domain=domain, config=config, training_files=training_files, output_path=output, force_training=force_training, fixed_model_name=fixed_model_name, persist_nlu_training_data=persist_nlu_training_data, additional_arguments=additional_arguments, ) ) async def train_async( domain: Union[Domain, Text], config: Dict[Text, Text], training_files: Optional[Union[Text, List[Text]]], output_path: Text = DEFAULT_MODELS_PATH, force_training: bool = False, fixed_model_name: Optional[Text] = None, persist_nlu_training_data: bool = False, additional_arguments: Optional[Dict] = None, ) -> Optional[Text]: """Trains a Rasa model (Core and NLU). Args: domain: Path to the domain file. config: Dict of paths to the config for Core and NLU. Keys are language codes training_files: Paths to the training data for Core and NLU. output_path: Output path. force_training: If `True` retrain model even if data has not changed. fixed_model_name: Name of model to be stored. persist_nlu_training_data: `True` if the NLU training data should be persisted with the model. additional_arguments: Additional training parameters. Returns: Path of the trained model archive. """ # file_importer = TrainingDataImporter.load_from_config( # config, domain, training_files # ) with ExitStack() as stack: train_path = stack.enter_context(TempDirectoryPath(tempfile.mkdtemp())) # bf mod from rasa_addons.importers import BotfrontFileImporter file_importer = BotfrontFileImporter(config, domain, training_files) # domain = await file_importer.get_domain() # if domain.is_empty(): # return await handle_domain_if_not_exists( # file_importer, output_path, fixed_model_name # ) # /bf mod return await _train_async_internal( file_importer, train_path, output_path, force_training, fixed_model_name, persist_nlu_training_data, additional_arguments, ) async def handle_domain_if_not_exists( file_importer: TrainingDataImporter, output_path, fixed_model_name ): nlu_model_only = await _train_nlu_with_validated_data( file_importer, output=output_path, fixed_model_name=fixed_model_name ) print_warning( "Core training was skipped because no valid domain file was found. Only an nlu-model was created." "Please specify a valid domain using '--domain' argument or check if the provided domain file exists." ) return nlu_model_only async def _train_async_internal( file_importer: TrainingDataImporter, train_path: Text, output_path: Text, force_training: bool, fixed_model_name: Optional[Text], persist_nlu_training_data: bool, additional_arguments: Optional[Dict], ) -> Optional[Text]: """Trains a Rasa model (Core and NLU). Use only from `train_async`. Args: file_importer: `TrainingDataImporter` which supplies the training data. train_path: Directory in which to train the model. output_path: Output path. force_training: If `True` retrain model even if data has not changed. persist_nlu_training_data: `True` if the NLU training data should be persisted with the model. fixed_model_name: Name of model to be stored. additional_arguments: Additional training parameters. Returns: Path of the trained model archive. """ stories, nlu_data = await asyncio.gather( file_importer.get_stories(), file_importer.get_nlu_data() ) # if stories.is_empty() and nlu_data.is_empty(): # print_error( # "No training data given. Please provide stories and NLU data in " # "order to train a Rasa model using the '--data' argument." # ) # return # if nlu_data.is_empty(): # print_warning("No NLU data present. Just a Rasa Core model will be trained.") # return await _train_core_with_validated_data( # file_importer, # output=output_path, # fixed_model_name=fixed_model_name, # additional_arguments=additional_arguments, # ) new_fingerprint = await model.model_fingerprint(file_importer) old_model = model.get_latest_model(output_path) fingerprint_comparison = FingerprintComparisonResult(force_training=force_training) if not force_training: fingerprint_comparison = model.should_retrain( new_fingerprint, old_model, train_path ) # bf mod > if fingerprint_comparison.nlu == True: # replace True with list of all langs fingerprint_comparison.nlu = list(new_fingerprint.get("nlu-config", {}).keys()) domain = await file_importer.get_domain() core_untrainable = domain.is_empty() or stories.is_empty() nlu_untrainable = [l for l, d in nlu_data.items() if d.is_empty()] fingerprint_comparison.core = fingerprint_comparison.core and not core_untrainable fingerprint_comparison.nlu = [l for l in fingerprint_comparison.nlu if l not in nlu_untrainable] if core_untrainable: print_color("Skipping Core training since domain or stories are empty.", color=bcolors.OKBLUE) for lang in nlu_untrainable: print_color("No NLU data found for language <{}>, skipping training...".format(lang), color=bcolors.OKBLUE) # </ bf mod if fingerprint_comparison.is_training_required(): await _do_training( file_importer, output_path=output_path, train_path=train_path, fingerprint_comparison_result=fingerprint_comparison, fixed_model_name=fixed_model_name, persist_nlu_training_data=persist_nlu_training_data, additional_arguments=additional_arguments, ) return model.package_model( fingerprint=new_fingerprint, output_directory=output_path, train_path=train_path, fixed_model_name=fixed_model_name, ) print_success( "Nothing changed. You can use the old model stored at '{}'." "".format(os.path.abspath(old_model)) ) return old_model async def _do_training( file_importer: TrainingDataImporter, output_path: Text, train_path: Text, fingerprint_comparison_result: Optional[FingerprintComparisonResult] = None, fixed_model_name: Optional[Text] = None, persist_nlu_training_data: bool = False, additional_arguments: Optional[Dict] = None, ): if not fingerprint_comparison_result: fingerprint_comparison_result = FingerprintComparisonResult() if fingerprint_comparison_result.should_retrain_core(): await _train_core_with_validated_data( file_importer, output=output_path, train_path=train_path, fixed_model_name=fixed_model_name, additional_arguments=additional_arguments, ) elif fingerprint_comparison_result.should_retrain_nlg(): print_color( "Core stories/configuration did not change. " "Only the templates section has been changed. A new model with " "the updated templates will be created.", color=bcolors.OKBLUE, ) await model.update_model_with_new_domain(file_importer, train_path) else: print_color( "Core stories/configuration did not change. No need to retrain Core model.", color=bcolors.OKBLUE, ) if fingerprint_comparison_result.should_retrain_nlu(): await _train_nlu_with_validated_data( file_importer, output=output_path, train_path=train_path, fixed_model_name=fixed_model_name, retrain_nlu=fingerprint_comparison_result.nlu, persist_nlu_training_data=persist_nlu_training_data, ) else: print_color( "NLU data/configuration did not change. No need to retrain NLU model.", color=bcolors.OKBLUE, ) def train_core( domain: Union[Domain, Text], config: Text, stories: Text, output: Text, train_path: Optional[Text] = None, fixed_model_name: Optional[Text] = None, additional_arguments: Optional[Dict] = None, ) -> Optional[Text]: loop = asyncio.get_event_loop() return loop.run_until_complete( train_core_async( domain=domain, config=config, stories=stories, output=output, train_path=train_path, fixed_model_name=fixed_model_name, additional_arguments=additional_arguments, ) ) async def train_core_async( domain: Union[Domain, Text], config: Text, stories: Text, output: Text, train_path: Optional[Text] = None, fixed_model_name: Optional[Text] = None, additional_arguments: Optional[Dict] = None, ) -> Optional[Text]: """Trains a Core model. Args: domain: Path to the domain file. config: Path to the config file for Core. stories: Path to the Core training data. output: Output path. train_path: If `None` the model will be trained in a temporary directory, otherwise in the provided directory. fixed_model_name: Name of model to be stored. uncompress: If `True` the model will not be compressed. additional_arguments: Additional training parameters. Returns: If `train_path` is given it returns the path to the model archive, otherwise the path to the directory with the trained model files. """ file_importer = TrainingDataImporter.load_core_importer_from_config( config, domain, [stories] ) domain = await file_importer.get_domain() if domain.is_empty(): print_error( "Core training was skipped because no valid domain file was found. " "Please specify a valid domain using '--domain' argument or check if the provided domain file exists." ) return None if not await file_importer.get_stories(): print_error( "No stories given. Please provide stories in order to " "train a Rasa Core model using the '--stories' argument." ) return return await _train_core_with_validated_data( file_importer, output=output, train_path=train_path, fixed_model_name=fixed_model_name, additional_arguments=additional_arguments, ) async def _train_core_with_validated_data( file_importer: TrainingDataImporter, output: Text, train_path: Optional[Text] = None, fixed_model_name: Optional[Text] = None, additional_arguments: Optional[Dict] = None, ) -> Optional[Text]: """Train Core with validated training and config data.""" import rasa.core.train with ExitStack() as stack: if train_path: # If the train path was provided, do nothing on exit. _train_path = train_path else: # Otherwise, create a temp train path and clean it up on exit. _train_path = stack.enter_context(TempDirectoryPath(tempfile.mkdtemp())) # normal (not compare) training print_color("Training Core model...", color=bcolors.OKBLUE) domain, config = await asyncio.gather( file_importer.get_domain(), file_importer.get_config() ) await rasa.core.train( domain_file=domain, training_resource=file_importer, output_path=os.path.join(_train_path, DEFAULT_CORE_SUBDIRECTORY_NAME), policy_config=config, additional_arguments=additional_arguments, ) print_color("Core model training completed.", color=bcolors.OKBLUE) if train_path is None: # Only Core was trained. new_fingerprint = await model.model_fingerprint(file_importer) return model.package_model( fingerprint=new_fingerprint, output_directory=output, train_path=_train_path, fixed_model_name=fixed_model_name, model_prefix="core-", ) return _train_path def train_nlu( config: Text, nlu_data: Text, output: Text, train_path: Optional[Text] = None, fixed_model_name: Optional[Text] = None, persist_nlu_training_data: bool = False, ) -> Optional[Text]: """Trains an NLU model. Args: config: Path to the config file for NLU. nlu_data: Path to the NLU training data. output: Output path. train_path: If `None` the model will be trained in a temporary directory, otherwise in the provided directory. fixed_model_name: Name of the model to be stored. persist_nlu_training_data: `True` if the NLU training data should be persisted with the model. Returns: If `train_path` is given it returns the path to the model archive, otherwise the path to the directory with the trained model files. """ loop = asyncio.get_event_loop() return loop.run_until_complete( _train_nlu_async( config, nlu_data, output, train_path, fixed_model_name, persist_nlu_training_data, ) ) async def _train_nlu_async( config: Text, nlu_data: Text, output: Text, train_path: Optional[Text] = None, fixed_model_name: Optional[Text] = None, persist_nlu_training_data: bool = False, ): if not nlu_data: print_error( "No NLU data given. Please provide NLU data in order to train " "a Rasa NLU model using the '--nlu' argument." ) return # training NLU only hence the training files still have to be selected file_importer = TrainingDataImporter.load_nlu_importer_from_config( config, training_data_paths=[nlu_data] ) training_datas = await file_importer.get_nlu_data() if training_datas.is_empty(): print_error( f"Path '{nlu_data}' doesn't contain valid NLU data in it. " "Please verify the data format. " "The NLU model training will be skipped now." ) return return await _train_nlu_with_validated_data( file_importer, output=output, train_path=train_path, fixed_model_name=fixed_model_name, persist_nlu_training_data=persist_nlu_training_data, ) async def _train_nlu_with_validated_data( file_importer: TrainingDataImporter, output: Text, train_path: Optional[Text] = None, fixed_model_name: Optional[Text] = None, persist_nlu_training_data: bool = False, retrain_nlu: Union[bool, List[Text]] = True ) -> Optional[Text]: """Train NLU with validated training and config data.""" import rasa.nlu.train with ExitStack() as stack: models = {} from rasa.nlu import config as cfg_loader if train_path: # If the train path was provided, do nothing on exit. _train_path = train_path else: # Otherwise, create a temp train path and clean it up on exit. _train_path = stack.enter_context(TempDirectoryPath(tempfile.mkdtemp())) # bf mod config = await file_importer.get_nlu_config(retrain_nlu) for lang in config: if config[lang]: print_color("Start training {} NLU model ...".format(lang), color=bcolors.OKBLUE) _, models[lang], _ = await rasa.nlu.train( config[lang], file_importer, _train_path, fixed_model_name="nlu-{}".format(lang), persist_nlu_training_data=persist_nlu_training_data, ) else: print_color("NLU data for language <{}> didn't change, skipping training...".format(lang), color=bcolors.OKBLUE) # /bf mod print_color("NLU model training completed.", color=bcolors.OKBLUE) if train_path is None: # Only NLU was trained new_fingerprint = await model.model_fingerprint(file_importer) return model.package_model( fingerprint=new_fingerprint, output_directory=output, train_path=_train_path, fixed_model_name=fixed_model_name, model_prefix="nlu-", ) return _train_path

34.673704

128

0.654027

2,139

18,065

5.259467

0.099579

0.0432

0.053511

0.043022

0.643644

0.565956

0.532178

0.5128

0.470756

0.448444

0

0.275505

18,065

520

129

34.740385

0.859566

0.102131

0

0.47619

0

0.097136

0.003

0

1

0.008403

false

0

0.123249

0

0.173669

0.120448

0

null

0

null

0

1

0

d99ed7256245422c7c5dd3c60b0661e4f78183ea

35,585

py

Python

rplugin/python3/denite/ui/default.py

timgates42/denite.nvim

12a9b5456f5a4600afeb0ba284ce1098bd35e501

[ "MIT" ]

null

rplugin/python3/denite/ui/default.py

timgates42/denite.nvim

12a9b5456f5a4600afeb0ba284ce1098bd35e501

[ "MIT" ]

null

rplugin/python3/denite/ui/default.py

timgates42/denite.nvim

12a9b5456f5a4600afeb0ba284ce1098bd35e501

[ "MIT" ]

null

# ============================================================================ # FILE: default.py # AUTHOR: Shougo Matsushita <Shougo.Matsu at gmail.com> # License: MIT license # ============================================================================ import re import typing from denite.util import echo, error, clearmatch, regex_convert_py_vim from denite.util import Nvim, UserContext, Candidates, Candidate from denite.parent import SyncParent class Default(object): @property def is_async(self) -> bool: return self._is_async def __init__(self, vim: Nvim) -> None: self._vim = vim self._denite: typing.Optional[SyncParent] = None self._selected_candidates: typing.List[int] = [] self._candidates: Candidates = [] self._cursor = 0 self._entire_len = 0 self._result: typing.List[typing.Any] = [] self._context: UserContext = {} self._bufnr = -1 self._winid = -1 self._winrestcmd = '' self._initialized = False self._winheight = 0 self._winwidth = 0 self._winminheight = -1 self._is_multi = False self._is_async = False self._matched_pattern = '' self._displayed_texts: typing.List[str] = [] self._statusline_sources = '' self._titlestring = '' self._ruler = False self._prev_action = '' self._prev_status: typing.Dict[str, typing.Any] = {} self._prev_curpos: typing.List[typing.Any] = [] self._save_window_options: typing.Dict[str, typing.Any] = {} self._sources_history: typing.List[typing.Any] = [] self._previous_text = '' self._floating = False self._filter_floating = False self._updated = False self._timers: typing.Dict[str, int] = {} self._matched_range_id = -1 self._matched_char_id = -1 self._check_matchdelete = bool(self._vim.call( 'denite#util#check_matchdelete')) def start(self, sources: typing.List[typing.Any], context: UserContext) -> typing.List[typing.Any]: if not self._denite: # if hasattr(self._vim, 'run_coroutine'): # self._denite = ASyncParent(self._vim) # else: self._denite = SyncParent(self._vim) self._result = [] context['sources_queue'] = [sources] self._start_sources_queue(context) return self._result def do_action(self, action_name: str, command: str = '', is_manual: bool = False) -> None: if is_manual: candidates = self._get_selected_candidates() elif self._get_cursor_candidate(): candidates = [self._get_cursor_candidate()] else: candidates = [] if not self._denite or not candidates or not action_name: return self._prev_action = action_name action = self._denite.get_action( self._context, action_name, candidates) if not action: return post_action = self._context['post_action'] is_quit = action['is_quit'] or post_action == 'quit' if is_quit: self.quit() self._denite.do_action(self._context, action_name, candidates) self._result = candidates if command != '': self._vim.command(command) if is_quit and post_action == 'open': # Re-open denite buffer prev_cursor = self._cursor cursor_candidate = self._get_cursor_candidate() self._init_buffer() self.redraw(False) if cursor_candidate == self._get_candidate(prev_cursor): # Restore the cursor self._move_to_pos(prev_cursor) # Disable quit flag is_quit = False if not is_quit and is_manual: self._selected_candidates = [] self.redraw(action['is_redraw']) if is_manual and self._context['sources_queue']: self._context['input'] = '' self._context['quick_move'] = '' self._start_sources_queue(self._context) return def redraw(self, is_force: bool = True) -> None: self._context['is_redraw'] = is_force if is_force: self._gather_candidates() if self._update_candidates(): self._update_buffer() else: self._update_status() self._context['is_redraw'] = False def quit(self) -> None: if self._denite: self._denite.on_close(self._context) self._quit_buffer() self._result = [] return def _restart(self) -> None: self._context['input'] = '' self._quit_buffer() self._init_denite() self._gather_candidates() self._init_buffer() self._update_candidates() self._update_buffer() def _start_sources_queue(self, context: UserContext) -> None: if not context['sources_queue']: return self._sources_history.append({ 'sources': context['sources_queue'][0], 'path': context['path'], }) self._start(context['sources_queue'][0], context) if context['sources_queue']: context['sources_queue'].pop(0) context['path'] = self._context['path'] def _start(self, sources: typing.List[typing.Any], context: UserContext) -> None: from denite.ui.map import do_map self._vim.command('silent! autocmd! denite') if re.search(r'\[Command Line\]$', self._vim.current.buffer.name): # Ignore command line window. return resume = self._initialized and context['resume'] if resume: # Skip the initialization update = ('immediately', 'immediately_1', 'cursor_pos', 'prev_winid', 'start_filter', 'quick_move') for key in update: self._context[key] = context[key] self._check_move_option() if self._check_do_option(): return self._init_buffer() if context['refresh']: self.redraw() self._move_to_pos(self._cursor) else: if self._context != context: self._context.clear() self._context.update(context) self._context['sources'] = sources self._context['is_redraw'] = False self._is_multi = len(sources) > 1 if not sources: # Ignore empty sources. error(self._vim, 'Empty sources') return self._init_denite() self._gather_candidates() self._update_candidates() self._init_cursor() self._check_move_option() if self._check_do_option(): return self._init_buffer() self._update_displayed_texts() self._update_buffer() self._move_to_pos(self._cursor) if self._context['quick_move'] and do_map(self, 'quick_move', []): return if self._context['start_filter']: do_map(self, 'open_filter_buffer', []) def _init_buffer(self) -> None: self._prev_status = dict() self._displayed_texts = [] self._prev_bufnr = self._vim.current.buffer.number self._prev_curpos = self._vim.call('getcurpos') self._prev_wininfo = self._get_wininfo() self._prev_winid = self._context['prev_winid'] self._winrestcmd = self._vim.call('winrestcmd') self._ruler = self._vim.options['ruler'] self._switch_buffer() self._bufnr = self._vim.current.buffer.number self._winid = self._vim.call('win_getid') self._resize_buffer(True) self._winheight = self._vim.current.window.height self._winwidth = self._vim.current.window.width self._bufvars = self._vim.current.buffer.vars self._bufvars['denite'] = { 'buffer_name': self._context['buffer_name'], } self._bufvars['denite_statusline'] = {} self._vim.vars['denite#_previewed_buffers'] = {} self._save_window_options = {} window_options = { 'colorcolumn', 'concealcursor', 'conceallevel', 'cursorcolumn', 'cursorline', 'foldcolumn', 'foldenable', 'list', 'number', 'relativenumber', 'signcolumn', 'spell', 'winfixheight', 'wrap', } for k in window_options: self._save_window_options[k] = self._vim.current.window.options[k] # Note: Have to use setlocal instead of "current.window.options" # "current.window.options" changes global value instead of local in # neovim. self._vim.command('setlocal colorcolumn=') self._vim.command('setlocal conceallevel=3') self._vim.command('setlocal concealcursor=inv') self._vim.command('setlocal nocursorcolumn') self._vim.command('setlocal nofoldenable') self._vim.command('setlocal foldcolumn=0') self._vim.command('setlocal nolist') self._vim.command('setlocal nonumber') self._vim.command('setlocal norelativenumber') self._vim.command('setlocal nospell') self._vim.command('setlocal winfixheight') self._vim.command('setlocal nowrap') if self._context['prompt']: self._vim.command('setlocal signcolumn=yes') else: self._vim.command('setlocal signcolumn=auto') if self._context['cursorline']: self._vim.command('setlocal cursorline') options = self._vim.current.buffer.options if self._floating: # Disable ruler self._vim.options['ruler'] = False options['buftype'] = 'nofile' options['bufhidden'] = 'delete' options['swapfile'] = False options['buflisted'] = False options['modeline'] = False options['modifiable'] = False options['filetype'] = 'denite' if self._vim.call('exists', '#WinEnter'): self._vim.command('doautocmd WinEnter') if self._vim.call('exists', '#BufWinEnter'): self._vim.command('doautocmd BufWinEnter') if not self._vim.call('has', 'nvim'): # In Vim8, FileType autocmd is not fired after set filetype option. self._vim.command('silent doautocmd FileType denite') if self._context['auto_action']: self._vim.command('autocmd denite ' 'CursorMoved <buffer> ' 'call denite#call_map("auto_action")') self._init_syntax() def _switch_buffer(self) -> None: split = self._context['split'] if (split != 'no' and self._winid > 0 and self._vim.call('win_gotoid', self._winid)): if split != 'vertical' and not self._floating: # Move the window to bottom self._vim.command('wincmd J') self._winrestcmd = '' return self._floating = split in [ 'floating', 'floating_relative_cursor', 'floating_relative_window', ] self._filter_floating = False if self._vim.current.buffer.options['filetype'] != 'denite': self._titlestring = self._vim.options['titlestring'] command = 'edit' if split == 'tab': self._vim.command('tabnew') elif self._floating: self._split_floating(split) elif self._context['filter_split_direction'] == 'floating': self._filter_floating = True elif split != 'no': command = self._get_direction() command += ' vsplit' if split == 'vertical' else ' split' bufname = '[denite]-' + self._context['buffer_name'] if self._vim.call('exists', '*bufadd'): bufnr = self._vim.call('bufadd', bufname) vertical = 'vertical' if split == 'vertical' else '' command = ( 'buffer' if split in ['no', 'tab', 'floating', 'floating_relative_window', 'floating_relative_cursor'] else 'sbuffer') self._vim.command( 'silent keepalt %s %s %s %s' % ( self._get_direction(), vertical, command, bufnr, ) ) else: self._vim.call( 'denite#util#execute_path', f'silent keepalt {command}', bufname) def _get_direction(self) -> str: direction = str(self._context['direction']) if direction == 'dynamictop' or direction == 'dynamicbottom': self._update_displayed_texts() winwidth = self._vim.call('winwidth', 0) is_fit = not [x for x in self._displayed_texts if self._vim.call('strwidth', x) > winwidth] if direction == 'dynamictop': direction = 'aboveleft' if is_fit else 'topleft' else: direction = 'belowright' if is_fit else 'botright' return direction def _get_wininfo(self) -> typing.List[typing.Any]: return [ self._vim.options['columns'], self._vim.options['lines'], self._vim.call('win_getid'), self._vim.call('tabpagebuflist') ] def _switch_prev_buffer(self) -> None: if (self._prev_bufnr == self._bufnr or self._vim.buffers[self._prev_bufnr].name == ''): self._vim.command('enew') else: self._vim.command('buffer ' + str(self._prev_bufnr)) def _init_syntax(self) -> None: self._vim.command('syntax case ignore') self._vim.command('highlight default link deniteInput ModeMsg') self._vim.command('highlight link deniteMatchedRange ' + self._context['highlight_matched_range']) self._vim.command('highlight link deniteMatchedChar ' + self._context['highlight_matched_char']) self._vim.command('highlight default link ' + 'deniteStatusLinePath Comment') self._vim.command('highlight default link ' + 'deniteStatusLineNumber LineNR') self._vim.command('highlight default link ' + 'deniteSelectedLine Statement') if self._floating: self._vim.current.window.options['winhighlight'] = ( 'Normal:' + self._context['highlight_window_background'] ) self._vim.command(('syntax match deniteSelectedLine /^[%s].*/' + ' contains=deniteConcealedMark') % ( self._context['selected_icon'])) self._vim.command(('syntax match deniteConcealedMark /^[ %s]/' + ' conceal contained') % ( self._context['selected_icon'])) if self._denite: self._denite.init_syntax(self._context, self._is_multi) def _update_candidates(self) -> bool: if not self._denite: return False [self._is_async, pattern, statuses, self._entire_len, self._candidates] = self._denite.filter_candidates(self._context) prev_displayed_texts = self._displayed_texts self._update_displayed_texts() prev_matched_pattern = self._matched_pattern self._matched_pattern = pattern prev_statusline_sources = self._statusline_sources self._statusline_sources = ' '.join(statuses) if self._is_async: self._start_timer('update_candidates') else: self._stop_timer('update_candidates') updated = (self._displayed_texts != prev_displayed_texts or self._matched_pattern != prev_matched_pattern or self._statusline_sources != prev_statusline_sources) if updated: self._updated = True self._start_timer('update_buffer') if self._context['search'] and self._context['input']: self._vim.call('setreg', '/', self._context['input']) return self._updated def _update_displayed_texts(self) -> None: candidates_len = len(self._candidates) if not self._is_async and self._context['auto_resize']: winminheight = self._context['winminheight'] max_height = min(self._context['winheight'], self._get_max_height()) if (winminheight != -1 and candidates_len < winminheight): self._winheight = winminheight elif candidates_len > max_height: self._winheight = max_height elif candidates_len != self._winheight: self._winheight = candidates_len max_source_name_len = 0 if self._candidates: max_source_name_len = max([ len(self._get_display_source_name(x['source_name'])) for x in self._candidates]) self._context['max_source_name_len'] = max_source_name_len self._context['max_source_name_format'] = ( '{:<' + str(self._context['max_source_name_len']) + '}') self._displayed_texts = [ self._get_candidate_display_text(i) for i in range(0, candidates_len) ] def _update_buffer(self) -> None: is_current_buffer = self._bufnr == self._vim.current.buffer.number self._update_status() if self._check_matchdelete and self._context['match_highlight']: matches = [x['id'] for x in self._vim.call('getmatches', self._winid)] if self._matched_range_id in matches: self._vim.call('matchdelete', self._matched_range_id, self._winid) self._matched_range_id = -1 if self._matched_char_id in matches: self._vim.call('matchdelete', self._matched_char_id, self._winid) self._matched_char_id = -1 if self._matched_pattern != '': self._matched_range_id = self._vim.call( 'matchadd', 'deniteMatchedRange', r'\c' + regex_convert_py_vim(self._matched_pattern), 10, -1, {'window': self._winid}) matched_char_pattern = '[{}]'.format(re.sub( r'([\[\]\\^-])', r'\\\1', self._context['input'].replace(' ', '') )) self._matched_char_id = self._vim.call( 'matchadd', 'deniteMatchedChar', matched_char_pattern, 10, -1, {'window': self._winid}) prev_linenr = self._vim.call('line', '.') prev_candidate = self._get_cursor_candidate() buffer = self._vim.buffers[self._bufnr] buffer.options['modifiable'] = True self._vim.vars['denite#_candidates'] = [ x['word'] for x in self._candidates] buffer[:] = self._displayed_texts buffer.options['modifiable'] = False self._previous_text = self._context['input'] self._resize_buffer(is_current_buffer) is_changed = (self._context['reversed'] or (is_current_buffer and self._previous_text != self._context['input'])) if self._updated and is_changed: if not is_current_buffer: save_winid = self._vim.call('win_getid') self._vim.call('win_gotoid', self._winid) self._init_cursor() self._move_to_pos(self._cursor) if not is_current_buffer: self._vim.call('win_gotoid', save_winid) elif is_current_buffer: self._vim.call('cursor', [prev_linenr, 0]) if is_current_buffer: if (self._context['auto_action'] and prev_candidate != self._get_cursor_candidate()): self.do_action(self._context['auto_action']) self._updated = False self._stop_timer('update_buffer') def _update_status(self) -> None: inpt = '' if self._context['input']: inpt = self._context['input'] + ' ' if self._context['error_messages']: inpt = '[ERROR] ' + inpt path = '[' + self._context['path'] + ']' status = { 'input': inpt, 'sources': self._statusline_sources, 'path': path, # Extra 'buffer_name': self._context['buffer_name'], 'line_total': len(self._candidates), } if status == self._prev_status: return self._bufvars['denite_statusline'] = status self._prev_status = status linenr = "printf('%'.(len(line('$'))+2).'d/%d',line('.'),line('$'))" if self._context['statusline']: if self._floating or self._filter_floating: self._vim.options['titlestring'] = ( "%{denite#get_status('input')}%* " + "%{denite#get_status('sources')} " + " %{denite#get_status('path')}%*" + "%{" + linenr + "}%*") else: winnr = self._vim.call('win_id2win', self._winid) self._vim.call('setwinvar', winnr, '&statusline', ( "%#deniteInput#%{denite#get_status('input')}%* " + "%{denite#get_status('sources')} %=" + "%#deniteStatusLinePath# %{denite#get_status('path')}%*" + "%#deniteStatusLineNumber#%{" + linenr + "}%*")) def _get_display_source_name(self, name: str) -> str: source_names = self._context['source_names'] if not self._is_multi or source_names == 'hide': source_name = '' else: short_name = (re.sub(r'([a-zA-Z])[a-zA-Z]+', r'\1', name) if re.search(r'[^a-zA-Z]', name) else name[:2]) source_name = short_name if source_names == 'short' else name return source_name def _get_candidate_display_text(self, index: int) -> str: source_names = self._context['source_names'] candidate = self._candidates[index] terms = [] if self._is_multi and source_names != 'hide': terms.append(self._context['max_source_name_format'].format( self._get_display_source_name(candidate['source_name']))) encoding = self._context['encoding'] abbr = candidate.get('abbr', candidate['word']).encode( encoding, errors='replace').decode(encoding, errors='replace') terms.append(abbr[:int(self._context['max_candidate_width'])]) return (str(self._context['selected_icon']) if index in self._selected_candidates else ' ') + ' '.join(terms).replace('\n', '') def _get_max_height(self) -> int: return int(self._vim.options['lines']) if not self._floating else ( int(self._vim.options['lines']) - int(self._context['winrow']) - int(self._vim.options['cmdheight'])) def _resize_buffer(self, is_current_buffer: bool) -> None: split = self._context['split'] if (split == 'no' or split == 'tab' or self._vim.call('winnr', '$') == 1): return winheight = max(self._winheight, 1) winwidth = max(self._winwidth, 1) is_vertical = split == 'vertical' if not is_current_buffer: restore = self._vim.call('win_getid') self._vim.call('win_gotoid', self._winid) if not is_vertical and self._vim.current.window.height != winheight: if self._floating: wincol = self._context['winrow'] row = wincol if split == 'floating': if self._context['auto_resize'] and row > 1: row += self._context['winheight'] row -= self._winheight self._vim.call('nvim_win_set_config', self._winid, { 'relative': 'editor', 'row': row, 'col': self._context['wincol'], 'width': winwidth, 'height': winheight, }) filter_row = 0 if wincol == 1 else row + winheight filter_col = self._context['wincol'] else: init_pos = self._vim.call('nvim_win_get_config', self._winid) self._vim.call('nvim_win_set_config', self._winid, { 'relative': 'win', 'win': init_pos['win'], 'row': init_pos['row'], 'col': init_pos['col'], 'width': winwidth, 'height': winheight, }) filter_col = init_pos['col'] if init_pos['anchor'] == 'NW': winpos = self._vim.call('nvim_win_get_position', self._winid) filter_row = winpos[0] + winheight filter_winid = self._vim.vars['denite#_filter_winid'] self._context['filter_winrow'] = row if self._vim.call('win_id2win', filter_winid) > 0: self._vim.call('nvim_win_set_config', filter_winid, { 'relative': 'editor', 'row': filter_row, 'col': filter_col, }) self._vim.command('resize ' + str(winheight)) if self._context['reversed']: self._vim.command('normal! zb') elif is_vertical and self._vim.current.window.width != winwidth: self._vim.command('vertical resize ' + str(winwidth)) if not is_current_buffer: self._vim.call('win_gotoid', restore) def _check_do_option(self) -> bool: if self._context['do'] != '': self._do_command(self._context['do']) return True elif (self._candidates and self._context['immediately'] or len(self._candidates) == 1 and self._context['immediately_1']): self._do_immediately() return True return not (self._context['empty'] or self._is_async or self._candidates) def _check_move_option(self) -> None: if self._context['cursor_pos'].isnumeric(): self._cursor = int(self._context['cursor_pos']) + 1 elif re.match(r'\+\d+', self._context['cursor_pos']): for _ in range(int(self._context['cursor_pos'][1:])): self._move_to_next_line() elif re.match(r'-\d+', self._context['cursor_pos']): for _ in range(int(self._context['cursor_pos'][1:])): self._move_to_prev_line() elif self._context['cursor_pos'] == '$': self._move_to_last_line() def _do_immediately(self) -> None: goto = self._winid > 0 and self._vim.call( 'win_gotoid', self._winid) if goto: # Jump to denite window self._init_buffer() self.do_action('default') candidate = self._get_cursor_candidate() if not candidate: return echo(self._vim, 'Normal', '[{}/{}] {}'.format( self._cursor, len(self._candidates), candidate.get('abbr', candidate['word']))) if goto: # Move to the previous window self._vim.command('wincmd p') def _do_command(self, command: str) -> None: self._init_cursor() cursor = 1 while cursor < len(self._candidates): self.do_action('default', command) self._move_to_next_line() self._quit_buffer() def _cleanup(self) -> None: self._stop_timer('update_candidates') self._stop_timer('update_buffer') if self._vim.current.buffer.number == self._bufnr: self._cursor = self._vim.call('line', '.') # Note: Close filter window before preview window self._vim.call('denite#filter#_close_filter_window') if not self._context['has_preview_window']: self._vim.command('pclose!') # Clear previewed buffers for bufnr in self._vim.vars['denite#_previewed_buffers'].keys(): if not self._vim.call('win_findbuf', bufnr): self._vim.command('silent bdelete ' + str(bufnr)) self._vim.vars['denite#_previewed_buffers'] = {} self._vim.command('highlight! link CursorLine CursorLine') if self._floating or self._filter_floating: self._vim.options['titlestring'] = self._titlestring self._vim.options['ruler'] = self._ruler def _close_current_window(self) -> None: if self._vim.call('winnr', '$') == 1: self._vim.command('buffer #') else: self._vim.command('close!') def _quit_buffer(self) -> None: self._cleanup() if self._vim.call('bufwinnr', self._bufnr) < 0: # Denite buffer is already closed return winids = self._vim.call('win_findbuf', self._vim.vars['denite#_filter_bufnr']) if winids: # Quit filter buffer self._vim.call('win_gotoid', winids[0]) self._close_current_window() # Move to denite window self._vim.call('win_gotoid', self._winid) # Restore the window if self._context['split'] == 'no': self._switch_prev_buffer() for k, v in self._save_window_options.items(): self._vim.current.window.options[k] = v else: if self._context['split'] == 'tab': self._vim.command('tabclose!') if self._context['split'] != 'tab': self._close_current_window() self._vim.call('win_gotoid', self._prev_winid) # Restore the position self._vim.call('setpos', '.', self._prev_curpos) if self._get_wininfo() and self._get_wininfo() == self._prev_wininfo: # Note: execute restcmd twice to restore layout properly self._vim.command(self._winrestcmd) self._vim.command(self._winrestcmd) clearmatch(self._vim) def _get_cursor_candidate(self) -> Candidate: return self._get_candidate(self._cursor) def _get_candidate(self, pos: int) -> Candidate: if not self._candidates or pos > len(self._candidates): return {} return self._candidates[pos - 1] def _get_selected_candidates(self) -> Candidates: if not self._selected_candidates: return [self._get_cursor_candidate() ] if self._get_cursor_candidate() else [] return [self._candidates[x] for x in self._selected_candidates] def _init_denite(self) -> None: if self._denite: self._denite.start(self._context) self._denite.on_init(self._context) self._initialized = True self._winheight = self._context['winheight'] self._winwidth = self._context['winwidth'] def _gather_candidates(self) -> None: self._selected_candidates = [] if self._denite: self._denite.gather_candidates(self._context) def _init_cursor(self) -> None: if self._context['reversed']: self._move_to_last_line() else: self._move_to_first_line() def _move_to_pos(self, pos: int) -> None: self._vim.call('cursor', pos, 0) self._cursor = pos if self._context['reversed']: self._vim.command('normal! zb') def _move_to_next_line(self) -> None: if self._cursor < len(self._candidates): self._cursor += 1 def _move_to_prev_line(self) -> None: if self._cursor >= 1: self._cursor -= 1 def _move_to_first_line(self) -> None: self._cursor = 1 def _move_to_last_line(self) -> None: self._cursor = len(self._candidates) def _start_timer(self, key: str) -> None: if key in self._timers: return if key == 'update_candidates': self._timers[key] = self._vim.call( 'denite#helper#_start_update_candidates_timer', self._bufnr) elif key == 'update_buffer': self._timers[key] = self._vim.call( 'denite#helper#_start_update_buffer_timer', self._bufnr) def _stop_timer(self, key: str) -> None: if key not in self._timers: return self._vim.call('timer_stop', self._timers[key]) # Note: After timer_stop is called, self._timers may be removed if key in self._timers: self._timers.pop(key) def _split_floating(self, split: str) -> None: # Use floating window if split == 'floating': self._vim.call( 'nvim_open_win', self._vim.call('bufnr', '%'), True, { 'relative': 'editor', 'row': self._context['winrow'], 'col': self._context['wincol'], 'width': self._context['winwidth'], 'height': self._context['winheight'], }) elif split == 'floating_relative_cursor': opened_pos = (self._vim.call('nvim_win_get_position', 0)[0] + self._vim.call('winline') - 1) if self._context['auto_resize']: height = max(self._winheight, 1) width = max(self._winwidth, 1) else: width = self._context['winwidth'] height = self._context['winheight'] if opened_pos + height + 3 > self._vim.options['lines']: anchor = 'SW' row = 0 self._context['filter_winrow'] = row + opened_pos else: anchor = 'NW' row = 1 self._context['filter_winrow'] = row + height + opened_pos self._vim.call( 'nvim_open_win', self._vim.call('bufnr', '%'), True, { 'relative': 'cursor', 'row': row, 'col': 0, 'width': width, 'height': height, 'anchor': anchor, }) elif split == 'floating_relative_window': self._vim.call( 'nvim_open_win', self._vim.call('bufnr', '%'), True, { 'relative': 'win', 'row': self._context['winrow'], 'col': self._context['wincol'], 'width': self._context['winwidth'], 'height': self._context['winheight'], })

37.816153

79

0.54863

3,700

35,585

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0.036637

0.012995

0.344253

0.19585

0.13448

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0.084084

0.06967

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0.00293

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35,585

940

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37.856383

0.763613

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0.001309

0

null

0

null

0

1

0

d99f875863138f11af1d76f0c753c198ad6d96bd

1,329

py

Python

PyDSTool/core/context_managers.py

yuanz271/PyDSTool

886c143cdd192aea204285f3a1cb4968c763c646

[ "Python-2.0", "OLDAP-2.7" ]

null

PyDSTool/core/context_managers.py

yuanz271/PyDSTool

886c143cdd192aea204285f3a1cb4968c763c646

[ "Python-2.0", "OLDAP-2.7" ]

null

PyDSTool/core/context_managers.py

yuanz271/PyDSTool

886c143cdd192aea204285f3a1cb4968c763c646

[ "Python-2.0", "OLDAP-2.7" ]

null

# -*- coding: utf-8 -*- """Context managers implemented for (mostly) internal use""" import contextlib import functools from io import UnsupportedOperation import os import sys __all__ = ["RedirectStdout", "RedirectStderr"] @contextlib.contextmanager def _stdchannel_redirected(stdchannel, dest_filename, mode="w"): """ A context manager to temporarily redirect stdout or stderr Originally by Marc Abramowitz, 2013 (http://marc-abramowitz.com/archives/2013/07/19/python-context-manager-for-redirected-stdout-and-stderr/) """ oldstdchannel = None dest_file = None try: if stdchannel is None: yield iter([None]) else: oldstdchannel = os.dup(stdchannel.fileno()) dest_file = open(dest_filename, mode) os.dup2(dest_file.fileno(), stdchannel.fileno()) yield except (UnsupportedOperation, AttributeError): yield iter([None]) finally: if oldstdchannel is not None: os.dup2(oldstdchannel, stdchannel.fileno()) if dest_file is not None: dest_file.close() RedirectStdout = functools.partial(_stdchannel_redirected, sys.stdout) RedirectStderr = functools.partial(_stdchannel_redirected, sys.stderr) RedirectNoOp = functools.partial(_stdchannel_redirected, None, "")

28.891304

109

0.68924

144

1,329

6.229167

0.479167

0.044593

0.086957

0.120401

0.086957

0

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0.210685

1,329

45

110

29.533333

0.840801

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0.071429

0

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false

0

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0

0.214286

0

null

0

null

0

1

0

d99ff34b5f61cee604590c456f40398d7da18182

3,215

py

Python

pos_kiosk/hooks.py

Muzzy73/pos_kiosk

1ed42cfaeb15f009293b76d05dd85bd322b42f03

[ "MIT" ]

1

2022-03-05T11:42:36.000Z

pos_kiosk/hooks.py

Muzzy73/pos_kiosk

1ed42cfaeb15f009293b76d05dd85bd322b42f03

[ "MIT" ]

null

pos_kiosk/hooks.py

Muzzy73/pos_kiosk

1ed42cfaeb15f009293b76d05dd85bd322b42f03

[ "MIT" ]

1

2022-03-05T11:42:37.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals from . import __version__ as app_version app_name = "pos_kiosk" app_title = "Pos Kiosk" app_publisher = "9t9it" app_description = "Kiosk App" app_icon = "octicon octicon-file-directory" app_color = "grey" app_email = "info@9t9it.com" app_license = "MIT" # Includes in <head> # ------------------ # include js, css files in header of desk.html # app_include_css = "/assets/pos_kiosk/css/pos_kiosk.css" # app_include_js = "/assets/pos_kiosk/js/pos_kiosk.js" # include js, css files in header of web template # web_include_css = "/assets/pos_kiosk/css/pos_kiosk.css" # web_include_js = "/assets/pos_kiosk/js/pos_kiosk.js" # include js in page # page_js = {"page" : "public/js/file.js"} # page_js = { # "kiosk": ["public/js/pos_page_js.js", "public/js/includes/number_to_words.js"] # } # include js in doctype views # doctype_js = {"doctype" : "public/js/doctype.js"} # doctype_list_js = {"doctype" : "public/js/doctype_list.js"} # doctype_tree_js = {"doctype" : "public/js/doctype_tree.js"} # doctype_calendar_js = {"doctype" : "public/js/doctype_calendar.js"} fixtures = [ { "doctype": "Custom Field", "filters": [ [ "name", "in", [ "Sales Invoice Item-pos_kiosk", "Mode of Payment-logo" ] ] ] } ] # Home Pages # ---------- # application home page (will override Website Settings) # home_page = "login" # website user home page (by Role) # role_home_page = { # "Role": "home_page" # } # Website user home page (by function) # get_website_user_home_page = "pos_kiosk.utils.get_home_page" # Generators # ---------- # automatically create page for each record of this doctype # website_generators = ["Web Page"] # Installation # ------------ # before_install = "pos_kiosk.install.before_install" # after_install = "pos_kiosk.install.after_install" # Desk Notifications # ------------------ # See frappe.core.notifications.get_notification_config # notification_config = "pos_kiosk.notifications.get_notification_config" # Permissions # ----------- # Permissions evaluated in scripted ways # permission_query_conditions = { # "Event": "frappe.desk.doctype.event.event.get_permission_query_conditions", # } # # has_permission = { # "Event": "frappe.desk.doctype.event.event.has_permission", # } # Document Events # --------------- # Hook on document methods and events # doc_events = { # "*": { # "on_update": "method", # "on_cancel": "method", # "on_trash": "method" # } # } # Scheduled Tasks # --------------- # scheduler_events = { # "all": [ # "pos_kiosk.tasks.all" # ], # "daily": [ # "pos_kiosk.tasks.daily" # ], # "hourly": [ # "pos_kiosk.tasks.hourly" # ], # "weekly": [ # "pos_kiosk.tasks.weekly" # ] # "monthly": [ # "pos_kiosk.tasks.monthly" # ] # } # Testing # ------- # before_tests = "pos_kiosk.install.before_tests" # Overriding Whitelisted Methods # ------------------------------ # # override_whitelisted_methods = { # "pos_bahrain.api.get_item_details.get_item_details": "pos_kiosk.api.item.get_item_details" # noqa # }

22.964286

101

0.631415

384

3,215

5.010417

0.338542

0.091476

0.033784

0.035343

0.235967

0.141892

0.108628

0.085239

0.045738

0

0.001911

0.186003

3,215

139

102

23.129496

0.733282

0.7521

0

0.231206

0.031206

0

1

0

false

0

0.08

0

0.08

0

null

0

null

0

1

0

d9a0c8935f1da040f76922b94d20a857d8b8cd7d

3,338

py

Python

easyai/model/backbone/cls/pnasnet.py

lpj0822/image_point_cloud_det

7b20e2f42f3f2ff4881485da58ad188a1f0d0e0f

[ "MIT" ]

1

2020-09-05T09:18:56.000Z

easyai/model/backbone/cls/pnasnet.py

lpj0822/image_point_cloud_det

7b20e2f42f3f2ff4881485da58ad188a1f0d0e0f

[ "MIT" ]

8

2020-04-20T02:18:55.000Z

2022-03-12T00:24:50.000Z

easyai/model/backbone/cls/pnasnet.py

lpj0822/image_point_cloud_det

7b20e2f42f3f2ff4881485da58ad188a1f0d0e0f

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding:utf-8 -*- # Author: ''' PNASNet in PyTorch. Paper: Progressive Neural Architecture Search ''' from easyai.base_name.block_name import NormalizationType, ActivationType from easyai.base_name.backbone_name import BackboneName from easyai.model.backbone.utility.base_backbone import * from easyai.model.base_block.utility.utility_block import ConvBNActivationBlock from easyai.model.base_block.cls.pnasnet_block import CellA, CellB __all__ = ['pnasnet_A', 'pnasnet_B'] class PNASNet(BaseBackbone): def __init__(self, data_channel=3, num_cells=6, num_planes=44, block=CellA, bnName=NormalizationType.BatchNormalize2d, activationName=ActivationType.ReLU): super().__init__() self.set_name(BackboneName.PNASNetA) self.data_channel = data_channel self.num_cells = num_cells self.block = block self.activation_name = activationName self.bn_name = bnName self.first_output = num_planes self.in_planes = self.first_output self.create_block_list() def create_block_list(self): self.block_out_channels = [] self.index = 0 layer1 = ConvBNActivationBlock(in_channels=self.data_channel, out_channels=self.first_output, kernel_size=3, stride=1, padding=1, bias=False, bnName=self.bn_name, activationName=self.activation_name) self.add_block_list(layer1.get_name(), layer1, self.first_output) self.make_layer(self.first_output, self.num_cells) self.downsample(self.first_output * 2) self.make_layer(self.first_output * 2, self.num_cells) self.downsample(self.first_output * 4) self.make_layer(self.first_output * 4, self.num_cells) def make_layer(self, planes, num_cells): for _ in range(num_cells): temp_block = self.block(self.in_planes, planes, stride=1, bn_name=self.bn_name, activation_name=self.activation_name) self.add_block_list(temp_block.get_name(), temp_block, planes) self.in_planes = planes def downsample(self, planes): down_block = self.block(self.in_planes, planes, stride=2, bn_name=self.bn_name, activation_name=self.activation_name) self.add_block_list(down_block.get_name(), down_block, planes) self.in_planes = planes def forward(self, x): output_list = [] for block in self._modules.values(): x = block(x) output_list.append(x) return output_list def pnasnet_A(data_channel): model = PNASNet(data_channel=data_channel, num_cells=6, num_planes=44, block=CellA) model.set_name(BackboneName.PNASNetA) return model def pnasnet_B(data_channel): model = PNASNet(data_channel=data_channel, num_cells=6, num_planes=32, block=CellB) model.set_name(BackboneName.PNASNetB) return model

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null

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null

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1

0

d9a268f19adc7700cf1335eb9dfc2c8d74c5a4dc

2,208

py

Python

tools/utils.py

vahini01/electoral_rolls

82e42a6ee68844b1c8ac7899e8e7bf7a24e48d44

[ "MIT" ]

16

2018-01-22T02:03:09.000Z

2022-02-24T07:16:47.000Z

tools/utils.py

vahini01/electoral_rolls

82e42a6ee68844b1c8ac7899e8e7bf7a24e48d44

[ "MIT" ]

2

2019-02-01T02:48:17.000Z

2020-09-06T06:09:35.000Z

tools/utils.py

vahini01/electoral_rolls

82e42a6ee68844b1c8ac7899e8e7bf7a24e48d44

[ "MIT" ]

8

2018-01-22T06:48:07.000Z

2021-08-08T16:26:12.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Fri Nov 10 23:28:58 2017 @author: dhingratul """ import urllib.request import os from selenium import webdriver from selenium.webdriver.support.ui import Select from bs4 import BeautifulSoup import ssl import requests import wget from PyPDF2 import PdfFileReader def download_file(pdf_url, mdir, filename, flag=False): if flag is True: context = ssl._create_unverified_context() response = urllib.request.urlopen(pdf_url, context=context) else: response = urllib.request.urlopen(pdf_url) filename = mdir + filename file = open(filename, 'wb') file.write(response.read()) if os.stat(filename).st_size == 0: flag = 0 else: flag = 1 file.close() return flag def download_file_R(pdf_url, mdir, filename, file_out): requests.packages.urllib3.disable_warnings() while True: # Keep trying until the webpage successfully downloads try: r = requests.get(pdf_url, verify=False, timeout=10) break # If it downloads, get out and get on with life # If it doesn't download after the timeout period, an exceptions is thrown, and we try again except requests.exceptions.RequestException as e: with open(file_out, "a") as myfile: myfile.write(pdf_url + '\n') filename = mdir + filename with open(filename, 'wb') as f: f.write(r.content) if os.stat(filename).st_size == 0: flag = 0 else: flag = 1 return flag def download_file_W(pdf_url, mdir, filename, flag=False): filename = mdir + filename ssl._create_default_https_context = ssl._create_unverified_context wget.download(pdf_url, filename) if os.stat(filename).st_size == 0: flag = 0 else: flag = 1 return flag def getDriver(url): driver = webdriver.Chrome() driver.get(url) return driver def is_valid_pdf(fn): """Check is the PDF valid """ try: with open(fn, 'rb') as f: pdf = PdfFileReader(f) numpages = pdf.numPages return (numpages > 0) except Exception as e: return False

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d9a3883f0ea5d080d5d4d2e05df6fadcaeb5c36e

1,956

py

Python

exp/viz_raw_manhattan.py

ellencwade/coronavirus-2020

b71e018deb8df8450b4d88ddbcd6ded6497aa8f9

[ "MIT" ]

null

exp/viz_raw_manhattan.py

ellencwade/coronavirus-2020

b71e018deb8df8450b4d88ddbcd6ded6497aa8f9

[ "MIT" ]

null

exp/viz_raw_manhattan.py

ellencwade/coronavirus-2020

b71e018deb8df8450b4d88ddbcd6ded6497aa8f9

[ "MIT" ]

null

""" Experiment summary ------------------ Treat each province/state in a country cases over time as a vector, do a simple K-Nearest Neighbor between countries. What country has the most similar trajectory to a given country? Plots similar countries """ import sys sys.path.insert(0, '..') from utils import data import os import sklearn import numpy as np import json import matplotlib.pyplot as plt plt.style.use('fivethirtyeight') # ------------ HYPERPARAMETERS ------------- BASE_PATH = '../COVID-19/csse_covid_19_data/' # ------------------------------------------ confirmed = os.path.join( BASE_PATH, 'csse_covid_19_time_series', 'time_series_covid19_confirmed_global.csv') confirmed = data.load_csv_data(confirmed) features = [] targets = [] fig = plt.figure(figsize=(12, 12)) ax = fig.add_subplot(111) cm = plt.get_cmap('jet') NUM_COLORS = 0 LINE_STYLES = ['solid', 'dashed', 'dotted'] NUM_STYLES = len(LINE_STYLES) dist_diff = os.path.join('../exp/results/', 'knn_raw.json') f = open(dist_diff,) dist_diff = json.load(f) for region, dist in dist_diff.items(): plt.style.use('fivethirtyeight') fig = plt.figure(figsize=(12, 12)) ax = fig.add_subplot(111) cm = plt.get_cmap('jet') other_region = dist['manhattan'][0] regions = [region, other_region] for val in regions: df = data.filter_by_attribute( confirmed, "Country/Region", val) cases, labels = data.get_cases_chronologically(df) cases = cases.sum(axis=0) lines = ax.plot(cases, label=val) ax.set_ylabel('# of confirmed cases') ax.set_xlabel("Time (days since Jan 22, 2020)") ax.set_yscale('log') ax.legend() plt.tight_layout() region = region.replace('*', '') other_region = other_region.replace('*', '') plt.title(f'Comparing confirmed cases in {region} and {other_region}') plt.savefig(f'results/raw_manhattan/{region}.png') plt.close() print(region)

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d9a428c026d2352f281b2b7ddd8ec8a286d37297

5,290

py

Python

rational/mxnet/rationals.py

steven-lang/rational_activations

234623dbb9360c215c430185b09e2237d5186b54

[ "MIT" ]

null

rational/mxnet/rationals.py

steven-lang/rational_activations

234623dbb9360c215c430185b09e2237d5186b54

[ "MIT" ]

null

rational/mxnet/rationals.py

steven-lang/rational_activations

234623dbb9360c215c430185b09e2237d5186b54

[ "MIT" ]

null

""" Rational Activation Functions for MXNET ======================================= This module allows you to create Rational Neural Networks using Learnable Rational activation functions with MXNET networks. """ import mxnet as mx from mxnet import initializer from mxnet.gluon import HybridBlock from rational.utils.get_weights import get_parameters from rational.mxnet.versions import _version_a, _version_b, _version_c, _version_d from rational._base.rational_base import Rational_base class Rational(Rational_base, HybridBlock): """ Rational Activation Function, inheriting from ``mxnet.gluon.HybridBlock``. Arguments: approx_func (str): The name of the approximated function for initialisation. The different functions are available in `rational.rationals_config.json`. Default: ``leaky_relu`` degrees (tuple of int): The degrees of the numerator (P) and denominator (Q). Default ``(5, 4)`` cuda (bool): whether to execute on cuda device. NOTE: THIS PARAMETER IS CURRENTLY NOT CONSIDERED. CUDA GPUS ARE USED WHEN IT IS POSSIBLE version (str): Version of Rational to use. Rational(x) = P(x)/Q(x), where P(x) = (a_0 + a_1 * x + a_2 * x^2 + ... + a_n * x^n) and `A`: Q(x) = (1 + |b_0 * x| + | b_1 * x^2| + ... + | b_m * x^{m+1}|) `B`: Q(x) = (1 + |b_0 * x + b_1 * x^2 + ... + b_m * x^{m + 1}|) `C`: Q(x) = (0.1 + |b_0 + b_1 * x + b_2 * x^2 + ... + b_m * x^m|) `D`: like `B` with noised coefficients b_i Default ``A`` trainable (bool): Whether the weights are trainable, i.e, if they are updated during backward pass. Default ``True`` Returns: HybridBlock: Rational hybrid block """ def __init__(self, approx_func='leaky_relu', degrees=(5, 4), cuda=False, version='A', trainable=True, **kwargs): super(Rational, self).__init__(**kwargs) # read initial parameter configuration from external files w_numerator, w_denominator = get_parameters( version, degrees, approx_func) # convert w_numerator and w_denominator to mxnet arrays w_numerator = mx.nd.array(w_numerator) w_denominator = mx.nd.array(w_denominator) # register the amount of weights in numerator and denominator, since we need them during # symbolic execution, but are unable to retrieve them at later stages self.numerator_length = len(w_numerator) self.denominator_length = len(w_denominator) self.training = trainable self.degrees = degrees self.version = version self.init_approximation = approx_func # set specified context (currently not happening, since unclear, how and why helpful) # self.device = gpu() if cuda else cpu() # register and configure weights (numerator and denominator coefficients) with self.name_scope(): self.numerator = self.params.get(name='w_numerator', shape=(len(w_numerator),), init=initializer.Constant( w_numerator), grad_req='write' if trainable else 'null', differentiable=trainable) self.denominator = self.params.get(name='w_denominator', shape=(len(w_denominator),), init=initializer.Constant( w_denominator), grad_req='write' if trainable else 'null', differentiable=trainable) # register whether function is trainable, since this information needs to be passed to # version D self.training = trainable self.init_approximation = approx_func # set rational activation function version self.rational_func = {'A': _version_a, 'B': _version_b, 'C': _version_c, 'D': _version_d} \ .get(version) if self.rational_func is None: raise ValueError( "rational activation function version %s not implemented" % version) def hybrid_forward(self, F, x, numerator, denominator): return self.rational_func(F, x, numerator, denominator, self.training, self.numerator_length, self.denominator_length) def numpy(self): """ Returns a numpy version of this activation function. """ from rational.numpy import Rational as Rational_numpy rational_n = Rational_numpy(self.init_approximation, self.degrees, self.version) rational_n.numerator = self.numerator.data().asnumpy().tolist() rational_n.denominator = self.denominator.data().asnumpy().tolist() return rational_n

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null

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null

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d9a6621d903359b14c87695eb4a1ac8dcea18138

844

py

Python

torchflare/criterion/utils.py

Neklaustares-tPtwP/torchflare

7af6b01ef7c26f0277a041619081f6df4eb1e42c

[ "Apache-2.0" ]

1

2021-09-14T08:38:05.000Z

torchflare/criterion/utils.py

weidao-Shi/torchflare

3c55b5a0761f2e85dd6da95767c6ec03f0f5baad

[ "Apache-2.0" ]

null

torchflare/criterion/utils.py

weidao-Shi/torchflare

3c55b5a0761f2e85dd6da95767c6ec03f0f5baad

[ "Apache-2.0" ]

1

2021-08-06T19:24:43.000Z

"""Utils for criterion.""" import torch import torch.nn.functional as F def normalize(x, axis=-1): """Performs L2-Norm.""" num = x denom = torch.norm(x, 2, axis, keepdim=True).expand_as(x) + 1e-12 return num / denom # Source : https://github.com/earhian/Humpback-Whale-Identification-1st-/blob/master/models/triplet_loss.py def euclidean_dist(x, y): """Computes Euclidean distance.""" m, n = x.size(0), y.size(0) xx = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n) yy = torch.pow(x, 2).sum(1, keepdim=True).expand(m, m).t() dist = xx + yy - 2 * torch.matmul(x, y.t()) dist = dist.clamp(min=1e-12).sqrt() return dist def cosine_dist(x, y): """Computes Cosine Distance.""" x = F.normalize(x, dim=1) y = F.normalize(y, dim=1) dist = 2 - 2 * torch.mm(x, y.t()) return dist

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d9a90a5af3f207f1020cbf41f94830b75e23fbc9

4,411

py

Python

readthedocs/donate/forms.py

gamearming/readthedocs

53d0094f657f549326a86b8bd0ccf924c2126941

[ "MIT" ]

null

readthedocs/donate/forms.py

gamearming/readthedocs

53d0094f657f549326a86b8bd0ccf924c2126941

[ "MIT" ]

null

readthedocs/donate/forms.py

gamearming/readthedocs

53d0094f657f549326a86b8bd0ccf924c2126941

[ "MIT" ]

null

"""Forms for RTD donations""" import logging from django import forms from django.conf import settings from django.utils.translation import ugettext_lazy as _ from readthedocs.payments.forms import StripeModelForm, StripeResourceMixin from readthedocs.payments.utils import stripe from .models import Supporter log = logging.getLogger(__name__) class SupporterForm(StripeResourceMixin, StripeModelForm): """Donation support sign up form This extends the basic payment form, giving fields for credit card number, expiry, and CVV. The proper Knockout data bindings are established on :py:class:`StripeModelForm` """ class Meta: model = Supporter fields = ( 'last_4_digits', 'name', 'email', 'dollars', 'logo_url', 'site_url', 'public', ) labels = { 'public': _('Make this donation public'), } help_texts = { 'public': _('Your name and image will be displayed on the donation page'), 'email': _('Your email is used for Gravatar and so we can send you a receipt'), 'logo_url': _("URL of your company's logo, images should be 300x300 pixels or less"), 'dollars': _('Companies donating over $400 can specify a logo URL and site link'), } widgets = { 'dollars': forms.HiddenInput(attrs={ 'data-bind': 'value: dollars' }), 'logo_url': forms.TextInput(attrs={ 'data-bind': 'value: logo_url, enable: urls_enabled' }), 'site_url': forms.TextInput(attrs={ 'data-bind': 'value: site_url, enable: urls_enabled' }), 'last_4_digits': forms.TextInput(attrs={ 'data-bind': 'valueInit: card_digits, value: card_digits' }), } last_4_digits = forms.CharField(widget=forms.HiddenInput(), required=True) name = forms.CharField(required=True) email = forms.CharField(required=True) def __init__(self, *args, **kwargs): self.user = kwargs.pop('user') super(SupporterForm, self).__init__(*args, **kwargs) def validate_stripe(self): """Call stripe for payment (not ideal here) and clean up logo < $200""" dollars = self.cleaned_data['dollars'] if dollars < 200: self.cleaned_data['logo_url'] = None self.cleaned_data['site_url'] = None stripe.Charge.create( amount=int(self.cleaned_data['dollars']) * 100, currency='usd', source=self.cleaned_data['stripe_token'], description='Read the Docs Sustained Engineering', receipt_email=self.cleaned_data['email'] ) def save(self, commit=True): supporter = super(SupporterForm, self).save(commit) if commit and self.user is not None and self.user.is_authenticated(): supporter.user = self.user supporter.save() return supporter class EthicalAdForm(StripeResourceMixin, StripeModelForm): """Payment form for ethical ads This extends the basic payment form, giving fields for credit card number, expiry, and CVV. The proper Knockout data bindings are established on :py:class:`StripeModelForm` """ class Meta: model = Supporter fields = ( 'last_4_digits', 'name', 'email', 'dollars', ) help_texts = { 'email': _('Your email is used so we can send you a receipt'), } widgets = { 'dollars': forms.HiddenInput(attrs={ 'data-bind': 'value: dollars' }), 'last_4_digits': forms.TextInput(attrs={ 'data-bind': 'valueInit: card_digits, value: card_digits' }), } last_4_digits = forms.CharField(widget=forms.HiddenInput(), required=True) name = forms.CharField(required=True) email = forms.CharField(required=True) def validate_stripe(self): stripe.Charge.create( amount=int(self.cleaned_data['dollars']) * 100, currency='usd', source=self.cleaned_data['stripe_token'], description='Read the Docs Sponsorship Payment', receipt_email=self.cleaned_data['email'] )

33.416667

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0.22449

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null

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null

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1

0

d9ad95f0461bd02e44c310b1381567e8524c288c

6,258

py

Python

pandas_datareaders_unofficial/datareaders/google_finance_options.py

movermeyer/pandas_datareaders_unofficial

458dcf473d070cd7686d53d4a9b479cbe0ab9218

[ "BSD-3-Clause" ]

18

2015-02-05T01:42:51.000Z

2020-12-27T19:24:25.000Z

pandas_datareaders_unofficial/datareaders/google_finance_options.py

movermeyer/pandas_datareaders_unofficial

458dcf473d070cd7686d53d4a9b479cbe0ab9218

[ "BSD-3-Clause" ]

1

2015-01-12T11:08:02.000Z

2015-01-13T09:14:47.000Z

pandas_datareaders_unofficial/datareaders/google_finance_options.py

femtotrader/pandas_datareaders

458dcf473d070cd7686d53d4a9b479cbe0ab9218

[ "BSD-3-Clause" ]

13

2015-09-10T19:39:51.000Z

2022-01-06T17:08:35.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- from .base import DataReaderBase from ..tools import COL, _get_dates, to_float, to_int import pandas as pd #from pandas.tseries.frequencies import to_offset from six.moves import cStringIO as StringIO import logging import traceback import datetime import json import token, tokenize def ymd_to_date(y, m, d): """ Returns date >>> expiration = {u'd': 1, u'm': 12, u'y': 2014} >>> ymd_to_date(**expiration) datetime.date(2014, 12, 1) >>> ymd_to_date(2014, 3, 1) datetime.date(2014, 3, 1) """ return(datetime.date(year=y, month=m, day=d)) def date_to_ymd(date): """ Returns dict like {'y': ..., 'm': ..., 'd': ...} >>> date_to_ymd(datetime.date(year=2010, month=1, day=3)) {'y': 2010, 'm': 1, 'd': 3} """ d = { 'y': date.year, 'm': date.month, 'd': date.day } return(d) def fix_lazy_json(in_text): """ Handle lazy JSON - to fix expecting property name this function fixes the json output from google http://stackoverflow.com/questions/4033633/handling-lazy-json-in-python-expecting-property-name """ tokengen = tokenize.generate_tokens(StringIO(in_text).readline) result = [] for tokid, tokval, _, _, _ in tokengen: # fix unquoted strings if (tokid == token.NAME): if tokval not in ['true', 'false', 'null', '-Infinity', 'Infinity', 'NaN']: tokid = token.STRING tokval = u'"%s"' % tokval # fix single-quoted strings elif (tokid == token.STRING): if tokval.startswith ("'"): tokval = u'"%s"' % tokval[1:-1].replace ('"', '\\"') # remove invalid commas elif (tokid == token.OP) and ((tokval == '}') or (tokval == ']')): if (len(result) > 0) and (result[-1][1] == ','): result.pop() # fix single-quoted strings elif (tokid == token.STRING): if tokval.startswith ("'"): tokval = u'"%s"' % tokval[1:-1].replace ('"', '\\"') result.append((tokid, tokval)) return tokenize.untokenize(result) def json_decode(json_string): try: ret = json.loads(json_string) except: json_string = fix_lazy_json(json_string) ret = json.loads(json_string) return ret class DataReaderGoogleFinanceOptions(DataReaderBase): """ DataReader to fetch data from Google Finance Options see https://www.google.com/finance/option_chain https://github.com/makmac213/python-google-option-chain http://www.drtomstarke.com/index.php/option-chains-from-google-finance-api """ def init(self, *args, **kwargs): self._get_multi = self._get_multi_todict def _get_one(self, name, *args, **kwargs): return(self._get_one_raw(name, 'All', 'json')) def _get_one_raw(self, symbol, typ='All', output='json', y='2014', m='12', d='1'): url = "https://www.google.com/finance/option_chain" params = { 'q': symbol, 'type': typ, 'output': output, } data = self._get_content(url, params) d = {} lst = [] for typ in [u'puts', u'calls']: df_typ = pd.DataFrame(data[typ]) df_typ['Type'] = typ lst.append(df_typ) del data[typ] for i, expiration in enumerate(data['expirations']): params = { 'q': symbol, 'output': output, 'expy': expiration['y'], 'expm': expiration['m'], 'expd': expiration['d'], } data = self._get_content(url, params) for typ in [u'puts', u'calls']: df_typ = pd.DataFrame(data[typ]) df_typ['Type'] = typ lst.append(df_typ) del data[typ] lst.append(df_typ) df = pd.concat(lst, axis=0, ignore_index=True) d_cols = { "a": "Ask", "b": "Bid", "p": "Last", "strike": "Strike", "expiry": "Expiry", "vol": "Volume", "name": "Name" } df = df.rename(columns=d_cols) """ d_cols = { "a": "ask", "b": "bid", "c": "change", "cid": "identity code", "cp": "cp" "cs": change direction. "chg" = up, "chr" = down, "chg"? "e": # I think this tells us something about what country where the stock is traded. "OPRA" means USA. "expiry": expiration date for this option "name": I don't know. I have never seen a value for this "oi": open interest. How many of these are currently being held by others. See, http://www.investopedia.com/terms/o/openinterest.asp "p": price, last "s": option code. Basically, Stock Symbol + 7 if mini option + date + "C" or "P" + price "strike": "strike price for this option" "vol": "the volume of options traded." } """ for col in ['Ask', 'Bid', 'c', 'cp', 'Last', 'Strike']: df[col] = df[col].map(to_float) for col in ['Volume', 'oi', 'cid']: df[col] = df[col].map(to_int) df['Expiry'] = pd.to_datetime(df['Expiry']) data['options'] = df data['underlying_id'] = int(data['underlying_id']) data['expiry'] = ymd_to_date(**data['expiry']) for i, expiration in enumerate(data['expirations']): data['expirations'][i] = ymd_to_date(**expiration) #for col in ['Volume']: # df[col] = df[col].fillna(0) #d = {} #d["options"] = df #return(d) return(data) def _get_content(self, url, params): #response = requests.get(url, params=params) response = self.session.get(url, params=params) if response.status_code == 200: content_json = response.text data = json_decode(content_json) return(data) if __name__ == "__main__": import doctest doctest.testmod()

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6,258

4.270557

0.324934

0.01087

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null

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null

0

1

0

d9adb9ef68a4c2ce5de1ed13aea3230964400996

5,039

py

Python

keras_textclassification/data_preprocess/generator_preprocess.py

Vail-qin/Keras-TextClassification

8acda5ae37db2647c8ecaa70027ffc6003d2abca

[ "MIT" ]

1

2019-12-27T16:59:16.000Z

keras_textclassification/data_preprocess/generator_preprocess.py

Yolo-Cultivate/Keras-TextClassification

183cf7b3483588bfe10d19b65124e52df5b338f8

[ "MIT" ]

null

keras_textclassification/data_preprocess/generator_preprocess.py

Yolo-Cultivate/Keras-TextClassification

183cf7b3483588bfe10d19b65124e52df5b338f8

[ "MIT" ]

1

2022-01-11T06:37:54.000Z

# !/usr/bin/python # -*- coding: utf-8 -*- # @time : 2019/11/2 21:08 # @author : Mo # @function: from keras_textclassification.data_preprocess.text_preprocess import load_json, save_json from keras_textclassification.conf.path_config import path_model_dir path_fast_text_model_vocab2index = path_model_dir + 'vocab2index.json' path_fast_text_model_l2i_i2l = path_model_dir + 'l2i_i2l.json' import numpy as np import os class PreprocessGenerator: """ 数据预处理, 输入为csv格式, [label,ques] """ def __init__(self): self.l2i_i2l = None if os.path.exists(path_fast_text_model_l2i_i2l): self.l2i_i2l = load_json(path_fast_text_model_l2i_i2l) def prereocess_idx(self, pred): if os.path.exists(path_fast_text_model_l2i_i2l): pred_i2l = {} i2l = self.l2i_i2l['i2l'] for i in range(len(pred)): pred_i2l[i2l[str(i)]] = pred[i] pred_i2l_rank = [sorted(pred_i2l.items(), key=lambda k: k[1], reverse=True)] return pred_i2l_rank else: raise RuntimeError("path_fast_text_model_label2index is None") def prereocess_pred_xid(self, pred): if os.path.exists(path_fast_text_model_l2i_i2l): pred_l2i = {} l2i = self.l2i_i2l['l2i'] for i in range(len(pred)): pred_l2i[pred[i]] = l2i[pred[i]] pred_l2i_rank = [sorted(pred_l2i.items(), key=lambda k: k[1], reverse=True)] return pred_l2i_rank else: raise RuntimeError("path_fast_text_model_label2index is None") def preprocess_get_label_set(self, path): # 首先获取label,set,即存在的具体类 label_set = set() len_all = 0 file_csv = open(path, "r", encoding="utf-8") for line in file_csv: len_all += 1 if len_all > 1: # 第一条是标签'label,ques'，不选择 line_sp = line.split(",") label_org = str(line_sp[0]).strip().upper() label_real = "NAN" if label_org=="" else label_org label_set.add(label_real) file_csv.close() return label_set, len_all def preprocess_label_ques_to_idx(self, embedding_type, batch_size, path, embed, rate=1): label_set, len_all = self.preprocess_get_label_set(path) # 获取label转index字典等, 如果label2index存在则不转换了, dev验证集合的时候用 if not os.path.exists(path_fast_text_model_l2i_i2l): count = 0 label2index = {} index2label = {} for label_one in label_set: label2index[label_one] = count index2label[count] = label_one count = count + 1 l2i_i2l = {} l2i_i2l['l2i'] = label2index l2i_i2l['i2l'] = index2label save_json(l2i_i2l, path_fast_text_model_l2i_i2l) else: l2i_i2l = load_json(path_fast_text_model_l2i_i2l) # 读取数据的比例 len_ql = int(rate * len_all) if len_ql <= 500: # sample时候不生效,使得语料足够训练 len_ql = len_all def process_line(line): # 对每一条数据操作，获取label和问句index line_sp = line.split(",") ques = str(line_sp[1]).strip().upper() label = str(line_sp[0]).strip().upper() label = "NAN" if label == "" else label que_embed = embed.sentence2idx(ques) label_zeros = [0] * len(l2i_i2l['l2i']) label_zeros[l2i_i2l['l2i'][label]] = 1 return que_embed, label_zeros while True: file_csv = open(path, "r", encoding="utf-8") cout_all_line = 0 cnt = 0 x, y = [], [] # 跳出循环 if len_ql < cout_all_line: break for line in file_csv: cout_all_line += 1 if cout_all_line > 1: # 第一条是标签'label,ques'，不选择 x_line, y_line = process_line(line) x.append(x_line) y.append(y_line) cnt += 1 if cnt == batch_size: if embedding_type in ['bert', 'albert']: x_, y_ = np.array(x), np.array(y) x_1 = np.array([x[0] for x in x_]) x_2 = np.array([x[1] for x in x_]) x_all = [x_1, x_2] elif embedding_type == 'xlnet': x_, y_ = x, np.array(y) x_1 = np.array([x[0][0] for x in x_]) x_2 = np.array([x[1][0] for x in x_]) x_3 = np.array([x[2][0] for x in x_]) x_all = [x_1, x_2, x_3] else: x_all, y_ = np.array(x), np.array(y) cnt = 0 yield (x_all, y_) x, y =[], [] file_csv.close() print("preprocess_label_ques_to_idx ok")

36.781022

92

0.523318

646

5,039

3.773994

0.218266

0.049221

0.054143

0.076702

0.370796

0.322395

0.30886

0.248975

0.226005

0.211649

0

0.039798

0.371701

5,039

136

93

37.051471

0.730259

0.059536

0

0.219048

0

0.040825

0.019562

0

1

0.057143

false

0

0.038095

0

0.142857

0.009524

0

null

0

null

0

1

0

d9b0df7f5ef294a68858d836af143c289d120187

4,375

py

Python

Object_detection_image.py

hiperus0988/pyao

72c56975a3d45aa033bdf7650b5369d59240395f

[ "Apache-2.0" ]

1

2021-06-09T22:17:57.000Z

Object_detection_image.py

hiperus0988/pyao

72c56975a3d45aa033bdf7650b5369d59240395f

[ "Apache-2.0" ]

null

Object_detection_image.py

hiperus0988/pyao

72c56975a3d45aa033bdf7650b5369d59240395f

[ "Apache-2.0" ]

null

######## Image Object Detection Using Tensorflow-trained Classifier ######### # # Author: Evan Juras # Date: 1/15/18 # Description: # This program uses a TensorFlow-trained classifier to perform object detection. # It loads the classifier uses it to perform object detection on an image. # It draws boxes and scores around the objects of interest in the image. ## Some of the code is copied from Google's example at ## https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb ## and some is copied from Dat Tran's example at ## https://github.com/datitran/object_detector_app/blob/master/object_detection_app.py ## but I changed it to make it more understandable to me. # Import packages import os import cv2 import numpy as np import tensorflow as tf import sys # This is needed since the notebook is stored in the object_detection folder. sys.path.append("..") # Import utilites from utils import label_map_util from utils import visualization_utils as vis_util # Name of the directory containing the object detection module we're using MODEL_NAME = 'inference_graph' IMAGE_NAME = 'test1.jpg' # Grab path to current working directory CWD_PATH = os.getcwd() # Path to frozen detection graph .pb file, which contains the model that is used # for object detection. PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb') # Path to label map file PATH_TO_LABELS = os.path.join(CWD_PATH,'training','labelmap.pbtxt') # Path to image PATH_TO_IMAGE = os.path.join(CWD_PATH,IMAGE_NAME) # Number of classes the object detector can identify NUM_CLASSES = 6 # Load the label map. # Label maps map indices to category names, so that when our convolution # network predicts `5`, we know that this corresponds to `king`. # Here we use internal utility functions, but anything that returns a # dictionary mapping integers to appropriate string labels would be fine label_map = label_map_util.load_labelmap(PATH_TO_LABELS) categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True) category_index = label_map_util.create_category_index(categories) # Load the Tensorflow model into memory. detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') sess = tf.Session(graph=detection_graph) # Define input and output tensors (i.e. data) for the object detection classifier # Input tensor is the image image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') # Output tensors are the detection boxes, scores, and classes # Each box represents a part of the image where a particular object was detected detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0') # Each score represents level of confidence for each of the objects. # The score is shown on the result image, together with the class label. detection_scores = detection_graph.get_tensor_by_name('detection_scores:0') detection_classes = detection_graph.get_tensor_by_name('detection_classes:0') # Number of objects detected num_detections = detection_graph.get_tensor_by_name('num_detections:0') # Load image using OpenCV and # expand image dimensions to have shape: [1, None, None, 3] # i.e. a single-column array, where each item in the column has the pixel RGB value image = cv2.imread(PATH_TO_IMAGE) image_expanded = np.expand_dims(image, axis=0) # Perform the actual detection by running the model with the image as input (boxes, scores, classes, num) = sess.run( [detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_expanded}) # Draw the results of the detection (aka 'visulaize the results') vis_util.visualize_boxes_and_labels_on_image_array( image, np.squeeze(boxes), np.squeeze(classes).astype(np.int32), np.squeeze(scores), category_index, use_normalized_coordinates=True, line_thickness=8, min_score_thresh=0.60) # All the results have been drawn on image. Now display the image. cv2.imshow('Object detector', image) # Press any key to close the image cv2.waitKey(0) # Clean up cv2.destroyAllWindows()

36.458333

122

0.779886

680

4,375

4.836765

0.377941

0.045607

0.025844

0.034965

0.082396

0.06689

0.034661

0

0.007451

0.141029

4,375

119

123

36.764706

0.867749

0.511314

0

0.083977

0.012066

0

1

0

false

0

0.166667

0

0.166667

0

null

0

null

0

1

0

d9b42bca24804913cf6908775c04bc29a0bec6df

1,469

py

Python

model/contact.py

hubogeri/python_training

7a918040e4c8bae5a031134911bc8b465f322699

[ "Apache-2.0" ]

null

model/contact.py

hubogeri/python_training

7a918040e4c8bae5a031134911bc8b465f322699

[ "Apache-2.0" ]

null

model/contact.py

hubogeri/python_training

7a918040e4c8bae5a031134911bc8b465f322699

[ "Apache-2.0" ]

null

from sys import maxsize class Contact: def __init__(self, fname=None, mname=None, lname=None, nick=None, title=None, comp=None, addr=None, home=None, mobile=None, work=None, fax=None, email1=None, email2=None, email3=None, homepage=None, bday=None, bmonth=None, byear=None, aday=None, amonth=None, ayear=None, secaddr=None, secphone=None, note=None, id =None): self.fname = fname self.mname = mname self.lname = lname self.nick = nick self.title = title self.comp = comp self.addr = addr self.home = home self.mobile = mobile self.work = work self.fax = fax self.email1 = email1 self.email2 = email2 self.email3 = email3 self.homepage = homepage self.bday = bday self.bmonth = bmonth self.byear = byear self.aday = aday self.amonth = amonth self.ayear = ayear self.secaddr = secaddr self.secphone = secphone self.note = note self.id = id def __repr__(self): return "%s:%s:%s" % (self.id, self.fname, self.lname) def __eq__(self, other): return (self.id is None or other.id is None or self.id == other.id) and self.fname == other.fname and self.lname == other.lname def id_or_max(self): if self.id: return int(self.id) else: return maxsize

30.604167

135

0.571137

191

1,469

4.319372

0.246073

0.043636

0.019394

0.024242

0

0.009091

0.326072

1,469

47

136

31.255319

0.824242

0

0.00545

0

1

0.1

false

0

0.025

0.05

0.25

0

null

0

null

0

1

0

d9b4cabd9071c90b544409b5b87e3302450b1278

11,342

py

Python

test/IECore/BasicPreset.py

ericmehl/cortex

054839cc709ce153d1bcaaefe7f340ebe641ec82

[ "BSD-3-Clause" ]

386

2015-01-02T11:10:43.000Z

2022-03-10T15:12:20.000Z

test/IECore/BasicPreset.py

ericmehl/cortex

054839cc709ce153d1bcaaefe7f340ebe641ec82

[ "BSD-3-Clause" ]

484

2015-01-09T18:28:06.000Z

2022-03-31T16:02:04.000Z

test/IECore/BasicPreset.py

ericmehl/cortex

054839cc709ce153d1bcaaefe7f340ebe641ec82

[ "BSD-3-Clause" ]

99

2015-01-28T23:18:04.000Z

2022-03-27T00:59:39.000Z

########################################################################## # # Copyright (c) 2010-2012, Image Engine Design Inc. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of Image Engine Design nor the names of any # other contributors to this software may be used to endorse or # promote products derived from this software without specific prior # written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS # IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING # NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # ########################################################################## from __future__ import with_statement import os import sys import shutil import unittest import IECore class TestBasicPreset( unittest.TestCase ) : def testCopy( self ) : testObj = IECore.Parameterised( "testParameterised1" ) testObj.parameters().addParameters( [ IECore.BoolParameter( "a", "", True ), IECore.FloatParameter( "b", "", 1.0 ), ] ) testObj2 = IECore.Parameterised( "testParameterised2" ) testObj2.parameters().addParameters( [ IECore.BoolParameter( "a", "", False ), IECore.FloatParameter( "c", "", 0.0 ), ] ) p = IECore.BasicPreset( testObj, testObj.parameters() ) self.assertTrue( p.applicableTo( testObj, testObj.parameters() ) ) self.assertFalse( p.applicableTo( testObj2, testObj2.parameters() ) ) testObj.parameters()["a"].setTypedValue( False ) testObj.parameters()["b"].setTypedValue( 0.0 ) p( testObj, testObj.parameters() ) self.assertEqual( testObj.parameters()["a"].getTypedValue(), True ) self.assertEqual( testObj.parameters()["b"].getTypedValue(), 1.0 ) p2 = IECore.BasicPreset( testObj, testObj.parameters(), parameters=( testObj.parameters()["a"], ) ) self.assertTrue( p2.applicableTo( testObj, testObj.parameters() ) ) self.assertTrue( p2.applicableTo( testObj2, testObj.parameters() ) ) p2( testObj2, testObj2.parameters() ) self.assertEqual( testObj2.parameters()["a"].getTypedValue(), True ) self.assertEqual( testObj2.parameters()["c"].getTypedValue(), 0.0 ) def testLoad( self ) : testObj = IECore.Parameterised( "testParameterised1" ) testObj.parameters().addParameters( [ IECore.BoolParameter( "a", "", True ), IECore.FloatParameter( "b", "", 1.0 ), ] ) testObj2 = IECore.Parameterised( "testParameterised1" ) testObj2.parameters().addParameters( [ IECore.BoolParameter( "a", "", False ), IECore.FloatParameter( "c", "", 0.0 ), ] ) savePath = os.path.abspath( os.path.join( os.path.dirname( __file__ ), "data", "basicPreset" ) ) messageHandler = IECore.CapturingMessageHandler() with messageHandler : p = IECore.BasicPreset( os.path.join( savePath, "basicPresetLoadTest", "basicPresetLoadTest-1.cob" ) ) self.assertEqual( len( messageHandler.messages ), 0 ) self.assertTrue( p.applicableTo( testObj, testObj.parameters() ) ) self.assertFalse( p.applicableTo( testObj2, testObj2.parameters() ) ) testObj.parameters()["a"].setTypedValue( False ) testObj.parameters()["b"].setTypedValue( 0.0 ) p( testObj, testObj.parameters() ) self.assertEqual( testObj.parameters()["a"].getTypedValue(), True ) self.assertEqual( testObj.parameters()["b"].getTypedValue(), 1.0 ) def testSave( self ) : testObj = IECore.Parameterised( "testParameterised1" ) testObj.parameters().addParameters( [ IECore.BoolParameter( "a", "", True ), IECore.FloatParameter( "b", "", 1.0 ), ] ) testObj2 = IECore.Parameterised( "testParameterised1" ) testObj2.parameters().addParameters( [ IECore.BoolParameter( "a", "", False ), IECore.FloatParameter( "c", "", 0.0 ), ] ) savePath = os.path.abspath( os.path.join( os.path.dirname( __file__ ), "data", "basicPreset" ) ) preset = IECore.BasicPreset( testObj, testObj.parameters() ) # Save for the classLoader and check its there, we test the 'loadability' later... preset.save( savePath, "basicPresetTest" ) self.assertTrue( os.path.isfile( os.path.join( savePath, "basicPresetTest", "basicPresetTest-1.cob" ) ) ) self.assertTrue( os.path.isfile( os.path.join( savePath, "basicPresetTest", "basicPresetTest-1.py" ) ) ) # save without the classLoader and check its there preset.save( savePath, "basicPresetTest", classLoadable=False ) self.assertTrue( os.path.isfile( os.path.join( savePath, "basicPresetTest.cob" ) ) ) # reload p = IECore.BasicPreset( os.path.join( savePath, "basicPresetTest.cob" ) ) self.assertTrue( p.applicableTo( testObj, testObj.parameters() ) ) self.assertFalse( p.applicableTo( testObj2, testObj2.parameters() ) ) testObj.parameters()["a"].setTypedValue( False ) testObj.parameters()["b"].setTypedValue( 0.0 ) p( testObj, testObj.parameters() ) self.assertEqual( testObj.parameters()["a"].getTypedValue(), True ) self.assertEqual( testObj.parameters()["b"].getTypedValue(), 1.0 ) preset2 = IECore.BasicPreset( testObj, testObj.parameters(), parameters=( testObj.parameters()["a"], ) ) preset2.save( savePath, "basicPresetTest2", classLoadable=False ) #reload p2 = IECore.BasicPreset( os.path.join( savePath, "basicPresetTest2.cob" ) ) self.assertTrue( p2.applicableTo( testObj, testObj.parameters() ) ) self.assertTrue( p2.applicableTo( testObj2, testObj.parameters() ) ) p2( testObj2, testObj2.parameters() ) self.assertEqual( testObj2.parameters()["a"].getTypedValue(), True ) self.assertEqual( testObj2.parameters()["c"].getTypedValue(), 0.0 ) def testClassLoader( self ) : testObj = IECore.Parameterised( "testParameterised1" ) testObj.parameters().addParameters( [ IECore.BoolParameter( "a", "", True ), IECore.FloatParameter( "b", "", 1.0 ), ] ) savePath = os.path.abspath( os.path.join( os.path.dirname( __file__ ), "data", "basicPreset" ) ) preset = IECore.BasicPreset( testObj, testObj.parameters() ) preset.save( savePath, "basicPresetTestClassLoader" ) # make sure that no messages are emitted during loading messageHandler = IECore.CapturingMessageHandler() with messageHandler : loader = IECore.ClassLoader( IECore.SearchPath( savePath ) ) p = loader.load( "basicPresetTestClassLoader" )() self.assertEqual( len( messageHandler.messages ), 0 ) self.assertTrue( isinstance( p, IECore.BasicPreset ) ) p.metadata() def testClasses( self ) : testObj = IECore.Parameterised( "testParameterised1" ) testObj.parameters().addParameters( [ IECore.BoolParameter( "a", "", True ), IECore.ClassParameter( "b", "", "IECORE_OP_PATHS", os.path.join( "maths", "multiply" ), 2 ), ] ) testObj2 = IECore.Parameterised( "testParameterised2" ) testObj2.parameters().addParameters( [ IECore.ClassParameter( "c", "", "IECORE_OP_PATHS" ), ] ) classes1 = testObj.parameters()["b"].getClass( True ) classes2 = testObj2.parameters()["c"].getClass( True ) self.assertNotEqual( classes1[1:], classes2[1:] ) p = IECore.BasicPreset( testObj, testObj.parameters()["b"] ) self.assertTrue( p.applicableTo( testObj, testObj.parameters()["b"] ) ) self.assertFalse( p.applicableTo( testObj, testObj.parameters() ) ) self.assertTrue( p.applicableTo( testObj2, testObj2.parameters()["c"] ) ) p( testObj2, testObj2.parameters()["c"] ) classes1 = testObj.parameters()["b"].getClass( True ) classes2 = testObj2.parameters()["c"].getClass( True ) self.assertEqual( classes1[1:], classes2[1:] ) def testClassVectors( self ) : testObj = IECore.Parameterised( "testParameterised1" ) testObj.parameters().addParameters( [ IECore.BoolParameter( "a", "", True ), IECore.ClassVectorParameter( "b", "", "IECORE_OP_PATHS" ), ] ) testObj.parameters()["b"].setClasses( [ ( "mult", os.path.join( "maths", "multiply" ), 2 ), ( "coIO", "compoundObjectInOut", 1 ), ] ) testObj2 = IECore.Parameterised( "testParameterised2" ) testObj2.parameters().addParameters( [ IECore.ClassVectorParameter( "c", "", "IECORE_OP_PATHS" ), ] ) classes1 = [ c[1:] for c in testObj.parameters()["b"].getClasses( True ) ] classes2 = [ c[1:] for c in testObj2.parameters()["c"].getClasses( True ) ] self.assertNotEqual( classes1, classes2 ) p = IECore.BasicPreset( testObj, testObj.parameters()["b"] ) self.assertTrue( p.applicableTo( testObj, testObj.parameters()["b"] ) ) self.assertFalse( p.applicableTo( testObj, testObj.parameters() ) ) self.assertTrue( p.applicableTo( testObj2, testObj2.parameters()["c"] ) ) p( testObj2, testObj2.parameters()["c"] ) classes1 = [ c[1:] for c in testObj.parameters()["b"].getClasses( True ) ] classes2 = [ c[1:] for c in testObj2.parameters()["c"].getClasses( True ) ] self.assertEqual( classes1, classes2 ) def testCompoundVectorParameter( self ) : p = IECore.Parameterised( "test" ) p.parameters().addParameters( [ IECore.BoolParameter( "a", "", False ), IECore.CompoundVectorParameter( "c", "", members = [ IECore.StringVectorParameter( "s", "", IECore.StringVectorData() ), IECore.BoolVectorParameter( "b", "", IECore.BoolVectorData() ), ] ) ] ) p["c"]["s"].setValue( IECore.StringVectorData( [ "1", "2", "3" ] ) ) p["c"]["b"].setValue( IECore.BoolVectorData( [ True, False, True ] ) ) v = p.parameters().getValue().copy() preset = IECore.BasicPreset( p, p.parameters() ) self.assertTrue( preset.applicableTo( p, p.parameters() ) ) p.parameters().setValue( p.parameters().defaultValue ) self.assertNotEqual( p.parameters().getValue(), v ) preset( p, p.parameters() ) self.assertEqual( p.parameters().getValue(), v ) def tearDown( self ) : savePath = os.path.abspath( os.path.join( os.path.dirname( __file__ ), "data", "basicPreset" ) ) paths = ( os.path.join( savePath, "basicPresetTest" ), os.path.join( savePath, "basicPresetTest.cob" ), os.path.join( savePath, "basicPresetTest2.cob" ), os.path.join( savePath, "basicPresetTestClassLoader" ), ) for p in paths : if os.path.isdir( p ) : shutil.rmtree( p ) elif os.path.isfile( p ) : os.remove( p ) if __name__ == "__main__": unittest.main()

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