text stringlengths 1 93.6k |
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possible_text = update.message.caption
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photo = get_closest(update.message.photo, [85, 85])
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file_id = photo.file_id
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self.downloadEvidence(updater, file_id, photo.file_unique_id)
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elif (update.message.sticker is not None):
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possible_text = None
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self.downloadEvidence(updater, update.message.sticker.file_id, update.message.sticker.file_unique_id)
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else:
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possible_text = update.message.text
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self.evidence = None
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self.text = re.sub(r'(https?)\S*', '(link)', possible_text or '...')
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self.user = User(update.message.forward_from or update.message.forward_sender_name)
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def downloadEvidence(self, updater, file_id, unique_id):
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newFile = updater.bot.get_file(file_id)
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file_name = unique_id + '.png'
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newFile.download(file_name)
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self.evidence = file_name
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def to_message(self):
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return Comment(text_content=self.text, user_id=self.user.id, user_name=self.user.name, evidence_path=self.evidence)
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class User:
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def __init__(self, user):
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if (isinstance(user, str)):
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self.name = user
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self.id = None
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else:
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self.name = user.first_name
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self.id = user.id
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# From python-telegram-bot documentation
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def get_closest(photos, desired_size):
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def diff(p): return p.width - desired_size[0], p.height - desired_size[1]
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def norm(t): return abs(t[0] + t[1] * 1j)
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return min(photos, key=lambda p: norm(diff(p)))
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# <FILESEP>
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import argparse
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import time
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import logging
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import sys
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import os
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import pickle
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import numpy as np
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import copy
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from params import *
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def run_experiments(args, save_dir):
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os.environ['search_space'] = args.search_space
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from nas_algorithms import run_nas_algorithm
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from data import Data
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trials = args.trials
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out_file = args.output_filename
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save_specs = args.save_specs
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metann_params = meta_neuralnet_params(args.search_space)
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algorithm_params = algo_params(args.algo_params)
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num_algos = len(algorithm_params)
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logging.info(algorithm_params)
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# set up search space
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mp = copy.deepcopy(metann_params)
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ss = mp.pop('search_space')
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dataset = mp.pop('dataset')
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search_space = Data(ss, dataset=dataset)
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for i in range(trials):
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results = []
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walltimes = []
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run_data = []
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for j in range(num_algos):
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# run NAS algorithm
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print('\n* Running algorithm: {}'.format(algorithm_params[j]))
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starttime = time.time()
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algo_result, run_datum = run_nas_algorithm(algorithm_params[j], search_space, mp)
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algo_result = np.round(algo_result, 5)
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# remove unnecessary dict entries that take up space
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for d in run_datum:
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if not save_specs:
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d.pop('spec')
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for key in ['encoding', 'adjacency', 'path', 'dist_to_min']:
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if key in d:
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d.pop(key)
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# add walltime, results, run_data
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walltimes.append(time.time()-starttime)
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results.append(algo_result)
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run_data.append(run_datum)
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# print and pickle results
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filename = os.path.join(save_dir, '{}_{}.pkl'.format(out_file, i))
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print('\n* Trial summary: (params, results, walltimes)')
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print(algorithm_params)
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